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1. Pandas, Numpy
2. matplotlib, seaborn
3. sklearn

1. Data import - pd.read_csv()
2. Data cleaning (columns dropping - df.drop())
3. Spotting outliers with sns.boxplot() and correlation with sns.regplot()
4. Using Linear regression to predict prices.
5. Calculating R and R^2 for different features.
6. Using Pipeline from sklearn.pipeline to build pipeline ( scaler -> polynomial features -> modeling).
7. Try Ridge regression model for prediction.
8. Performing second order polynomial transform to training data for better score, then RR again.
9. Valuation using train_test_split from sklearn.model_selection.