2.8 Baseline model for car price prediction project

Slides

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

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.

2.8 Baseline model for car price prediction project