Jigyaasa
  • Home
No Result
View All Result
Jigyaasa
  • Home
No Result
View All Result
Jigyaasa
No Result
View All Result

How to Perform Feature Selection With Numerical Input Data

Subhanshu Singh by Subhanshu Singh
June 26, 2020
in Artificial Intelligence
0
how-to-perform-feature-selection-with-numerical-input-data
1
VIEWS
Share on FacebookShare on Twitter

Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable.

Feature selection is often straightforward when working with real-valued input and output data, such as using the Pearson’s correlation coefficient, but can be challenging when working with numerical input data and a categorical target variable.

The two most commonly used feature selection methods for numerical input data when the target variable is categorical (e.g. classification predictive modeling) are the ANOVA f-test statistic and the mutual information statistic.

In this tutorial, you will discover how to perform feature selection with numerical input data for classification.

After completing this tutorial, you will know:

  • The diabetes predictive modeling problem with numerical inputs and binary classification target variables.
  • How to evaluate the importance of numerical features using the ANOVA f-test and mutual information statistics.
  • How to perform feature selection for numerical data when fitting and evaluating a classification model.

Let’s get started.

How to Perform Feature Selection With Numerical Input Data

How to Perform Feature Selection With Numerical Input Data

Photo by Susanne Nilsson, some rights reserved.

Tutorial Overview

This tutorial is divided into four parts; they are:

  • Diabetes Numerical Dataset
  • Numerical Feature Selection
    • ANOVA f-test Feature Selection
    • Mutual Information Feature Selection
  • Modeling With Selected Features
    • Model Built Using All Features
    • Model Built Using ANOVA f-test Features
    • Model Built Using Mutual Information Features
  • Tune the Number of Selected Features

Diabetes Numerical Dataset

As the basis of this tutorial, we will use the so-called “diabetes” dataset that has been widely studied as a machine learning dataset since 1990.

The dataset classifies patients’ data as either an onset of diabetes within five years or not. There are 768 examples and eight input variables. It is a binary classification problem.

A naive model can achieve an accuracy of about 65 percent on this dataset. A good score is about 77 percent +/- 5 percent. We will aim for this region but note that the models in this tutorial are not optimized; they are designed to demonstrate feature selection schemes.

You can download the dataset and save the file as “pima-indians-diabetes.csv” in your current working directory.

  • Diabetes Dataset (pima-indians-diabetes.csv)
  • Diabetes Dataset Description (pima-indians-diabetes.names)

Looking at the data, we can see that all nine input variables are numerical.

6,148,72,35,0,33.6,0.627,50,1

1,85,66,29,0,26.6,0.351,31,0

8,183,64,0,0,23.3,0.672,32,1

1,89,66,23,94,28.1,0.167,21,0

0,137,40,35,168,43.1,2.288,33,1

…

We can load this dataset into memory using the Pandas library.

...

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

Once loaded, we can split the columns into input (X) and output (y) for modeling.

...

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

We can tie all of this together into a helpful function that we can reuse later.

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

Once loaded, we can split the data into training and test sets so we can fit and evaluate a learning model.

We will use the train_test_split() function form scikit-learn and use 67 percent of the data for training and 33 percent for testing.

...

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

Tying all of these elements together, the complete example of loading, splitting, and summarizing the raw categorical dataset is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

# load and summarize the dataset

from pandas import read_csv

from sklearn.model_selection import train_test_split

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# summarize

print(‘Train’, X_train.shape, y_train.shape)

print(‘Test’, X_test.shape, y_test.shape)

Running the example reports the size of the input and output elements of the train and test sets.

We can see that we have 514 examples for training and 254 for testing.

Train (514, 8) (514, 1)

Test (254, 8) (254, 1)

Now that we have loaded and prepared the diabetes dataset, we can explore feature selection.

Numerical Feature Selection

There are two popular feature selection techniques that can be used for numerical input data and a categorical (class) target variable.

They are:

  • ANOVA-f Statistic.
  • Mutual Information Statistics.

Let’s take a closer look at each in turn.

ANOVA f-test Feature Selection

ANOVA is an acronym for “analysis of variance” and is a parametric statistical hypothesis test for determining whether the means from two or more samples of data (often three or more) come from the same distribution or not.

An F-statistic, or F-test, is a class of statistical tests that calculate the ratio between variances values, such as the variance from two different samples or the explained and unexplained variance by a statistical test, like ANOVA. The ANOVA method is a type of F-statistic referred to here as an ANOVA f-test.

Importantly, ANOVA is used when one variable is numeric and one is categorical, such as numerical input variables and a classification target variable in a classification task.

The results of this test can be used for feature selection where those features that are independent of the target variable can be removed from the dataset.

When the outcome is numeric, and […] the predictor has more than two levels, the traditional ANOVA F-statistic can be calculated.

— Page 242, Feature Engineering and Selection, 2019.

The scikit-learn machine library provides an implementation of the ANOVA f-test in the f_classif() function. This function can be used in a feature selection strategy, such as selecting the top k most relevant features (largest values) via the SelectKBest class.

For example, we can define the SelectKBest class to use the f_classif() function and select all features, then transform the train and test sets.

...

# configure to select all features

fs = SelectKBest(score_func=f_classif, k=‘all’)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

We can then print the scores for each variable (larger is better) and plot the scores for each variable as a bar graph to get an idea of how many features we should select.

