Housing Prices Prediction

Analysis of the housing prices from the Ames dataset

Here's the Kaggle page for this challenge.

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My Approach:

1. Data Exploration -

Here, I separate out the features as belonging to one of the 3 classes -

* `num_cont_columns` for numerical continuous features
* `num_discrete_columns` for numerical discrete features
* `categ_columns` for categorical features

I do this because there are different steps of preprocessing required for each of those features. By separating them, I am able to create a clean analysis of the data.

2. Preprocessing -

After the separation, I divide the preprocessing stage into 3 steps where I analyse-

 * Continuous valued numeric features
 * Discrete valued numeric features
 * And finally, the Categorical features

In each of those steps, I do the missing values imputation and feature engineering as required.

3. Modelling -

I will train the following regression models -

* Simple regression
* Ridge regression
* ElasticNet regression
* Lasso regression

And the following ensemble models -

* Random Forest
* XGBoost

Finally, I took the top 3 best performing models and stacked them up for the final submission model.

Results:

My final model scored an RMSE score of 0.12562 and is in the top 26% of the Kaggle leaderboard.

I have included my final predictions for the test data along with the kernel that I used and the datasets, with this repository.