This repository contains essential techniques and implementations for Data Preprocessing using Python and Jupyter Notebook. Data preprocessing is a critical step in any data science or machine learning workflow, ensuring raw data is clean, structured, and ready for analysis.

📂 Repository Contents

🧹 Data Cleaning – Handling missing values, duplicates, and inconsistencies

🔄 Data Transformation – Scaling, normalization, and encoding categorical data

🏗️ Feature Engineering – Creating, modifying, and selecting important features

🔻 Dimensionality Reduction – PCA, LDA, and other techniques

🚨 Outlier Detection & Handling – Identifying and dealing with anomalies

📊 Real-world Case Studies – Applying preprocessing techniques on real datasets

🛠 Tools & Technologies Used

Programming Language: Python 🐍

Notebook Environment: Jupyter Notebook 📒

Key Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, etc.

This repository serves as a valuable reference for anyone working with data, from beginners to experienced data scientists