Machine Learning: Classification, Regression & NLP

A curated repository of end-to-end machine learning projects demonstrating the application of classification, regression, and natural language processing (NLP) techniques using real-world datasets.

Each notebook showcases not just model training and evaluation, but also proper data preprocessing, visualization, and pipeline design—ideal for applying ML in practical environments.

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Key Features

• Supervised ML (Classification & Regression)
• Text preprocessing & sentiment analysis (NLP)
• Feature engineering and selection
• Model evaluation with precision, recall, F1, ROC-AUC, and MSE
• Clean and annotated Jupyter Notebooks

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Technologies Used

Tool/Library	Purpose
scikit-learn	ML models, pipelines, metrics
pandas / numpy	Data preprocessing and manipulation
matplotlib	Visualization
nltk / re	Text preprocessing and tokenization
Jupyter Notebook	Interactive experimentation environment

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Project Structure

machine-learning-classification-regression-nlp ├── classfication_regression_nlp-Report-Samuel-Pillai.ipynb # Final report notebook with analysis ├── classification_regression_nlp.ipynb # Cleaned version for public viewing ├── README.md

How to Run

Recommended environment: Python 3.9+ with Jupyter installed.

bash:

(Optional) Create a virtual environment

python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows

Install dependencies

pip install -r requirements.txt

Launch Jupyter Notebook

jupyter notebook

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Demo Highlights

•	 Predicting numeric targets using regression (e.g., housing prices)
•	 Classifying sentiments or labels from text
•	 Text cleaning, tokenization, stopword removal
•	 Model comparisons: Logistic Regression, Decision Trees, Random Forests

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License

This project is released under the MIT License.

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Author

Samuel Pillai Email: [email protected]