{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Perkenalan**\n",
    "---\n",
    "**MILESTONE 2**  \n",
    "**Nama**       : Devin Lee   \n",
    "**Batch**       : HCK-009  \n",
    "**Dataset**    : E-Commerce Shipping Data  \n",
    "**Objective**   : \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. Import Libraries**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from numpy import mean, median\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, precision_score, f1_score\n",
    "from sklearn.compose import make_column_transformer\n",
    "from feature_engine.outliers import Winsorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from time import time\n",
    "import warnings\n",
    "import pickle\n",
    "from sklearn.pipeline import Pipeline,make_pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3. Data Loading**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Data Contains :***\n",
    "- ***ID***                    : ID Number of Customers.\n",
    "- ***Warehouse block***       : The Company have big Warehouse which is divided in to block such as A,B,C,D,E.\n",
    "- ***Mode of shipment***      : The Company Ships the products in multiple way such as Ship, Flight and Road.\n",
    "- ***Customer care calls***   : The number of calls made from enquiry for enquiry of the shipment.\n",
    "- ***Customer rating***       : The company has rated from every customer. 1 is the lowest (Worst), 5 is the highest (Best).\n",
    "- ***Cost of the product***   : Cost of the Product in US Dollars.\n",
    "- ***Prior purchases***       : The Number of Prior Purchase.\n",
    "- ***Product importance***   : The company has categorized the product in the various parameter such as low, medium, high.\n",
    "Gender                  : Male and Female.\n",
    "- ***Discount offered***      : Discount offered on that specific product.\n",
    "- ***Weight in gms***         : It is the weight in grams.\n",
    "- ***Reached on time***       : It is the target variable, where 1 Indicates that the product has NOT reached on time and 0 indicates it has reached on time.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Loading Data:***\n",
    "> Pada proses ini akan melakukan loading data menggunakan fungsi dari pandas. Dimana yang akan dilakukan adalah untuk melakukan read_csv karena dataset memiliki format csv. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Warehouse_block</th>\n",
       "      <th>Mode_of_Shipment</th>\n",
       "      <th>Customer_care_calls</th>\n",
       "      <th>Customer_rating</th>\n",
       "      <th>Cost_of_the_Product</th>\n",
       "      <th>Prior_purchases</th>\n",
       "      <th>Product_importance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Discount_offered</th>\n",
       "      <th>Weight_in_gms</th>\n",
       "      <th>Reached.on.Time_Y.N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>D</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>1233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>216</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>59</td>\n",
       "      <td>3088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>4</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>48</td>\n",
       "      <td>3374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>Flight</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>176</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>10</td>\n",
       "      <td>1177</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>46</td>\n",
       "      <td>2484</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10994</th>\n",
       "      <td>10995</td>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>5</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1538</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10995</th>\n",
       "      <td>10996</td>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>232</td>\n",
       "      <td>5</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>1247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10996</th>\n",
       "      <td>10997</td>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1155</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>10998</td>\n",
       "      <td>F</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>223</td>\n",
       "      <td>6</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10998</th>\n",
       "      <td>10999</td>\n",
       "      <td>D</td>\n",
       "      <td>Ship</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "      <td>5</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>1639</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10999 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Warehouse_block Mode_of_Shipment  Customer_care_calls  \\\n",
       "0          1               D           Flight                    4   \n",
       "1          2               F           Flight                    4   \n",
       "2          3               A           Flight                    2   \n",
       "3          4               B           Flight                    3   \n",
       "4          5               C           Flight                    2   \n",
       "...      ...             ...              ...                  ...   \n",
       "10994  10995               A             Ship                    4   \n",
       "10995  10996               B             Ship                    4   \n",
       "10996  10997               C             Ship                    5   \n",
       "10997  10998               F             Ship                    5   \n",
       "10998  10999               D             Ship                    2   \n",
       "\n",
       "       Customer_rating  Cost_of_the_Product  Prior_purchases  \\\n",
       "0                    2                  177                3   \n",
       "1                    5                  216                2   \n",
       "2                    2                  183                4   \n",
       "3                    3                  176                4   \n",
       "4                    2                  184                3   \n",
       "...                ...                  ...              ...   \n",
       "10994                1                  252                5   \n",
       "10995                1                  232                5   \n",
       "10996                4                  242                5   \n",
       "10997                2                  223                6   \n",
       "10998                5                  155                5   \n",
       "\n",
       "      Product_importance Gender  Discount_offered  Weight_in_gms  \\\n",
       "0                    low      F                44           1233   \n",
       "1                    low      M                59           3088   \n",
       "2                    low      M                48           3374   \n",
       "3                 medium      M                10           1177   \n",
       "4                 medium      F                46           2484   \n",
       "...                  ...    ...               ...            ...   \n",
       "10994             medium      F                 1           1538   \n",
       "10995             medium      F                 6           1247   \n",
       "10996                low      F                 4           1155   \n",
       "10997             medium      M                 2           1210   \n",
       "10998                low      F                 6           1639   \n",
       "\n",
       "       Reached.on.Time_Y.N  \n",
       "0                        1  \n",
       "1                        1  \n",
       "2                        1  \n",
       "3                        1  \n",
       "4                        1  \n",
       "...                    ...  \n",
       "10994                    1  \n",
       "10995                    0  \n",
       "10996                    0  \n",
       "10997                    0  \n",
       "10998                    0  \n",
       "\n",
       "[10999 rows x 12 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading Data\n",
    "df = pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4. Exploratory Data Analysis (EDA)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Data Exploration***\n",
    "> Pada tahap ini akan dilakukan explorasi data. Explorasi data ini sangat berguna untuk lebih mengenal bagaimana distribusi atau penyebaran data yang akan dianalisa. Tahapan ini juga sangat membantu untuk melihat dari sisi bisnis. Untuk melakukan EDA (Exploratory Data Analysis) ini, akan dilakukan beberapa hal diantaranya adalah untuk melihat berapa total rows dan column data, informasi data yang diantaranya adalah mean, median, standard deviasi, skewness, dan sebagainya yang dapat menunjang analisa untuk kedepannya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lowercases columns\n",
    "''' \n",
    "tujuannya adalah untuk membuat lowercase pada seluruh nama column, hal ini untuk mempermudah dalam penulisan, karena akan dikhawatirkan jika data sangat case sensitive. \n",
    "'''\n",
    "df.columns = df.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10999 entries, 0 to 10998\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   id                   10999 non-null  int64 \n",
      " 1   warehouse_block      10999 non-null  object\n",
      " 2   mode_of_shipment     10999 non-null  object\n",
      " 3   customer_care_calls  10999 non-null  int64 \n",
      " 4   customer_rating      10999 non-null  int64 \n",
      " 5   cost_of_the_product  10999 non-null  int64 \n",
      " 6   prior_purchases      10999 non-null  int64 \n",
      " 7   product_importance   10999 non-null  object\n",
      " 8   gender               10999 non-null  object\n",
      " 9   discount_offered     10999 non-null  int64 \n",
      " 10  weight_in_gms        10999 non-null  int64 \n",
      " 11  reached.on.time_y.n  10999 non-null  int64 \n",
      "dtypes: int64(8), object(4)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Data Types and Missing Values \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "warehouse_block        0\n",
       "mode_of_shipment       0\n",
       "customer_care_calls    0\n",
       "customer_rating        0\n",
       "cost_of_the_product    0\n",
       "prior_purchases        0\n",
       "product_importance     0\n",
       "gender                 0\n",
       "discount_offered       0\n",
       "weight_in_gms          0\n",
       "reached.on.time_y.n    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For Checking Total Missing Values \n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>3175.282140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2750.5</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>8249.5</td>\n",
       "      <td>10999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_care_calls</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>4.054459</td>\n",
       "      <td>1.141490</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_rating</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>2.990545</td>\n",
       "      <td>1.413603</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost_of_the_product</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>210.196836</td>\n",
       "      <td>48.063272</td>\n",
       "      <td>96.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prior_purchases</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>3.567597</td>\n",
       "      <td>1.522860</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discount_offered</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>13.373216</td>\n",
       "      <td>16.205527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight_in_gms</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>3634.016729</td>\n",
       "      <td>1635.377251</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>1839.5</td>\n",
       "      <td>4149.0</td>\n",
       "      <td>5050.0</td>\n",
       "      <td>7846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reached.on.time_y.n</th>\n",
       "      <td>10999.0</td>\n",
       "      <td>0.596691</td>\n",
       "      <td>0.490584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count         mean          std     min     25%  \\\n",
       "id                   10999.0  5500.000000  3175.282140     1.0  2750.5   \n",
       "customer_care_calls  10999.0     4.054459     1.141490     2.0     3.0   \n",
       "customer_rating      10999.0     2.990545     1.413603     1.0     2.0   \n",
       "cost_of_the_product  10999.0   210.196836    48.063272    96.0   169.0   \n",
       "prior_purchases      10999.0     3.567597     1.522860     2.0     3.0   \n",
       "discount_offered     10999.0    13.373216    16.205527     1.0     4.0   \n",
       "weight_in_gms        10999.0  3634.016729  1635.377251  1001.0  1839.5   \n",
       "reached.on.time_y.n  10999.0     0.596691     0.490584     0.0     0.0   \n",
       "\n",
       "                        50%     75%      max  \n",
       "id                   5500.0  8249.5  10999.0  \n",
       "customer_care_calls     4.0     5.0      7.0  \n",
       "customer_rating         3.0     4.0      5.0  \n",
       "cost_of_the_product   214.0   251.0    310.0  \n",
       "prior_purchases         3.0     4.0     10.0  \n",
       "discount_offered        7.0    10.0     65.0  \n",
       "weight_in_gms        4149.0  5050.0   7846.0  \n",
       "reached.on.time_y.n     1.0     1.0      1.0  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe\n",
    "''' \n",
    "tujuannya adalah untuk melihat informasi seperti count, mean, standard deviation, min, 25%, 50%, 75%, max.\n",
    "'''\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                     0.000000\n",
      "customer_care_calls    0.391926\n",
      "customer_rating        0.004360\n",
      "cost_of_the_product   -0.157117\n",
      "prior_purchases        1.681897\n",
      "discount_offered       1.798929\n",
      "weight_in_gms         -0.249747\n",
      "reached.on.time_y.n   -0.394257\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Selecting only numerical columns\n",
    "numerical_df = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Calculating skewness only for numerical columns\n",
