Python ML guided project Level 1, 24min - simple linear regression house price prediction
- Brandyn Ewanek
- 30 nov 2022
- 1 Min. de lectura
This intro project is great for those new to Sklearn. Learn how to set up an ML workflow. From exploratory data analysis to predictions. Use pandas and seaborn in Python to perform your data analysis. Then use Sklearn to do the train test split and make your predictions. Here we also look at the R-squared score to evaluate our model.
Send data science teacher brandyn a message if you have any questions
Use seaborn swamplot to detail valuable insights.
Use seaborn's Violinplot to inspect our distributions.
Use the heatmap in seaborn with the correlation matrix from pandas to plot and inspect all the correlations in your dataset.
Use sklearn's StandardScaler to standardize your data and get it ready for modeling.
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