...

# what are scores for the features

for i in range(len(fs.scores_)):

print(‘Feature %d: %f’ % (i, fs.scores_[i]))

# plot the scores

pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)

pyplot.show()

Tying this together with the data preparation for the diabetes dataset in the previous section, the complete example is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

# example of anova f-test feature selection for numerical data

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import f_classif

from matplotlib import pyplot

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select all features

fs = SelectKBest(score_func=f_classif, k=‘all’)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# feature selection

X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)

# what are scores for the features

for i in range(len(fs.scores_)):

print(‘Feature %d: %f’ % (i, fs.scores_[i]))

# plot the scores

pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)

pyplot.show()

Running the example first prints the scores calculated for each input feature and the target variable.

Note that your specific results may differ given the stochastic nature of the learning algorithm. Try running the example a few times.

In this case, we can see that some features stand out as perhaps being more relevant than others, with much larger test statistic values.

Perhaps features 1, 5, and 7 are most relevant.

Feature 0: 16.527385

Feature 1: 131.325562

Feature 2: 0.042371

Feature 3: 1.415216

Feature 4: 12.778966

Feature 5: 49.209523

Feature 6: 13.377142

Feature 7: 25.126440

A bar chart of the feature importance scores for each input feature is created.

This clearly shows that feature 1 might be the most relevant (according to test) and that perhaps six of the eight input features are the more relevant.

We could set k=6 when configuring the SelectKBest to select these top four features.

Bar Chart of the Input Features (x) vs The ANOVA f-test Feature Importance (y)

Bar Chart of the Input Features (x) vs The ANOVA f-test Feature Importance (y)

Mutual Information Feature Selection

Mutual information from the field of information theory is the application of information gain (typically used in the construction of decision trees) to feature selection.

Mutual information is calculated between two variables and measures the reduction in uncertainty for one variable given a known value of the other variable.

You can learn more about mutual information in the following tutorial.

  • What Is Information Gain and Mutual Information for Machine Learning

Mutual information is straightforward when considering the distribution of two discrete (categorical or ordinal) variables, such as categorical input and categorical output data. Nevertheless, it can be adapted for use with numerical input and categorical output.

For technical details on how this can be achieved, see the 2014 paper titled “Mutual Information between Discrete and Continuous Data Sets.”

The scikit-learn machine learning library provides an implementation of mutual information for feature selection with numeric input and categorical output variables via the mutual_info_classif() function.

Like f_classif(), it can be used in the SelectKBest feature selection strategy (and other strategies).

...

# configure to select all features

fs = SelectKBest(score_func=mutual_info_classif, k=‘all’)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

We can perform feature selection using mutual information on the diabetes dataset and print and plot the scores (larger is better) as we did in the previous section.

The complete example of using mutual information for numerical feature selection is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

# example of mutual information feature selection for numerical input data

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import mutual_info_classif

from matplotlib import pyplot

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select all features

fs = SelectKBest(score_func=mutual_info_classif, k=‘all’)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# feature selection

X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)

# what are scores for the features

for i in range(len(fs.scores_)):

print(‘Feature %d: %f’ % (i, fs.scores_[i]))

# plot the scores

pyplot.bar([i for i in range(len(fs.scores_))], fs.scores_)

pyplot.show()

Running the example first prints the scores calculated for each input feature and the target variable.

Note: your specific results may differ. Try running the example a few times.

In this case, we can see that some of the features have a modestly low score, suggesting that perhaps they can be removed.

Perhaps features 1 and 5 are most relevant.

Feature 1: 0.118431

Feature 2: 0.019966

Feature 3: 0.041791

Feature 4: 0.019858

Feature 5: 0.084719

Feature 6: 0.018079

Feature 7: 0.033098

A bar chart of the feature importance scores for each input feature is created.

Importantly, a different mixture of features is promoted.

Bar Chart of the Input Features (x) vs. the Mutual Information Feature Importance (y)

Bar Chart of the Input Features (x) vs. the Mutual Information Feature Importance (y)

Now that we know how to perform feature selection on numerical input data for a classification predictive modeling problem, we can try developing a model using the selected features and compare the results.

Modeling With Selected Features

There are many different techniques for scoring features and selecting features based on scores; how do you know which one to use?

A robust approach is to evaluate models using different feature selection methods (and numbers of features) and select the method that results in a model with the best performance.

In this section, we will evaluate a Logistic Regression model with all features compared to a model built from features selected by ANOVA f-test and those features selected via mutual information.

Logistic regression is a good model for testing feature selection methods as it can perform better if irrelevant features are removed from the model.

Model Built Using All Features

As a first step, we will evaluate a LogisticRegression model using all the available features.

The model is fit on the training dataset and evaluated on the test dataset.

The complete example is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

# evaluation of a model using all input features

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# fit the model

model = LogisticRegression(solver=‘liblinear’)

model.fit(X_train, y_train)

# evaluate the model

yhat = model.predict(X_test)

# evaluate predictions

accuracy = accuracy_score(y_test, yhat)

print(‘Accuracy: %.2f’ % (accuracy*100))

Running the example prints the accuracy of the model on the training dataset.

Note: your specific results may vary given the stochastic nature of the learning algorithm. Try running the example a few times.

In this case, we can see that the model achieves a classification accuracy of about 77 percent.

We would prefer to use a subset of features that achieves a classification accuracy that is as good or better than this.

