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| import streamlit as st | |
| import pandas as pd | |
| from sklearn.linear_model import LinearRegression | |
| import matplotlib.pyplot as plt | |
| # Page configuration | |
| st.set_page_config(page_title="Crime Rate Predictor", layout="centered") | |
| st.title("🔮 Crime Rate Prediction for Indian States") | |
| # CSV path (Hosted online) | |
| csv_path = "https://huggingface.co/spaces/MLDeveloper/crime_rate_predicition/resolve/main/RS_Session_255_AS_116.1%20(2).csv" | |
| try: | |
| # Load and preprocess data | |
| df = pd.read_csv(csv_path) | |
| data = df[[ | |
| 'State/UT', | |
| 'Number of Cases Registered - 2018-19', | |
| 'Number of Cases Registered - 2019-20', | |
| 'Number of Cases Registered - 2020-21', | |
| 'Number of Cases Registered - 2021-22 (up to 31.10.2021)' | |
| ]].copy() | |
| data.columns = ['State/UT', '2018', '2019', '2020', '2021'] | |
| for col in ['2018', '2019', '2020', '2021']: | |
| data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0).astype(int) | |
| # --- User Inputs --- | |
| st.subheader("📝 Enter Details to Predict Future Crime Rates") | |
| # Dropdown for State selection | |
| state_input = st.selectbox("Select State/UT", sorted(data['State/UT'].unique())) | |
| # Slider for year selection | |
| year_input = st.slider("Select Starting Year", 2022, 2026, 2022) | |
| if state_input: | |
| if state_input in data['State/UT'].values: | |
| selected_row = data[data['State/UT'] == state_input].iloc[0] | |
| X_train = pd.DataFrame({'Year': [2018, 2019, 2020, 2021]}) | |
| y_train = selected_row[['2018', '2019', '2020', '2021']].values | |
| # Train model and predict | |
| model = LinearRegression() | |
| model.fit(X_train, y_train) | |
| future_years = list(range(year_input, 2028)) | |
| predictions = model.predict(pd.DataFrame({'Year': future_years})) | |
| result_df = pd.DataFrame({ | |
| 'Year': future_years, | |
| 'Predicted Crime Cases': [max(0, int(pred)) for pred in predictions] | |
| }) | |
| # # Show predictions | |
| st.subheader(f"📈 Predicted Crime Rate for {state_input} ({year_input} to 2027)") | |
| st.dataframe(result_df, use_container_width=True) | |
| # Plot | |
| fig, ax = plt.subplots() | |
| ax.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='orangered') | |
| ax.set_xlabel("Year") | |
| ax.set_ylabel("Predicted Crime Cases") | |
| ax.set_title(f"{state_input} Crime Rate Prediction") | |
| st.pyplot(fig) | |
| else: | |
| st.warning("⚠️ Please enter a valid State/UT name from the dataset.") | |
| else: | |
| st.info("👈 Please enter a State/UT name to begin prediction.") | |
| except FileNotFoundError: | |
| st.error(f"❌ File not found at path: {csv_path}. Please check the path.") | |