ml-model-training

# 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) y = df['target'] X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3) X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5) # Scale features scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_val = scaler.transform(X_val) X_test = scaler.transform(X_test) ``` ## Scikit-learn Training ```python from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, accuracy_score model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) y_pred = model.predict(X_val) print(classification_report(y_val, y_pred)) ``` ## PyTorch Training ```python import torch import torch.nn as nn class Model(nn.Module): def __init__(self, input_dim): super().__init__() self.layers = nn.Sequential( nn.Linear(input_dim, 64), n...