Ensemble Methods

• 11_random_forest/ - Random Forest classifier and regressor

• 19_Bagging/ - Bagging ensemble technique

  • diabetes.csv

Model Evaluation & Optimization

• 12_KFold_Cross_Validation/ - K-Fold cross-validation for model evaluation

  • Exercise/ - Practice exercises

• 15_gridsearch/ - Grid Search for hyperparameter tuning

Clustering

• 13_kmeans/ - K-Means clustering algorithm
  • income.csv

Advanced Topics

• 16_regularization/ - L1 (Lasso) and L2 (Ridge) regularization

  • Melbourne_housing_FULL.csv

• 18_PCA/ - Principal Component Analysis for dimensionality reduction

  • PCA_tutorial_digits.ipynb

• 20_FeatureEngineering/ - Feature engineering techniques

  • 1_outliers/ - Percentile-based outlier detection
  • 2_outliers_z_score/ - Z-score based outlier detection
  • 3_outlier_IQR/ - Interquartile Range outlier detection
  • Includes: bhp.csv, heights.csv

🌐 Live site (GitHub Pages)

The landing page is built from the src/ folder and deployed automatically on every push to main.

Site URL: https://lokeshpuma.github.io/Machine_Learning/

Enable Pages (one-time)

1. Open Settings → Pages on github.com/lokeshpuma/Machine_Learning
2. Under Build and deployment, set Source to GitHub Actions
3. Push to main — the Deploy GitHub Pages workflow publishes src/

🚀 Getting Started

1. Navigate to any folder to find Jupyter notebooks (.ipynb files)
2. Each notebook contains:
   • Explanations of the algorithm/concept
   • Code examples with sample datasets
   • Visualizations and results
3. Opening a notebook requires Jupyter or VSCode with Jupyter extension

📊 Topics Covered

• Linear & Logistic Regression
• Decision Trees & Random Forests
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier
• Clustering (K-Means)
• Dimensionality Reduction (PCA)
• Ensemble Methods (Bagging, Random Forest)
• Cross-validation & Model Evaluation
• Hyperparameter Tuning (GridSearch)
• Regularization (L1/L2)
• Feature Engineering & Outlier Detection

💾 Data Files

The collection includes various CSV datasets for practice:

• homeprices.csv - Housing prices
• carprices.csv - Car prices
• insurance_data.csv - Insurance data
• titanic.csv - Titanic survival dataset
• spam.csv - Email spam classification dataset
• income.csv - Income clustering data
• diabetes.csv - Diabetes prediction data
• salaries.csv - Salary classification
• bhp.csv, heights.csv - Outlier detection samples
• Melbourne_housing_FULL.csv - Real estate data for regularization

📘 Prerequisites

• Python 3.x
• Libraries: scikit-learn, pandas, numpy, matplotlib, seaborn
• Jupyter Notebook or Jupyter Lab

🔧 Installation

pip install jupyter scikit-learn pandas numpy matplotlib seaborn

📂 Repository layout

📝 Notes

• Each folder is self-contained with its own notebooks and datasets
• Notebooks include both theory and practical implementations
• Code examples are ready to run with provided datasets
• Perfect for learning machine learning concepts sequentially

This collection serves as a learning resource for machine learning fundamentals and practical applications.