    "skewness = numerical_df.skew()\n",
    "print(skewness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Didapatkan bahwa pada saat melalakukan pengecheckan nilai skewness, column prior_purchases dan discount_offered didapatkan bahwa nilai skewness pada column tersebut mengalami skew, hal ini kemungkinan dikarenakan terdapat outliers pada kedua columns tersebut. Maka perlu dilakukan handling outliers. Pada kasus ini jika tidak ingin menghilangkan data, maka handling outliers yang paling cocok adalah dengan menggunakan winsorizer yang nanti akan dilakukan di proses selanjutnya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Data Cleaning***\n",
    "> Pada proses ini akan dilakukan data cleaning pada dataset yang tersedia. Dimana langkah-langkah yang akan dilakukan adalah change value name, dan dropping column. Penggantian value akan dilakukan pada column block_warehouse. Dimana pada value ini, value yang terdapat pada column ini terdiri atas 'A','B','C','D','F'. Hal ini tidak sesuai dengan urutan Alphabet, dimana seharunya setelah huruf 'D' adalah 'E', bukan 'F'. Maka perlu dilakukan penggantian value tersebut\n",
    ">\n",
    "> Lalu pada langkah selanjutnya adalah dropping column_id. Karena Column ID tidak terlalu dibutuhkan dalam pembuatan pemodelan. Maka columns ID perlu didrop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Change Value Name\n",
    "> Pada value block warehouse, didapatkan nama yang terdiri dari A,B,C,D,F. Berdasarkan dari urutan alphabet, urutan ini memiliki kesalahan, jadi akan dilakukan perubahan nama value F menjadi E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['D', 'F', 'A', 'B', 'C'], dtype=object)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique Value in Warehouse Block\n",
    "df['warehouse_block'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing Value F into E\n",
    "df['warehouse_block'] = df['warehouse_block'].replace('F', 'E')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['D', 'E', 'A', 'B', 'C'], dtype=object)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value already changed\n",
    "df['warehouse_block'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Value F sudah terganti sesuai dengan huruf 'E', dan sudah sesuai dengan urutan alphabet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Drop Column 'id'\n",
    "> Dilakukan dropping column ID karena column ini tidak terlalu dibutuhkan untuk pemodelan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Column ID\n",
    "df.drop(columns='id', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ***Data Visualization***\n",
    "> Proses ini akan melakukan analisa secara visualisasi. Dimana pada analisa akan menggunakan beberapa metode visualisasi seperti boxplot, histogram, dan scatterplot. Tujuan dari visualisasi data ini adalah untuk memudahkan dan melihat lebih jelas persebaran ataupun korelasi antar data yang ada. Pada proses ini akan dilakukan beberapa analisa, diantaranya adalah :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1. Heatmap Correlation*\n",
    "> Kegunaan dari heatmap correlation ini adalah untuk melihat correlation secara keseluruhan. Hal ini dapat mempermudah analisa korelasi dari seluruh columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2700x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only numerical columns for correlation\n",
    "numerical_df = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Set the size and context of the seaborn plot\n",
    "sns.set(rc={'figure.figsize':(27,10)})\n",
    "sns.set_context(\"talk\", font_scale=0.7)\n",
    "\n",
    "# Create the heatmap using the correlation matrix of the numerical dataframe\n",
    "sns.heatmap(numerical_df.corr(), cmap='Greens', annot=True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil ini dapat dikatakan ada beberapa column yang memiliki korelasi. Ada beberapa column yang memiliki korelasi sampai 30% - 40%, dan ada juga korelasi minus yang dapat dikatakan bahwa column tersebut tidak memiliki atau hampir tidak memiliki korelasi satu sama lain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2. Distribution of Customer Rating*\n",
    "> Proses ini akan melakukan visualisasi distribusi dari customer rating, dimana distribusi ini perseberannya berdasarkan dari frequency data customer_rating seluruh rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histogram Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2700x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['customer_rating'].hist(bins=10, color='skyblue', edgecolor='black')\n",
    "\n",
    "plt.title('Customer Rating Histogram')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil data histogram, didapatkan bahwa persebaran nilai customer rating memiliki persebaran yang relatif sama besar. Hal ini ada beberapa kemungkinan data ini terbagi rata untuk pada saat data entry. Dan jika dilihat secara detail, didapatkan nilai tertinggi persebaran/distribusinya adalah rating 1 dan rating 3. Besar kemungkinan pada proses shipping ini sering terjadi problem yang membuat customer tidak merasa puas dengan pelayanan shippingnya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*3. Correlation between Gender and Customer Rating*\n",
    "> Proses ini akan melakukan correlation antara gender dengan rating. Hal ini bertujuan untuk melihat kepuasaan pelanggan berdasarkan dari gender, yang didasari/terbagi berdasarkan dari gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(9,7)})\n",
    "sns.set_context(\"talk\", font_scale=0.8)\n",
    "\n",
    "edu = sns.countplot(x='customer_rating', hue='gender', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Didapatkan bahwa pengguna shipping ini lebih banyak perempuan, dibanding dengan laki-laki, Namun persebarannya hampir merata secara menyeluruh. Hal dapat dikatakan bahwa gender yang sering melakukan penilaian adalah gender wanita/female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*4. Distribution Cost of the Product*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histogram Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cost_of_the_product'].hist(bins=10, color='skyblue', edgecolor='black')\n",
    "\n",
    "plt.title('Customer Rating Histogram')\n",
    "plt.xlabel('Cost of the Product ($)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot for the 'cost_of_the_product' column\n",
    "plt.boxplot(df['cost_of_the_product'])  # Using dropna() to remove NaN values which can't be plotted\n",
    "\n",
    "plt.title('Boxplot of Cost of the Product')\n",
    "plt.ylabel('Cost of the Product ($)')\n",
    "plt.xticks([1], ['Cost of the Product'])  # This sets the x-tick label to 'Cost of the Product'\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil diatas didapatkan bahwa distribusi untuk cost of the product rata-rata berada dikisaran harga $250, hal ini dapat dikatakan bahwa besarnya biaya per product ini dapat dikarenakan biaya pengiriman yang mahal ataupun jarak tempuh pengiriman"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*5. Correlation between Mode of Shipment and Cost of the Product*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "# Box Plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.boxplot(x='mode_of_shipment', y='cost_of_the_product', data=df, palette='viridis')\n",
    "plt.title('Box Plot of Customer Ratings by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Customer Rating')\n",
    "\n",
    "# Point Plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.pointplot(x='mode_of_shipment', y='cost_of_the_product', data=df, join=False, palette='viridis')\n",
    "plt.title('Point Plot of Average Customer Rating by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Average Customer Rating')\n",
    "\n",
    "# Show the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil dari analisa ini didapatkan bahwa boxplot memiliki persebaran mediannya tersebar secara merata, namun pada analisa point plot, dapat dilihat bahwa rata-rata pengiriman menggunakan jalur darat, dan tidak tidak jauh dari hasil pengiriman menggunakan kapal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*6. Correlation between Warehouse Block and Cost of the Product*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "# You can plot a histogram for 'cost_of_the_product' for each category in 'warehouse_block'\n",
    "g = sns.FacetGrid(df, col='warehouse_block', col_wrap=3, sharex=True, sharey=True)  # Adjust col_wrap as needed\n",
    "g.map(plt.hist, 'cost_of_the_product', color='skyblue', edgecolor='black')\n",
    "\n",
    "# Adding titles and labels\n",
    "g.set_titles('{col_name} block')\n",
    "g.set_xlabels('cost of the product ($)')\n",
    "g.set_ylabels('frequency')\n",
    "\n",
    "# Display the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil histogram pada tiap block, didapatkan bahwa proses shipping sering dilakukan pada block warehouse E. Hal ini dapat kemungkinan bahwa jarak pengiriman yang paling dekat dengan customer berada pada warehouse block.\\E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*7. Correlation between Warehouse Block and Mode of Shipment*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the 'warehouse_block' column in the desired order\n",
    "sorted_order = ['A', 'B', 'C', 'D', 'E']\n",
    "df['warehouse_block'] = pd.Categorical(df['warehouse_block'], categories=sorted_order, ordered=True)\n",
    "data = df.sort_values('warehouse_block')\n",
    "\n",
    "# Now plot the sorted data\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='warehouse_block', data=data, order=sorted_order)\n",
    "plt.title('Sorted Warehouse Blocks')\n",
    "plt.xlabel('Warehouse Block')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mode_of_shipment\n",
       "Ship      7462\n",
       "Flight    1777\n",
       "Road      1760\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mode_of_shipment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "warehouse_block\n",
       "E    3666\n",
       "D    1834\n",
       "A    1833\n",
       "B    1833\n",
       "C    1833\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['warehouse_block'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*8. Correlation between Mode of Shipment and Rating*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "# Count Plot\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.countplot(x='mode_of_shipment', hue='customer_rating', data=df, palette='viridis')\n",
    "plt.title('Count Plot of Customer Ratings by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Box Plot\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.boxplot(x='mode_of_shipment', y='customer_rating', data=df, palette='viridis')\n",
    "plt.title('Box Plot of Customer Ratings by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Customer Rating')\n",
    "\n",
    "# Point Plot\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.pointplot(x='mode_of_shipment', y='customer_rating', data=df, join=False, palette='viridis')\n",
    "plt.title('Point Plot of Average Customer Rating by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Average Customer Rating')\n",
    "\n",
    "# Show the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil distribusi antara korelasi mode of shipment dan rating, didapatkan rating tertinggi didapatkan pada mode pengiriman dengan menggunakan kapal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*8. Correlation between Mode of Shipment and On time Shipping*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "# Count Plot\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.countplot(x='mode_of_shipment', hue='reached.on.time_y.n', data=df, palette='viridis')\n",
    "plt.title('Count Plot of Customer Ratings by Mode of Shipment')\n",
    "plt.xlabel('Mode of Shipment')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Show the plots\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil analisa distribusi korelasi antara mode of shipment dengan customer rating, didapatkan bahwa pengiriman dengan rating terbaik rata-rata menggunakan pengiriman melalui kapal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*9. Boxplot for Each Numerical Columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_cols = df[['customer_care_calls','customer_rating', 'cost_of_the_product', 'prior_purchases','discount_offered', 'weight_in_gms']]\n",
    "for col in numerical_cols:\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.boxplot(y=data[col], palette='viridis')\n",
    "    plt.title(f'Outliers in {col}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Dari analisa boxplot didapatkan bahwa terdapat outliers pada column discount_offered dan prior purchase, maka kedua hal ini harus dilakukan handling outliers, karena ada beberapa model yang sangat sensitive terhadap outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5. Feature Engineering**\n",
    "> Feature engineering adalah proses transformasi dan pembuatan fitur (variabel) baru untuk meningkatkan performa model machine learning. Ini adalah salah satu aspek kritis dalam pemodelan prediktif karena kualitas fitur yang digunakan seringkali lebih penting daripada kecanggihan model itu sendiri. Proses feature engineering mencakup pemahaman mendalam tentang data dan domain masalah. Pada proses ini akan dilakukan splitting data, scaling data, handling outliers, dan splitting data untuk data train dan test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*5.1. Checking Balancing Data*\n",
    "> Berdasarkan hasil checking based on value counts, didapatkan bahwa data ini masih dikatakan balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'reached.on.time_y.n\\n1    59.669061\\n0    40.330939%'"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the value counts and the percentage of each unique value in the column 'reached.on.time_y.n'\n",
    "value_counts = df['reached.on.time_y.n'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Display the percentages\n",
    "value_counts_percentage = value_counts.to_string() + \"%\"\n",
    "value_counts_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a pie chart for the distribution of 'Reached.on.Time_Y.N'\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.pie(value_counts, labels=value_counts.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Distribution of Reached on Time (Yes=1 / No=0)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reached.on.time_y.n\n",
