A Streamlit application for training and evaluating machine learning models on various datasets.

• Dataset selection (Iris, Tips, Diamonds, Titanic, MPG, Penguins) and custom CSV upload.
• Automatic detection of classification or regression tasks based on the target variable.
• Exploratory Data Analysis (EDA) tab with data summaries, type overviews, missing value analysis, correlation heatmaps, feature distributions, and more.
• Configurable data preprocessing options (missing value handling, scaling, encoding).
• Selection from various classification and regression models (Random Forest, Gradient Boosting, SVM, KNN, Linear/Logistic Regression, etc.).
• Optional Grid Search for hyperparameter tuning.
• Model performance visualization (metrics, prediction plots, confusion matrix, ROC curve, feature importance).
• Experiment history tracking and comparison.
• Downloadable model reports (JSON format).

1. Clone the repository:

   git clone https://github.com/rayen003/ML_model_visualization.git
   cd ML_model_visualization

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run a1/A1.py