In this notebook I explained how to tackle a machine learning from the beginning to the end, at the example of predicting housing prices. I covered everything you need, to build regression system and a lot of tools & techniques, that are common in the machine learning landscape. I got everything I cover in this notebook from the book "Hands-On Machine Learning with Scikit_learn & Tensorflow" from Aurelien Geron. I used the California Housing Dataset from the statlib repository.
To see the project, just open the Jupyter Notebook: "Housing-Price-Predictions.ipynb".