       "1    6563\n",
       "0    4436\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \n",
    "df['reached.on.time_y.n'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*5.2. Splitting Data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse_block</th>\n",
       "      <th>mode_of_shipment</th>\n",
       "      <th>customer_care_calls</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>cost_of_the_product</th>\n",
       "      <th>prior_purchases</th>\n",
       "      <th>product_importance</th>\n",
       "      <th>gender</th>\n",
       "      <th>discount_offered</th>\n",
       "      <th>weight_in_gms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>177</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>44</td>\n",
       "      <td>1233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E</td>\n",
       "      <td>Flight</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>216</td>\n",
       "      <td>2</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>59</td>\n",
       "      <td>3088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>4</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>48</td>\n",
       "      <td>3374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>Flight</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>176</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>10</td>\n",
       "      <td>1177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>Flight</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>46</td>\n",
       "      <td>2484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  warehouse_block mode_of_shipment  customer_care_calls  customer_rating  \\\n",
       "0               D           Flight                    4                2   \n",
       "1               E           Flight                    4                5   \n",
       "2               A           Flight                    2                2   \n",
       "3               B           Flight                    3                3   \n",
       "4               C           Flight                    2                2   \n",
       "\n",
       "   cost_of_the_product  prior_purchases product_importance gender  \\\n",
       "0                  177                3                low      F   \n",
       "1                  216                2                low      M   \n",
       "2                  183                4                low      M   \n",
       "3                  176                4             medium      M   \n",
       "4                  184                3             medium      F   \n",
       "\n",
       "   discount_offered  weight_in_gms  \n",
       "0                44           1233  \n",
       "1                59           3088  \n",
       "2                48           3374  \n",
       "3                10           1177  \n",
       "4                46           2484  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['reached.on.time_y.n'], axis = 1)\n",
    "y = df['reached.on.time_y.n']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*5.3. Splitting Between Train and Test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-train (8799, 10)\n",
      "X-Test (2200, 10)\n"
     ]
    }
   ],
   "source": [
    "# Splitting train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 14)\n",
    "print('X-train', X_train.shape)\n",
    "print('X-Test', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse_block</th>\n",
       "      <th>mode_of_shipment</th>\n",
       "      <th>customer_care_calls</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>cost_of_the_product</th>\n",
       "      <th>prior_purchases</th>\n",
       "      <th>product_importance</th>\n",
       "      <th>gender</th>\n",
       "      <th>discount_offered</th>\n",
       "      <th>weight_in_gms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>11</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4558</th>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10791</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>259</td>\n",
       "      <td>5</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4310</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>246</td>\n",
       "      <td>6</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>4846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>B</td>\n",
       "      <td>Flight</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>5807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7526</th>\n",
       "      <td>A</td>\n",
       "      <td>Road</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>5187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6471</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>266</td>\n",
       "      <td>3</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>5531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>D</td>\n",
       "      <td>Road</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>219</td>\n",
       "      <td>2</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9484</th>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>218</td>\n",
       "      <td>2</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>4072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>38</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8799 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      warehouse_block mode_of_shipment  customer_care_calls  customer_rating  \\\n",
       "2240                A             Ship                    3                3   \n",
       "4558                C             Ship                    5                1   \n",
       "10791               B             Ship                    4                2   \n",
       "4310                A             Ship                    6                2   \n",
       "5211                B           Flight                    3                2   \n",
       "...               ...              ...                  ...              ...   \n",
       "7526                A             Road                    3                3   \n",
       "6471                B             Ship                    4                4   \n",
       "2454                D             Road                    4                5   \n",
       "9484                C             Ship                    3                5   \n",
       "2667                B             Ship                    5                3   \n",
       "\n",
       "       cost_of_the_product  prior_purchases product_importance gender  \\\n",
       "2240                   168                3             medium      M   \n",
       "4558                   252                4             medium      M   \n",
       "10791                  259                5             medium      M   \n",
       "4310                   246                6                low      F   \n",
       "5211                   160                3             medium      M   \n",
       "...                    ...              ...                ...    ...   \n",
       "7526                   157                3                low      F   \n",
       "6471                   266                3                low      M   \n",
       "2454                   219                2             medium      F   \n",
       "9484                   218                2             medium      F   \n",
       "2667                   162                4             medium      M   \n",
       "\n",
       "       discount_offered  weight_in_gms  \n",
       "2240                 11           1008  \n",
       "4558                  4           1837  \n",
       "10791                 7           1042  \n",
       "4310                  1           4846  \n",
       "5211                  3           5807  \n",
       "...                 ...            ...  \n",
       "7526                  6           5187  \n",
       "6471                  3           5531  \n",
       "2454                 28           2164  \n",
       "9484                  6           4072  \n",
       "2667                 38           1407  \n",
       "\n",
       "[8799 rows x 10 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['warehouse_block', 'mode_of_shipment', 'customer_care_calls',\n",
       "       'customer_rating', 'cost_of_the_product', 'prior_purchases',\n",
       "       'product_importance', 'gender', 'discount_offered', 'weight_in_gms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*5.3. Splitting Categorical Column and Numerical Column*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorical Columns\n",
    "cat_col = ['warehouse_block','mode_of_shipment','product_importance','gender']\n",
    "\n",
    "# Numerical Columns\n",
    "num_col = ['customer_care_calls','customer_rating', 'cost_of_the_product', 'prior_purchases','discount_offered', 'weight_in_gms']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Handling Outliers***\n",
    "> Pada proses ini akan dilakukan handling outliers pada columns 'prior_purchase' dan 'discount_offered'. Handling outliers/Extreme values ini akan mempengaruhi beberapa model yang akan digunakan dikarenakan ada beberapa model yang sensitive terhadap outliers seperti model KNN, SVM, dsb.\n",
    ">\n",
    "> Proses Handling Outliers ini akan dilakukan dengan menggunakan metode Winsorizer. Keunggulan metode Winsorizer ini adalah data yang melebihi atau lebih kurang dari kuartil tertinggi dan terendah, tidak akan dihapus/dihilangkan, melainkan akan dicapping terhadap nilai tertinggi dan terendah. Metode ini akan mengurangi jumlah outliers tanpa mengurangi data yang membuat proses analisa dan modeling akan lebih baik"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- *Before Handling Outliers:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bafore Doing Winsorizer: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "customer_care_calls    0.389524\n",
       "customer_rating        0.005025\n",
       "cost_of_the_product   -0.153765\n",
       "prior_purchases        1.674824\n",
       "discount_offered       1.806311\n",
       "weight_in_gms         -0.245154\n",
       "dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Bafore Doing Winsorizer: ')\n",
    "X_train[num_col].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Winsorizer for the subset of columns\n",
    "windsoriser = Winsorizer(capping_method='iqr',\n",
    "                          tail='both',\n",
    "                          fold=1.5,\n",
    "                          variables=['prior_purchases', 'discount_offered'])\n",
    "\n",
    "# Fit the Winsorizer to the subset of columns\n",
    "windsoriser.fit(X_train)\n",
    "\n",
    "# Apply the transformation to the training data\n",
    "X_train_winsorised = windsoriser.transform(X_train)\n",
    "\n",
    "# Apply the transformation to the test data\n",
    "X_test_winsorised = windsoriser.transform(X_test)\n",
    "\n",
    "# If you want to replace the original data with the winsorised data, you can do so:\n",
    "X_train = X_train_winsorised\n",
    "X_test = X_test_winsorised"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- *After Handling Outliers:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Doing Winsorizer: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "customer_care_calls    0.389524\n",
       "customer_rating        0.005025\n",
       "cost_of_the_product   -0.153765\n",
       "prior_purchases        0.411550\n",
       "discount_offered       0.710615\n",
       "weight_in_gms         -0.245154\n",
       "dtype: float64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('After Doing Winsorizer: ')\n",
    "X_train[num_col].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>warehouse_block</th>\n",
       "      <th>mode_of_shipment</th>\n",
       "      <th>customer_care_calls</th>\n",
       "      <th>customer_rating</th>\n",
       "      <th>cost_of_the_product</th>\n",
       "      <th>prior_purchases</th>\n",
       "      <th>product_importance</th>\n",
       "      <th>gender</th>\n",
       "      <th>discount_offered</th>\n",
       "      <th>weight_in_gms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>168</td>\n",
       "      <td>3.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>11</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4558</th>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>4.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>1837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10791</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>259</td>\n",
       "      <td>5.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>7</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4310</th>\n",
       "      <td>A</td>\n",
       "      <td>Ship</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>246</td>\n",
       "      <td>5.5</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>4846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>B</td>\n",
       "      <td>Flight</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>5807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7526</th>\n",
       "      <td>A</td>\n",
       "      <td>Road</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>157</td>\n",
       "      <td>3.0</td>\n",
       "      <td>low</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>5187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6471</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>266</td>\n",
       "      <td>3.0</td>\n",
       "      <td>low</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>5531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>D</td>\n",
       "      <td>Road</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>219</td>\n",
       "      <td>2.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>19</td>\n",
       "      <td>2164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9484</th>\n",
       "      <td>C</td>\n",
       "      <td>Ship</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>218</td>\n",
       "      <td>2.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>F</td>\n",
       "      <td>6</td>\n",
       "      <td>4072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>B</td>\n",