Model Built Using ANOVA f-test Features

We can use the ANOVA f-test to score the features and select the four most relevant features.

The select_features() function below is updated to achieve this.

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select a subset of features

fs = SelectKBest(score_func=f_classif, k=4)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

The complete example of evaluating a logistic regression model fit and evaluated on data using this feature selection method is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

# evaluation of a model using 4 features chosen with anova f-test

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import f_classif

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select a subset of features

fs = SelectKBest(score_func=f_classif, k=4)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# feature selection

X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)

# fit the model

model = LogisticRegression(solver=‘liblinear’)

model.fit(X_train_fs, y_train)

# evaluate the model

yhat = model.predict(X_test_fs)

# evaluate predictions

accuracy = accuracy_score(y_test, yhat)

print(‘Accuracy: %.2f’ % (accuracy*100))

Running the example reports the performance of the model on just four of the eight input features selected using the ANOVA f-test statistic.

Note: your specific results may vary given the stochastic nature of the learning algorithm. Try running the example a few times.

In this case, we see that the model achieved an accuracy of about 78.74 percent, a lift in performance compared to the baseline that achieved 77.56 percent.

Model Built Using Mutual Information Features

We can repeat the experiment and select the top four features using a mutual information statistic.

The updated version of the select_features() function to achieve this is listed below.

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select a subset of features

fs = SelectKBest(score_func=mutual_info_classif, k=4)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

The complete example of using mutual information for feature selection to fit a logistic regression model is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

# evaluation of a model using 4 features chosen with mutual information

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import mutual_info_classif

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# feature selection

def select_features(X_train, y_train, X_test):

# configure to select a subset of features

fs = SelectKBest(score_func=mutual_info_classif, k=4)

# learn relationship from training data

fs.fit(X_train, y_train)

# transform train input data

X_train_fs = fs.transform(X_train)

# transform test input data

X_test_fs = fs.transform(X_test)

return X_train_fs, X_test_fs, fs

# load the dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# split into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)

# feature selection

X_train_fs, X_test_fs, fs = select_features(X_train, y_train, X_test)

# fit the model

model = LogisticRegression(solver=‘liblinear’)

model.fit(X_train_fs, y_train)

# evaluate the model

yhat = model.predict(X_test_fs)

# evaluate predictions

accuracy = accuracy_score(y_test, yhat)

print(‘Accuracy: %.2f’ % (accuracy*100))

Running the example fits the model on the four top selected features chosen using mutual information.

Note that your specific results may vary given the stochastic nature of the learning algorithm. Try running the example a few times.

In this case, we can make no difference compared to the baseline model. This is interesting as we know the method chose a different four features compared to the previous method.

Tune the Number of Selected Features

In the previous example, we selected four features, but how do we know that is a good or best number of features to select?

Instead of guessing, we can systematically test a range of different numbers of selected features and discover which results in the best performing model. This is called a grid search, where the k argument to the SelectKBest class can be tuned.

It is good practice to evaluate model configurations on classification tasks using repeated stratified k-fold cross-validation. We will use three repeats of 10-fold cross-validation via the RepeatedStratifiedKFold class.

...

# define the evaluation method

cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)

We can define a Pipeline that correctly prepares the feature selection transform on the training set and applies it to the train set and test set for each fold of the cross-validation.

In this case, we will use the ANOVA f-test statistical method for selecting features.

...

# define the pipeline to evaluate

model = LogisticRegression(solver=‘liblinear’)

fs = SelectKBest(score_func=f_classif)

pipeline = Pipeline(steps=[(‘anova’,fs), (‘lr’, model)])

We can then define the grid of values to evaluate as 1 to 8.

Note that the grid is a dictionary of parameters to values to search, and given that we are using a Pipeline, we can access the SelectKBest object via the name we gave it, ‘anova‘, and then the parameter name ‘k‘, separated by two underscores, or ‘anova__k‘.

...

# define the grid

grid = dict()

grid[‘anova__k’] = [i+1 for i in range(X.shape[1])]

We can then define and run the search.

...

# define the grid search

search = GridSearchCV(pipeline, grid, scoring=‘accuracy’, n_jobs=–1, cv=cv)

# perform the search

results = search.fit(X, y)

Tying this together, the complete example is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

# compare different numbers of features selected using anova f-test

from pandas import read_csv

from sklearn.model_selection import RepeatedStratifiedKFold

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import f_classif

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

from sklearn.model_selection import GridSearchCV

from matplotlib import pyplot

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# define dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# define the evaluation method

cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)

# define the pipeline to evaluate

model = LogisticRegression(solver=‘liblinear’)

fs = SelectKBest(score_func=f_classif)

pipeline = Pipeline(steps=[(‘anova’,fs), (‘lr’, model)])

# define the grid

grid = dict()

grid[‘anova__k’] = [i+1 for i in range(X.shape[1])]

# define the grid search

search = GridSearchCV(pipeline, grid, scoring=‘accuracy’, n_jobs=–1, cv=cv)

# perform the search

results = search.fit(X, y)

# summarize best

print(‘Best Mean Accuracy: %.3f’ % results.best_score_)

print(‘Best Config: %s’ % results.best_params_)

Running the example grid searches different numbers of selected features using ANOVA f-test, where each modeling pipeline is evaluated using repeated cross-validation.

