AI — Model Training

Training Pipeline

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

Data Preparation

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

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

# Split
X_train, X_test, y_train, y_test = train_test_split(
    df.drop('target', axis=1), df['target']
)

# Scale
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)

Training

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20]
}

grid = GridSearchCV(model, param_grid, cv=5)
grid.fit(X_train, y_train)

Mini Practice

1. Prepare dataset
2. Split and scale data
3. Train model
4. Tune hyperparameters

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