       "      <td>Ship</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>162</td>\n",
       "      <td>4.0</td>\n",
       "      <td>medium</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>1407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8799 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      warehouse_block mode_of_shipment  customer_care_calls  customer_rating  \\\n",
       "2240                A             Ship                    3                3   \n",
       "4558                C             Ship                    5                1   \n",
       "10791               B             Ship                    4                2   \n",
       "4310                A             Ship                    6                2   \n",
       "5211                B           Flight                    3                2   \n",
       "...               ...              ...                  ...              ...   \n",
       "7526                A             Road                    3                3   \n",
       "6471                B             Ship                    4                4   \n",
       "2454                D             Road                    4                5   \n",
       "9484                C             Ship                    3                5   \n",
       "2667                B             Ship                    5                3   \n",
       "\n",
       "       cost_of_the_product  prior_purchases product_importance gender  \\\n",
       "2240                   168              3.0             medium      M   \n",
       "4558                   252              4.0             medium      M   \n",
       "10791                  259              5.0             medium      M   \n",
       "4310                   246              5.5                low      F   \n",
       "5211                   160              3.0             medium      M   \n",
       "...                    ...              ...                ...    ...   \n",
       "7526                   157              3.0                low      F   \n",
       "6471                   266              3.0                low      M   \n",
       "2454                   219              2.0             medium      F   \n",
       "9484                   218              2.0             medium      F   \n",
       "2667                   162              4.0             medium      M   \n",
       "\n",
       "       discount_offered  weight_in_gms  \n",
       "2240                 11           1008  \n",
       "4558                  4           1837  \n",
       "10791                 7           1042  \n",
       "4310                  1           4846  \n",
       "5211                  3           5807  \n",
       "...                 ...            ...  \n",
       "7526                  6           5187  \n",
       "6471                  3           5531  \n",
       "2454                 19           2164  \n",
       "9484                  6           4072  \n",
       "2667                 19           1407  \n",
       "\n",
       "[8799 rows x 10 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from imblearn.over_sampling import SMOTENC\n",
    "\n",
    "\n",
    "# smotenc = SMOTENC([0, 1, 6, 7], random_state = 42)\n",
    "# X_train_balanced, y_train_balanced = smotenc.fit_resample(X_train, y_train)\n",
    "# y_train_balanced.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Feature Scalling***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Scalling Numerical Columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. *Standard Scaler*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_column = num_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Encoding Categorical Columns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. *One Hot Encoding*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_column_ohe = ['warehouse_block','mode_of_shipment','gender']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. *Ordinal Encoding*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct the ord_order to be a list of lists\n",
    "ord_col = ['product_importance']  # Ordinal column\n",
    "ord_order = [['low', 'medium', 'high']]  # Correct ordinal order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Pada proses scaling ini, akan melakukan scalling dan ecoding, dimana feature scalling akan dilakukan pada seluruh numerical column dengan menggunakan standard scaller. hal ini karena standard scaler adalah sangat cocok dengan data yang memiliki distribusi normal maupun hampir yang mendekati distribusi normal. Dan dilakukan juga Encoding pada categorical column, dimana ada encoding akan dilakukan menggunakan One Hot Encoding dan Ordinal Encoding, karena masing-masing column memiliki sifat yang berbeda seperti urutan dan sebagainya."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Preprocessing***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Kegunaan preprocessing adalah untuk mendefine dan melakukan column tranformer, dimana pada preprosessor terdiri atas data-data feature scalling yang terdiri standard scaler untuk numerical column, dan menggunakan ordinal encoder, dan one hot encoder pada tiap kategorial column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor = make_column_transformer(\n",
    "    (StandardScaler(), numerical_column),  # Asumsi numerical_columns adalah list dari nama kolom numerik\n",
    "    (OrdinalEncoder(), ord_col),  # Gunakan instance OrdinalEncoder yang sudah dibuat\n",
    "    (OneHotEncoder(), categorical_column_ohe),  # Asumsi categorical_columns_ohe adalah list dari nama kolom kategorikal untuk one-hot encoding\n",
    "    remainder=\"passthrough\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **6. Model Definition**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Pada proses ini adalah dengan membuat pipeline berdasarkan dari hasil preprocessor dan 4 model, yang terdiri dari KNN, SVM, Decision Tree, dan Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pipeline = make_pipeline(preprocessor, KNeighborsClassifier(n_jobs=-1))\n",
    "# knn_pipeline.fit(X_train, y_train) -> masuknya ke model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pipeline = make_pipeline(preprocessor, SVC(random_state=42))\n",
    "# svm_pipeline.fit(X_train, y_train) -> masuknya ke model training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Membuat pipeline untuk Decision Tree\n",
    "# decision_tree_pipeline = make_pipeline(preprocessor, DecisionTreeClassifier(random_state=42))\n",
    "\n",
    "decision_tree_pipeline = make_pipeline(\n",
    "    preprocessor,\n",
    "    DecisionTreeClassifier(\n",
    "        criterion='entropy',\n",
    "        max_depth=3,\n",
    "        min_samples_split=5,\n",
    "        min_samples_leaf=2,\n",
    "        random_state=42\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Membuat pipeline untuk Random Forest\n",
    "# random_forest_pipeline = make_pipeline(preprocessor, RandomForestClassifier(random_state=42, n_jobs=-1))\n",
    "\n",
    "# Random Forest with custom parameters\n",
    "random_forest_pipeline = make_pipeline(\n",
    "    preprocessor,\n",
    "    RandomForestClassifier(\n",
    "        n_estimators=100,\n",
    "        max_depth=5,\n",
    "        min_samples_split=5,\n",
    "        min_samples_leaf=2,\n",
    "        n_jobs=-1,\n",
    "        random_state=42\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **7. Model Training**\n",
    "> Proses ini akan melakukan fitting antara hasil pipeline dengan X_train dan y_train. Kegunaannya adalah untuk memasukan nilai X_train dan y_train pada tiap model pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KNN Training pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;kneighborsclassifier&#x27;, KNeighborsClassifier(n_jobs=-1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-185\" type=\"checkbox\" ><label for=\"sk-estimator-id-185\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;kneighborsclassifier&#x27;, KNeighborsClassifier(n_jobs=-1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-186\" type=\"checkbox\" ><label for=\"sk-estimator-id-186\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-187\" type=\"checkbox\" ><label for=\"sk-estimator-id-187\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-188\" type=\"checkbox\" ><label for=\"sk-estimator-id-188\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-189\" type=\"checkbox\" ><label for=\"sk-estimator-id-189\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-190\" type=\"checkbox\" ><label for=\"sk-estimator-id-190\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-191\" type=\"checkbox\" ><label for=\"sk-estimator-id-191\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-192\" type=\"checkbox\" ><label for=\"sk-estimator-id-192\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-193\" type=\"checkbox\" ><label for=\"sk-estimator-id-193\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-194\" type=\"checkbox\" ><label for=\"sk-estimator-id-194\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-195\" type=\"checkbox\" ><label for=\"sk-estimator-id-195\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_jobs=-1)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('kneighborsclassifier', KNeighborsClassifier(n_jobs=-1))])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM Training pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;svc&#x27;, SVC(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-196\" type=\"checkbox\" ><label for=\"sk-estimator-id-196\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-197\" type=\"checkbox\" ><label for=\"sk-estimator-id-197\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-198\" type=\"checkbox\" ><label for=\"sk-estimator-id-198\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-199\" type=\"checkbox\" ><label for=\"sk-estimator-id-199\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-200\" type=\"checkbox\" ><label for=\"sk-estimator-id-200\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-201\" type=\"checkbox\" ><label for=\"sk-estimator-id-201\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-202\" type=\"checkbox\" ><label for=\"sk-estimator-id-202\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-203\" type=\"checkbox\" ><label for=\"sk-estimator-id-203\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-204\" type=\"checkbox\" ><label for=\"sk-estimator-id-204\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-205\" type=\"checkbox\" ><label for=\"sk-estimator-id-205\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-206\" type=\"checkbox\" ><label for=\"sk-estimator-id-206\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('svc', SVC(random_state=42))])"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decision Tree Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3,\n",
       "                                        min_samples_leaf=2, min_samples_split=5,\n",
       "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-207\" type=\"checkbox\" ><label for=\"sk-estimator-id-207\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3,\n",
       "                                        min_samples_leaf=2, min_samples_split=5,\n",
       "                                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-208\" type=\"checkbox\" ><label for=\"sk-estimator-id-208\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-209\" type=\"checkbox\" ><label for=\"sk-estimator-id-209\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-210\" type=\"checkbox\" ><label for=\"sk-estimator-id-210\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-211\" type=\"checkbox\" ><label for=\"sk-estimator-id-211\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-212\" type=\"checkbox\" ><label for=\"sk-estimator-id-212\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-213\" type=\"checkbox\" ><label for=\"sk-estimator-id-213\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-214\" type=\"checkbox\" ><label for=\"sk-estimator-id-214\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-215\" type=\"checkbox\" ><label for=\"sk-estimator-id-215\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-216\" type=\"checkbox\" ><label for=\"sk-estimator-id-216\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-217\" type=\"checkbox\" ><label for=\"sk-estimator-id-217\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, min_samples_leaf=2,\n",
       "                       min_samples_split=5, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(criterion='entropy', max_depth=3,\n",
       "                                        min_samples_leaf=2, min_samples_split=5,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(max_depth=5, min_samples_leaf=2,\n",
       "                                        min_samples_split=5, n_jobs=-1,\n",
       "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-218\" type=\"checkbox\" ><label for=\"sk-estimator-id-218\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;randomforestclassifier&#x27;,\n",
       "                 RandomForestClassifier(max_depth=5, min_samples_leaf=2,\n",
       "                                        min_samples_split=5, n_jobs=-1,\n",
       "                                        random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-219\" type=\"checkbox\" ><label for=\"sk-estimator-id-219\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-220\" type=\"checkbox\" ><label for=\"sk-estimator-id-220\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-221\" type=\"checkbox\" ><label for=\"sk-estimator-id-221\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-222\" type=\"checkbox\" ><label for=\"sk-estimator-id-222\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-223\" type=\"checkbox\" ><label for=\"sk-estimator-id-223\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-224\" type=\"checkbox\" ><label for=\"sk-estimator-id-224\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-225\" type=\"checkbox\" ><label for=\"sk-estimator-id-225\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-226\" type=\"checkbox\" ><label for=\"sk-estimator-id-226\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-227\" type=\"checkbox\" ><label for=\"sk-estimator-id-227\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-228\" type=\"checkbox\" ><label for=\"sk-estimator-id-228\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, min_samples_leaf=2, min_samples_split=5,\n",
