Applied Machine Learning

A comprehensive collection of Jupyter notebooks covering fundamental and advanced machine learning techniques, algorithms, and data preprocessing methods.

📋 Overview

This repository contains practical implementations of various machine learning algorithms and techniques using Python. Each notebook includes hands-on examples with real-world datasets, making it an excellent resource for learning and reference.

📚 Contents

Data Preprocessing

• NumPy and Pandas Fundamentals - Core data manipulation libraries
• Data Preprocessing - Handling missing data, feature scaling, and encoding
• Bestseller Dataset Preprocessing - Real-world data cleaning and preparation

Regression Analysis

• Simple Linear Regression - Income prediction and salary analysis
• Ordinary Least Squares (OLS) - Student score prediction modeling

Classification Algorithms

• Logistic Regression - Social network ads analysis and iris classification
• Decision Trees - Multiple implementations including:
  • Social network ads classification
  • Diabetes prediction
  • Customer churn modeling
• Random Forest - Social network ads classification with ensemble methods

Clustering Techniques

• K-Means Clustering - Customer segmentation using mall dataset
• Agglomerative Clustering - Hierarchical clustering with dendrograms
• Ward Linkage - Advanced hierarchical clustering methods

Ensemble Learning

• AdaBoost - Mushroom classification using boosting techniques

Dimensionality Reduction

• Principal Component Analysis (PCA) - Wine dataset feature reduction and visualization

Practice & Assignments

• Lab assignments and exercises
• Final exam practice notebooks
• Comprehensive practice sessions

🛠️ Technologies & Libraries

• Python 3.x
• Jupyter Notebook
• Core Libraries:
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn

📊 Datasets

The repository includes various datasets for different machine learning tasks:

• Income and salary data
• Social network advertising data
• Mall customer segmentation data
• Diabetes prediction data
• Customer churn data (banking)
• Iris flower dataset
• Wine classification data
• Mushroom classification data

🚀 Getting Started

Prerequisites

pip install numpy pandas scikit-learn matplotlib seaborn jupyter

Running the Notebooks

1. Clone the repository:

git clone https://github.com/suhaasd/applied-machine-learning.git
cd applied-machine-learning

2. Launch Jupyter Notebook:

jupyter notebook

3. Open any notebook and run the cells sequentially

📖 Learning Path

For beginners, recommended learning sequence:

1. Foundation: Start with NumPy and Pandas basics
2. Preprocessing: Learn data cleaning and preparation techniques
3. Regression: Begin with simple linear regression
4. Classification: Progress through logistic regression and decision trees
5. Advanced Topics: Explore ensemble methods, clustering, and PCA

🎯 Key Concepts Covered

• Supervised Learning: Regression and Classification
• Unsupervised Learning: Clustering and Dimensionality Reduction
• Model Evaluation: Cross-validation, accuracy metrics, confusion matrices
• Feature Engineering: Scaling, encoding, and transformation
• Ensemble Methods: Random Forest, AdaBoost
• Visualization: Decision boundaries, dendrograms, scatter plots

📝 Notebook Structure

Each notebook typically includes:

• Problem statement and objectives
• Data loading and exploration
• Data preprocessing and cleaning
• Model implementation
• Training and evaluation
• Visualization of results
• Performance metrics

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page or submit a pull request.

📧 Contact

Suhaas D - @suhaasd

⭐ Acknowledgments

• Datasets used are for educational purposes
• Implementations follow best practices from scikit-learn documentation
• Inspired by practical machine learning applications

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This repository is maintained as part of learning and practicing applied machine learning concepts.