Your specific results may vary given the stochastic nature of the learning algorithm and evaluating procedure. Try running the example a few times.

In this case, we can see that the best number of selected features is seven; that achieves an accuracy of about 77 percent.

Best Mean Accuracy: 0.770

Best Config: {‘anova__k’: 7}

We might want to see the relationship between the number of selected features and classification accuracy. In this relationship, we may expect that more features result in a better performance to a point.

This relationship can be explored by manually evaluating each configuration of k for the SelectKBest from 1 to 8, gathering the sample of accuracy scores, and plotting the results using box and whisker plots side-by-side. The spread and mean of these box plots would be expected to show any interesting relationship between the number of selected features and the classification accuracy of the pipeline.

The complete example of achieving this is listed below.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

# compare different numbers of features selected using anova f-test

from numpy import mean

from numpy import std

from pandas import read_csv

from sklearn.model_selection import cross_val_score

from sklearn.model_selection import RepeatedStratifiedKFold

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import f_classif

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

from matplotlib import pyplot

# load the dataset

def load_dataset(filename):

# load the dataset as a pandas DataFrame

data = read_csv(filename, header=None)

# retrieve numpy array

dataset = data.values

# split into input (X) and output (y) variables

X = dataset[:, :–1]

y = dataset[:,–1]

return X, y

# evaluate a give model using cross-validation

def evaluate_model(model):

cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)

scores = cross_val_score(model, X, y, scoring=‘accuracy’, cv=cv, n_jobs=–1, error_score=‘raise’)

return scores

# define dataset

X, y = load_dataset(‘pima-indians-diabetes.csv’)

# define number of features to evaluate

num_features = [i+1 for i in range(X.shape[1])]

# enumerate each number of features

results = list()

for k in num_features:

# create pipeline

model = LogisticRegression(solver=‘liblinear’)

fs = SelectKBest(score_func=f_classif, k=k)

pipeline = Pipeline(steps=[(‘anova’,fs), (‘lr’, model)])

# evaluate the model

scores = evaluate_model(pipeline)

results.append(scores)

# summarize the results

print(‘>%d %.3f (%.3f)’ % (k, mean(scores), std(scores)))

# plot model performance for comparison

pyplot.boxplot(results, labels=num_features, showmeans=True)

pyplot.show()

Running the example first reports the mean and standard deviation accuracy for each number of selected features.

Your specific results may vary given the stochastic nature of the learning algorithm and evaluation procedure. Try running the example a few times.

In this case, it looks like selecting five and seven features results in roughly the same accuracy.

>1 0.748 (0.048)

>2 0.756 (0.042)

>3 0.761 (0.044)

>4 0.759 (0.042)

>5 0.770 (0.041)

>6 0.766 (0.042)

>7 0.770 (0.042)

>8 0.768 (0.040)

Box and whisker plots are created side-by-side showing the trend of increasing mean accuracy with the number of selected features to five features, after which it may become less stable.

Selecting five features might be an appropriate configuration in this case.

Box and Whisker Plots of Classification Accuracy for Each Number of Selected Features Using ANOVA f-test

Box and Whisker Plots of Classification Accuracy for Each Number of Selected Features Using ANOVA f-test

Further Reading

This section provides more resources on the topic if you are looking to go deeper.

Tutorials

  • How to Choose a Feature Selection Method For Machine Learning
  • How to Perform Feature Selection with Categorical Data
  • What Is Information Gain and Mutual Information for Machine Learning

Books

  • Feature Engineering and Selection, 2019.

Papers

  • Mutual Information between Discrete and Continuous Data Sets, 2014.

APIs

  • Feature selection, Scikit-Learn User Guide.
  • sklearn.feature_selection.f_classif API.
  • sklearn.feature_selection.mutual_info_classif API.
  • sklearn.feature_selection.SelectKBest API.

Articles

  • F-test, Wikipedia.
  • One-way analysis of variance, Wikipedia.

Datasets

  • Diabetes Dataset (pima-indians-diabetes.csv)
  • Diabetes Dataset Description (pima-indians-diabetes.names)

Summary

In this tutorial, you discovered how to perform feature selection with numerical input data for classification.

Specifically, you learned:

  • The diabetes predictive modeling problem with numerical inputs and binary classification target variables.
  • How to evaluate the importance of numerical features using the ANOVA f-test and mutual information statistics.
  • How to perform feature selection for numerical data when fitting and evaluating a classification model.

Do you have any questions?


Ask your questions in the comments below and I will do my best to answer.

Tags: aiartificial intelligenceData Preparationmachine learning
Previous Post

EHRs with Machine Learning Deciphering Drug Effects In Pregnant Women

Next Post

Feature Engineering and Selection (Book Review)

Next Post
feature-engineering-and-selection-(book-review)

Feature Engineering and Selection (Book Review)