       "                       n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('randomforestclassifier',\n",
       "                 RandomForestClassifier(max_depth=5, min_samples_leaf=2,\n",
       "                                        min_samples_split=5, n_jobs=-1,\n",
       "                                        random_state=42))])"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **8. Model Evaluation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KNN Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.8096967317695554 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.77      0.74      3564\n",
      "           1       0.83      0.79      0.81      5235\n",
      "\n",
      "    accuracy                           0.78      8799\n",
      "   macro avg       0.77      0.78      0.77      8799\n",
      "weighted avg       0.78      0.78      0.78      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208C7B5F10>\n",
      "F1 Score - Test Set  :  0.6824534161490683 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.58      0.55       872\n",
      "           1       0.70      0.66      0.68      1328\n",
      "\n",
      "    accuracy                           0.63      2200\n",
      "   macro avg       0.62      0.62      0.62      2200\n",
      "weighted avg       0.63      0.63      0.63      2200\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208C7FBE10>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = knn_pipeline.predict(X_train)\n",
    "y_pred_test = knn_pipeline.predict(X_test)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(knn_pipeline, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('F1 Score - Test Set  : ', f1_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(knn_pipeline, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.6950788792088534 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.91      0.72      3564\n",
      "           1       0.91      0.56      0.70      5235\n",
      "\n",
      "    accuracy                           0.71      8799\n",
      "   macro avg       0.75      0.74      0.71      8799\n",
      "weighted avg       0.78      0.71      0.70      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208CDB7D50>\n",
      "F1 Score - Test Set  :  0.6543438077634011 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      0.85      0.67       872\n",
      "           1       0.85      0.53      0.65      1328\n",
      "\n",
      "    accuracy                           0.66      2200\n",
      "   macro avg       0.70      0.69      0.66      2200\n",
      "weighted avg       0.73      0.66      0.66      2200\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208C74FDD0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = svm_pipeline.predict(X_train)\n",
    "y_pred_test = svm_pipeline.predict(X_test)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(svm_pipeline, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('F1 Score - Test Set  : ', f1_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(svm_pipeline, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decision Tree Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.6417098773238902 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.96      0.71      3564\n",
      "           1       0.95      0.48      0.64      5235\n",
      "\n",
      "    accuracy                           0.68      8799\n",
      "   macro avg       0.75      0.72      0.67      8799\n",
      "weighted avg       0.79      0.68      0.67      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208C887DD0>\n",
      "F1 Score - Test Set  :  0.6427145708582834 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      0.96      0.70       872\n",
      "           1       0.95      0.48      0.64      1328\n",
      "\n",
      "    accuracy                           0.67      2200\n",
      "   macro avg       0.75      0.72      0.67      2200\n",
      "weighted avg       0.79      0.67      0.67      2200\n",
      " \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x00000220F43D6010>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = decision_tree_pipeline.predict(X_train)\n",
    "y_pred_test = decision_tree_pipeline.predict(X_test)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(decision_tree_pipeline, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('F1 Score - Test Set  : ', f1_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(decision_tree_pipeline, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score - Train Set  :  0.6950028719126938 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.88      0.70      3564\n",
      "           1       0.87      0.58      0.70      5235\n",
      "\n",
      "    accuracy                           0.70      8799\n",
      "   macro avg       0.73      0.73      0.70      8799\n",
      "weighted avg       0.76      0.70      0.70      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208D05E850>\n",
      "F1 Score - Test Set  :  0.6794171220400728 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.86      0.68       872\n",
      "           1       0.86      0.56      0.68      1328\n",
      "\n",
      "    accuracy                           0.68      2200\n",
      "   macro avg       0.71      0.71      0.68      2200\n",
      "weighted avg       0.74      0.68      0.68      2200\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208CE7AF10>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_train = random_forest_pipeline.predict(X_train)\n",
    "y_pred_test = random_forest_pipeline.predict(X_test)\n",
    "\n",
    "print('F1 Score - Train Set  : ', f1_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(random_forest_pipeline, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('F1 Score - Test Set  : ', f1_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(random_forest_pipeline, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari confusion matrix, target adalah shipping on time atau tidak. Untuk 1 adalah ontime, dan 0 adalah tidak. \n",
    "> 1. False Positive = Barang diprediksi on time, tetapi faktanya tidak on time\n",
    "> 2. False Negative = Barang diprediksi tidak on time, tetapi faktanya on time\n",
    ">\n",
    "> Jika melihat dari segi kepuasan pelanggan/customer, service level agreement, ataupun penalty, False Positive (Barang diprediksi on time, tetapi faktanya tidak on time) Lebih berbahaya. Karena jika customer membeli suatu barang, dimana barang akan diprediksi datang secara on time, tetapi ternyata secara fakta bahwa barang tidak datang on time, itu akan membuat customer hilang rasa kepercayaan terhadap perusahaan. Hal ini akan memiliki effect juga terhadap satisfaction pelanggan, dan juga loss of business. Karena salah satu hal yang paling penting adalah kepercayaan dan kepuasan customer. Maka yang paling berbahaya adalah \"False Positive\"\n",
    ">\n",
    "> Bedasarkan dari hasil ke 4 model, didapatkan bahwa nilai Precision (false positive) pada 1 (on time) adalah :\n",
    ">\n",
    ">| No | Model | Train | Test |\n",
    ">| --- | --- | --- | --- |\n",
    ">| 1 | KNN | 0.83 | 0.70 |\n",
    ">| 2 | SVM | 0.91 | 0.85 |\n",
    ">| 3 | Decision Tree | 0.95 | 0.95 |\n",
    ">| 4 | Random Forest | 0.87 | 0.86 |\n",
    ">\n",
    "> Jika dilihat dari model ini, KNN dan SVM memiliki nilai train test overfitting. Tetapi pada model Decision Tree dan Random Forest memiliki nilai Train-Test yang sama. untuk memastikan apakah model ini memang yang terbaik, maka perlu dilakukan Cross Validation.\n",
    ">\n",
    "> Secara flow pengerjaan, maka hal pertama yang akan dilakukan adalah dengan melihat Cross Validation menggunakan fungsi *cross_val_score*. Hal ini berguna untuk melihat range train - test yang sebenarnya dari coss_val_score. Selanjutnya akan dilakukan hyperparameter tuning untuk dengan menggunakan grid search guna mendapatkan parameter-parameter model terbaik. Dan selanjutnya akan dilakukan cross validation lagi untuk tiap model setelah dilakukan hyperparameter tuning.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Cross Validation***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KNN Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1. Train*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.71489621 0.715874   0.71588089]\n",
      "precision Score - Mean - Cross Validation :  0.7155503681634906\n",
      "precision Score - Std - Cross Validation  :  0.00046256478104104743\n",
      "precision Score - Range of Train-Set       :  0.7150878033824496 - 0.7160129329445316\n"
     ]
    }
   ],
   "source": [
    "precision_train_cross_val_knn = cross_val_score(knn_pipeline,\n",
    "                                         X_train,\n",
    "                                         y_train,  # Make sure to use y_train here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_train_cross_val_knn)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_train_cross_val_knn.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_train_cross_val_knn.std())\n",
    "print('precision Score - Range of Train-Set       : ', (precision_train_cross_val_knn.mean() - precision_train_cross_val_knn.std()), '-', (precision_train_cross_val_knn.mean() + precision_train_cross_val_knn.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2. Test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.70238095 0.69863014 0.69300226]\n",
      "precision Score - Mean - Cross Validation :  0.698004448901199\n",
      "precision Score - Std - Cross Validation  :  0.0038543131138871465\n",
      "precision Score - Range of test-Set       :  0.6941501357873118 - 0.7018587620150861\n"
     ]
    }
   ],
   "source": [
    "precision_test_cross_val_knn = cross_val_score(knn_pipeline,\n",
    "                                         X_test,\n",
    "                                         y_test,  # Make sure to use y_test here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_test_cross_val_knn)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_test_cross_val_knn.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_test_cross_val_knn.std())\n",
    "print('precision Score - Range of test-Set       : ', (precision_test_cross_val_knn.mean() - precision_test_cross_val_knn.std()), '-', (precision_test_cross_val_knn.mean() + precision_test_cross_val_knn.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1. Train*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.82962329 0.78438949 0.85914179]\n",
      "precision Score - Mean - Cross Validation :  0.8243848562232138\n",
      "precision Score - Std - Cross Validation  :  0.030741475811807962\n",
      "precision Score - Range of Train-Set       :  0.7936433804114058 - 0.8551263320350218\n"
     ]
    }
   ],
   "source": [
    "precision_train_cross_val_svm = cross_val_score(svm_pipeline,\n",
    "                                         X_train,\n",
    "                                         y_train,  # Make sure to use y_train here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_train_cross_val_svm)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_train_cross_val_svm.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_train_cross_val_svm.std())\n",
    "print('precision Score - Range of Train-Set       : ', (precision_train_cross_val_svm.mean() - precision_train_cross_val_svm.std()), '-', (precision_train_cross_val_svm.mean() + precision_train_cross_val_svm.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2. Test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.73994638 0.74424552 0.7493188 ]\n",
      "precision Score - Mean - Cross Validation :  0.7445035686945481\n",
      "precision Score - Std - Cross Validation  :  0.0038306227682204695\n",
      "precision Score - Range of test-Set       :  0.7406729459263277 - 0.7483341914627686\n"
     ]
    }
   ],
   "source": [
    "precision_test_cross_val_svm = cross_val_score(svm_pipeline,\n",
    "                                         X_test,\n",
    "                                         y_test,  # Make sure to use y_test here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_test_cross_val_svm)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_test_cross_val_svm.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_test_cross_val_svm.std())\n",
    "print('precision Score - Range of test-Set       : ', (precision_test_cross_val_svm.mean() - precision_test_cross_val_svm.std()), '-', (precision_test_cross_val_svm.mean() + precision_test_cross_val_svm.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Decision Tree Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1. Train*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.94194962 0.97596154 0.97482014]\n",
      "precision Score - Mean - Cross Validation :  0.9642437663316142\n",
      "precision Score - Std - Cross Validation  :  0.01577122967025023\n",
      "precision Score - Range of Train-Set       :  0.948472536661364 - 0.9800149960018645\n"
     ]
    }
   ],
   "source": [
    "precision_train_cross_val_decision_tree = cross_val_score(decision_tree_pipeline,\n",
    "                                         X_train,\n",
    "                                         y_train,  # Make sure to use y_train here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_train_cross_val_decision_tree)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_train_cross_val_decision_tree.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_train_cross_val_decision_tree.std())\n",
    "print('precision Score - Range of Train-Set       : ', (precision_train_cross_val_decision_tree.mean() - precision_train_cross_val_decision_tree.std()), '-', (precision_train_cross_val_decision_tree.mean() + precision_train_cross_val_decision_tree.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2. Test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.6627907  0.97487437 0.96017699]\n",
      "precision Score - Mean - Cross Validation :  0.8659473535613857\n",
      "precision Score - Std - Cross Validation  :  0.1437787035071768\n",
      "precision Score - Range of test-Set       :  0.7221686500542089 - 1.0097260570685624\n"
     ]
    }
   ],
   "source": [
