Overview

This project aims to predict future crime rates in Chicago using time series forecasting methods. The dataset consists of historical crime data, which is processed and fed into Facebook Prophet, a robust forecasting tool developed by Facebook. The model is used to forecast future crime rates based on past trends and seasonal patterns.

Project Objective

• To analyze and forecast the crime rate in Chicago using historical crime data.
• To explore seasonal patterns, weekly trends, and potential anomalies in crime data.
• To implement Facebook Prophet for time series forecasting and visualize the results.

Key Features

• Data Preprocessing: Cleaning and transforming raw crime data into a time-series format.
• Exploratory Data Analysis (EDA): Visualization of crime trends over time, monthly/weekly trends, and outliers.
• Facebook Prophet Forecasting: Implementation of Prophet for predicting future crime rates.
• Evaluation: Model performance evaluation using metrics like Mean Absolute Error (MAE) and visualization of forecast vs. actual crime data.
• Visualizations: Trend plots, seasonality analysis, and forecast results.

Dataset

The dataset used for this project contains historical crime data from the City of Chicago. You can access the dataset from Chicago Data Portal.

Columns of Interest:

• Date: The date of the crime incident.
• Primary Type: The type of crime (e.g., theft, assault, robbery).
• Description: Detailed description of the crime.
• Location Description: The location where the crime occurred.
• Year: The year of the incident.
• Arrest: Whether an arrest was made or not.

System Requirements

To run this project, you'll need the following:

• Python 3.6 or higher
• Facebook Prophet (v0.7 or higher)
• pandas (v1.1 or higher)
• matplotlib (v3.3 or higher)
• seaborn (v0.11 or higher)

You can install the required packages using the following command:

pip install pandas matplotlib seaborn prophet