Acknowledgement: O'Reilly Hands-on ML Projects book, Chapter 2
Overview:
The dataset contains the median housing prices of California in 1990 and 10 features that can be used to predict future prices. The data was visualized, prepped, and cleaned using packages from pandas, matplotlib and scikit-learn, and analyzed using four supervised ML models (linear regression, decision trees, random forest, and SVM). Hyperparameters tuning was done by using GridSearch and RandomSearch on different hyperparameters combinations.
Acknowledgement: The Ames Housing dataset was compiled by Dean De Cock for use in data science education.
Overview:
This is a personal follow-up project to apply the lessons learned from Project 1. The data was downloaded from this Kaggle competition https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview/description
The Ames dataset contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this dataset can be used to predict the final price of future homes. The data was visualized, prepped, and cleaned using packages from pandas, matplotlib seaborn, scipy, and scikit-learn and analyzed using four supervised ML models (linear regression, decision trees, random forest, and SVM).