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