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Aaditya Bansal
Writer's pictureAaditya Bansal
Feb 22, 20233 min read

Ames Housing Price Prediction Project

Updated: Feb 23, 2023

Introduction


The Ames Housing Price Prediction Project is a well-known challenge on Kaggle, where the objective is to predict the final sale price of homes in Ames, Iowa, using a set of features such as neighborhood, year built, square footage, and more.


In this blog post, I will share my experience completing this project and how I applied various machine learning techniques and models to build the best model and make accurate predictions.


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Data Extraction, Exploration and Preprocessing


The first step in any machine learning project is to explore and preprocess the data.


In this project, I began by importing the dataset, checking for missing values, and performing feature engineering. I imputed missing values for other features and replaced the Null values in dataset using information given in the data description, performed One hot encoding for categorical features and transformed some categorical variables stored as numbers into category (string) variables.


Exploratory data analysis (EDA) is also essential to understand the data and identify patterns.


I used various visualizations and statistical tests to explore the relationship between each feature and the target variable (sale price). Some interesting findings from the EDA include:

  • The sale price is positively correlated with the overall quality of the house, the size of the lot, and the living area.

Feature Selection and Engineering


After preprocessing the data, the next step is to select the most relevant features and engineer new features that can improve the model's performance.


I engineered new features, such as the total overall quality, the area per room, total rooms, and the total number of bathrooms.

 

Model Selection and Evaluation


Once the data is prepared, it's time to choose the best model that can accurately predict the sale price of the houses.


In this project, I experimented with various machine learning models, including linear regression, ridge regression, decision tree, random forest, gradient boosting, Xgboost regression and cat boost Regression.


I used k-fold cross-validation to find the best model and evaluated their performance using metrics, root mean squared error (RMSE).


After comparing the performance of all models, I found that the cat boosting model had the lowest RMSE, indicating that it is the best model for this task.


I also performed feature importance analysis to understand which features have the most significant impact on the predictions.


The top features that contribute to the model's accuracy include the overall quality of the house, the total area of the house, and the year build.

 

Conclusion


The Ames Housing Price Prediction Project on Kaggle was a great learning experience for me to apply various machine learning techniques and models to build the best model and make accurate predictions.


Through data exploration and preprocessing, feature selection and engineering, and model selection and evaluation, I was able to build a categorical boosting model that can predict the sale price of the houses with high accuracy.


I also gained insights into the factors that affect the house's value and how to interpret the model's predictions. I highly recommend this project to anyone who wants to improve their skills in machine learning and data analysis.



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You can also see this on Kaggle: Ames Housing Price Prediction | Kaggle

 

Thank you for your time 🤗

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