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| import streamlit as st | |
| import pandas as pd | |
| from sklearn.linear_model import LinearRegression | |
| import matplotlib.pyplot as plt | |
| # Page config | |
| st.set_page_config(page_title="Crime Rate Prediction", layout="wide") | |
| st.title("📊 Crime Rate Prediction Based on Past Data") | |
| # CSV path (Make sure this file is uploaded in Streamlit cloud if deployed) | |
| csv_path = "crime_data.csv" | |
| try: | |
| # Load the dataset | |
| df = pd.read_csv(csv_path) | |
| st.subheader("📄 Raw Dataset") | |
| st.dataframe(df) | |
| # Preprocess | |
| 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'] | |
| # Convert string numbers to integers (if needed) | |
| for col in ['2018', '2019', '2020', '2021']: | |
| data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0).astype(int) | |
| # Sidebar for user input | |
| st.sidebar.header("🔍 Predict Future Crime") | |
| selected_state = st.sidebar.selectbox("Select a State/UT", data['State/UT'].unique()) | |
| start_year = st.sidebar.slider("Select starting year for prediction", 2022, 2026, 2022) | |
| # Perform prediction for selected state | |
| selected_row = data[data['State/UT'] == selected_state].iloc[0] | |
| years = [2018, 2019, 2020, 2021] | |
| X_train = pd.DataFrame({'Year': years}) | |
| y_train = selected_row[['2018', '2019', '2020', '2021']].values | |
| model = LinearRegression() | |
| model.fit(X_train, y_train) | |
| future_years = list(range(start_year, 2028)) | |
| predictions = model.predict(pd.DataFrame({'Year': future_years})) | |
| # Prepare result DataFrame | |
| result_df = pd.DataFrame({ | |
| 'Year': future_years, | |
| 'Predicted Crime Cases': [max(0, int(pred)) for pred in predictions] | |
| }) | |
| st.subheader(f"📈 Predicted Crime Rate in {selected_state} ({start_year}–2027)") | |
| st.dataframe(result_df) | |
| # Plotting | |
| fig2, ax2 = plt.subplots() | |
| ax2.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='teal') | |
| ax2.set_xlabel("Year") | |
| ax2.set_ylabel("Predicted Crime Cases") | |
| ax2.set_title(f"Crime Trend Prediction for {selected_state}") | |
| st.pyplot(fig2) | |
| except FileNotFoundError: | |
| st.error(f"❌ File not found at path: {csv_path}. Please check the path.") | |