Toronto Crime Analytics & Prediction

Analyze and forecast Toronto crime using Python. This repo contains two Jupyter notebooks: one for exploratory data analysis (EDA) with geospatial mapping and one for training/benchmarking predictive models.

🔎 Overview

• Explore Assault and Auto Theft patterns by area and time.
• Build and compare ML models (Gradient Boosting, Random Forest, Linear/XGBoost).
• Evaluate with MSE, MAE, RMSE, R² and export clean result tables.
• Produce interactive Folium maps (GeoPandas) for spatial insight.

📁 Notebooks

1. Gson Data Analytics Toronto Crime Statistics.ipynb

   • Cleans and joins geospatial data (GeoPandas), renders Folium maps.
   • Trains an XGBoost Regressor (e.g., predicting assault counts).
   • Outputs metrics and an “Actual vs Predicted” comparison table.

2. Toronto Crime Statiscal Prediction.ipynb

   • Ingests Assault and Auto Theft datasets.
   • Trains/compares GradientBoostingRegressor, RandomForestRegressor, LinearRegression with train/test split, cross-validation, and GridSearchCV.
   • Exports performance summaries and error tables; saves plots to /mnt/data/plots.

🧰 Tech Stack

• Data/ML: pandas, numpy, scikit-learn, xgboost
• Geo/Maps: geopandas, folium
• Viz: matplotlib, seaborn
• (Some notebooks also import panel, pyngrok, gradio, jupyter_bokeh.)

📦 Data

• Expected CSVs (example): Assault.csv, AutoTheft.csv
• Place in a local data/ folder (or update paths inside the notebooks).
• If using Colab, replace upload cells with direct pd.read_csv("data/<file>.csv") or use the built-in upload prompt.

▶️ How to Run

git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>
# (optional) python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter notebook