Crime Forecasting Project

Overview

This project focuses on analyzing and forecasting crime patterns using data analytics and machine learning techniques. The goal is to uncover meaningful insights and provide actionable predictions to support policy-making and crime prevention strategies.

Key Features

• Exploratory Data Analysis (EDA): Identified missing values, outliers, and data inconsistencies to ensure data quality.
• Data Cleaning: Addressed issues such as null values and outliers in key columns like cross streets, modus operandi, victim demographics, and weapon descriptions.
• Feature Analysis: Investigated relationships between features to uncover trends and correlations.
• Crime Prediction Model: Developed and evaluated machine learning models to predict crime likelihood and patterns.
• Data Visualization: Created interactive visualizations to communicate findings effectively.

Technologies Used

• Python: For data analysis and modeling.
• Pandas & NumPy: For data manipulation and cleaning.
• Matplotlib & Seaborn: For creating insightful visualizations.
• Scikit-learn: For machine learning model implementation.
• Jupyter Notebook: For an interactive and reproducible workflow.

Dataset

The dataset includes detailed records of crime incidents with features such as:

• Cross Street
• Modus Operandi
• Victim Demographics (Age, Sex, Descent)
• Weapon Used

How to Run the Project

1. Clone the repository:

   git clone https://github.com/your-username/crime-forecasting.git

2. Navigate to the project directory:

   cd crime-forecasting

3. Install the required dependencies:

   pip install -r requirements.txt

4. Open the Jupyter Notebook:

   jupyter notebook crime_forecasting.ipynb

Results

• Key Insights:
  • Identified significant factors influencing crime trends.
  • Highlighted areas and times with higher crime likelihood.
• Model Performance: Achieved [insert metric, e.g., accuracy] on test data.

Future Work

• Extend analysis to include additional datasets.
• Improve model performance with advanced algorithms.
• Deploy the model as a web application for real-time predictions.

Acknowledgments

• Thank You to the Machine Learning Practice faculty at IIT Madras for all the support to complete this Project.

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Feel free to reach out for any questions or suggestions!