Model Training Guide 🤖⚙️

1. What is Model Training?

Model Training is the process of feeding data into an algorithm so it can learn patterns and make predictions.

2. Steps

1. Prepare data
2. Split data
3. Train model
4. Evaluate model
5. Improve model

3. Train-Test Split

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.2)

4. Train Model

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

5. Prediction

y_pred = model.predict(X_test)

6. Evaluation

from sklearn.metrics import mean_squared_error

print(mean_squared_error(y_test, y_pred))

7. Deep Learning Training

model.fit(X_train, y_train, epochs=10)

8. Overfitting vs Underfitting

Overfitting → Too accurate on training, fails on test
Underfitting → Poor performance overall

9. Improve Model

- More data
- Feature engineering
- Hyperparameter tuning

10. Real Use Cases

• Prediction systems 📈
• Recommendation engines 🎯
• AI apps 🤖
• Fraud detection 🔐