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| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import pickle | |
| import feature_engine | |
| with open('RandomForest.pkl', 'rb') as file_1: | |
| model_rfr = pickle.load(file_1) | |
| with open('model_scaler.pkl', 'rb') as file_2: | |
| model_scaler = pickle.load(file_2) | |
| st.write('Price Prediction based on random forest regression') | |
| location = st.selectbox(label='Location', | |
| options=['Delhi', | |
| 'Coimbatore', | |
| 'Mumbai', | |
| 'Hyderabad', | |
| 'Pune', | |
| 'Jaipur', | |
| 'Chennai', | |
| 'Kochi', | |
| 'Bangalore', | |
| 'Kolkata', | |
| 'Ahmedabad']) | |
| year = st.selectbox(label='Year', | |
| options= | |
| ['2014', '2013', '2017', '2012', '2016', '2015', '2011', '2007', | |
| '2008', '2009', '2019', '2010', '2005', '2006', '2018', '2003', | |
| '2004', 'less than 2003']) | |
| fuel_type = st.selectbox(label='Fuel_Type', options=['CNG', 'Petrol', 'Diesel', 'LPG']) | |
| transmission = st.selectbox(label='Transmission', options=['Manual', 'Automatic']) | |
| owner_type = st.selectbox(label='Owner_Type', options=['First', 'Second', 'Third']) | |
| power = st.number_input(label='Power', min_value=34.20, max_value=616.00, value=420.69, step=0.01) | |
| model = st.selectbox(label='Model', | |
| options=['Alto', 'Innova', 'Etios', 'i20', 'XUV500', 'Fortuner', 'EON', | |
| 'City', 'Q5', 'Grand', 'Corolla', 'Swift', 'Terrano', 'X1', '3', | |
| 'Ikon', 'Ritz', 'Micra', 'Laura', 'Civic', 'Jazz', 'Rapid', 'Polo', | |
| 'i10', 'Pajero', 'Creta', '5', 'Baleno', 'A4', 'Ertiga', 'Ciaz', | |
| 'A', 'Ecosport', 'Endeavour', 'Verna', 'Vitara', 'Indigo', | |
| 'Celerio', 'Sail', 'Scorpio', 'Ameo', 'Wagon', 'Brio', 'Santro', | |
| 'Figo', 'Tiago', 'Beat', 'Xcent', 'Camry', 'Vento', 'Teana', | |
| 'Manza', 'Fiesta', 'B', 'Accord', 'Zen', 'Elantra', 'Indica', | |
| 'Nano', 'Amaze', 'Q3', 'New', 'BRV', 'X3', 'GL-Class', 'GO', | |
| 'Ignis', 'A-Star', 'XE', 'EcoSport', 'KUV', 'Avventura', 'CR-V', | |
| 'Accent', 'XF', 'E-Class', 'M-Class', 'Xylo', 'Mobilio', 'S', | |
| 'Passat', 'Sunny', 'A8', '7', 'Duster', 'Dzire', 'A6', 'Linea', | |
| 'Sumo', 'Grande', 'Cooper', 'Thar', 'Xenon', 'Aveo', 'Tavera', | |
| 'Superb', '800', 'BR-V', 'Verito', 'Q7', 'Eeco', 'Bolero', 'Jetta', | |
| 'Elite', 'Flying', 'Tigor', 'Rover', 'XC90', 'X6', 'Land', | |
| 'Tucson', 'Omni', 'Spark', 'Cruze', 'GLC', 'SX4', 'KWID', 'Getz', | |
| 'Optra', 'Enjoy', 'TUV', 'Compass', 'GLE', 'CLA', 'S60', 'Captur', | |
| 'A3', 'Pulse', 'Punto', 'XC60', 'WRV', 'CrossPolo', 'Cayenne', | |
| 'Octavia', 'Quanto', 'S80', '370Z', '6', 'Sonata', 'Classic', | |
| 'Koleos', 'V40', 'GLA', 'Qualis', 'Freestyle', 'Abarth', 'Versa', | |
| 'redi-GO', 'SLC', 'Lodgy', '1.4Gsi', 'Zest', 'Ssangyong', 'Esteem', | |
| 'Clubman', 'X5', 'Captiva', 'Renault'] | |
| ) | |
| manufacturer = st.selectbox(label='Manufacturer', | |
| options=['Maruti', 'Toyota', 'Hyundai', 'Mahindra', 'Honda', 'Audi', | |
| 'Nissan', 'BMW', 'Ford', 'Skoda', 'Volkswagen', 'Mitsubishi', | |
| 'Mercedes-Benz', 'Tata', 'Chevrolet', 'Datsun', 'Jaguar', 'Fiat', | |
| 'Renault', 'Mini', 'Bentley', 'Land', 'Volvo', 'Jeep', 'Porsche', | |
| 'OpelCorsa' | |
| ]) | |
| df = pd.DataFrame({'Location': [location], | |
| 'Year': [year], | |
| 'Fuel_Type': [fuel_type], | |
| 'Transmission': [transmission], | |
| 'Owner_Type':[owner_type], | |
| 'Power': [power], | |
| 'Model': [model], | |
| 'Manufacturer': [manufacturer], | |
| }) | |
| from sklearn.preprocessing import LabelEncoder | |
| label = LabelEncoder() | |
| label_lst = ['Location', 'Fuel_Type','Transmission','Model','Manufacturer'] | |
| for col in label_lst : | |
| df[col] = label.fit_transform(df[col]) | |
| dic = {'First':2,'Second':1,'Third':0} | |
| df['Owner_Type']=df['Owner_Type'].map(dic) | |
| dic1 = {'2019':17,"2018":16,"2017":15,"2016":14,"2015":13,"2014":12,"2013":11,"2012":10,"2011":9,"2010":8,"2009":7,"2008":6,"2007":5,"2006":4,"2005":3,"2004":2,"2003":1,"less than 2003":0 } | |
| df['Year']=df['Year'].map(dic1) | |
| # Predict using Random Forest Regression | |
| data_inf_scaled = model_scaler.transform(df) | |
| y_pred_inf = model_rfr.predict(data_inf_scaled) | |
| y_pred_inf | |
| st.write('Price Predicted: ') | |
| st.text(y_pred_inf) | |