Waterfront
", unsafe_allow_html=True) waterfront_input = st.sidebar.radio("Select Yes or No", ["Yes", "No"], index=1) waterfront = 1 if waterfront_input == 'Yes' else 0 st.sidebar.write("View
", unsafe_allow_html=True) view_input = st.sidebar.radio("Select Yes or No", ["Yes", "No"], index=1, key='Yes') view = 1 if view_input == 'Yes' else 0 st.sidebar.write("Condition
", unsafe_allow_html=True) st.sidebar.write("1: Poor
", unsafe_allow_html=True) st.sidebar.write("2: Fair
", unsafe_allow_html=True) st.sidebar.write("3: Average
", unsafe_allow_html=True) st.sidebar.write("4: Good
", unsafe_allow_html=True) st.sidebar.write("5: Excellent
", unsafe_allow_html=True) condition = st.sidebar.slider('Select from above',1,5,3,1,key=5) st.sidebar.write("Grades
", unsafe_allow_html=True) st.sidebar.write("(1-3): Poor Construction
", unsafe_allow_html=True) st.sidebar.write("(4-7): Standard Construction
", unsafe_allow_html=True) st.sidebar.write("(8-9): Good to Very Good Quality
", unsafe_allow_html=True) st.sidebar.write("(10-12): High Quality with Luxury Features
", unsafe_allow_html=True) grade = st.sidebar.slider('Select from above',1,12,6,1,key=6) sqft_above = st.sidebar.slider('Sqft Above', 300, 10000, 560, 50,key=7) sqft_basement = st.sidebar.slider('Sqft Basement', 0, 5000, 0, 50,key=8) yr_built = st.sidebar.slider('Year Built', 1900, 2022, 1970, 1,key=9) yr_renovated = st.sidebar.slider('Year Renovated', 1900, 2022, 1970, 1, key=10) sqft_living15 = st.sidebar.slider("Average interior square footage of the 15 nearest neighbors' living spaces", 300, 10000, 560, 50, key=11) sqft_lot15 = st.sidebar.slider('The average square footage of the land lots of the 15 nearest neighbors', 400, 20000, 700, 100, key=12) zipcode_options = ['98002', '98003', '98004', '98005', '98006', '98007', '98008', '98010', '98011', '98014', '98019', '98022', '98023', '98024', '98027', '98028', '98029', '98030', '98031', '98032', '98033', '98034', '98038', '98039', '98040', '98042', '98045', '98052', '98053', '98055', '98056', '98058', '98059', '98065', '98070', '98072', '98074', '98075', '98077', '98092', '98102', '98103', '98105', '98106', '98107', '98108', '98109', '98112', '98115', '98116', '98117', '98118', '98119', '98122', '98125', '98126', '98133', '98136', '98144', '98146', '98148', '98155', '98166', '98168', '98177', '98178', '98188', '98198', '98199'] selected_zipcode = st.sidebar.selectbox('Zipcode', zipcode_options) data = { 'bedrooms': bedrooms, 'bathrooms': bathrooms, 'sqft_living': sqft_living, 'sqft_lot': sqft_land, 'floors': floors, 'waterfront': waterfront, 'view': view, 'condition': condition, 'grade': grade, 'sqft_above': sqft_above, 'sqft_basement': sqft_basement, 'yr_built': yr_built, 'yr_renovated': yr_renovated, 'sqft_living15': sqft_living15, 'sqft_lot15': sqft_lot15, f'zipcode_{selected_zipcode}': True, # Set the selected zipcode to True } # Set all zipcodes to False for zipcode_option in zipcode_options: data[f'zipcode_{zipcode_option}'] = False # Set the selected zipcode to True data[f'zipcode_{selected_zipcode}'] = True features = pd.DataFrame(data, index=[0]) return features user_features = user_input_features() st.subheader('User Input Parameters') st.write(user_features) #loading dataset @st.cache_data def load_dataset(): return pd.read_csv('house_data.csv') df = load_dataset() y=df['price'] X=df.drop("price",axis=1) #splitting dataset @st.cache_data def split_dataset(): return train_test_split(X, y, test_size=0.2, random_state=0) X_train, X_test, y_train, y_test = split_dataset() #model Training @st.cache_data def train_model(): rfregressor = RandomForestRegressor(n_estimators=500, random_state=0, n_jobs=4, max_depth=30, max_features='sqrt', min_samples_leaf=1, min_samples_split=10) rfregressor.fit(X_train, y_train) return rfregressor model = train_model() #Prediction @st.cache_data def make_prediction(features): # Load the model inside the function to avoid UnhashableParamError model = train_model() # Extract feature names from the training data feature_names = X.columns.tolist() # Ensure the input features have the same order as the training data features = features[feature_names] return model.predict(features) y_pred = make_prediction(X_test) prediction = make_prediction(user_features) st.write('## Prediction') st.write(f'Predicted House Price: **${int(prediction[0]):,}**') st.write("## Data and Result Analysis") st.subheader('Dataframe Sample') st.write(df.head(10)) #Model Analysis def analyze_model(X_test, y_test, model): model_params = (model.get_params(),) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) mae = mean_absolute_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) return mse, rmse, mae, r2 mse, rmse, mae, r2 = analyze_model(X_test, y_test, model) # Display the mathematical result analysis st.subheader('Mathematical Result Analysis') st.write(f'R-squared (R2): **{r2:.2f}**') st.write(f'Mean Squared Error (MSE): **{mse:.2f}**') st.write(f'Root Mean Squared Error (RMSE): **{rmse:.2f}**') st.write(f'Mean Absolute Error (MAE): **{mae:.2f}**') # Add space to push the footer to the bottom st.markdown("