A project leveraging the UCI Adult Income dataset to predict income brackets using a RandomForestClassifier. Emphasis is on feature engineering, data preprocessing with One-Hot Encoding, and model optimization through hyperparameter tuning.

An analytical approach to predicting California housing prices using the RandomForestRegressor and LinearRegressor, with a focus on data preprocessing and feature engineering.

Implemented various machine learning algorithms and techniques learned during the course, such as Nearest Neighbours, conformal prediction, linear regression, Ridge Regression, Lasso, data preprocessing, parameter selection, kernels, neural networks, support vector machines, scikit-learn pipelines, and cross-conformal predictors.

Jupyter Notebook containing various searching and sorting algorithms. Each algorithms is explained. All the algorithms are also compared to each other.

This is a custom backend for the first iteration of the discussion platform. This was created to learn how to create a custom backend using Python and Flask.

A simple Flask app to learn how to use JWT for authentication. This serves as a foundation to using JWT in other projects using Flask.

A simple Django app to learn how to use Django with tokens for authentication. This serves as a foundation to using Django in other projects.