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🧠 Machine Learning Practice

Welcome to my personal Machine Learning repository! This space is dedicated to my continuous journey in Data Science, serving as a collection of projects, experiments, and code snippets where I apply theoretical concepts to real-world data.

🎯 Key Focus Areas

  • 📊 Exploratory Data Analysis (EDA): Uncovering patterns and insights using Pandas, Matplotlib, and Seaborn.
  • ⚙️ Feature Engineering: Transforming raw data into meaningful features (Scaling, Encoding, Imputation).
  • 🤖 Machine Learning Algorithms: Implementing models from Linear Regression to Random Forests and Boosting techniques.

🚀 Goal

To consistently practice ML concepts, document my learning progress, and build a robust library of reference code for future projects.