This project analyzes the Major Crime Indicators (MCI) dataset from the Toronto Police Service to identify crime trends and high-risk areas, enhancing public safety through data-driven insights.
project_name/
│
├── data/ # Dataset storage
│ └── MCI_data.csv # MCI dataset file
│
├── notebooks/ # Jupyter notebooks for exploration and analysis
│ └── analysis.ipynb
│
├── scripts/ # Python scripts for preprocessing and modeling
│ └── models.py
│
├── requirements.txt # Project dependencies
│
└── README.md # Project documentation
To get started with this project:
- Clone the repository:
git clone https://github.com/mohaimenhasan/Toronto-Crime-Predictor.git- Navigate to the project directory:
cd Toronto-Crime-Predictor- Set up a virtual environment:
python -m venv env
source env/bin/activate # Use `.\env\Scripts\activate` on Windows- Install dependencies:
pip install -r requirements.txt- Launch Jupyter Notebook to access the notebooks:
jupyter notebookThe data/ directory contains the crime dataset provided by the Toronto Police Service. To download the dataset vist - https://www.tps.ca/data-maps/open-data/
The CSVs I collected for this project are:
- Major_Crime_Indicators_Open_Data.csv
- Major_Crime_Indicators_Open_Data.geojson
- Major_Crime_Indicators_Open_Data.kml
The notebooks/ directory includes Jupyter notebooks with detailed data analysis and visualization.
The scripts/ directory contains Python code for data preprocessing and machine learning models.
Dependencies are listed in the requirements.txt file and can be installed using pip.
This project is released under the MIT License.
Contributions to this project are welcome! Please refer to CONTRIBUTING.md for guidelines.
- Toronto Police Service for providing the dataset.
- Contributors who helped with the project.
For any inquiries, please reach out to mohaimenhasan@gmail.com.