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app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+pipe = pickle.load(open('pipe.pkl','rb'))
+df = pickle.load(open('df.pkl','rb'))
+
+st.title("Laptop Price Predictor")
+
+company = st.selectbox('Brand',df['Company'].unique())
+
+type = st.selectbox('Type',df['TypeName'].unique())
+
+ram = st.selectbox('RAM(in GB)',[2,4,6,8,12,16,24,32,64])
+
+weight = st.number_input('Weight of the Laptop ')
+
+touchscreen = st.selectbox('Touchscreen',['No','Yes'])
+
+ips = st.selectbox('IPS',['No','Yes'])
+
+screen_size = st.number_input('Screen Size')
+
+resolution = st.selectbox('Screen Resolution',['1920x1080','1366x768','1600x900','3840x2160','3200x1800','2880x1800','2560x1600','2560x1440','2304x1440'])
+
+cpu = st.selectbox('CPU',df['Cpu brand'].unique())
+
+hdd = st.selectbox('HDD(in GB)',[0,128,256,512,1024,2048])
+
+ssd = st.selectbox('SSD(in GB)',[0,8,128,256,512,1024])
+
+gpu = st.selectbox('GPU',df['Gpu Brand'].unique())
+
+os = st.selectbox('OS',df['OS_Brand'].unique())
+
+if st.button('Predict Price'):
+    ppi = None
+    if touchscreen == 'Yes':
+        touchscreen = 1
+    else:
+        touchscreen = 0
+        
+    if ips == 'Yes':
+        ips = 1
+    else:
+        ips = 0
+        
+    X_res = int(resolution.split('x')[0])
+    Y_res = int(resolution.split('x')[1])
+    ppi = ((X_res**2) + (Y_res**2))**0.5/screen_size
+        
+    query = np.array([company,type,ram,weight,touchscreen,ips,ppi,cpu,hdd,ssd,gpu,os])
+    query = query.reshape(1,12)
+    st.title("The predicted Price of this Configuration is Rs." + str(int(np.exp(pipe.predict(query)[0]))))

df.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e0554a6fb0c97293bd11079ef2ce8e4a7f7c6d77c81f0dea913cd4e836efda90
+size 124872

pipe.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b3b92429694bcfd7429a25bac76393739b2586a9f6fd8204bbeae57a270fcf0
+size 4868909