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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import joblib | |
| import matplotlib.pyplot as plt | |
| # Load the model and columns | |
| lr_clf = joblib.load("bengaluru_home_prices_model.joblib") | |
| X_columns = pd.read_csv("dora.csv") | |
| OHE = pd.read_csv("B5.csv") | |
| locations = OHE['location'].tolist() | |
| # Non-changeable variables | |
| bhk1 = 5 | |
| bath1 = 5 | |
| def predict_price(location, sqft, bath, bhk): | |
| loc_index = np.where(X_columns.columns == location)[0][0] | |
| x = np.zeros(len(X_columns.columns)) | |
| x[0] = sqft | |
| x[1] = bath | |
| x[2] = bhk | |
| if loc_index >= 0: | |
| x[loc_index] = 1 | |
| return lr_clf.predict([x])[0] | |
| def get_price_predictions(location, sqft, bhk): | |
| all_predictions = [] | |
| for bhk_val in range(1, bhk+1): | |
| predictions = [] | |
| for bath in range(1, 6): | |
| price_prediction = predict_price(location, sqft, bath, bhk_val) | |
| predictions.append(price_prediction) | |
| all_predictions.append(predictions) | |
| return all_predictions | |
| st.title('House Price Prediction') | |
| # Sidebar with area and location selection | |
| sqft = st.sidebar.slider('Select the area in sq meters:', min_value=500.0, max_value=3000.0, value=500.0) | |
| location = st.sidebar.selectbox('Select a location:', locations) | |
| bhk = st.sidebar.slider('Select BHK (1-5):', min_value=1, max_value=5) | |
| bath = st.sidebar.slider('Select Bathrooms (1-5):', min_value=1, max_value=5) | |
| estimated_price = predict_price(location, sqft, bath, bhk) | |
| st.write(f"Estimated Price per sqft : ₹ {estimated_price}") | |
| # Predict prices for different numbers of BHKs | |
| predictions = get_price_predictions(location, sqft, bhk1) | |
| # Display a spreadsheet-like table of prices | |
| prices_table = pd.DataFrame(predictions, columns=[f"{i+1} BHK" for i in range(bhk1)], index=[f"{i} Bathrooms" for i in range(1, bath1+1)]) | |
| st.table(prices_table) | |
| # Google Map embedding | |
| st.components.v1.html(""" | |
| <iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d248849.56659330425!2d77.46612584428054!3d12.954280236534027!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x3bae1670c9b44e6d%3A0xf8dfc3e8517e4fe0!2sBengaluru%2C%20Karnataka!5e0!3m2!1sen!2sin!4v1721196570239!5m2!1sen!2sin" | |
| width="600" | |
| height="450" | |
| style="border:0;" | |
| allowfullscreen="" | |
| loading="lazy" | |
| referrerpolicy="no-referrer-when-downgrade"> | |
| </iframe> | |
| """, height=500) | |
| # Plot graphs for each number of BHKs | |
| fig, axs = plt.subplots(bhk1, 1, figsize=(10, bhk1*5), sharex=True) | |
| bath_values = range(1, 6) | |
| colors = ['blue', 'green', 'red', 'purple', 'orange'] # Define different colors for each BHK | |
| for i in range(bhk1): | |
| axs[i].plot(bath_values, predictions[i], label=f'{i+1} BHK', color=colors[i]) # Use a different color for each BHK | |
| axs[i].set_ylabel('Predicted Price per sqft (in ₹)') | |
| axs[i].set_title(f'Predicted Price for {i+1} BHK (in ₹)') | |
| axs[i].legend(loc='center left', bbox_to_anchor=(1, 0.5)) # Position legend to the right of the graph | |
| # Set common x-axis label | |
| fig.text(0.5, 0.04, 'Number of Bathrooms', ha='center', va='center') | |
| plt.tight_layout(pad=3.0) | |
| st.pyplot(fig) | |