EDA_Data_Preprocessing

This repository contains notebooks for performing Exploratory Data Analysis (EDA) and data preprocessing. The aim is to provide a comprehensive set of tools and techniques to clean, transform and analyze datasets, making them ready for further analysis or machine learning modeling.

Contents

1. Data Cleaning: Handling missing values, data inconsistencies and outliers.
2. Data Transformation: Encoding categorical variables, feature scaling and creating new features.
3. Exploratory Data Analysis: Visualizations, Insights extraction and data summary.
4. Data Integration: Combining data from multiple sources.

Tools and Libraries

1. Pandas: Data manipulation and analysis
2. Numpy: Numerical operations.
3. Scikit-learn: Feature engineering and preprocessing.
4. Matplotlib and seaborn: Data visualization.