aitraining tabular --train \
  --model xgboost \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --project-name tabular-model

aitraining tabular --train \
  --model xgboost \
  --data-path ./customers.csv \
  --target-columns churn \
  --valid-split validation \
  --task classification \
  --project-name churn-predictor

aitraining tabular --train \
  --model xgboost \
  --data-path ./houses.csv \
  --target-columns price \
  --valid-split validation \
  --task regression \
  --project-name price-predictor

feature1,feature2,feature3,target
1.5,category_a,100,1
2.3,category_b,200,0

aitraining tabular --train \
  --model gradient_boosting \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --categorical-columns "color,size,region" \
  --project-name model

aitraining tabular --train \
  --model xgboost \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --id-column customer_id \
  --project-name model

aitraining tabular --train \
  --model xgboost \
  --data-path ./customers.csv \
  --target-columns churned \
  --valid-split validation \
  --id-column customer_id \
  --task classification \
  --project-name churn-model

aitraining tabular --train \
  --model gradient_boosting \
  --data-path ./houses.csv \
  --target-columns sale_price \
  --valid-split validation \
  --id-column house_id \
  --task regression \
  --project-name house-prices

aitraining tabular --train \
  --model extra_trees \
  --data-path ./products.csv \
  --target-columns category \
  --valid-split validation \
  --categorical-columns "brand,color,material" \
  --task classification \
  --project-name product-classifier

# Train XGBoost
aitraining tabular --train \
  --model xgboost \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --project-name model-xgb

# Train Random Forest
aitraining tabular --train \
  --model random_forest \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --project-name model-rf

# Train Gradient Boosting
aitraining tabular --train \
  --model gradient_boosting \
  --data-path ./data.csv \
  --target-columns target \
  --valid-split validation \
  --project-name model-gb

project-name/
├── model.joblib        # Trained model
├── metrics.json        # Evaluation metrics
├── feature_importance.json
└── config.yaml         # Training config

import joblib

model = joblib.load("project-name/model.joblib")
predictions = model.predict(new_data)