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Category

  • -core
  • -inch
  • -year-old
  • 'anti-procrastination'
  • 'bang'
  • 'gold'
  • 'plug
  • 'Trending'
  • 0
  • 000 mah battery
  • 1
  • 10 billion dollars
  • 100gb
  • 11th gen
  • 1mii
  • 1mii deals
  • 2
  • 2-in-1
  • 2020 election
  • 2020 elections
  • 2020 presidential election
  • 20th century fox
  • 20th century studios
  • 2d
  • 2in1
  • 3.5 ghz
  • 35th
  • 360hz
  • 3d printing
  • 3dprinting
  • 4-series
  • 4k
  • 50 states of fright
  • 5g
  • 64-megapixel camera
  • 65
  • 8bitdo
  • 8k
  • a dark path
  • a10
  • a20
  • a20 gen 2
  • a40
  • a40tr
  • a50 wireless
  • abide
  • abortion
  • absentee ballots
  • Academy
  • acadiana mall
  • accelerated
  • accept sender
  • accepting
  • accessibility
  • accessibility center of excellence
  • acer
  • acer deals
  • acer spin 7
  • acerspin7
  • Acorn
  • action camera
  • action figures
  • active noise cancellation
  • Activision
  • activision blizzard
  • Activists
  • actually
  • ada
  • adam savage
  • addicted
  • administration
  • adobe
  • adopt
  • adrian smith
  • ads
  • adult swim
  • advanced optimus
  • advertising
  • affect
  • affordable
  • African
  • After
  • after math
  • aftermath
  • agriculture
  • ai
  • air carrier
  • air pollution
  • air quality
  • air travel
  • aircraft
  • AirDrop
  • airline
  • airplanes
  • airpods pro
  • airports
  • Airtel
  • ajit pai
  • alex winter
  • alexa
  • alexa for residential
  • alibaba
  • alice braga
  • alien addiction
  • aliens
  • Alienware
  • alienware 25
  • alienware 27
  • alienware 38
  • alienware deals
  • alipay
  • all up
  • all-electric
  • alphabay
  • alphabet
  • alter
  • Amateur
  • amazfit
  • amazing
  • amazon
  • amazon alexa
  • amazon deals
  • amazon echo
  • amazon flex
  • amazon pay
  • amazon prime
  • amazon prime air
  • amazon prime video
  • amazon.subtember
  • Amazon's
  • amazonalexa
  • amazonpay
  • Ambient
  • amd
  • AMD's
  • American
  • american horror story
  • Amnesia
  • Among
  • amongst
  • ampere
  • analysis
  • anarchists
  • anc
  • Ancient
  • andrea riseborough
  • andrea stewart
  • android
  • android 10
  • android auto
  • android automotive
  • android tablet
  • Android's
  • android10
  • androidtablet
  • animal crossing
  • animation
  • anime
  • anker
  • anker deals
  • annihilation
  • anniversary
  • announcements
  • announces
  • anode
  • Another
  • ant man 3
  • antenna
  • anthony carrigan
  • anti-cheat
  • anti-tracking
  • antibiotics
  • antibodies
  • antibody
  • anticipated
  • antifa
  • antitrust
  • Antiviral
  • antlers
  • anwr
  • anxiety
  • anxious
  • anya taylor joy
  • anyone
  • Aorus
  • apartments
  • apollo 11
  • apologizes
  • app
  • app store
  • apparel
  • appeals
  • apple
  • apple arcade
  • apple arm
  • apple deals
  • apple health
  • apple rumors
  • apple safari
  • apple silicon
  • apple store
  • apple tv
  • apple vs epic
  • apple watch
  • apple watch series 3
  • Apple's
  • application
  • approaching
  • approximately
  • apps
  • ar
  • Arcade
  • arcade stick
  • archives
  • arctic
  • arctic national wildlife refuge
  • area51m
  • argument
  • arm
  • arnold schwarzenneger
  • art gallery
  • artemis mission
  • Artificial Intelligence
  • arturia
  • asobo studio
  • asphalt
  • Assassin's
  • astro
  • astro gaming
  • Astro's
  • astrogaming
  • Astronomers
  • astronomy
  • Astrophysics
  • asus rog strix scar g15
  • asus rog strix scar g15 review
  • Asus'
  • at home fitness
  • at&t
  • atomic
  • attach
  • Attackers
  • Attacking
  • attempts
  • attorney general
  • auction
  • audible
  • audio
  • audiobooks
  • augmented reality
  • augmented reality glasses
  • august
  • august wifi smart lock
  • aukey
  • aukey deals
  • Aurora
  • australia
  • Australia's
  • Australian
  • australian police
  • Authenticator
  • Authorities
  • authors
  • autofocus
  • automation
  • Autonomous
  • autonomous vehicle
  • Autophagy
  • autopilot
  • autoplay
  • av
  • available
  • avatar the last airbender
  • Avengers
  • aviation
  • ayo edibiri
  • azula
  • babies
  • baby yoda
  • backhaul
  • backwards compatibility
  • bacteria
  • balloon
  • ban
  • barrierfree
  • bass
  • batman
  • batman the animated series
  • batmobile
  • batteries
  • battery
  • Battery-free
  • battle
  • battle royale
  • bay area
  • be prepared
  • Beating
  • beats
  • beats deals
  • beautiful
  • beauty
  • bedding
  • bedroom
  • beer
  • behavior
  • behind the scenes
  • Being
  • Belgian
  • believed
  • Bering
  • bering sea
  • best buy deals
  • best of gizmodo
