Readme for house-prices-advanced-regression-techniques.ipynb This Jupyter notebook is a data analysis and machine learning project that aims to predict house prices based on various features. The project uses the House Prices: Advanced Regression Techniques dataset from Kaggle. Feel free to use it for project.
Dependencies The following dependencies are required to run the notebook:
jovian
opendatasets
pandas
numpy
scikit-learn
xgboost
seaborn
matplotlib
Installation
The dependencies can be installed using pip:
css
pip install jovian opendatasets pandas numpy scikit-learn xgboost seaborn matplotlib --quiet
Dataset
The House Prices: Advanced Regression Techniques dataset can be downloaded from Kaggle. The dataset contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa. The dataset has 1,460 rows and 81 columns, where each row represents a single home and each column represents an attribute. The target variable is the SalePrice column, which represents the sale price of the home in dollars.
Usage Download the dataset from Kaggle and save it to the same directory as the Jupyter notebook. Open the Jupyter notebook using Jupyter Notebook or JupyterLab. Run the code cells in the notebook in sequential order to reproduce the analysis.