This repository contains a Jupyter notebook titled "Crime classification task.ipynb," which focuses on analyzing and visualizing crime data. The aim is to classiffy crime using machine lerning techniques. The classification of the crime into violent and property based on the UCR document obtained from Data Lacity.
The data cleaning and exploratory analysis for this project were performed in a separate notebook, which can be found here It is recommended to review this notebook for a comprehensive understanding of the initial data processing and exploratory steps.
- Robust Scaling: Applies scaling techniques to make the models less sensitive to outliers.
- Model Training and Evaluation: Utilizes various machine learning classifiers like Random Forest, SVM, Logistic Regression, and KNN.
- Hyperparameter Tuning: Improves model performance by tuning the hyperparameters of the classifiers.
- Robust Scaling: Applies scaling techniques to make the models less sensitive to outliers.
Once you have opened the notebook, you can run each cell to see the analysis step-by-step. Feel free to modify the code to suit your specific data set or analysis requirements.
Contributions to this project are welcome. Please follow these steps to contribute:
- Fork the repository.
- Create a new branch.
- Commit your changes.
- Push to the branch.
- Create a new Pull Request.
Contact For any queries or further discussion, feel free to contact me at lindaooby@gmail.com.