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The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

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1. DIMENSIONALITY REDUCTION(PCA)

2. SCALING (STANDARDSCALER, NORMALIZER, MINMAX)

3. CATEGORICAL ENCODING (DUMMY/ONE-HOT)

4. BINNING (GROUPING/AGGREGATING)

5. CLUSTERING

6. FEATURE SELECTION(COMBINATION)

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The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

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python linear-regression pca dimensionality-reduction normalizer binning feature-engineering kmeans-clustering random-forest-regressor mean-absolute-error standardscaler one-hot-encoding categorical-encoding minmaxscaler

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