    "precision_test_cross_val_decision_tree = cross_val_score(decision_tree_pipeline,\n",
    "                                         X_test,\n",
    "                                         y_test,  # Make sure to use y_test here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_test_cross_val_decision_tree)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_test_cross_val_decision_tree.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_test_cross_val_decision_tree.std())\n",
    "print('precision Score - Range of test-Set       : ', (precision_test_cross_val_decision_tree.mean() - precision_test_cross_val_decision_tree.std()), '-', (precision_test_cross_val_decision_tree.mean() + precision_test_cross_val_decision_tree.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Random Forest Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*1. Train*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.82643958 0.79418345 0.85636856]\n",
      "precision Score - Mean - Cross Validation :  0.825663862380063\n",
      "precision Score - Std - Cross Validation  :  0.025392893256764245\n",
      "precision Score - Range of Train-Set       :  0.8002709691232988 - 0.8510567556368271\n"
     ]
    }
   ],
   "source": [
    "precision_train_cross_val_random_forest = cross_val_score(random_forest_pipeline,\n",
    "                                         X_train,\n",
    "                                         y_train,  # Make sure to use y_train here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_train_cross_val_random_forest)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_train_cross_val_random_forest.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_train_cross_val_random_forest.std())\n",
    "print('precision Score - Range of Train-Set       : ', (precision_train_cross_val_random_forest.mean() - precision_train_cross_val_random_forest.std()), '-', (precision_train_cross_val_random_forest.mean() + precision_train_cross_val_random_forest.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*2. Test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision Score - All - Cross Validation  :  [0.76584022 0.77188329 0.74324324]\n",
      "precision Score - Mean - Cross Validation :  0.7603222509178623\n",
      "precision Score - Std - Cross Validation  :  0.012326098255553012\n",
      "precision Score - Range of test-Set       :  0.7479961526623093 - 0.7726483491734153\n"
     ]
    }
   ],
   "source": [
    "precision_test_cross_val_random_forest = cross_val_score(random_forest_pipeline,\n",
    "                                         X_test,\n",
    "                                         y_test,  # Make sure to use y_test here\n",
    "                                         cv=3,\n",
    "                                         scoring=\"precision\")\n",
    "\n",
    "# Print the precision scores and statistics\n",
    "print('precision Score - All - Cross Validation  : ', precision_test_cross_val_random_forest)\n",
    "print('precision Score - Mean - Cross Validation : ', precision_test_cross_val_random_forest.mean())\n",
    "print('precision Score - Std - Cross Validation  : ', precision_test_cross_val_random_forest.std())\n",
    "print('precision Score - Range of test-Set       : ', (precision_test_cross_val_random_forest.mean() - precision_test_cross_val_random_forest.std()), '-', (precision_test_cross_val_random_forest.mean() + precision_test_cross_val_random_forest.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Bedasarkan dari hasil ke 4 model setelah dilakukan Cross Validation, didapatkan bahwa nilai Precision (false positive) pada 1 (on time) adalah :\n",
    ">\n",
    ">| No | Model | Train | Test | Type |\n",
    ">| --- | --- | --- | --- | --- |\n",
    ">| 1 | KNN | 0.71 | 0.70 | Good Fitting |\n",
    ">| 2 | SVM | 0.79 - 0.85 | 0.74 | Slightly Overfitting |\n",
    ">| 3 | Decision Tree | 0.95 - 0.98 | 0.72 - 1 | Overfitting |\n",
    ">| 4 | Random Forest | 0.80 - 0.85 | 0.74 - 0.77 | Overfitting |\n",
    ">\n",
    "> Maka selanjutnya model yang akan digunakan adalah KNN, dan akan dilakukan hyperparameter tuning untuk melihat apakah hasilnya akan lebih baik lagi setelah di hyperparameter tuning atau tidak, jika ternyata hasil hyperparameter tuning untuk model KNN/SVM ternyata lebih buruk, maka untuk modeling KNN/SVM akan menggunakan default/baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Hyperparameter Tuning***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter grid for KNN\n",
    "n_neighbors_list = list(range(1, 31))\n",
    "knn_grid_search_params = {\n",
    "    'kneighborsclassifier__n_neighbors': n_neighbors_list,\n",
    "    'kneighborsclassifier__weights': ['uniform', 'distance'],\n",
    "    'kneighborsclassifier__metric': ['euclidean', 'manhattan']\n",
    "}\n",
    "\n",
    "# Setup the grid search\n",
    "knn_grid_search = GridSearchCV(\n",
    "    estimator=knn_pipeline,\n",
    "    param_grid=knn_grid_search_params,\n",
    "    scoring='precision',  # or 'f1', 'precision', 'recall', etc.\n",
    "    cv=50,\n",
    "    n_jobs=-1\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;customer_care_calls&#x27;,\n",
       "                                                                          &#x27;customer_rating&#x27;,\n",
       "                                                                          &#x27;cost_of_the_product&#x27;,\n",
       "                                                                          &#x27;prior_purchases&#x27;,\n",
       "                                                                          &#x27;discount_offered&#x27;,\n",
       "                                                                          &#x27;weight_in_gms&#x27;]),\n",
       "                                                                        (&#x27;ordinalencoder&#x27;,\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         [&#x27;product_importance&#x27;]),\n",
       "                                                                        (&#x27;onehotencoder&#x27;...\n",
       "                                                                          &#x27;mode_of_shipment&#x27;,\n",
       "                                                                          &#x27;gender&#x27;])])),\n",
       "                                       (&#x27;kneighborsclassifier&#x27;,\n",
       "                                        KNeighborsClassifier(n_jobs=-1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;kneighborsclassifier__metric&#x27;: [&#x27;euclidean&#x27;,\n",
       "                                                          &#x27;manhattan&#x27;],\n",
       "                         &#x27;kneighborsclassifier__n_neighbors&#x27;: [1, 2, 3, 4, 5, 6,\n",
       "                                                               7, 8, 9, 10, 11,\n",
       "                                                               12, 13, 14, 15,\n",
       "                                                               16, 17, 18, 19,\n",
       "                                                               20, 21, 22, 23,\n",
       "                                                               24, 25, 26, 27,\n",
       "                                                               28, 29, 30],\n",
       "                         &#x27;kneighborsclassifier__weights&#x27;: [&#x27;uniform&#x27;,\n",
       "                                                           &#x27;distance&#x27;]},\n",
       "             scoring=&#x27;precision&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-229\" type=\"checkbox\" ><label for=\"sk-estimator-id-229\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=50,\n",
       "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;customer_care_calls&#x27;,\n",
       "                                                                          &#x27;customer_rating&#x27;,\n",
       "                                                                          &#x27;cost_of_the_product&#x27;,\n",
       "                                                                          &#x27;prior_purchases&#x27;,\n",
       "                                                                          &#x27;discount_offered&#x27;,\n",
       "                                                                          &#x27;weight_in_gms&#x27;]),\n",
       "                                                                        (&#x27;ordinalencoder&#x27;,\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         [&#x27;product_importance&#x27;]),\n",
       "                                                                        (&#x27;onehotencoder&#x27;...\n",
       "                                                                          &#x27;mode_of_shipment&#x27;,\n",
       "                                                                          &#x27;gender&#x27;])])),\n",
       "                                       (&#x27;kneighborsclassifier&#x27;,\n",
       "                                        KNeighborsClassifier(n_jobs=-1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;kneighborsclassifier__metric&#x27;: [&#x27;euclidean&#x27;,\n",
       "                                                          &#x27;manhattan&#x27;],\n",
       "                         &#x27;kneighborsclassifier__n_neighbors&#x27;: [1, 2, 3, 4, 5, 6,\n",
       "                                                               7, 8, 9, 10, 11,\n",
       "                                                               12, 13, 14, 15,\n",
       "                                                               16, 17, 18, 19,\n",
       "                                                               20, 21, 22, 23,\n",
       "                                                               24, 25, 26, 27,\n",
       "                                                               28, 29, 30],\n",
       "                         &#x27;kneighborsclassifier__weights&#x27;: [&#x27;uniform&#x27;,\n",
       "                                                           &#x27;distance&#x27;]},\n",
       "             scoring=&#x27;precision&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-230\" type=\"checkbox\" ><label for=\"sk-estimator-id-230\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;kneighborsclassifier&#x27;, KNeighborsClassifier(n_jobs=-1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-231\" type=\"checkbox\" ><label for=\"sk-estimator-id-231\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-232\" type=\"checkbox\" ><label for=\"sk-estimator-id-232\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-233\" type=\"checkbox\" ><label for=\"sk-estimator-id-233\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-234\" type=\"checkbox\" ><label for=\"sk-estimator-id-234\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-235\" type=\"checkbox\" ><label for=\"sk-estimator-id-235\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-236\" type=\"checkbox\" ><label for=\"sk-estimator-id-236\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-237\" type=\"checkbox\" ><label for=\"sk-estimator-id-237\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-238\" type=\"checkbox\" ><label for=\"sk-estimator-id-238\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-239\" type=\"checkbox\" ><label for=\"sk-estimator-id-239\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-240\" type=\"checkbox\" ><label for=\"sk-estimator-id-240\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_jobs=-1)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=50,\n",
       "             estimator=Pipeline(steps=[('columntransformer',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('standardscaler',\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         ['customer_care_calls',\n",
       "                                                                          'customer_rating',\n",
       "                                                                          'cost_of_the_product',\n",
       "                                                                          'prior_purchases',\n",
       "                                                                          'discount_offered',\n",
       "                                                                          'weight_in_gms']),\n",
       "                                                                        ('ordinalencoder',\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         ['product_importance']),\n",
       "                                                                        ('onehotencoder'...\n",
       "                                                                          'mode_of_shipment',\n",
       "                                                                          'gender'])])),\n",
       "                                       ('kneighborsclassifier',\n",
       "                                        KNeighborsClassifier(n_jobs=-1))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'kneighborsclassifier__metric': ['euclidean',\n",
       "                                                          'manhattan'],\n",
       "                         'kneighborsclassifier__n_neighbors': [1, 2, 3, 4, 5, 6,\n",
       "                                                               7, 8, 9, 10, 11,\n",
       "                                                               12, 13, 14, 15,\n",
       "                                                               16, 17, 18, 19,\n",
       "                                                               20, 21, 22, 23,\n",
       "                                                               24, 25, 26, 27,\n",
       "                                                               28, 29, 30],\n",
       "                         'kneighborsclassifier__weights': ['uniform',\n",
       "                                                           'distance']},\n",
       "             scoring='precision')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "knn_grid_search.fit(X_train, y_train)  # Uncomment and use the correct feature and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kneighborsclassifier__metric': 'manhattan',\n",
       " 'kneighborsclassifier__n_neighbors': 2,\n",
