Predict house prices with regression algorithms and sklearn
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Learn how to predict house prices using regression algorithms in Python with Scikit-learn. Discover the power of linear regression, decision trees, and other models to build accurate predictive models for real estate and housing markets.
At a Glance
Learn various regression algorithms using Python and scikit-learn, including multiple linear regression, random forest, and decision trees. Visualize your results with Matplotlib and perform a comparative study of different regression models, highlighting their importance in predicting house prices. Use Pandas and scikit-learn to understand and implement these regression techniques and produce insightful visualizations to enhance your analysis.
This hands-on project is based on the Learn regression algorithms using Python and scikit-learn tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.
A look at the project ahead
- Implement regression models: Use Python and scikit-learn to develop various regression models.
- Master data preparation: Acquire skills in cleaning and preparing data for regression analysis.
- Evaluate model performance: Learn to use metrics like MSE and R-squared to assess model accuracy.
- Apply regression to real estate: Demonstrate how regression predicts real estate prices, which aids in investment decisions.
What you’ll need
- No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
- Basic understanding of Python: Some basic understanding of Python is beneficial.
- Some understanding of statistical concepts: It’s helpful to have some understanding of regression concepts, particularly linear, multiple, and polynomial regression as well as random forest and decision trees.
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