SelectFromModel Feature Selection Example in Python

     Scikit-learn API provides SelectFromModel class for extracting best features of given dataset according to the importance of weights. The SelectFromModel is a meta-estimator that determines the weight importance by comparing to the given threshold value. 

    In this tutorial, we'll briefly learn how to select best features of regression data by using the SelectFromModel in Python. The tutorial covers:

1. SelectFromModel for regression data
   
2. Source code listing

   We'll start by loading the required libraries and functions.

Recursive Feature Elimination (RFE) Example in Python

     Extracting influential features of dataset is essential part of data preparation to train model in machine learning. Scikit-learn API provides RFE class that ranks features by recursive feature elimination to select best features. The method recursively eliminates the least important features based on specific attributes taken by estimator.

    In this tutorial, we'll briefly learn how to select best features of dataset by using the RFE in Python. The tutorial covers:

1. RFE Example with Boston dataset
2. Source code listing

   We'll start by loading the required libraries and functions.

Reading Texts on Image by Using Tesseract and PyOCR in Python

    Optical Character Recognition (OCR) is a conversion of typed or handwritten letters on an image into the machine encoded texts.  There are several methods and libraries that can be used to read text on image.

    In this tutorial, we'll briefly learn how to read letters in an image by using the Tesseract and PyOCR in Python. The tutorial covers:

1. Installing Tesseract and PyOCR
   
2. Reading texts on image
   
3. Source code listing

   Let's get started.

SelectKBest Feature Selection Example in Python

     Scikit-learn API provides SelectKBest class for extracting best features of given dataset. The SelectKBest method selects the features according to the k highest score. By changing the 'score_func' parameter we can apply the method for both classification and regression data. Selecting best features is important process when we prepare a large dataset for training. It helps us to eliminate less important part of the data and reduce a training time.

    In this tutorial, we'll briefly learn how to select best features of classification and regression data by using the SelectKBest in Python. The tutorial covers:

1. SelectKBest for classification data
2. SelectKBest for regression data
   
3. Source code listing

   We'll start by loading the required libraries and functions.

Dimensionality Reduction Example with Factor Analysis in Python

     Factor Analysis is a technique that used to express data with reduced number of variables. Reducing the number of variables in a data is helpful method to simplify large dataset by decreasing the variables without loosing the generality of it. 

    The Scikit-learn API provides the FactorAnalysis model that performs a maximum likelihood estimate of  loading matrix using SVD based approach. In this tutorial, we'll briefly learn how to use FactorAnalysis model to reduce the data dimension and visualize the output in Python. The tutorials covers:

1. MNIST dataset Projection with Factor Analysis
   
2. Image data Factor Analysis and visualizing
   
3. Source code listing
   

SparsePCA Projection Example in Python

     Sparse Principal Component Analysis is a type of PCA analysis method. SparsePCA extracts sparse components to build the data.

    The Scikit-learn API provides SparsePCA class to apply Sparse PCA method in Python. In this tutorial, we'll briefly learn how to project data by using SparsePCA and visualize the projected data in Python. The tutorials covers:

1. Iris dataset SparsePCA projection and visualizing
   
2. MNIST dataset SparsePCA projection and visualizing
   
3. Source code listing