Day #99 - Statistics and distance based features

Stats

• Calculate statistics of derived features from neighborhood analysis
• User_id / Page_id / Ad_price / Ad_position
• Use label encoder
• Treat data points implicitly
• Add lowest and highest price for position of add
• maximum and minimum price values
• Pages user visited
• Standard deviation of prices
• Most visited page
• Many more features
• Introduce new information

Neighbors

• Number of houses in 500m, 1000m
• Average price per sq.m
• Number of schools / supermarkets / parking lots in 500m / 1000m
• Distance to closest substation
• Embrace both group-by and nearest neighbor methods

Matrix Factorizations

• Approach for feature extraction
• User / Items mapping matrix
• User - Attributes matrix
• U X M = R
• Row and column related features
• BOW represent larger parse vector
• Document represented by small dense vector (Dimensionality reduction)
• Matrix Factorizations
• SVD, PCA, TruncatedSVD for sparse matrices
• NMF (Non-Negative Matrix Factorization) - Zero or Positive Number
• NMF makes data suitable for decision trees
• Used for Dimensionality reduction

Example Code

x_all = np.cancatenate([x_train,x_test])

pca.fit(x_all)

x_train_pca = pca.transform(x_train)

x_test_pca = pca.transform(x_test)

Happy Learning!!!