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arima.py

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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(r"C:\Users\hp\Desktop\project\model\arima\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)
+

arima_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a0dc6d9b51cb4a8dccddc7b865981b7d140392f975d8818efaecbac3426cf18
+size 724531