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| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| import pickle | |
| Operating_Airline= ["American Airlines", "Delta Air Lines", "American Eagle Airlines", "United Airlines", "Southwest Airlines"] | |
| Origin = ["Hartsfield-Jackson Atlanta International Airport", "Charlotte Douglas International Airport", "Denver International Airport", "Dallas/Fort Worth International Airport", "George Bush Intercontinental Airport", "Los Angeles International Airport", "Chicago O'Hare International Airport", "Phoenix Sky Harbor International Airport", "San Francisco International Airport"] | |
| Dest = ["Hartsfield-Jackson Atlanta International Airport", "Charlotte Douglas International Airport", "Denver International Airport", "Dallas/Fort Worth International Airport", "George Bush Intercontinental Airport", "Los Angeles International Airport", "Chicago O'Hare International Airport", "Phoenix Sky Harbor International Airport", "San Francisco International Airport"] | |
| airports = { | |
| "Hartsfield-Jackson Atlanta International Airport": "ATL", | |
| "Charlotte Douglas International Airport": "CLT", | |
| "Denver International Airport": "DEN", | |
| "Dallas/Fort Worth International Airport": "DFW", | |
| "George Bush Intercontinental Airport": "IAH", | |
| "Los Angeles International Airport": "LAX", | |
| "Chicago O'Hare International Airport": "ORD", | |
| "Phoenix Sky Harbor International Airport": "PHX", | |
| "San Francisco International Airport": "SFO" | |
| } | |
| airlines = { | |
| "American Airlines": "AA", | |
| "Delta Air Lines": "DL", | |
| "American Eagle Airlines": "OO", | |
| "United Airlines": "UA", | |
| "Southwest Airlines": "WN" | |
| } | |
| data_pivot = { | |
| 'origin': ['ATL', 'ATL', 'ATL', 'ATL', 'ATL', 'ATL', 'ATL', 'CLT', 'CLT', 'CLT', 'CLT', 'CLT', 'CLT', 'CLT', 'DEN', 'DEN', 'DEN', 'DEN', 'DEN', 'DEN', 'DEN', 'DFW', 'DFW', 'DFW', 'DFW', 'DFW', 'DFW', 'IAH', 'IAH', 'IAH', 'IAH', 'IAH', 'IAH', 'LAX', 'LAX', 'LAX', 'LAX', 'LAX', 'LAX', 'LAX', 'ORD', 'ORD', 'ORD', 'ORD', 'ORD', 'ORD', 'ORD', 'PHX', 'PHX', 'PHX', 'PHX', 'PHX', 'PHX', 'PHX', 'SFO', 'SFO', 'SFO', 'SFO', 'SFO', 'SFO', 'SFO'], | |
| 'dest': ['CLT', 'DEN', 'DFW', 'IAH', 'LAX', 'ORD', 'PHX', 'ATL', 'DEN', 'DFW', 'IAH', 'LAX', 'ORD', 'PHX', 'ATL', 'CLT', 'DFW', 'IAH', 'LAX', 'ORD', 'PHX', 'ATL', 'CLT', 'DEN', 'IAH', 'LAX', 'ORD', 'ATL', 'CLT', 'DEN', 'DFW', 'LAX', 'ORD', 'ATL', 'CLT', 'DEN', 'DFW', 'IAH', 'ORD', 'PHX', 'ATL', 'CLT', 'DEN', 'DFW', 'IAH', 'LAX', 'PHX', 'ATL', 'CLT', 'DEN', 'DFW', 'IAH', 'LAX', 'SFO', 'ATL', 'CLT', 'DEN', 'DFW', 'IAH', 'LAX', 'ORD'], | |
| 'distance': [226.0, 1199.0, 731.0, 689.0, 1947.0, 606.0, 1587.0, 226.0, 1337.0, 936.0, 912.0, 2125.0, 599.0, 1773.0, 1199.0, 1337.0, 641.0, 862.0, 862.0, 888.0, 602.0, 731.0, 936.0, 641.0, 224.0, 1235.0, 801.0, 689.0, 912.0, 862.0, 224.0, 1379.0, 925.0, 1947.0, 2125.0, 862.0, 1235.0, 1379.0, 1744.0, 370.0, 606.0, 599.0, 888.0, 802.0, 925.0, 1744.0, 1440.0, 1587.0, 1773.0, 602.0, 868.0, 1009.0, 370.0, 651.0, 2139.0, 2296.0, 967.0, 1464.0, 1635.0, 337.0, 1846.0] | |
