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| import streamlit as st | |
| import pickle | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| st.title("Future crime pattern prediction ") | |
| # Load the trained ARIMA model | |
| with open("arima_model.pkl", 'rb') as f: | |
| model_fit = pickle.load(f) | |
| # Define number of future periods for forecasting | |
| future_steps = st.number_input("Enter number of months to forecast:", value=12, min_value=1, step=1) | |
| #start_date_input = st.date_input("Enter start date for the forecast:", value=pd.Timestamp.today()) | |
| # Convert the user input date to a pandas Timestamp for compatibility | |
| start_date = pd.to_datetime("2024-03-2") | |
| # Forecast future crime patterns | |
| future_forecast = model_fit.forecast(steps=future_steps) | |
| # Get the dates for the forecasted period | |
| future_dates = pd.date_range(start=start_date, periods=future_steps + 1, freq='ME')[1:] | |
| # Plot forecasted values | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(future_dates, future_forecast, label="Forecast") | |
| #plt.fill_between(future_dates, conf_int[:, 0], conf_int[:, 1], color='gray', alpha=0.3, label='95% Confidence Interval') | |
| plt.legend() | |
| plt.title("Future Crime Forecast") | |
| plt.xlabel("Date") | |
| plt.ylabel("Predicted Crime Count") | |
| st.pyplot(plt) | |
| # Display forecasted values | |
| st.write(f"Forecasted values for the next {future_steps} months:") | |
| st.write(future_forecast) | |