BIA — Business Intelligence & Analytics

Machine learning and data science learning projects.

Projects

1. MachineLearningIntroduction

Simple Linear Regression Model for Housing Price Prediction

A comprehensive introduction to the machine learning pipeline covering data exploration, preprocessing, model training, and evaluation.

• Techniques: Exploratory Data Analysis (EDA), Data Preprocessing, Feature Scaling, Linear Regression, Model Evaluation
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Dataset: 128 housing records (125 after cleaning)
• Performance: R² = 0.7928 (79.28% variance explained), MAE = $9,165, RMSE = $11,548

Quick Start:

cd MachineLearningIntroduction
python main.py

Full Documentation: MachineLearningIntroduction/README.md

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Getting Started

Prerequisites

• Python 3.14+
• Git
• GitHub account

Setup GitHub Credentials

1. Create a GitHub Personal Access Token:

   • Go to GitHub Settings → Personal Access Tokens
   • Click Generate new token
   • Select scope: repo (full control of private repositories)
   • Copy the token (you won't be able to see it again!)

2. Configure local .env file:

   # Copy template
   cp .env.example .env
   
   # Edit .env with your credentials
   # (Windows) Notepad .env
   # (Linux/Mac) nano .env

3. Fill in .env:

   GITHUB_USERNAME=your_username
   GITHUB_TOKEN=your_personal_access_token
   GITHUB_REPO=BIA

⚠️ Security: The .env file is in .gitignore and will NEVER be committed to GitHub. Keep your credentials safe!

Clone & Setup

# Clone the repository
git clone https://github.com/yourusername/BIA.git
cd BIA

# Navigate to a project
cd MachineLearningIntroduction

# (Optional) Create a virtual environment
python -m venv venv
venv\Scripts\Activate

# Install dependencies
pip install -r requirements.txt

# Run the project
python main.py

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Project Structure

BIA/
├── README.md                          (this file)
├── LICENSE                            (MIT License)
├── .gitignore                         (Python .gitignore)
│
└── MachineLearningIntroduction/
    ├── README.md                      (Project documentation)
    ├── requirements.txt               (Project dependencies)
    ├── main.py                        (Entry point)
    ├── data_loader.py                 (Data loading & inspection)
    ├── preprocessing.py               (Feature engineering & scaling)
    ├── eda.py                         (Exploratory data analysis)
    ├── model.py                       (Model training & evaluation)
    │
    └── data/
        └── housing_dataset.csv        (Dataset)

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Future Projects

Additional ML/data science projects will be added to this repository:

• Advanced Regression Models (Polynomial, Ridge, Lasso)
• Classification Models (Logistic Regression, Decision Trees)
• Time Series Forecasting
• Clustering & Unsupervised Learning
• Natural Language Processing
• Deep Learning

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Technologies

• Data Processing: Pandas, NumPy
• Machine Learning: Scikit-learn
• Visualization: Matplotlib, Seaborn
• Environment: Python 3.14+, Virtual Environment

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Learning Objectives

This repository demonstrates:

• ✓ End-to-end ML pipeline development
• ✓ Data exploration & visualization
• ✓ Data preprocessing & feature engineering
• ✓ Model training & evaluation
• ✓ Metric interpretation
• ✓ Code documentation & organization
• ✓ Best practices in machine learning

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License

This project is licensed under the MIT License — see LICENSE for details.

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Contributing

This is a personal learning repository. Feedback and suggestions are welcome!

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Contact

For questions or discussions about these projects, feel free to reach out.

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Last Updated: 2026-07-20