Course Wiki

Exploratory data analysis — Machine Learning Zoomcamp Module 2

df[col].unique() -> return a list of unique values in the series df[col].nunique() -> return the number of unique values in the series df.isnull().sum() -> return the number of null values in the dataframe

Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Exploratory data analysis

Notes

Pandas attributes and methods:

• df[col].unique() -> return a list of unique values in the series
• df[col].nunique() -> return the number of unique values in the series
• df.isnull().sum() -> return the number of null values in the dataframe

Matplotlib and seaborn methods:

• %matplotlib inline -> assure that plots are displayed in jupyter notebook’s cells
• sns.histplot() -> show the histogram of a series

Numpy methods:

• np.log1p() -> apply log transformation to a variable, after adding one to each input value.

Long-tail distributions usually confuse the ML models, so the recommendation is to transform the target variable distribution to a normal one whenever possible.

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-car-price-prediction-project
• mlz-m02-car-price-prediction-project-summary
• mlz-m02-categorical-variables
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-data-preparation
• mlz-m02-explore-more
• 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-data-preparation
• mlz-m03-eda
• 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-training-logistic-regression-with-scikit-learn
• 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