ML Project (Tabular Data)

This project was initialized with ML Assistant CLI using the tabular plugin.

Quick Start

# 1. Add your dataset
cp /path/to/your/data.csv data/raw/

# 2. Preprocess data
mlcli preprocess --input data/raw/data.csv --target target_column

# 3. Train models
mlcli train

# 4. Evaluate performance
mlcli evaluate

# 5. Get AI suggestions
mlcli suggest

# 6. Make predictions
mlcli predict --input new_data.csv --output predictions.csv

Project Structure

├── data/
│   └── raw/           # Your raw datasets
├── mlcli.yaml         # ML pipeline configuration
└── README.md

Additional directories are created automatically:

• data/processed/ - Preprocessed data
• models/ - Trained models
• reports/ - Evaluation reports

Configuration

Edit mlcli.yaml to customize:

• Target column name
• Model algorithms to try
• Preprocessing strategies
• Hyperparameter tuning settings

Supported Algorithms

• Logistic Regression
• Random Forest
• XGBoost
• Gradient Boosting
• Support Vector Machine

Next Steps

1. Add your dataset to data/raw/
2. Edit mlcli.yaml to set your target column
3. Run mlcli preprocess to prepare your data