🔎 Exploratory Data Analysis & AutoML App

This application allows you to upload a dataset (CSV or Excel), perform Exploratory Data Analysis (EDA), and then train Machine Learning models automatically and interactively using Streamlit.

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✨ Features

🧮 Exploratory Data Analysis (EDA)

• Upload dataset (.csv, .xlsx)
• Select the target variable (y) from the available columns
• Univariate analysis (numerical and categorical distributions)
• Bivariate analysis (numerical vs target, categorical vs target)
• Correlation matrix
• PCA Analysis (dimensionality reduction)
• Clustering with KMeans and DBSCAN (with Silhouette Score)
• Normality test (Shapiro-Wilk)
• Download the dataset enriched with cluster labels

🤖 AutoML

• Automatic detection of the problem type (Classification or Regression)

• Customizable Train / Validation / Test split via slider

• Automatic Feature Selection with SelectKBest

• Train the following models (user-selectable):

  • Random Forest
  • Gradient Boosting
  • XGBoost
  • LightGBM
  • CatBoost
  • Linear Regression

• Customizable evaluation metric selection for model training

• Classification model calibration (e.g., Platt scaling, isotonic regression)

• Evaluation on Train, Validation, and Test sets to detect overfitting

• Evaluation metrics:

  • Classification: Accuracy, Precision, Recall, F1, AUC, Brier Score, Expected Calibration Error (ECE)
  • Regression: RMSE, MAE, R²

• Interactive visualizations:

  • Distributions, scatter plots, heatmaps
  • Comparative chart of model metrics
  • Scatter plot to assess overfitting (Train vs Test)

🔮 GPT Insight

• Automatic generation of a professional report with GPT that analyzes the results, highlights overfitting, and selects the best model.

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📦 Requirements

Install the necessary packages with:

pip install -r requirements.txt

Contents of requirements.txt:

streamlit>=1.28.0
pandas>=2.0.3
numpy>=1.25.0
matplotlib>=3.7.2
seaborn>=0.12.2
scipy>=1.11.1
scikit-learn>=1.3.0
xgboost>=1.7.6
lightgbm>=4.0.0
catboost>=1.2
joblib>=1.3.2
openpyxl>=3.1.2
openai>=1.12.0

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🚀 Run the App

Launch the application with:

streamlit run app.py