Course Wiki

EDA — Machine Learning Zoomcamp Module 3

The EDA for this project consisted of: Checking missing values Looking at the distribution of the target variable (churn) Looking at numerical and categorical variables

Machine Learning Zoomcamp › Module 3: Machine Learning for Classification › EDA

Notes

The EDA for this project consisted of:

• Checking missing values
• Looking at the distribution of the target variable (churn)
• Looking at numerical and categorical variables

Functions and methods:

• df.isnull().sum() - returns the number of null values in the dataframe.
• df.x.value_counts() returns the number of values for each category in x series. The normalize=True argument retrieves the percentage of each category. In this project, the mean of churn is equal to the churn rate obtained with the value_counts method.
• round(x, y) - round an x number with y decimal places
• df[x].nunique() - returns the number of unique values in x series

The entire code of this project is available in this jupyter notebook.

• Notes from Peter Ernicke

Key concepts

• Pandas
• Jupyter Notebooks

Related notes

• mlz-m01-introduction-to-numpy
• mlz-m01-introduction-to-pandas
• mlz-m01-linear-algebra-refresher
• mlz-m01-setting-up-the-environment
• mlz-m01-summary
• mlz-m02-baseline-model-for-car-price-prediction-project
• mlz-m02-categorical-variables
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-data-preparation
• mlz-m02-exploratory-data-analysis
• mlz-m02-feature-engineering
• mlz-m02-linear-regression
• mlz-m02-linear-regression-vector-form
• mlz-m02-regularization
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-setting-up-the-validation-framework
• mlz-m02-training-linear-regression-normal-equation
• mlz-m02-tuning-the-model
• mlz-m02-using-the-model
• mlz-m03-churn-prediction-project
• mlz-m03-data-preparation
• mlz-m03-explore-more
• mlz-m03-feature-importance-churn-rate-and-risk-ratio
• mlz-m03-feature-importance-correlation
• mlz-m03-feature-importance-mutual-information
• mlz-m03-logistic-regression
• mlz-m03-model-interpretation
• mlz-m03-one-hot-encoding
• mlz-m03-setting-up-the-validation-framework
• mlz-m03-summary
• mlz-m03-training-logistic-regression-with-scikit-learn
• mlz-m03-using-the-model
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-data-cleaning-and-preparation
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-gradient-boosting-and-xgboost
• mlz-m06-xgboost-parameter-tuning
• mlz-m08-installation-of-tensorflow
• mlz-m10-creating-a-pre-processing-service

Sources

• Video
• Lesson file