| } | |
| df_pivot = pd.DataFrame(data_pivot) | |
| pivot_table = pd.pivot_table(df_pivot, values='distance', index=['origin'], columns=['dest'], fill_value=0) | |
| filename = "rf.pkl" | |
| with open(filename, "rb") as pickle_file: | |
| model = pickle.load(pickle_file) | |
| airport_codes = { | |
| 'LAX': 'USW00023174', | |
| 'IAH': 'USW00012960', | |
| 'DEN': 'USW00003017', | |
| 'ORD': 'USW00094846', | |
| 'ATL': 'USW00013874', | |
| 'SFO': 'USW00023234', | |
| 'DFW': 'USW00003927', | |
| 'PHX': 'USW00023183', | |
| 'CLT': 'USW00013881' | |
| } | |
| def processResponse(a): | |
| data = a.text.replace('"', ' ').splitlines() | |
| data = [line.strip() for line in data] | |
| header = data[0].split(',') | |
| header = [line.strip() for line in header] | |
| rows = [row.split(',') for row in data[1:] if row] | |
| rows[0] = [line.strip() for line in rows[0]] | |
| rows[1] = [line.strip() for line in rows[1]] | |
| df = pd.DataFrame(rows, columns=header) | |
| columns_to_convert = ['AWND', 'PRCP', 'SNOW', 'TAVG'] | |
| df[columns_to_convert] = df[columns_to_convert].apply(pd.to_numeric, errors='coerce') | |
| df.fillna(0,inplace=True) | |
| return df | |
| def weather_info(origin,dest,date): | |
| url = 'https://www.ncei.noaa.gov/access/services/data/v1' | |
| params = { | |
| 'dataset': 'daily-summaries', | |
| 'stations': f'{origin}, {dest}', | |
| 'dataTypes': 'AWND,PRCP,SNOW,TAVG', | |
| 'startDate': f'{date}', | |
| 'endDate': f'{date}' | |
| } | |
| response = requests.get(url, params=params) | |
| if response.status_code == 200: | |
| df = processResponse(response) | |
| awnd_o, prcp_o, tavg_o, awnd_d, prcp_d, tavg_d,snow_o, snow_d = df['AWND'][0], df['PRCP'][0], df['TAVG'][0], df['AWND'][1], df['PRCP'][1], df['TAVG'][1], df['SNOW'][0], df['SNOW'][1] | |
| return awnd_o, prcp_o, tavg_o, awnd_d, prcp_d, tavg_d,snow_o, snow_d | |
| return 0,0,0,0,0,0,0,0 | |
| def preprocess_input(date, operating_airline, origin, dest, dep_time, distance): | |
| quarter = (date.month - 1) // 3 + 1 | |
| month = date.month | |
| day_of_month = date.day | |
| day_of_week = date.weekday() + 1 | |
| processed_time = dep_time.hour * 100 + dep_time.minute | |
| dep_hour_of_day = int(processed_time) // 100 | |
| awnd_o, prcp_o, tavg_o, awnd_d, prcp_d, tavg_d,snow_o, snow_d = weather_info(airport_codes[origin],airport_codes[dest],date) | |
| format = { | |
| "Distance": False, "DepHourofDay": False, "AWND_O": False, "PRCP_O": False, "TAVG_O": False, "AWND_D": False, | |
| "PRCP_D": False, "TAVG_D": False, "SNOW_O": False, "SNOW_D": False, "Quarter_1": False, "Quarter_2": False, | |
| "Quarter_3": False, "Quarter_4": False, "Month_1": False, "Month_2": False, "Month_3": False, "Month_4": False, | |
| "Month_5": False, "Month_6": False, "Month_7": False, "Month_8": False, "Month_9": False, "Month_10": False, | |
| "Month_11": False, "Month_12": False, "DayofMonth_1": False, "DayofMonth_2": False, "DayofMonth_3": False, | |
