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Baseline model for car price prediction project — Machine Learning Zoomcamp Module 2

In this lesson we build a baseline model and apply the df_train dataset to derive weights for the bias (w0) and the features (w). For this, we use the train_linear_regression(X, y) function from the previous lesson. Linear regression only...

Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Baseline model for car price prediction project

Notes

• In this lesson we build a baseline model and apply the df_train dataset to derive weights for the bias (w0) and the features (w). For this, we use the train_linear_regression(X, y) function from the previous lesson.
• Linear regression only applies to numerical features. Therefore, only the numerical features from df_train are used for the feature matrix.
• We notice some of the features in df_train are nan. We set them to 0 for the sake of simplicity, so the model is solvable, but it will be appropriate if a non-zeo value is used as the filler (e.g. mean value of the feature).
• Once the weights are calculated, then we apply them on \(\\\\ \large g(X) = w_0 + X \cdot w\) to derive the predicted y vector.
• Then we plot both predicted y and the actual y on the same histogram for a visual comparison.

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

• Notes from Peter Ernicke

Key concepts

• Linear Regression
• 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-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-exploratory-data-analysis
• 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-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-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-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