Learn how to build and evaluate machine learning models using scikit-learn, from data preprocessing to model selection and evaluation.

Mastering Machine Learning with scikit-learn.png

tarball.tar.gz

Mlxtend

This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Python for machine learning tasks. It is an excellent resource to help you develop practical machine learning applications using Python and scikit-learn.

In this course, you’ll learn fundamental concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. You’ll also learn how to implement popular machine learning algorithms, including regression, classification, and clustering, using scikit-learn’s user-friendly API. The course also introduces advanced topics such as ensemble methods, model interpretation, and hyperparameter optimization.

After taking this course, you’ll gain hands-on experience in applying machine learning techniques to solve diverse data-driven problems. You’ll also be equipped with the expertise to confidently leverage scikit-learn for a wide range of machine learning applications in industry as well as academia.

Scikit-Learn for Machine Learning

customerID,gender,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Churn
7402-VSVEK,Male,0,Yes,No,1,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,25.42,29.85,No

import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
import joblib

# Load the dataset
data = pd.read_csv('data.csv')

# Defining column types
id_col = 'customerID'
num_cols = ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges']
cat_cols = [
    'gender', 'Partner', 'Dependents',
    'PhoneService', 'MultipleLines', 'InternetService',
    'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',
    'TechSupport', 'StreamingTV', 'StreamingMovies',
    'Contract', 'PaperlessBilling', 'PaymentMethod'
]

# Define X (features) and y (target variable)
X = data.drop(['Churn', id_col], axis=1)
y = data['Churn']

# Convert labels to numeric form
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Define transformers
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder(handle_unknown='error', drop='first'))
])

# Create the column transformer
preprocessor = ColumnTransformer(transformers=[
    ('numeric', numeric_transformer, num_cols),
    ('categorical', categorical_transformer, cat_cols)
])

# Create the pipeline with the preprocessor and classifier
pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression())
])

# Fit the pipeline
pipeline.fit(X_train, y_train)

# Calculate feature importance using permutation
result = permutation_importance(
    pipeline, X_test, y_test, scoring='f1', n_repeats=10, random_state=42
)
importance_scores = result.importances_mean

for col, score in zip([col for col in list(X)], importance_scores):
    print(f"{col}: {score}")

# Export the pipeline using joblib
joblib.dump(pipeline, 'pipeline.joblib')

# Get a new observation for testing
new_data = pd.read_csv('new_data.csv')
new_data.drop(id_col, axis=1, inplace=True)

# Apply the pipeline to the new observation
prediction = pipeline.predict(new_data)
final_prediction=label_encoder.inverse_transform(prediction)
print("Final prediction:", final_prediction)

Follow the instructions for building pipelines, calculating feature importance, and exporting models.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

How to Predict the Traffic Volume Using Machine Learning

Tips and Tricks

Conclusion

Customer Segmentation with K-Means Clustering

Solution: Tips and Tricks