A learning project, based on the Kaggle knowledge competition House Prices: Advanced Regression Techniques. The aim is to experience and document all the steps in an end-to-end predictive modeling problem in great detail.
Think of it as an overblown kernel :)
The original dataset is available here. A version of the dataset is available on Kaggle - this is the dataset we'll be working with.
The project consists of three stages, processing, exploratory analysis, and predictive modeling. It has the following directories:
/notebooks- Jupyter notebooks for processing, exploratory analysis, and predictive modeling/codes- Supplemental code for the notebooks/data- Datasets./training- Model training artifacts for persistence purposes/submissions- Model predictions for submission to Kaggle competition (neccesary for evaluating performance predictive models since Kaggle has the test set)
There are several related data files in /data:
train.csv,test.csv- Original Kaggle train and test dataorig.csv- Train and test data together with some metadata (dtypesandMultiIndex) for convenience in loading topandas.DataFrameclean.csv- Processed and cleaned version oforig.csv
Both orig.csv and clean.csv are created and discussed in the notebook process.ipynb. They can also be built by running the script process.py