Machine Learning Projects

Welcome to my Machine Learning repository! This repository is a collection of my projects and experiments as I learn and explore various machine learning concepts and techniques. I will continuously update this repository with new projects, code, and insights as I progress in my machine learning journey. All the codes will primarily be in R.

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Introduction and Overview

This repository covers a wide range of machine learning methods, starting with classical linear and generalized linear models and progressing to modern non-linear statistical learning methods. Below is an overview of the topics that will be covered:

1. Classical Linear and Generalized Linear Models:

   • Linear Regression
   • Logistic Regression
   • Linear Discriminant Analysis

2. Modern Resampling and Variable Selection Methods:

   • Bootstrapping
   • Shrinkage Methods (e.g., Lasso, Ridge Regression)

3. Traditional Multivariate Analysis Methods:

   • Cluster Analysis
   • Principal Component Analysis (PCA)
   • Multivariate Regression Methods (e.g., Structural Equation Modeling)

4. Modern Non-Linear Statistical Learning Methods:

   • Generalized Additive Models (GAM)
   • Decision Trees
   • Random Forest
   • Boosting and Bagging
   • Support Vector Machines (SVM)
   • Neural Networks

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Repository Structure

The repository is organized into folders based on the type of machine learning task. Below is the directory structure:

└── sriramv1212-machine-learning/
    ├── README.md
    ├── Linear Regression/
    │   ├── Ames_Housing_Data.csv
    │   └── Linear Regression 1_ Housing price prediction.Rmd
    └── Logistic Regression/
        ├── Logistic Regression 2- Titanic.Rmd
        └── Titanic2.csv

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Projects

1. Linear Regression: Housing Price Prediction

• Description: This project focuses on predicting house prices using the Ames Housing dataset. Two variable selection methods—stepwise selection and best subset selection—are used to build and compare linear regression models.
• Files:
  • Ames_Housing_Data.csv: Dataset containing housing features and sale prices.
  • Linear Regression 1_ Housing price prediction.Rmd: R Markdown file with the analysis, model building, and evaluation.
• Key Steps:
  • Data splitting into training and testing sets.
  • Stepwise variable selection based on BIC.
  • Best subset selection based on SSE.
  • Model comparison using BIC, RMSE, and R².
• Results:
  • Stepwise model and best subset model coefficients.
  • RMSE and R² values for both models on the test data.
  • Comparison of model performance.

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2. Logistic Regression: Titanic Survival Prediction

• Description: This project predicts passenger survival on the Titanic using logistic regression. The dataset is cleaned, and a logistic regression model is built to classify passengers as survivors or non-survivors.
• Files:
  • Titanic2.csv: Dataset containing passenger information and survival status.
  • Logistic Regression 2- Titanic.Rmd: R Markdown file with the analysis, model building, and evaluation.
• Key Steps:
  • Data cleaning and preprocessing.
  • Splitting data into training and testing sets.
  • Fitting a logistic regression model with 7 predictors.
  • Evaluating the model using a confusion matrix, accuracy, sensitivity, and specificity.
  • Predicting survival for additional passengers.
• Results:
  • Confusion matrix and performance metrics (accuracy, sensitivity, specificity).
  • Predicted survival for additional passengers.

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Status of Machine Learning Methods

Method	Status	Details
Linear Regression	✅ Completed	Link to Project
Logistic Regression	✅ Completed	Link to Project
Linear Discriminant Analysis	⏳ Upcoming	Planned for future updates.
Bootstrapping	⏳ Upcoming	Planned for future updates.
Shrinkage Methods	⏳ Upcoming	Planned for future updates.
Cluster Analysis	⏳ Upcoming	Planned for future updates.
Principal Component Analysis	⏳ Upcoming	Planned for future updates.
Decision Trees	⏳ Upcoming	Planned for future updates.
Random Forest	⏳ Upcoming	Planned for future updates.
Boosting and Bagging	⏳ Upcoming	Planned for future updates.
Support Vector Machines	⏳ Upcoming	Planned for future updates.
Neural Networks	⏳ Upcoming	Planned for future updates.

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Recommended Textbooks

1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2008)
   Trevor Hastie, Robert Tibshirani, Jerome Friedman. Springer.
   Download PDF

2. An Introduction to Statistical Learning with Applications in R (2017)
   Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. Springer.
   Book Website

3. Applied Multivariate Statistics with R (Optional) (2015)
   Daniel Zelterman. Springer.
   ISBN-13: 978-3319361635, ISBN-10: 3319361635.

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How to Use This Repository

1. Clone the Repository:

   git clone https://github.com/sriramv1212/sriramv1212-machine-learning.git

2. Navigate to the Project Folder:

   • For Linear Regression:

     cd sriramv1212-machine-learning/Linear Regression

   • For Logistic Regression:

     cd sriramv1212-machine-learning/Logistic Regression

3. Open the R Markdown File:

   • Use RStudio or any compatible IDE to open and run the .Rmd files.

4. Reproduce the Analysis:

   • Ensure all required libraries are installed.
   • Run the code chunks in the R Markdown file to reproduce the analysis and results.

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Dependencies

• R Libraries:
  • caret
  • MASS
  • leaps
  • dplyr
  • tidyverse

Install the required libraries using:

install.packages(c("caret", "MASS", "leaps", "dplyr", "tidyverse"))

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Author

• Sriram Vivek
• GitHub: sriramv1212
• Email: [email protected]

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgments

• The Ames Housing dataset and Titanic dataset are publicly available and widely used for educational purposes.
• Special thanks to the R community for providing excellent resources and libraries for machine learning.

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Happy Learning!