Machine Learning Practice Repository

This repository contains my hands-on practice work in Machine Learning and Data Preprocessing using Python.
It covers a wide range of data cleaning, feature engineering, transformation techniques, and model-related experiments, primarily implemented in Jupyter Notebooks.

The goal of this repo is to build strong practical foundations for real-world ML projects.

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🧠 Topics Covered

• Data Cleaning & Missing Value Handling
• Feature Engineering & Feature Construction
• Encoding Techniques
  • One-Hot Encoding
  • Ordinal Encoding
  • Binary Encoding
• Imputation Methods
  • Simple Imputer
  • KNN Imputer
  • Random Imputation
  • Indicator Variables
• Feature Scaling & Transformation
  • Power Transform
  • Binning
• Date & Time Feature Extraction
• ColumnTransformer & Pipelines
• PCA (Dimensionality Reduction)
• Handling Outliers
• Small end-to-end ML experiments

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🛠️ Tech Stack

• Python
• Jupyter Notebook
• NumPy
• Pandas
• Scikit-learn
• Matplotlib / Seaborn

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🚀 How to Run Locally

1. Clone the repository:

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

2. Navigate to the project directory:

cd your-repo-name

3. Create a virtual environment (optional but recommended):

python -m venv venv

4. Activate the environment:

Windows: venv\Scripts\activate

Linux/Mac: source venv/bin/activate

5. Install dependencies:

pip install -r requirements.txt

6. Launch Jupyter Notebook:

jupyter notebook

📌 Notes

This repository is for learning and experimentation purposes

Datasets included are mostly toy or practice datasets

Code focuses on clarity and understanding, not production optimization

📬 Contact

Author: Anurag Prajapati If you find this repository useful or have suggestions, feel free to connect!

⭐ If you like this repo, consider giving it a star!