🤖 The Ultimate Scikit-Learn Machine Learning Handbook

100% Runnable, Production-Ready Machine Learning Notebooks — No External Data Needed

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🎯 Project Mission

This project is a comprehensive, beginner-to-senior level Machine Learning guide built entirely using scikit-learn (sklearn) — the most popular ML library in Python.

Every algorithm is implemented using in-built sklearn datasets, so you can run every notebook instantly — no external data downloads, no setup issues, just clean and working ML code.

Our goal:
To take you from basic ML concepts to production-ready engineering workflows that reflect the real work of a Senior Machine Learning Engineer.

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✅ Why This Project Exists

🚫 No More Data Setup Problems

Every example runs instantly with sklearn’s built-in datasets like load_iris, load_diabetes, and fetch_california_housing.

🧩 Go Beyond .fit() and .predict()

Each notebook explores hyperparameters, regularization, bias-variance tradeoff, and model interpretability — not just model training.

🔗 Learn Production-Ready Pipelines

Every workflow includes Pipeline and ColumnTransformer to show how real-world ML systems prevent data leakage and ensure reproducibility.

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👨‍💻 Who Should Use This Repository?

Audience Level	Learning Focus	Key Takeaway
Beginner / Intermediate	Understanding ML basics, model training, evaluation metrics.	Focus on Sections 1 & 2
Advanced Learner	Cross-validation, scaling, hyperparameter tuning, and ensemble methods.	Focus on Sections 3 & 4
Senior ML Engineer	Model interpretability (SHAP/LIME), feature importance, and full ML pipelines.	Focus on Section 5

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📘 Repository Overview

This repository contains 24 Jupyter Notebooks, grouped into 6 learning modules.
Each notebook includes theory, implementation, evaluation, and interpretation.

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📌 Module 01 — Regression Algorithms

Notebook	Topic
🔗 01_Linear_Regression.ipynb	Simple & Multiple Linear Regression
🔗 02_Ridge_Lasso_ElasticNet.ipynb	Regularization Techniques
🔗 03_SVR_Support_Vector_Regression.ipynb	Kernel SVR
🔗 04_KNN_Regression.ipynb	K-Nearest Neighbor Regression

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📌 Module 02 — Classification Algorithms

Notebook	Topic
🔗 05_Logistic_Regression.ipynb	Binary & Multi-class Classification
🔗 06_Decision_Tree_Classifier.ipynb	Tree Splits & Pruning
🔗 07_KNN_Classifier.ipynb	Distance-based Classification
🔗 08_SVM_Classifier.ipynb	Margin Optimization
🔗 09_Naive_Bayes.ipynb	Probabilistic Classifiers

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📌 Module 03 — Ensemble Techniques

Notebook	Topic
🔗 10_Random_Forest.ipynb	Bagging & OOB Scoring
🔗 11_AdaBoost.ipynb	Adaptive Boosting
🔗 12_Gradient_Boosting.ipynb	Residual Learning
🔗 13_XGBoost_LightGBM.ipynb	Fast Gradient Boosting
🔗 14_Stacking_Voting_Classifier.ipynb	Hybrid Model Stacking

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📌 Module 04 — Clustering (Unsupervised ML)

Notebook	Topic
🔗 15_KMeans_Clustering.ipynb	Cluster Partitioning
🔗 16_Hierarchical_Clustering.ipynb	Dendrograms
🔗 17_DBSCAN.ipynb	Density-Based Clustering
🔗 18_Gaussian_Mixture_Models.ipynb	Soft Clustering

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📌 Module 05 — Dimensionality Reduction

Notebook	Topic
🔗 19_PCA.ipynb	Principal Component Analysis
🔗 20_ICA.ipynb	Independent Component Analysis
🔗 21_tSNE_and_UMAP.ipynb	Non-linear Visualization

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📌 Module 06 — Machine Learning Engineering

Notebook	Topic
🔗 22_Feature_Engineering_and_Preprocessing.ipynb	Encoding, Scaling & Transformers
🔗 23_Pipelines_and_ColumnTransformer.ipynb	Data Leakage Prevention
🔗 24_Model_Selection_and_Tuning.ipynb	Grid Search + CV

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🔬 Notebook Format (Consistent Across All Notebooks)

Each notebook follows a standardized 5-section structure:

Section	Focus	Skill Level
1. Theoretical Foundation	Intuition, math formula, cost function, optimization concept.	Beginner
2. Setup & Dataset	Import libraries, load sklearn datasets, and train-test split.	All
3. Preprocessing & Modeling	Feature scaling, encoding, and model training.	Intermediate
4. Evaluation & Metrics	Metrics like MSE, R², ROC-AUC, F1, confusion matrix, ROC curve.	Advanced
5. Interpretation & Next Steps	SHAP/LIME analysis, regularization, bias-variance, feature importance.	Senior

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🚀 Getting Started

Step 1: Clone the Repository

git clone https://github.com/rohanmistry231/Scikit-Learn-Machine-Learning-Handbook.git
cd Scikit-Learn-Machine-Learning-Handbook

Step 2: Launch Jupyter Notebook

jupyter notebook

Then open any notebook (e.g., Module_01_Regression/01_Linear_Regression.ipynb).

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🤝 Contributing

We welcome contributions from the ML community! If you’d like to add new algorithms, improve explanations, or enhance interpretability sections:

• Follow the 5-section notebook structure
• Use only scikit-learn datasets
• Write clear, documented, and reproducible code

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🌟 Vision

“To build the most practical, instantly runnable, and production-focused Machine Learning resource for Python and scikit-learn.”

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Author: Rohan Mistry License: MIT Framework: Scikit-learn, Python 3.9+