For this project the California Housing Prices dataset was used. It contains 10 attributes (link: https://www.kaggle.com/camnugent/california-housing-prices). More specifically the independent variables are longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, ocean proximity while the dependent variable is median house value. That is why the goal of the analysis was predicting the housing prices of the California dataset by applying regression, bagging, boosting and ANN techniques with the KNIME software.
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For this project I decided to use the California Housing Prices dataset, which contains 10 attributes (link: https://www.kaggle.com/camnugent/california-housing-prices). More specifically the independent variables used are longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, ocean proximity…
Palazoff/California-Housing-Price-Prediction
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For this project I decided to use the California Housing Prices dataset, which contains 10 attributes (link: https://www.kaggle.com/camnugent/california-housing-prices). More specifically the independent variables used are longitude, latitude, housing median age, total rooms, total bedrooms, population, households, median income, ocean proximity…
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