How to do variance thresholding in Python for feature selection?
FEATURE EXTRACTION DATA CLEANING PYTHON DATA MUNGING MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to do variance thresholding in Python for feature selection?

How to do variance thresholding in Python for feature selection?

This recipe helps you do variance thresholding in Python for feature selection

0
This data science python source code does the following: 1. Uses Variance for selecting the best features. 2. Visualizes the final result
In [1]:
## How to do variance thresholding in Python for feature selection
def Snippet_130():
    print()
    print(format('How to do variance thresholding in Python for feature selection','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn.feature_selection import VarianceThreshold

    # Load iris data
    iris = datasets.load_iris()

    # Create features and target
    X = iris.data; print(); print(X[0:7])
    y = iris.target; print(); print(y[0:7])

    # Create VarianceThreshold object with a variance with a threshold of 0.5
    thresholder = VarianceThreshold(threshold=.5)

    # Conduct variance thresholding
    X_high_variance = thresholder.fit_transform(X)

    # View first five rows with features with variances above threshold
    print(); print(X_high_variance[0:7])

Snippet_130()
*********How to do variance thresholding in Python for feature selection**********

[[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]]

[0 0 0 0 0 0 0]

[[5.1 1.4 0.2]
 [4.9 1.4 0.2]
 [4.7 1.3 0.2]
 [4.6 1.5 0.2]
 [5.  1.4 0.2]
 [5.4 1.7 0.4]
 [4.6 1.4 0.3]]

Relevant Projects

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Forecast Inventory demand using historical sales data in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.