Ames-housing dataset description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Regression Task !

Project phases

• Cleaning
• Data Preprocessing
• Analysis of the dataset and creation of different models : CatBoost, ANN, DecisionTree, RandomForest, LinearRegression, Knn

Cleaning

• Analyzing null/NaN
• Removing redundant features
• Removing skewed features
• Transformation of categorical qualitative features
• Feature correlation and observation
• Splitting with Stratified K-fold

Data PreProcessing

• Creation of multiple new features that add information to the different models
• One-Hot encoding
• Min-Max scaler option
• Standard scaler option

Training and analysis on multiple algorithms

• Best accuracy and stability achivied with CatBoost algorithm
• ANN with adjusted LR can be a fast option to train the model and immediately obtain results on new data