This mini-project contains an implementation from scratch of some Multi-Class Classification Algorithms. The data is already cleaned, and doesn't need any further pre-processing, it was encoded using Tf-idf (Term Frequency Inverse Document Frequency).
Firstly, I implemented the Logistic Regression algorithm with One-vs-All strategy to adapt the algorithm for the multi-classification task. I used the Momentum with SGD optimizer for optimizing the Binary Cross-Entropy loss used to get the optimal weight matrix.
Secondly, I implemented Multi-Class SVM with the same strategy as Logistic Regression, and since I chose soft margin SVM to deal with non-linearly separable data, I used the same previous optimizer to optimize the L2 reguralized Hinge loss.

The repository contains the following files & directories:

• Notebooks directory: It contains a jupyter notebook where the main functions of the project are called, and where results are displayed.
• Loss directory: It contains an implementation of the various loss functions mentioned in the description, and their corresponding gradient calculus.
• Algorithms directory: This directory contains an implementation of the various ML algorithms mentioned in the description.
• The dataset: This mini-project was taken from a HackerRank challenge, you can refer to the following link, to get the dataset as well as the instructions to solve the problem.

Implementation of SVM with the Kernel trick, to deal with non-linearly separable data using various Kernel functions (Gaussian, Polynomial, etc..).

For any information, feedback or questions, please contact me