       " 'kneighborsclassifier__weights': 'uniform'}"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Best Hyperparameters\n",
    "\n",
    "knn_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Best Estimator\n",
    "\n",
    "knn_grid_best = knn_grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score - train Set   :  1.0 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      1.00      0.81      3564\n",
      "           1       1.00      0.69      0.82      5235\n",
      "\n",
      "    accuracy                           0.81      8799\n",
      "   macro avg       0.84      0.84      0.81      8799\n",
      "weighted avg       0.87      0.81      0.81      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208E310E90>\n",
      "Precision Score - Test Set   :  0.8119369369369369 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.81      0.65       872\n",
      "           1       0.81      0.54      0.65      1328\n",
      "\n",
      "    accuracy                           0.65      2200\n",
      "   macro avg       0.67      0.68      0.65      2200\n",
      "weighted avg       0.70      0.65      0.65      2200\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208ACACD90>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check Performance Model against Test-Set\n",
    "y_pred_train = knn_grid_best.predict(X_train)\n",
    "y_pred_test = knn_grid_best.predict(X_test)\n",
    "\n",
    "print('Precision Score - train Set   : ', precision_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(knn_grid_best, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('Precision Score - Test Set   : ', precision_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(knn_grid_best, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter grid for SVM\n",
    "svm_grid_search_params = {\n",
    "    'svc__C': [0.1, 1, 10, 100],\n",
    "    'svc__kernel': ['linear', 'rbf', 'poly'],\n",
    "    'svc__gamma': ['scale', 'auto']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup the grid search for SVM\n",
    "svm_grid_search = GridSearchCV(\n",
    "    estimator=svm_pipeline,\n",
    "    param_grid=svm_grid_search_params,\n",
    "    scoring='precision',  # Choose an appropriate scoring metric for your problem\n",
    "    cv=5,  # 5-fold cross-validation is a common choice\n",
    "    n_jobs=-1,\n",
    "    verbose=1  # This will print out more information during the grid search\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-22 {color: black;}#sk-container-id-22 pre{padding: 0;}#sk-container-id-22 div.sk-toggleable {background-color: white;}#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-22 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-22 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-22 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-22 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-22 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-22 div.sk-item {position: relative;z-index: 1;}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-22 div.sk-item::before, #sk-container-id-22 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-22 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-22 div.sk-label-container {text-align: center;}#sk-container-id-22 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-22 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;customer_care_calls&#x27;,\n",
       "                                                                          &#x27;customer_rating&#x27;,\n",
       "                                                                          &#x27;cost_of_the_product&#x27;,\n",
       "                                                                          &#x27;prior_purchases&#x27;,\n",
       "                                                                          &#x27;discount_offered&#x27;,\n",
       "                                                                          &#x27;weight_in_gms&#x27;]),\n",
       "                                                                        (&#x27;ordinalencoder&#x27;,\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         [&#x27;product_importance&#x27;]),\n",
       "                                                                        (&#x27;onehotencoder&#x27;,\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         [&#x27;warehouse_block&#x27;,\n",
       "                                                                          &#x27;mode_of_shipment&#x27;,\n",
       "                                                                          &#x27;gender&#x27;])])),\n",
       "                                       (&#x27;svc&#x27;, SVC(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;svc__C&#x27;: [0.1, 1, 10, 100],\n",
       "                         &#x27;svc__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                         &#x27;svc__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
       "             scoring=&#x27;precision&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-241\" type=\"checkbox\" ><label for=\"sk-estimator-id-241\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                                        ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                          transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         [&#x27;customer_care_calls&#x27;,\n",
       "                                                                          &#x27;customer_rating&#x27;,\n",
       "                                                                          &#x27;cost_of_the_product&#x27;,\n",
       "                                                                          &#x27;prior_purchases&#x27;,\n",
       "                                                                          &#x27;discount_offered&#x27;,\n",
       "                                                                          &#x27;weight_in_gms&#x27;]),\n",
       "                                                                        (&#x27;ordinalencoder&#x27;,\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         [&#x27;product_importance&#x27;]),\n",
       "                                                                        (&#x27;onehotencoder&#x27;,\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         [&#x27;warehouse_block&#x27;,\n",
       "                                                                          &#x27;mode_of_shipment&#x27;,\n",
       "                                                                          &#x27;gender&#x27;])])),\n",
       "                                       (&#x27;svc&#x27;, SVC(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;svc__C&#x27;: [0.1, 1, 10, 100],\n",
       "                         &#x27;svc__gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;],\n",
       "                         &#x27;svc__kernel&#x27;: [&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;]},\n",
       "             scoring=&#x27;precision&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-242\" type=\"checkbox\" ><label for=\"sk-estimator-id-242\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;svc&#x27;, SVC(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-243\" type=\"checkbox\" ><label for=\"sk-estimator-id-243\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-244\" type=\"checkbox\" ><label for=\"sk-estimator-id-244\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-245\" type=\"checkbox\" ><label for=\"sk-estimator-id-245\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-246\" type=\"checkbox\" ><label for=\"sk-estimator-id-246\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-247\" type=\"checkbox\" ><label for=\"sk-estimator-id-247\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-248\" type=\"checkbox\" ><label for=\"sk-estimator-id-248\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-249\" type=\"checkbox\" ><label for=\"sk-estimator-id-249\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-250\" type=\"checkbox\" ><label for=\"sk-estimator-id-250\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-251\" type=\"checkbox\" ><label for=\"sk-estimator-id-251\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-252\" type=\"checkbox\" ><label for=\"sk-estimator-id-252\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('columntransformer',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('standardscaler',\n",
       "                                                                         StandardScaler(),\n",
       "                                                                         ['customer_care_calls',\n",
       "                                                                          'customer_rating',\n",
       "                                                                          'cost_of_the_product',\n",
       "                                                                          'prior_purchases',\n",
       "                                                                          'discount_offered',\n",
       "                                                                          'weight_in_gms']),\n",
       "                                                                        ('ordinalencoder',\n",
       "                                                                         OrdinalEncoder(),\n",
       "                                                                         ['product_importance']),\n",
       "                                                                        ('onehotencoder',\n",
       "                                                                         OneHotEncoder(),\n",
       "                                                                         ['warehouse_block',\n",
       "                                                                          'mode_of_shipment',\n",
       "                                                                          'gender'])])),\n",
       "                                       ('svc', SVC(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'svc__C': [0.1, 1, 10, 100],\n",
       "                         'svc__gamma': ['scale', 'auto'],\n",
       "                         'svc__kernel': ['linear', 'rbf', 'poly']},\n",
       "             scoring='precision', verbose=1)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the grid search to the data\n",
    "svm_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 0.1, 'svc__gamma': 'scale', 'svc__kernel': 'rbf'}"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display Best Hyperparamters from Random Search\n",
    "\n",
    "svm_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-23 {color: black;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;svc&#x27;, SVC(C=0.1, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-253\" type=\"checkbox\" ><label for=\"sk-estimator-id-253\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;svc&#x27;, SVC(C=0.1, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-254\" type=\"checkbox\" ><label for=\"sk-estimator-id-254\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-255\" type=\"checkbox\" ><label for=\"sk-estimator-id-255\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-256\" type=\"checkbox\" ><label for=\"sk-estimator-id-256\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-257\" type=\"checkbox\" ><label for=\"sk-estimator-id-257\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-258\" type=\"checkbox\" ><label for=\"sk-estimator-id-258\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-259\" type=\"checkbox\" ><label for=\"sk-estimator-id-259\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-260\" type=\"checkbox\" ><label for=\"sk-estimator-id-260\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-261\" type=\"checkbox\" ><label for=\"sk-estimator-id-261\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-262\" type=\"checkbox\" ><label for=\"sk-estimator-id-262\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-263\" type=\"checkbox\" ><label for=\"sk-estimator-id-263\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('svc', SVC(C=0.1, random_state=42))])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Best Estimator\n",
    "\n",
    "svm_gridcv_best = svm_grid_search.best_estimator_\n",
    "svm_gridcv_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision Score - train Set   :  0.9128065395095368 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.93      0.70      3564\n",
      "           1       0.91      0.51      0.66      5235\n",
      "\n",
      "    accuracy                           0.68      8799\n",
      "   macro avg       0.74      0.72      0.68      8799\n",
      "weighted avg       0.77      0.68      0.67      8799\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208E30BE90>\n",
      "Precision Score - Test Set   :  0.8838451268357811 \n",
      "\n",
      "Classification Report : \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.90      0.68       872\n",
      "           1       0.88      0.50      0.64      1328\n",
      "\n",
      "    accuracy                           0.66      2200\n",
      "   macro avg       0.71      0.70      0.66      2200\n",
      "weighted avg       0.75      0.66      0.65      2200\n",
      " \n",
      "\n",
      "Confusion Matrix      : \n",
      " <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000002208EFF1850>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # Check Performance Model against Test-Set\n",
    "y_pred_train = svm_gridcv_best.predict(X_train)\n",
    "y_pred_test = svm_gridcv_best.predict(X_test)\n",
    "\n",
    "print('Precision Score - train Set   : ', precision_score(y_train, y_pred_train), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_train, y_pred_train), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(svm_gridcv_best, X_train, y_train, cmap='Reds'))\n",
    "\n",
    "print('Precision Score - Test Set   : ', precision_score(y_test, y_pred_test), '\\n')\n",
    "print('Classification Report : \\n', classification_report(y_test, y_pred_test), '\\n')\n",
