📊 Data Science Portfolio — LunarTech Case Studies

A collection of Data Science projects built as part of my learning journey through the LunarTech Data Science course. Each project covers a different core concept — from regression and hypothesis testing to exploratory data analysis.

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🗂️ Projects Overview

🏠 1. California Housing Price Prediction — Linear Regression

Predicting median house values using OLS and Scikit-learn Linear Regression

• Data cleaning, outlier removal (IQR), feature engineering
• OLS regression with full assumption checking (linearity, exogeneity, homoscedasticity)
• Model evaluation using RMSE
• Folder: linear-regression/

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🧪 2. A/B Testing — Webpage Click Rate Analysis

Statistical hypothesis testing to evaluate a new webpage design

• Z-test, p-value, confidence intervals
• Practical vs statistical significance
• Bell curve visualization with rejection regions
• Folder: ab-testing/

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🛒 3. Superstore Sales — Exploratory Data Analysis

Deep-dive EDA on retail transactional data to uncover business insights

• Customer segmentation, shipping analysis
• Geographic sales breakdown (state & city level)
• Product category performance
• Yearly, quarterly, and monthly sales trends
• Folder: superstore-eda/

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🔧 Tech Stack

Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • Statsmodels • SciPy

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🚀 How to Run Any Project

1. Clone the repository

   git clone https://github.com/your-username/your-repo-name.git

2. Navigate to the project folder

   cd linear-regression   # or ab-testing / superstore-eda

3. Install dependencies

   pip install pandas numpy matplotlib seaborn scikit-learn statsmodels scipy

4. Open the notebook

   jupyter notebook

5. Run all cells: Kernel → Restart & Run All

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👤 Author

[Reshal Menezes]
BSc IT Student | Aspiring Data Scientist
LinkedIn • GitHub

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🙏 Acknowledgements

All projects built by following the LunarTech Data Science course.