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

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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

import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, KBinsDiscretizer

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',
    'Churn'
    ]

# Imputation of missing values
imputer = SimpleImputer(strategy='mean')
data[num_cols] = imputer.fit_transform(data[num_cols])

# Encoding
encoder = OneHotEncoder(handle_unknown='error', drop='first')
encoded = encoder.fit_transform(data[cat_cols])
encoded_df = pd.DataFrame(
    encoded.toarray(),
    columns=encoder.get_feature_names(cat_cols)
)
data = pd.concat([data[id_col+num_cols], encoded_df], axis=1)

# Discretizing
discretizer = KBinsDiscretizer(n_bins=3, encode='ordinal')
data[num_cols] = discretizer.fit_transform(data[num_cols])

# Scaling
scaler = StandardScaler()
data[num_cols] = scaler.fit_transform(data[num_cols])

print(data.head())

Follow the instructions for preprocessing data from a csv file.

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: Preprocessing