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| import pandas as pd | |
| import streamlit as st | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| import pickle | |
| import sklearn | |
| from PIL import Image | |
| # Load the saved components | |
| with open("dt_model.pkl", "rb") as f: | |
| components = pickle.load(f) | |
| # Extract the individual components | |
| num_imputer = components["num_imputer"] | |
| cat_imputer = components["cat_imputer"] | |
| encoder = components["encoder"] | |
| scaler = components["scaler"] | |
| dt_model = components["models"] | |
| # Create the app | |
| st.set_page_config( | |
| layout="wide" | |
| ) | |
| # Add an image or logo to the app | |
| image = Image.open('grocery_store.jpg') | |
| # Open the image file | |
| st.image(image) | |
| #add app title | |
| st.title(":moneybag: SALES PREDICTION MACHINE LEARNING APP :moneybag:") | |
| # Add some text | |
| st.write("Enter some data for Prediction.") | |
| # Create the input fields | |
| input_data = {} | |
| col1,col2,col3 = st.columns(3) | |
| with col1: | |
| input_data['store_nbr'] = st.slider("store_nbr",0,54) | |
| input_data['products'] = st.selectbox("products", ['AUTOMOTIVE', 'CLEANING', 'BEAUTY', 'FOODS', 'STATIONERY', | |
| 'CELEBRATION', 'GROCERY', 'HARDWARE', 'HOME', 'LADIESWEAR', | |
| 'LAWN AND GARDEN', 'CLOTHING', 'LIQUOR,WINE,BEER', 'PET SUPPLIES']) | |
| input_data['onpromotion'] =st.number_input("onpromotion",step=1) | |
| input_data['state'] = st.selectbox("state", ['Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura', | |
| 'Santo Domingo de los Tsachilas', 'Bolivar', 'Pastaza', | |
| 'Tungurahua', 'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja', | |
| 'El Oro', 'Esmeraldas', 'Manabi']) | |
| with col2: | |
| input_data['store_type'] = st.selectbox("store_type",['D', 'C', 'B', 'E', 'A']) | |
| input_data['cluster'] = st.number_input("cluster",step=1) | |
| input_data['dcoilwtico'] = st.number_input("dcoilwtico",step=1) | |
| input_data['year'] = st.number_input("year",step=1) | |
| with col3: | |
| input_data['month'] = st.slider("month",1,12) | |
| input_data['day'] = st.slider("day",1,31) | |
| input_data['dayofweek'] = st.number_input("dayofweek,0=Sun and 6=Sat",step=1) | |
| input_data['end_month'] = st.selectbox("end_month",['True','False']) | |
| # Create a button to make a prediction | |
| if st.button("Predict"): | |
| # Convert the input data to a pandas DataFrame | |
| input_df = pd.DataFrame([input_data]) | |
| # Selecting categorical and numerical columns separately | |
| cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object'] | |
| num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object'] | |
| # Apply the imputers | |
| input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns]) | |
| input_df_imputed_num = num_imputer.transform(input_df[num_columns]) | |
| # Encode the categorical columns | |
| input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(), | |
| columns=encoder.get_feature_names(cat_columns)) | |
| # Scale the numerical columns | |
| input_df_scaled = scaler.transform(input_df_imputed_num) | |
| input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns) | |
| #joining the cat encoded and num scaled | |
| final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1) | |
| # Make a prediction | |
| prediction =dt_model.predict(final_df)[0] | |
| # Display the prediction | |
| st.write(f"The predicted sales are: {prediction}.") | |
| input_df.to_csv("data.csv", index=False) | |
| st.table(input_df) | |