    "print('Confusion Matrix      : \\n', ConfusionMatrixDisplay.from_estimator(svm_gridcv_best, X_test, y_test, cmap='Reds'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Berdasarkan dari hasil hyperparameter tuning yang menggunakan model terbaik dari hasil cross validation didapatkan bahwa model terbaik yaitu SVM model. Maka pada proses hyperparameter tuning didapatkan bahwa nilai Train pada model ini setelah hyperparameter tuning adalah 0.91 dan nilai Test sebesar 0.88"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Boosting*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your SVC classifier\n",
    "svc = SVC(kernel='rbf', probability=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wrap the SVC classifier with AdaBoost\n",
    "ada_clf = AdaBoostClassifier(\n",
    "    base_estimator=svc,\n",
    "    n_estimators=5,\n",
    "    algorithm=\"SAMME.R\",  # Use SAMME.R as the algorithm for AdaBoost with SVM\n",
    "    random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-24 {color: black;}#sk-container-id-24 pre{padding: 0;}#sk-container-id-24 div.sk-toggleable {background-color: white;}#sk-container-id-24 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-24 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-24 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-24 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-24 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-24 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-24 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-24 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-24 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-24 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-24 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-24 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-24 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-24 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-24 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-24 div.sk-item {position: relative;z-index: 1;}#sk-container-id-24 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-24 div.sk-item::before, #sk-container-id-24 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-24 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-24 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-24 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-24 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-24 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-24 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-24 div.sk-label-container {text-align: center;}#sk-container-id-24 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-24 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-24\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;ada_boost_svc&#x27;,\n",
       "                 AdaBoostClassifier(base_estimator=SVC(probability=True,\n",
       "                                                       random_state=42),\n",
       "                                    n_estimators=5, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-264\" type=\"checkbox\" ><label for=\"sk-estimator-id-264\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;standardscaler&#x27;,\n",
       "                                                  StandardScaler(),\n",
       "                                                  [&#x27;customer_care_calls&#x27;,\n",
       "                                                   &#x27;customer_rating&#x27;,\n",
       "                                                   &#x27;cost_of_the_product&#x27;,\n",
       "                                                   &#x27;prior_purchases&#x27;,\n",
       "                                                   &#x27;discount_offered&#x27;,\n",
       "                                                   &#x27;weight_in_gms&#x27;]),\n",
       "                                                 (&#x27;ordinalencoder&#x27;,\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  [&#x27;product_importance&#x27;]),\n",
       "                                                 (&#x27;onehotencoder&#x27;,\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  [&#x27;warehouse_block&#x27;,\n",
       "                                                   &#x27;mode_of_shipment&#x27;,\n",
       "                                                   &#x27;gender&#x27;])])),\n",
       "                (&#x27;ada_boost_svc&#x27;,\n",
       "                 AdaBoostClassifier(base_estimator=SVC(probability=True,\n",
       "                                                       random_state=42),\n",
       "                                    n_estimators=5, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-265\" type=\"checkbox\" ><label for=\"sk-estimator-id-265\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;standardscaler&#x27;, StandardScaler(),\n",
       "                                 [&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;,\n",
       "                                  &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;,\n",
       "                                  &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]),\n",
       "                                (&#x27;ordinalencoder&#x27;, OrdinalEncoder(),\n",
       "                                 [&#x27;product_importance&#x27;]),\n",
       "                                (&#x27;onehotencoder&#x27;, OneHotEncoder(),\n",
       "                                 [&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;,\n",
       "                                  &#x27;gender&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-266\" type=\"checkbox\" ><label for=\"sk-estimator-id-266\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">standardscaler</label><div class=\"sk-toggleable__content\"><pre>[&#x27;customer_care_calls&#x27;, &#x27;customer_rating&#x27;, &#x27;cost_of_the_product&#x27;, &#x27;prior_purchases&#x27;, &#x27;discount_offered&#x27;, &#x27;weight_in_gms&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-267\" type=\"checkbox\" ><label for=\"sk-estimator-id-267\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-268\" type=\"checkbox\" ><label for=\"sk-estimator-id-268\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinalencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;product_importance&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-269\" type=\"checkbox\" ><label for=\"sk-estimator-id-269\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-270\" type=\"checkbox\" ><label for=\"sk-estimator-id-270\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;warehouse_block&#x27;, &#x27;mode_of_shipment&#x27;, &#x27;gender&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-271\" type=\"checkbox\" ><label for=\"sk-estimator-id-271\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-272\" type=\"checkbox\" ><label for=\"sk-estimator-id-272\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-273\" type=\"checkbox\" ><label for=\"sk-estimator-id-273\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-274\" type=\"checkbox\" ><label for=\"sk-estimator-id-274\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ada_boost_svc: AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(base_estimator=SVC(probability=True, random_state=42),\n",
       "                   n_estimators=5, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-275\" type=\"checkbox\" ><label for=\"sk-estimator-id-275\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-276\" type=\"checkbox\" ><label for=\"sk-estimator-id-276\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('standardscaler',\n",
       "                                                  StandardScaler(),\n",
       "                                                  ['customer_care_calls',\n",
       "                                                   'customer_rating',\n",
       "                                                   'cost_of_the_product',\n",
       "                                                   'prior_purchases',\n",
       "                                                   'discount_offered',\n",
       "                                                   'weight_in_gms']),\n",
       "                                                 ('ordinalencoder',\n",
       "                                                  OrdinalEncoder(),\n",
       "                                                  ['product_importance']),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['warehouse_block',\n",
       "                                                   'mode_of_shipment',\n",
       "                                                   'gender'])])),\n",
       "                ('ada_boost_svc',\n",
       "                 AdaBoostClassifier(base_estimator=SVC(probability=True,\n",
       "                                                       random_state=42),\n",
       "                                    n_estimators=5, random_state=42))])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pipeline that first preprocesses the data then applies AdaBoost with SVC\n",
    "svm_ada_pipeline = Pipeline([\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('ada_boost_svc', ada_clf)\n",
    "])\n",
    "\n",
    "# Fit the pipeline to your training data\n",
    "svm_ada_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision - Train - AdaBoost :  0.6218858892359387\n",
      "Precision - Test - AdaBoost  :  0.6198723640928502\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "\n",
    "y_train_pred = svm_ada_pipeline.predict(X_train)\n",
    "y_test_pred = svm_ada_pipeline.predict(X_test)\n",
    "\n",
    "print('Precision - Train - AdaBoost : ', precision_score(y_train, y_train_pred, average='weighted'))\n",
    "print('Precision - Test - AdaBoost  : ', precision_score(y_test, y_test_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Dan pada hasil boosting dari model SVM ini didapatkan bahwa nilai adaboost Train sebesar 0.62 dan Test 0.619. Proses boosting ini sangat good fitting, tetapi pada modeling ini akan menggunakan hasil Hyperparameter Tuning, dimana hasil Hyperparameter Tuning ini lebih tinggi dibanding dengan hasil boosting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **9. Model Saving**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model.pkl', 'wb') as file_1:\n",
    "  pickle.dump(svm_gridcv_best, file_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **10. Model Inference**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dilanjutkan ke file : 'model_inference_devin_lee.ipynb'**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **11. Pengambilan Kesimpulan**\n",
    "> Dataset ini membahas tentang E-Commerce Shipping Data. Dimana data ini membahas tentang bagaimana proses pegiriman data yang berbeda-beda, dimana ada jalur darat, jalur laut, dan jalur udara. Dari ketiga hal ini akan dilihat juga bagaimana rating dari tiap-tiap proses jalur, dan tujuan utama pembuatan model ini adalah untuk mengetahui apakah dari parameter-parameter column ini, pengiriman on time atau tidak. \n",
    ">\n",
    "> Dari analis data ini, didapatkan bahwa:\n",
    "> - Didapatkan bahwa pada saat melalakukan pengecheckan nilai skewness, column prior_purchases dan discount_offered didapatkan bahwa nilai skewness pada column tersebut mengalami skew, hal ini kemungkinan dikarenakan terdapat outliers pada kedua columns tersebut. Maka perlu dilakukan handling outliers. Pada kasus ini jika tidak ingin menghilangkan data, maka handling outliers yang paling cocok adalah dengan menggunakan winsorizer yang nanti akan dilakukan di proses selanjutnya.\n",
    ">\n",
    "> - Berdasarkan dari hasil visualisasi didapatkan bahwa ada keterdapatan outliers pada beberapa column tertentu, dan dilakukan handling outliers menggunakan winsorizer\n",
    ">\n",
    "> - Berdasarkan dari analisa model, dari baseline model, hyperparameter tuning, dan boosting, didapatkan bahwa model terbaik adalah menggunakan model SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conceptual Problems**\n",
    "> 1. Jelaskan latar belakang adanya bagging dan cara kerja bagging ! \n",
    ">\n",
    "> Jawab: Bagging, atau Bootstrap Aggregating, merupakan metode ensemble learning yang dirancang untuk meningkatkan stabilitas dan akurasi dari algoritma machine learning yang digunakan pada klasifikasi dan regresi. Metode ini mengurangi varians dan membantu mencegah overfitting. Bagging bekerja dengan membuat multiple versi dari predictor (model pembelajaran mesin) dengan melatihnya secara terpisah pada subset data yang berbeda. Subset ini dibuat dengan proses sampling dengan penggantian dari dataset asli, yang dikenal sebagai bootstrap sample. Kemudian, prediksi dari masing-masing model diagregasikan melalui voting untuk klasifikasi atau rata-rata untuk regresi untuk membentuk prediksi final.\n",
    ">\n",
    "> 2. Jelaskan perbedaan cara kerja algoritma Random Forest dengan algoritma boosting yang Anda pilih !  \n",
    ">\n",
    "> Jawab:Random Forest adalah algoritma bagging yang menggunakan kumpulan pohon keputusan dimana setiap pohon dilatih pada subset data yang secara acak dipilih dengan penggantian. Dalam klasifikasi, mode dari kelas output oleh pohon individu adalah kelas yang diprediksi oleh ensemble. Dalam regresi, rata-rata prediksi dari pohon individu diambil. Sebaliknya, algoritma boosting, seperti AdaBoost atau Gradient Boosting, bekerja secara iteratif dengan menambahkan model baru yang berusaha memperbaiki kesalahan dari model sebelumnya. Model baru ini memberikan bobot yang lebih tinggi pada contoh yang salah diklasifikasikan atau residual, sehingga algoritma secara berurutan fokus pada aspek paling sulit dari masalah pelatihan.\n",
    ">\n",
    "> 3. Jelaskan apa yang dimaksud dengan Cross Validation !  \n",
    ">\n",
    "> Jawab:teknik statistik yang digunakan untuk mengevaluasi kemampuan generalisasi dari suatu model, dan juga untuk melindungi terhadap overfitting. Teknik ini melibatkan pembagian dataset menjadi dua segmen: satu digunakan untuk melatih model dan yang lain digunakan untuk menguji model. Dalam k-fold cross-validation, data dibagi menjadi k subset yang disebut 'folds'. Model dilatih pada k-1 fold dan diuji pada fold yang tersisa. Proses ini diulangi k kali, dengan setiap fold digunakan tepat satu kali sebagai data uji. Hasilnya adalah k estimasi kinerja model, yang biasanya dirangkum dalam bentuk rata-rata.\n"
   ]
  }
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