/ml-model-training

---
name: ml-model-training
description: Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
license: MIT
metadata:
  keywords: "machine learning, model training, PyTorch, TensorFlow, scikit-learn, neural networks, deep learning, classification, regression, hyperparameter tuning, cross-validation, model evaluation, data preprocessing, feature engineering"
---

# ML Model Training

Train machine learning models with proper data handling and evaluation.

## Training Workflow

1. Data Preparation → 2. Feature Engineering → 3. Model Selection → 4. Training → 5. Evaluation

## Data Preparation

```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

# Load and clean data
df = pd.read_csv('data.csv')
df = df.dropna()

# Encode categorical variables
le = LabelEncoder()
df['category'] = le.fit_transform(df['category'])

# Split data (70/15/15)
X = df.drop('target', axis=1)