Advanced scikit-learn: Take Your ML Skills to the Next Level!

 

1️⃣ Feature Scaling & Normalization

• Many ML models perform better with scaled data!

from sklearn.preprocessing import StandardScaler  
scaler = StandardScaler() 
X_train_scaled = scaler.fit_transform(X_train) 
X_test_scaled = scaler.transform(X_test) 

2️⃣ Hyperparameter Tuning with GridSearchCV

• Find the best model parameters automatically!

from sklearn.model_selection import GridSearchCV  
params = {'n_estimators': [50, 100, 150]}  
grid = GridSearchCV(RandomForestClassifier(), param_grid=params, cv=5) 
grid.fit(X_train, y_train) 
print(grid.best_params_) 

3️⃣ Cross-Validation for Reliable Evaluation

from sklearn.model_selection import cross_val_score  
scores = cross_val_score(model, X_train, y_train, cv=5) 
print("Average accuracy:", scores.mean()) 

4️⃣ Dimensionality Reduction with PCA

• Reduce dataset features while retaining information

from sklearn.decomposition import PCA  
pca = PCA(n_components=2) 
X_reduced = pca.fit_transform(X_train) 

5️⃣ Handling Imbalanced Datasets with SMOTE

• When one class has way more samples than another

from imblearn.over_sampling import SMOTE  
smote = SMOTE() 
X_resampled, y_resampled = smote.fit_resample(X_train, y_train) 

6️⃣ Model Pipelines for Automation

• Combine preprocessing & training into one pipeline!

from sklearn.pipeline import Pipeline  
pipe = Pipeline([('scaler', StandardScaler()), 
                 ('model', RandomForestClassifier())]) 
pipe.fit(X_train, y_train) 

7️⃣ Feature Selection to Improve Performance

from sklearn.feature_selection import SelectKBest, f_classif  
selector = SelectKBest(score_func=f_classif, k=2) 
X_new = selector.fit_transform(X_train, y_train) 

8️⃣ Deploying ML Models with Joblib

• Save & reload your trained models!

import joblib  
joblib.dump(model, 'model.pkl') 
model = joblib.load('model.pkl')