  • beta
  • bethesda
  • bh deals
  • bh photo deals
  • bicycle
  • biden
  • biden-harris
  • big boys
  • big mouth
  • big oil
  • bigger
  • biggest
  • bike
  • bill and ted face the music
  • bill barr
  • bill sienkiewicz
  • billion
  • billy crystal
  • biocontainment
  • biodiversity
  • biohackers
  • biohacking
  • biological
  • bird box
  • Birds
  • bitcoin
  • Black
  • black christmas
  • black hole
  • black lives matter
  • black panther
  • black widow
  • Blade
  • blast
  • blink
  • blink indoor
  • blink outdoor
  • block
  • blocks
  • blogging
  • Blood
  • blood-clotting
  • blu de barrio
  • blu hunt
  • Blu-ray
  • bluetooth
  • bluetooth headphones
  • bluetooth speaker
  • bluetooth speakers
  • bmi
  • board games
  • boat parade
  • boats
  • bob mcleod
  • Bobble
  • bolsonaro
  • bomberman
  • boneless chicken wings
  • book review
  • books
  • bookshelf injection
  • boosts
  • bounce music
  • box office
  • boxes
  • boycott
  • Boyega
  • braille
  • brain
  • brain computer interface
  • brain computer interfaces
  • brain-machine interface
  • Brainstem
  • brand-new
  • branding
  • brandon cronenberg
  • brazil
  • breakdown
  • breaks
  • breast
  • brigette lundy paine
  • brings
  • broadband
  • broadcast
  • brookings institution press
  • brooklyn
  • browser
  • budget
  • budget laptops
  • bug
  • bug fixes
  • bugs
  • build
  • bulk collection
  • bulk data collection
  • bullshit resistance school
  • burned
  • burning
  • burrowing
  • business
  • business laptops
  • butterfly
  • buyers guide
  • bytedance
  • cadmium
  • cake
  • california
  • california wildfires
  • call of duty
  • call of duty black ops cold war
  • call of duty league
  • call of duty: warzone
  • called
  • callofduty
  • callofdutyblackopscoldwar
  • callofdutyleague
  • Calls
  • calltoaction
  • Cambrian
  • camera
  • cameras
  • campaign
  • campaign signs
  • Can't
  • cancer
  • cancer alley
  • canine
  • Canon
  • captain america
  • captions
  • capture
  • Capturing
  • carbon
  • carcinogens
  • cars
  • cartivator
  • cary elwes
  • cases
  • cassandra clare
  • catnap
  • cbs
  • cbs all access
  • cd projekt red
  • cdc
  • cdl
  • cdpr
  • celebrates
  • Cells
  • Celtics
  • censorship
  • century
  • centurylink
  • chadwick boseman
  • chair
  • chairs
  • chamois
  • Champion
  • Championship
  • chance
  • Change
  • changes
  • channel zero
  • charging stations
  • charity
  • charlie heaton
  • cheap
  • cheaper
  • cheapest
  • cheating
  • Check
  • checked
  • cher wang
  • chest
  • chicago
  • chicken wings
  • Children
  • childrens books
  • childs play
  • china
  • Chinese
  • chips
  • chipset
  • choir
  • Cholesterol
  • chris claremont
  • chris matheson
  • christmas
  • christopher abbott
  • christopher nolan
  • chrome
  • Chromebooks
  • chucky
  • CineBeam
  • citadel
  • cities
  • city council meeting
  • civil liberties
  • Clarifying
  • class
  • classes
  • Classic
  • clean
  • Cleaning
  • clients
  • Climate
  • climate change
  • climate policy
  • clint barton
  • Clippers
  • clothing
  • cloud
  • cloud computing
  • cloud storage
  • Cloudflare
  • club pro+ tws
  • clusterfucks
  • coastal communities
  • Coaxing
  • cobra jet
  • cobra kai
  • cod
  • coffee
  • collaborative
  • college sports
  • Color
  • colorado
  • colors
  • comcast
  • Comey
  • comics
  • comixology
  • commerce
  • commerce department
  • common
  • commutes
  • company
  • competition
  • complaint
  • completely
  • complimentary
  • compound
  • Comprehensive
  • computational
  • computer
  • computer building
  • computers
  • concept art
  • concerning
  • confirmed
  • confirms
  • Connacht
  • connected home
  • connectedhome
  • consciousness
  • conservation
  • Conserve
  • conspiracies
  • conspirators
  • Constant
  • construct
  • Consume
  • consumer tech
  • contact tracing
  • contaminated
  • contamination
  • content moderation
  • continuous
  • contract
  • contractor
  • contractors
  • contracts
  • control
  • controller
  • convert
  • convertible
  • cooking
  • cops
  • cord cutters
  • cordless
  • coronavirus
  • corsair
  • cortisone
  • cosplay
  • costs
  • Could
  • countless
  • courts
  • covertly
  • covid 19
  • covid 19 reopening
  • COVID-
  • cpu
  • cpus
  • created
  • Creativity
  • Creed
  • creepypasta
  • crime
  • criteria
  • critical race theory
  • Croatia
  • cross-site tracking
  • crossover
  • crowdfunding
  • crunchyroll
  • crusher evo
  • Crysis
  • crystal dynamics
  • current
  • cx 400bt
  • CyberGhost
  • Cyberpunk
  • cybersecurity
  • cytokine
  • dangerous
  • daniel prude
