Capstone Project – Exploratory Data Analysis and Machine Learning

Author: Abiodun Samuel Akozo-Emiri
Program: Full Stack Data Science Certification
Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook

📘 Overview

This capstone project focuses on solving a real-world data challenge using end-to-end data science methodology — from data exploration to model building and evaluation. The goal was to extract insights, visualize trends, and build predictive models to guide decisions.

🧰 Project Features

• ✔ Exploratory Data Analysis (EDA)
• ✔ Data cleaning & preprocessing
• ✔ Visualizations using Matplotlib and Seaborn
• ✔ Correlation analysis
• ✔ Model building (Linear Regression, Decision Tree, etc.)
• ✔ Model evaluation (RMSE, R² Score)

📁 File Structure

capstone-project/ ├── capstone_notebook.ipynb # Main Jupyter notebook ├── data/ # Raw datasets (CSV, Excel, etc.) ├── images/ # Charts and visuals ├── README.md # Project description and guide

🧠 Key Learnings

• Data storytelling through EDA
• Real-world model interpretation
• Python-based data science workflow
• Git and version control for project delivery

🚀 How to Run

1. Clone the repo
2. Open capstone_notebook.ipynb in Jupyter
3. Follow step-by-step from data loading to final evaluation

📞 Contact

📧 biosam04@gmail.com
🔗 LinkedIn

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