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This repository is meant to document my hands-on experience with supervised learning algorithms and techniques. It includes a variety of exercises, and experiments using different types of data and tools. Each file represents a step forward in building my machine learning skills.

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Decision Tree (REG).ipynb
Decision Tree (REG).ipynb
 
 
Decision Tree (class).ipynb
Decision Tree (class).ipynb
 
 
Imbalanced datasets.ipynb
Imbalanced datasets.ipynb
 
 
K_nearest neighbor (Reg).ipynb
K_nearest neighbor (Reg).ipynb
 
 
Lasso&Ridge_regularization technique.ipynb
Lasso&Ridge_regularization technique.ipynb
 
 
Logistic Regression (Multi class).ipynb
Logistic Regression (Multi class).ipynb
 
 
Logistic_Regression(Binary class).ipynb
Logistic_Regression(Binary class).ipynb
 
 
Multiple_linear_regression.ipynb
Multiple_linear_regression.ipynb
 
 
Naive Bayes.ipynb
Naive Bayes.ipynb
 
 
Polynomial regression.ipynb
Polynomial regression.ipynb
 
 
README.md
README.md
 
 
Recall , precision , F1 score.ipynb
Recall , precision , F1 score.ipynb
 
 
SVM Linear Regression.ipynb
SVM Linear Regression.ipynb
 
 
Simple_linear_regression.ipynb
Simple_linear_regression.ipynb
 
 
k_nearest neighbor (class).ipynb
k_nearest neighbor (class).ipynb
 
 
pre & post pruning.ipynb
pre & post pruning.ipynb
 
 
svm linear classification.ipynb
svm linear classification.ipynb
 
 
svm non-linear classification.ipynb
svm non-linear classification.ipynb
 
 
svm non-linear regression.ipynb
svm non-linear regression.ipynb
 
 

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Supervised Machine Learning Practice Repository

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.

Topics Covered

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

Why I Built This

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.

Tools and Technologies Used

Python

Scikit-learn

Pandas and NumPy

Matplotlib and Seaborn

Contributions

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.

Connect with Me

If you're interested in machine learning or have feedback, feel free to connect with me on GitHub or LinkedIn.

Support

If you find this repository helpful, consider giving it a star on GitHub! Jupyter Notebook

About

This repository is meant to document my hands-on experience with supervised learning algorithms and techniques. It includes a variety of exercises, and experiments using different types of data and tools. Each file represents a step forward in building my machine learning skills.

Topics

python data-science machine-learning machine-learning-algorithms jupyter-notebook data-analysis supervised-machine-learning sciket-learn

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