Python data cleaning cheat sheet

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# 2. Import libraries and modules

importnumpy asnp

importpandas aspd

fromsklearn.model_selection importtrain_test_split

fromsklearn importpreprocessing

fromsklearn.ensemble importRandomForestRegressor

fromsklearn.pipeline importmake_pipeline

fromsklearn.model_selection import GridSearchCV

fromsklearn.metrics importmean_squared_error,r2_score

importjoblib

# 3. Load red wine data.

dataset_url= 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'

data=pd.read_csv(dataset_url,sep=';')

# 4. Split data into training and test sets

y= data.quality

X=data.drop('quality',axis=1)

X_train,X_test,y_train,y_test= train_test_split(X,y,

                                                    test_size=0.2,

                                                    random_state=123,

                                                    stratify=y)

# 5. Declare data preprocessing steps

pipeline=make_pipeline(preprocessing.StandardScaler(),

                         RandomForestRegressor(n_estimators=100,

                                               random_state=123))

# 6. Declare hyperparameters to tune

hyperparameters={'randomforestregressor__max_features':['auto','sqrt','log2'],

                  'randomforestregressor__max_depth': [None,5,3,1]}

# 7. Tune model using cross-validation pipeline

clf=GridSearchCV(pipeline,hyperparameters, cv=10)

clf.fit(X_train,y_train)

# 8. Refit on the entire training set

# No additional code needed if clf.refit == True (default is True)

# 9. Evaluate model pipeline on test data

pred= clf.predict(X_test)

print(r2_score(y_test,pred))

print(mean_squared_error(y_test,pred))

# 10. Save model for future use

joblib.dump(clf,'rf_regressor.pkl')

# To load: clf2 = joblib.load('rf_regressor.pkl')