🧠 EDA Assistant
EDA Assistant is a Python-based toolkit that automates the Exploratory Data Analysis (EDA) process. It helps you clean datasets, visualize insights, and generate analysis reports automatically. Ideal for data scientists, analysts, and students working on machine learning or data analysis projects.
📁 Project Structure eda_assistant/ │ ├── check_environment.py # Checks Python and package setup ├── debug_imports.py # Debugging module imports ├── run_analysis.py # Main entry point to start EDA ├── test_final.py # Final testing script │ ├── config/ │ └── settings.py # Configuration file for file paths and parameters │ ├── data/ │ ├── raw/ # Unprocessed input data │ ├── processed/ # Cleaned datasets │ └── sample_data/ # Example datasets for demo/testing │ ├── notebooks/ │ ├── 01_data_exploration.ipynb │ ├── 02_analysis_prototyping.ipynb │ └── 03_final_pipeline.ipynb │ ├── reports/ │ ├── analysis_*/ # Auto-generated visual reports │ ├── html_reports/ │ ├── pdf_reports/ │ └── images/ │ └── src/ ├── data_loader.py # Loads datasets ├── data_cleaner.py # Cleans and preprocesses data ├── bivariate_analysis.py # Visualizes variable relationships └── main.py # Central EDA workflow script
- Create a virtual environment python -m venv venv
Activate it:
Windows
venv\Scripts\activate
macOS/Linux
source venv/bin/activate
- Install dependencies pip install -r requirements.txt
🚀 How to Run the Project
python src/data_loader.py --input data/raw/ python run_analysis.py --input data/raw/
📊 Output
Cleaned datasets → data/processed/
Graphs and images → reports/images/
HTML/PDF reports → reports/html_reports/, reports/pdf_reports/
🧩 Example python run_analysis.py --input data/raw/movies.csv --output reports/analysis_latest/
🧰 Requirements
See requirements.txt for all dependencies.
Key libraries include: pandas numpy matplotlib seaborn scikit-learn jupyter
🧑💻 Author
Developed by: Sarvesh Tharun