  • dark shadows
  • dark web
  • darling
  • darpa
  • das
  • data
  • data portability
  • data privacy
  • data security
  • data transfer project
  • dating
  • david benioff
  • david polfeldt
  • davidbenioff
  • Daylight
  • daylight saving time
  • db weiss
  • dbweiss
  • dc
  • dc comics
  • dc fandome
  • ddos
  • ddos attacks
  • deadly
  • deals
  • dean parisot
  • death
  • debunks
  • debuts
  • Decades-old
  • Deciphering
  • decisions
  • declares
  • deep learning
  • deepfake
  • deepfakes
  • deepmind
  • DeepMind's
  • defending democracy program
  • deficiency
  • deforestation
  • del rey
  • delay
  • delays
  • deletes
  • deliveries
  • delivery
  • dell
  • dell deals
  • demanding
  • democratic party
  • demonstrate
  • demonstrates
  • Demonstrating
  • denim
  • Department
  • department of commerce
  • department of defense
  • Dependence
  • Depot
  • Depression
  • deron j powell
  • Descent
  • describes
  • design
  • designation
  • designers
  • details
  • detecting
  • detection
  • determine
  • dev patel
  • develop
  • developers
  • development
  • developmental
  • device
  • devices
  • dexamethasone
  • diabetes
  • Diabetes-in-a-dish
  • didn't
  • diesel
  • diets
  • differing
  • digital
  • digital cameras
  • digital diversions
  • Digital's
  • Dimensity
  • dinosaur
  • dipayan ghosh
  • direct
  • disabilities
  • disasters
  • Discord
  • discount
  • discover
  • discovered
  • Discovering
  • discovers
  • discovery
  • disenchantment
  • disney
  • disney plus
  • disney plus hotstar
  • disneyplus
  • display
  • displayhdr 600
  • Disrespect
  • dissociation
  • distance learning
  • ditch
  • Division
  • diy
  • dji
  • Djokovic
  • dlc
  • dlss
  • dna
  • do all the letters of the alphabet next you cowards
  • docs
  • dod
  • Dodder
  • doesn't
  • dogs
  • doing
  • doj
  • Dollars
  • dolphins
  • don mancini
  • don't
  • donald trump
  • donation
  • donnie yen
  • doom
  • doom eternal
  • doom ii
  • doometernal
  • doorbell
  • doorbell cams
  • doorbells
  • dorm
  • download
  • dragoncon
  • dragster
  • dramatically
  • dream edition
  • Dreamcast
  • drivers
  • driving
  • drone
  • drone delivery
  • drones
  • dropbox
  • drug-resistant
  • drugs
  • dryer
  • dual-screen
  • dune
  • dungeons and dragons
  • duo evo plus
  • Dynabook
  • dynamics
  • Dyson
  • dystopia
  • e-commerce
  • e-ink
  • e-mail
  • ea
  • earbuds
  • earlier
  • Earliest
  • Early
  • earth league international
  • earth observation
  • Earth's
  • easter
  • easter eggs
  • ecg
  • echo auto
  • echo buds
  • echoauto
  • ecofascism
  • economy
  • ed solomon
  • edgar wright
  • edge
  • Edinburgh
  • Edison
  • edison software
  • Edition
  • education
  • edward snowden
  • Effective
  • Elderly
  • election
  • election 2020
  • elections
  • electric
  • electric car
  • electric scooters
  • electric truck
  • electric vehicle
  • electrical
  • electrolyte
  • electron
  • electronic
  • electronic arts
  • electronic skin
  • elephant
  • elephants
  • elon musk
  • emails
  • embedded
  • Emergency
  • emissions
  • enables
  • enc
  • ending
  • endurance peak 2
  • endurance peak ii
  • energy
  • engadget podcast
  • engadgetdeals
  • engadgetpodcast
  • engadgetupscaled
  • Engineers
  • England
  • enhance
  • Enjoy
  • entertainment
  • Entry-level
  • environment
  • environmental protection agency
  • eoin colfer
  • epa
  • epic
  • epic games
  • epic vs apple
  • Epic’s
  • epicgames
  • episode
  • equipped
  • Erangel
  • eshop
  • espionage
  • esports
  • esportssg
  • establish
  • Estrogen
  • eta
  • Europe's
  • European
  • eurorack
  • euthanasia
  • euv
  • ev
  • Every
  • evictions
  • evidence
  • evolution
  • examines
  • excellent
  • exclusive
  • exercise
  • exist
  • expanded universe
  • expands
  • expensive
  • experience accessibility team
  • Experimental
  • explains
  • explorer project
  • export
  • exposure
  • exposure notification
  • extension
  • extinction
  • extreme e
  • extreme ultraviolet
  • extremee
  • exxon
  • exxonmobil
  • faa
  • face masks
  • face shields
  • facebook
  • facebook live
  • facebook wrote a press release
  • Facebook's
  • facilities
  • factors
  • failure
  • Failures
  • fainting
  • fake
  • fake events
  • fake news
  • fakes
  • falcon 9
  • fall 2020
  • fall guys
  • families
  • fascism
  • fast
  • fastest
  • Fastly
  • FAU-G
  • fbi
  • fcc
  • fda
  • FDA's
  • feature
  • federal communications commission
  • federalcommunicationscommission
  • fediverse
  • fedot tumusov
  • Felix
  • Females
  • femtech
  • fertility tech
  • fibre
