link to the project: https://automatic-exploratory-data-analysis-rfzmcrpraqxdvfnuuyms6k.streamlit.app

📌 Automatic Exploratory Data Analysis (EDA) Tool 🚀 A simple and interactive Streamlit app for automatic exploratory data analysis (EDA), including PCA and feature importance analysis.

📖 Overview

This project is a Streamlit-based Exploratory Data Analysis (EDA) tool that allows users to upload CSV datasets and perform various analyses, including: ✅ Data Summary & Statistics ✅ Histograms of Numeric Features ✅ Correlation Matrix Heatmap ✅ Principal Component Analysis (PCA) ✅ Feature Importance using Random Forest

The tool is modularized inside the eda_tool/ package, making it easy to extend and maintain.

📂 Project Structure

Automatic-Exploratory-Data-Analysis/ │── eda_tool/ │ ├── init.py │ ├── data_loader.py # Handles CSV data loading │ ├── eda_summary.py # Provides summary statistics │ ├── missing_values.py # Handles missing value analysis │ ├── outlier_detection.py # Detects outliers │ ├── visualization.py # Visualization functions (histograms, PCA, feature importance) │── app.py # Streamlit app │── requirements.txt # Dependencies │── README.md # Project documentation

🚀 Features

🔹 Data Summary & Insights Displays key statistics such as mean, median, and standard deviation. Identifies missing values in the dataset. 🔹 Data Visualization Histograms for numerical columns. Correlation Matrix heatmap for feature relationships. 🔹 Advanced Analysis Principal Component Analysis (PCA): Visualizes explained variance. Feature Importance Analysis: Uses Random Forest to determine the most important features. 💻 Installation & Setup

1️⃣ Clone the repository git clone https://github.com/your-username/Automatic-Exploratory-Data-Analysis.git cd Automatic-Exploratory-Data-Analysis 2️⃣ Create a virtual environment (Optional but recommended) python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate 3️⃣ Install dependencies pip install -r requirements.txt 4️⃣ Run the Streamlit app streamlit run app.py 📌 Usage

Open the Streamlit interface in your browser. Upload a CSV file. Explore the dataset using the available analysis options. Select a target variable for feature importance analysis. 🔧 Configuration

🛠️ Technologies Used

Python 3.x Pandas, NumPy (Data Processing) Matplotlib, Seaborn (Visualizations) Scikit-learn (Machine Learning for PCA & Feature Importance) Streamlit (Interactive Web App) 📌 Future Improvements

✅ Support for time-series analysis. ✅ More outlier detection techniques. ✅ Dynamic feature selection for ML model training.