ML Pipeline Projects 🚀

Welcome to the ML_Pipeline repository, a collection of multiple machine learning projects and implementations. This repository includes a variety of end-to-end machine learning pipelines focused on solving real-world problems such as disease detection, sentiment analysis, recommendation systems, and more.

Each project follows a structured approach, with data preprocessing, feature engineering, model training, evaluation, and prediction. You'll find Jupyter notebooks, datasets, and scripts to understand and reproduce each project.

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📁 Project Structure

ML_Pipeline/
├── Book Recommendations System/
│   ├── Book Recommendation System with Python.ipynb
│   ├── books_data.csv
│
├── Breast Cancer Prediction Using ML/
│   ├── Advance Project Breast Cancer Prediction Using ML.ipynb
│   ├── breast_cancer.csv
│   ├── brest_cancer.pkl
│   ├── PE_breast_cancer.jpeg
│   └── roc_breast_cancer.jpeg
│
├── Heart Disease Prediction/
│   ├── Heart Disease Prediction With Machine Learning.ipynb
│   └── heart.csv
│
├── Hybrid Recommendation System using Python/
│   ├── Hybrid Recommendation System using Python.ipynb
│   └── fashion_products.csv
│
├── Liver Disease Prediction Using ML/
│   ├── Advance Project Liver Disease Prediction Using ML.ipynb
│   ├── liver.csv
│   ├── liver.pkl
│   ├── PE_liver.jpeg
│   └── roc_liver.jpeg
│
├── Movie Recommender System Using Python/
│   ├── Movie Recommendation System.ipynb
│   ├── movies.csv
│   └── ratings.csv
│
├── Online Payments Fraud Detection/
│   └── Online Payments Fraud Detection.ipynb
│
├── Parkinson's Disease Detection/
│   ├── Parkinson's Disease Detection.ipynb
│   └── dataset.csv
│
├── Text Emotions Classification/
│   ├── Text Emotions Classification using Python.ipynb
│   ├── train.txt
│   ├── val.txt
│   └── test.txt
│
├── Tiktok Reviews Sentimental Analysis/
│   └── TikTok Reviews Sentiment Analysis using Python.ipynb
│
├── energy_consumption_yt-master/
│   ├── 0_yt_EDA.ipynb
│   ├── README.md
│   └── dataset/
│       ├── LoureiroDataset.zip
│       ├── LoureiroDataset/loureiro_energy.csv
│       └── LoureiroDataset/weather_aveiro_final.csv
│
└── house-prices-advanced-regression-techniques/
    └── house-prices-advanced-regression-techniques.zip

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📚 Projects Overview

Here is a brief description of each project included in this repository.

Project	Description
Book Recommendation System	Collaborative filtering-based system to recommend books.
Breast Cancer Prediction	Predicts if a tumor is benign or malignant.
Heart Disease Prediction	Predicts heart disease risk using ML models.
Hybrid Recommendation System	Combines collaborative and content-based recommendations.
Liver Disease Prediction	Detects liver disease risk based on clinical data.
Movie Recommender System	Recommends movies using collaborative filtering.
Online Payments Fraud Detection	Detects fraudulent payment transactions.
Parkinson's Disease Detection	Classifies Parkinson's disease using biomedical data.
Text Emotions Classification	Classifies emotions from text into categories.
TikTok Sentiment Analysis	Analyzes TikTok reviews for positive/negative sentiment.
Energy Consumption Analysis	Analyzes energy consumption trends for forecasting.
House Prices Regression	Predicts house prices using regression techniques.

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🔧 Installation Instructions

To run the projects locally, you will need to install the required libraries.

1. Clone the repository

   git clone [email protected]:Amiche02/ML_Pipeline.git
   cd ML_Pipeline

2. Set up a virtual environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows use .\venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run Jupyter Notebook

   jupyter notebook

Note: Each subdirectory may have its own set of dependencies specified within the notebooks. You may need to install additional libraries as required (e.g., scikit-learn, pandas, numpy, matplotlib, etc.).

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💻 Usage

To explore the projects, follow these steps:

1. Open the project folder you're interested in (e.g., Breast Cancer Prediction Using ML).
2. Launch the Jupyter Notebook file (.ipynb).
3. Follow the steps inside the notebook, from data loading to model evaluation.

Each notebook is self-explanatory, with comments and explanations to guide you through the entire machine learning pipeline.

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📈 Machine Learning Techniques

This repository demonstrates several machine learning concepts and techniques, including:

• Data Preprocessing: Handling missing values, encoding categorical data, and feature scaling.
• Data Visualization: Using Matplotlib, Seaborn, and other libraries to visualize data distributions.
• Model Building: Regression, classification, and clustering models.
• Evaluation Metrics: Confusion matrices, accuracy, precision, recall, F1-score, and ROC curves.
• Feature Engineering: Feature selection and dimensionality reduction.
• Deployment: Preparing models for deployment (exporting to .pkl files).

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📂 Datasets

Each project contains its own dataset, found within its corresponding folder. The datasets have been sourced from public repositories like Kaggle and other public sources. Here are some of the datasets used:

• books_data.csv - Data on books for the recommendation system.
• breast_cancer.csv - Clinical data to predict breast cancer.
• liver.csv - Liver disease prediction dataset.
• heart.csv - Heart disease prediction dataset.
• fashion_products.csv - E-commerce product data for recommendations.
• tiktok_reviews.csv - Sentiment analysis on TikTok reviews.
• energy_consumption.csv - Energy consumption dataset for trend analysis.

Note: Some datasets may require unpacking (.zip files). Follow the instructions inside each notebook.

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📦 Dependencies

These are the key Python libraries used in this repository. Each project may have additional dependencies.

• NumPy: Numerical computations
• Pandas: Data manipulation and analysis
• Matplotlib & Seaborn: Data visualization
• Scikit-learn: Machine learning models
• XGBoost & LightGBM: Advanced machine learning models
• NLTK & TextBlob: Natural Language Processing (NLP)
• TensorFlow & Keras: Deep learning models
• Jupyter Notebook: Interactive Python notebooks

To install all required dependencies:

pip install -r requirements.txt

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🛠️ Features

• Multiple machine learning projects in one place.
• End-to-end pipelines with data cleaning, modeling, and evaluation.
• Easy to understand, reproducible Jupyter Notebooks.
• Pre-trained models and serialized files (.pkl files) to save time.
• Datasets included in each project directory.

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🤝 Contribution

If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Make changes and commit (git commit -m 'Add a new feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a Pull Request.

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📜 License

This project is licensed under the MIT License. Feel free to use, modify, and distribute it as you like.

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🎉 Acknowledgments

A big thank you to all the contributors and public datasets from Kaggle and other sources.

If this project has helped you, please give it a ⭐️ on GitHub.

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📞 Contact

If you have any questions, issues, or feedback, feel free to reach out.

Author: Stéphane Kpoviessi
Email: [email protected]