| "DayofMonth_4": False, "DayofMonth_5": False, "DayofMonth_6": False, "DayofMonth_7": False, "DayofMonth_8": False, | |
| "DayofMonth_9": False, "DayofMonth_10": False, "DayofMonth_11": False, "DayofMonth_12": False, "DayofMonth_13": False, | |
| "DayofMonth_14": False, "DayofMonth_15": False, "DayofMonth_16": False, "DayofMonth_17": False, "DayofMonth_18": False, | |
| "DayofMonth_19": False, "DayofMonth_20": False, "DayofMonth_21": False, "DayofMonth_22": False, "DayofMonth_23": False, | |
| "DayofMonth_24": False, "DayofMonth_25": False, "DayofMonth_26": False, "DayofMonth_27": False, "DayofMonth_28": False, | |
| "DayofMonth_29": False, "DayofMonth_30": False, "DayofMonth_31": False, "DayOfWeek_1": False, "DayOfWeek_2": False, | |
| "DayOfWeek_3": False, "DayOfWeek_4": False, "DayOfWeek_5": False, "DayOfWeek_6": False, "DayOfWeek_7": False, | |
| "Operating_Airline _AA": False, "Operating_Airline _DL": False, "Operating_Airline _OO": False, "Operating_Airline _UA": False, | |
| "Operating_Airline _WN": False, "Origin_ATL": False, "Origin_CLT": False, "Origin_DEN": False, "Origin_DFW": False, | |
| "Origin_IAH": False, "Origin_LAX": False, "Origin_ORD": False, "Origin_PHX": False, "Origin_SFO": False, | |
| "Dest_ATL": False, "Dest_CLT": False, "Dest_DEN": False, "Dest_DFW": False, "Dest_IAH": False, "Dest_LAX": False, | |
| "Dest_ORD": False, "Dest_PHX": False, "Dest_SFO": False} | |
| format["Distance"] = distance | |
| format["DepHourofDay"] = dep_hour_of_day | |
| format["AWND_O"] = awnd_o | |
| format["PRCP_O"] = prcp_o | |
| format["TAVG_O"] = tavg_o | |
| format["AWND_D"] = awnd_d | |
| format["PRCP_D"] = prcp_d | |
| format["TAVG_D"] = tavg_d | |
| format["SNOW_O"] = snow_o | |
| format["SNOW_D"] = snow_d | |
| format[f"Quarter_{quarter}"] = True | |
| format[f"Month_{month}"] = True | |
| format[f"DayofMonth_{day_of_month}"] = True | |
| format[f"DayOfWeek_{day_of_week}"] = True | |
| format[f"Operating_Airline _{operating_airline}"] = True | |
| format[f"Origin_{origin}"] = True | |
| format[f"Dest_{dest}"] = True | |
| return pd.DataFrame(format, index=[0]) | |
| def predict(data): | |
| pred = model.predict(data.iloc[:, :]) | |
| return pred[0] | |
| # Streamlit Code | |
| st.title("Flight Delay Prediction") | |
| input1 = st.selectbox("Please Select Your Airline", Operating_Airline) | |
| input2 = st.selectbox("Please Select your Origin Airport", Origin) | |
| input3 = st.selectbox("Please Select your Destination Airport", Dest) | |
| date = st.date_input("Please Pick Date of your Journey") | |
| time = st.time_input("Please Select Scheduled Departure Time") | |
| input1 = airlines[f"{input1}"] | |
| input2 = airports[f"{input2}"] | |
| input3 = airports[f"{input3}"] | |
| if st.button("Predict"): | |
| df = preprocess_input(date,input1,input2,input3,time, pivot_table[input2][input3]) | |
| prediction = predict(df) | |
| if prediction == 1: | |
| st.error("Your Flight is Most Likely to be delayed more than 15 minutes") | |
| else: | |
| st.success("Your flight is likely to be on time") |