NdumbiData/Help-International.-

This repository focuses on data preprocessing with exploratory data analysis (EDA) and feature engineering. It includes data cleaning, feature creation, and transformation to optimize datasets for machine learning models, providing insights and preparing data for better performance.

Help International.

Data EDA & Feature Engineering This repository focuses on exploratory data analysis (EDA) and feature engineering to prepare data for machine learning models.

Project Structure Data Cleaning: Addressing missing values, outliers, and normalizing data. Exploratory Data Analysis (EDA): Visualizing key features and trends in the dataset. Feature Engineering: Creating new features and transforming data to improve model performance.

Key Features Dataset: Analyzed for trends, distributions, and relationships among variables. Feature Engineering: Includes techniques such as feature scaling, encoding categorical variables, and creating interaction terms. Results: Insights from EDA are used to guide feature engineering and improve the dataset for machine learning.

How to Run Clone the repository. Install necessary dependencies from requirements.txt. Run the Jupyter notebook to explore the data and apply feature engineering techniques.