🧠 Machine Learning From Scratch

This repository contains implementations of core Machine Learning algorithms from scratch, without relying on high-level ML libraries like scikit-learn. Each folder contains one algorithm, its dataset (if applicable), and Jupyter notebooks for step-by-step explanation and code.

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## 📂 Project Structure
from scratch/ 
├── linear regression/ 
├── multiple linear regression/ 
├── logistic regression/ 
├── polynomial_regression/ 
├── anaconda_projects/ 
└── README.md

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This repo needs an overhaul as i will be writing the files again in pure python and avoiding the use of notebooks. I will try to use google collab for any gpu-required tasks

✅ Completed Modules

📈 Linear Regression

• linear_regression.ipynb: Simple linear regression from scratch
• linereg_normal.ipynb: Implementation using the Normal Equation
• Dataset: data.csv : study hours vs marks

🔢 Multiple Linear Regression

• multiple_linear_regression.ipynb: Handles multivariate input
• Dataset: California Housing dataset from sklearn

🔁 Logistic Regression

• logistic_regression.ipynb: Binary classification from scratch
• Dataset: Social_Network_Ads.csv

📊 Polynomial Regression

• polynomial_regression.ipynb: Captures non-linear relationships
• Dataset: Fish.csv

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🚧 Upcoming / In Progress

• Decision Trees
• Naive Bayes
• K-Means Clustering
• SVM

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📌 Goals of This Repository

• Deepen understanding by implementing algorithms step-by-step
• Visualize how models work under the hood
• Create a growing ML reference from scratch
• Use minimal libraries (only numpy, pandas, matplotlib)

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📦 Environment

All notebooks are tested using:

• Python 3.x
• Jupyter Notebook
• (Optional) Anaconda for environment management

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🧩 Tips

• Each folder includes a .ipynb notebook with math, logic, and implementation.
• Check .ipynb_checkpoints/ if you're resuming work or looking for autosaves.
• Some folders include datasets used for demonstration.

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🧠 Author

Ethan Shibu
🔗 LinkedIn

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🏷️ Tags

#MachineLearning #FromScratch #Regression #Python #BeginnerProjects #DataScience

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