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Data Preprocessing and Exploratory Data Analysis

This repository contains a collection of Jupyter notebooks that demonstrate different techniques for data preprocessing, exploratory data analysis (EDA), feature engineering, and predictive modeling.
The notebooks cover a wide range of tasks commonly performed in a data science workflow — from cleaning and transforming raw datasets to building and evaluating machine learning models.

📂 Repository Structure

Notebook Description
Classification_and_prediction_regression.ipynb Builds classification and regression models, evaluates predictive performance.
Data_Aggregation.ipynb Demonstrates grouping, summarizing, and aggregating datasets.
Data_Transformation.ipynb Shows how to transform raw data (scaling, normalization, handling missing values, encoding).
Feature_Selection.ipynb Implements techniques for selecting the most relevant features.
Feature_Transformation.ipynb Feature engineering and transformations (e.g., encoding, PCA).
Handle_Imbalanced_Classes.ipynb Methods to deal with imbalanced datasets (oversampling, undersampling, SMOTE).
Market_Basket_Analysis.ipynb Association rule mining and frequent itemset analysis.
PCA_Data_Mine.ipynb Principal Component Analysis (PCA) for dimensionality reduction.
Webscrapping.ipynb Collecting datasets via web scraping (typo in filename: should be Webscraping).

📊 Workflow

The general data science workflow demonstrated in this repository is:

  1. Data Collection → e.g. Webscrapping.ipynb
  2. Exploration & Cleaning → Data_Transformation.ipynb, Data_Aggregation.ipynb
  3. Feature Engineering → Feature_Selection.ipynb, Feature_Transformation.ipynb
  4. Dimensionality Reduction → PCA_Data_Mine.ipynb
  5. Modeling & Evaluation → Classification_and_prediction_regression.ipynb, Handle_Imbalanced_Classes.ipynb
  6. Advanced Analysis → Market_Basket_Analysis.ipynb

🛠️ Requirements

  • Python 3.8+
  • Jupyter Notebook / JupyterLab
  • Common ML/data libraries:
    • pandas, numpy, matplotlib, seaborn
    • scikit-learn
    • imbalanced-learn
    • mlxtend
    • beautifulsoup4, requests (for web scraping)

📌 Notes

  • Some notebooks may rely on sample datasets (e.g., CSV files or scikit-learn’s built-in datasets).
  • Ensure file paths are updated if you use your own datasets.
  • The notebook Webscrapping.ipynb should be renamed to Webscraping.ipynb for clarity.

🤝 Contributing

Contributions are welcome!

  • Open an issue if you find a bug or want to suggest improvements.
  • Submit a pull request if you want to add more preprocessing or EDA techniques.