Welcome to my Supervised Machine Learning repository!
This project is a collection of all the files, notebooks, and scripts I created while learning and practicing supervised machine learning. It reflects my personal journey — from understanding fundamental concepts to implementing models and evaluating their performance on real-world datasets.
Linear Regression (Simple and Multiple)
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes Classifier
model-selection
Performance evaluation (Accuracy, Precision, Recall, F1-Score etc)
Data preprocessing and feature engineering
I created this repository as part of my machine learning self-study journey. It helped me reinforce theoretical knowledge by applying it practically. It also serves as a portfolio to demonstrate my learning progress and a reference for future projects.
Python
Scikit-learn
Pandas and NumPy
Matplotlib and Seaborn
This repository is mainly for personal learning, but feel free to explore, fork, or adapt it for your own educational purposes. Suggestions for improvement are always welcome.
If you're interested in machine learning or have feedback, feel free to connect with me on GitHub or LinkedIn.
If you find this repository helpful, consider giving it a star on GitHub! Jupyter Notebook