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In this Supervised Machine Learning project I analyze a dataset of housing data and train machine learning algorithms to predict house prices.

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housing-prices kaggle-competition supervised-machine-learning

supervised_ml_house_prices's Introduction

Supervised_ML_House_prices

"Tell me about your dream house and I'll tell you what it costs." ๐Ÿ 

In this Supervised Machine Learning project I analyze a dataset of housing data and train machine learning algorithms to predict house prices. It is a regression approach, meaning that the predicted labels (house prices) are on a continuous scale.

This is a learning project, in which I practice machine learning techniques such as feature selection, grid search with cross validation and pipelines consisting of preprocessing and modeling steps. I test a variety of models with different architectures to find the best performing ones.

I use the best model to submit a prediction to the associated indefinitely running Kaggle challenge.

Content

The project is separated into 4 working notebooks:

  • 01: Data Preparation and Exploration
  • 02: Baseline Model
  • 03: Feature Selection
  • 04: Model Training and Selection

Dataset

The dataset originates from the challenge's website and can be downloaded from the "Data" tab. A detailed description of the features can be found at the end of this notebook and stems from the data_description.txt file from Kaggle.

Context

This Machine Learning project was carried out in the context of a 4.5 month-long Data Science bootcamp with WBS Coding School. Many thanks to WBS Coding School and to my instructors for the guidance.

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