🤖 Machine Learning Projects

A curated collection of Machine Learning projects built using Python, Scikit-learn, Pandas, NumPy, and other popular data science libraries.

This repository demonstrates practical implementations of various Machine Learning concepts, including Regression, Classification, Clustering, Recommendation Systems, and Natural Language Processing (NLP).

Note: To keep the repository lightweight, large datasets, trained model files (.pkl), cache files, virtual environments, and other generated artifacts are excluded using .gitignore.

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📁 Repository Structure

Machine-Learning-Projects/
│
├── Advertising Sales/
├── Advertising Sales MLR/
├── Book Recommender/
├── Boston House Price/
├── Breast Cancer Diagnosis/
├── Car Price/
├── Customer Segmentation/
├── Diamond Price/
├── Email Spam Classifier/
├── Flight Fare/
├── Movie Recommender/
├── Movie Sentiment Analysis/
├── Rock vs Mine Prediction/
└── Song Recommender/

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🚀 Projects Included

Project	Category	Description
📈 Advertising Sales	Regression	Predict sales using advertising expenditure.
📊 Advertising Sales MLR	Multiple Linear Regression	Predict sales using multiple advertising channels.
📚 Book Recommender	Recommendation System	Recommend books using similarity-based filtering.
🏠 Boston House Price	Regression	Predict housing prices from property features.
🎗 Breast Cancer Diagnosis	Classification	Classify tumors as benign or malignant.
🚗 Car Price Prediction	Regression	Estimate used car prices using machine learning.
👥 Customer Segmentation	Clustering	Segment customers using K-Means clustering.
💎 Diamond Price Prediction	Regression	Predict diamond prices from physical attributes.
📧 Email Spam Classifier	NLP	Classify emails as spam or ham.
✈ Flight Fare Prediction	Regression	Predict airline ticket prices.
🎬 Movie Recommender	Recommendation System	Recommend similar movies using content-based filtering.
😊 Movie Sentiment Analysis	NLP	Perform sentiment analysis on movie reviews.
⛏ Rock vs Mine Prediction	Classification	Predict whether a sonar signal represents a rock or a mine.
🎵 Song Recommender	Recommendation System	Recommend songs based on similarity metrics.

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🛠️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook
• Streamlit
• Pickle
• Git & GitHub

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📚 Machine Learning Concepts

Regression

• Linear Regression
• Multiple Linear Regression

Classification

• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machine (SVM)
• Naive Bayes

Clustering

• K-Means Clustering

Recommendation Systems

• Content-Based Filtering
• Cosine Similarity

Natural Language Processing

• Text Preprocessing
• Count Vectorization
• TF-IDF Vectorization
• Spam Detection
• Sentiment Analysis

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📦 Repository Notes

To reduce repository size and improve cloning speed, several files are intentionally excluded from version control through the .gitignore file.

The following are typically not included:

• Large datasets (.csv, .xlsx, .json, etc.)
• Trained machine learning models (.pkl, .joblib)
• Python cache files (__pycache__/)
• Virtual environments (venv/, .venv/)
• IDE configuration files
• Temporary files and logs
• Generated outputs and cache directories

If you would like to run a project locally, you may need to:

• Download the required dataset.
• Train the model to generate the serialized .pkl file (if applicable).
• Update dataset paths as required.

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▶️ Getting Started

1. Clone the repository

git clone https://github.com/<your-username>/Machine-Learning-Projects.git

2. Navigate to the repository

cd Machine-Learning-Projects

3. Open a project

cd "Movie Recommender"

4. Install dependencies

pip install -r requirements.txt

If a project doesn't include a requirements.txt, install the common libraries:

pip install numpy pandas matplotlib seaborn scikit-learn notebook streamlit

5. Run the notebook or application

jupyter notebook

or

streamlit run app.py

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🎯 Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Feature Scaling
• Machine Learning Model Development
• Model Evaluation
• Recommendation Systems
• Natural Language Processing
• Data Visualization
• Model Serialization
• Git & GitHub Version Control

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🔮 Future Enhancements

• Deep Learning Projects
• Time Series Forecasting
• Computer Vision
• Explainable AI (SHAP & LIME)
• Model Deployment
• MLOps Pipelines
• Docker Support
• CI/CD Integration

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🤝 Contributing

Contributions are welcome!

1. Fork this repository.
2. Create a feature branch.
3. Commit your changes.
4. Push to your fork.
5. Open a Pull Request.

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⭐ Show Your Support

If you found this repository helpful, please consider giving it a ⭐ on GitHub.

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👨‍💻 Author

Jeremiah Ranen

• GitHub: https://github.com/JeremiahRanen7