Welcome to my comprehensive Data Science learning repository! 📊
This project documents my journey from the fundamentals of Statistics and Probability to building advanced Machine Learning models. It serves as both a theoretical archive and a practical portfolio, demonstrating my ability to handle data pipelines from scratch.
The repository is organized into two main sections to separate theory from practice:
Foundations, Algorithms, and Mathematics. This section contains theoretical notes and algorithms implemented from scratch. It covers:
- Statistics & Probability: Distributions, Central Limit Theorem, Hypothesis Testing.
- Regression Analysis: Linear & Logistic Regression logic and math.
Real-world Applications and Hands-on Labs. This section contains practical mini-projects and data analysis notebooks. It covers:
- Data Preprocessing: Cleaning, filtering, and feature engineering with Pandas.
- Visualization: Insightful charts using Matplotlib and Seaborn.
- Modeling: Building predictive models using Scikit-Learn.
To maintain a lightweight repository, large datasets (.csv files) are not hosted here. Please refer to the inner README files for instructions on how to obtain the necessary data for the examples.
Maintained by [İsmail İbican]