  • Fidelio
  • Fidelity
  • fields
  • Figuring
  • film
  • finally
  • finally multicolor hue lightstrips
  • Finding
  • finds
  • Finest
  • fingerprint reader
  • fire tv
  • first
  • first amendment
  • fisa
  • fitbit
  • fitbit charge 4
  • fitness
  • fitness bands
  • fitness gear
  • fitness trackers
  • Fitter
  • five eyes
  • flash
  • flaunts
  • flexible
  • flexible display
  • Flight
  • flight simulator 2020
  • flint
  • flood
  • Floppy'
  • florida
  • flowering
  • flying car
  • flying taxis
  • fold 2
  • foldable
  • foldable phone
  • foldables
  • folding
  • Following
  • food
  • food justice
  • food security
  • Food-web
  • football
  • footwear
  • forces
  • forcibly
  • ford
  • fordpass
  • forecast
  • foreign
  • forests
  • Forget
  • fortnite
  • Fortnite's
  • Forty-Year-Old
  • Forward-thinking
  • forwarding limit
  • Fossil
  • fossils
  • found
  • fountain pens
  • fox news
  • fox soccer plus
  • France
  • fraud
  • free
  • free comics
  • free speech
  • free-to-play
  • freshwater
  • Friday
  • frontier
  • fuck fossil fuels
  • Fujifilm
  • full frame cameras
  • full-frame
  • Functions
  • Fungi
  • future
  • g-sync
  • g-sync ultimate
  • g9
  • gadgetry
  • gadgets
  • Galaxy
  • galaxy a42 5g
  • galaxy book flex
  • galaxy book flex 5g
  • galaxy buds plus
  • galaxy fit
  • galaxy fit 2
  • galaxy fold
  • galaxy s20
  • galaxy s20 fan edition
  • galaxy s20 ultra
  • galaxy tab a7
  • galaxy watch 3
  • galaxy z fold 2
  • galaxy z fold 2 5g
  • galaxy z fold2
  • galaxybookflex
  • galaxybookflex5g
  • gallery
  • game & watch
  • game boy
  • game of thrones
  • game-breaking
  • gameboy
  • gameofthrones
  • Gamers
  • games
  • Gamifying
  • gaming
  • gaming desktops
  • gaming gear
  • gaming laptop
  • gaming laptops
  • gaming monitor
  • gaming shelf
  • gas pump
  • gas station
  • gaspump
  • gasstation
  • gear
  • geforce
  • geforce rtx
  • geforce rtx 2060
  • geforce rtx 3080
  • geforcertx3080
  • gene kozicki
  • generous
  • Genes
  • Genetic
  • genetics
  • Genome
  • Genomic
  • Germany
  • Germany's
  • getting
  • getting out
  • giancarlo esposito
  • Giant
  • gig economy
  • gig workers
  • gizmos
  • glaciers
  • glitch
  • global tel link
  • Globalization
  • Gmail
  • go vacation
  • godzilla vs kong
  • gofundme
  • goltv
  • gong li
  • google
  • google ad policy
  • google ads
  • google assistant
  • google assistant snapshot
  • google chrome
  • google docs
  • google drive
  • google images
  • google kids space
  • google magenta
  • google maps
  • google play
  • google podcasts
  • Google's
  • googlekidsspace
  • gopro
  • gorilla glass
  • gotten
  • gpu
  • gpus
  • Graduate
  • Grand
  • grand central publishing
  • graphic neural network
  • graphically-impressive
  • graphics
  • graphics card
  • graphics cards
  • gravitational wave
  • Gravity
  • gravity waves
  • green drone
  • grills
  • groceries
  • growth
  • guidance
  • guidelines
  • guides
  • Guilt
  • Gulls
  • gwichin
  • hackers
  • hacking
  • hairdye
  • halloween
  • Handgrip
  • handing
  • handle
  • happens
  • happier
  • haptics
  • hard truths
  • harder
  • hardware
  • harvard
  • harvard university
  • harvarduniversity
  • hashes
  • Hastings
  • have your cake and eat it too
  • hawc
  • hawk rev vampire slayers
  • hawkeye
  • hbo
  • hbo max
  • hdr10+
  • headache
  • headed
  • headphones
  • headpohones
  • headset
  • headsets
  • health
  • Hearing
  • heart
  • heat wave
  • heat-free
  • Heavy
  • Hedge
  • heliophysics
  • hell to the no
  • hellfeed
  • hello games
  • Helminth
  • Helping
  • henry zaga
  • hepa
  • Here's
  • herman cain
  • heroes
  • hey email app
  • higher
  • highfire
  • hillary clinton
  • hints
  • hisense
  • history
  • hitting the books
  • hittingthebooks
  • holiday
  • holidays
  • home
  • home fitness
  • home schooling
  • home security
  • home theater
  • homepage
  • homepod
  • homesecurity
  • homework gap
  • honeybees
  • honeysuckle
  • honor
  • Honor's
  • horror
  • horsepower
  • Hostgator
  • hosting
  • hosts
  • hot toys
  • Hotspots'
  • hotstar
  • House
  • households
  • hp
  • hp deals
  • htc
  • Huawei
  • Hubble
  • hue play gradient
  • hugo weaving
  • human
  • Hunter
  • hunters
  • hurricane katrina
  • hurricane laura
  • hurricane season
  • hybrid
  • hypersonic
  • hypersonic missiles
  • hypertension
  • hyperx
  • Hyrule
  • i miss midi music
  • ian alexander
  • iap
  • ice ice maybe
  • ice on thin ice
  • Iceland
  • icloud
  • id software
  • id.4
  • ideas
  • Identification
  • identified
  • identify
  • idw
  • ifa
  • ifa 2020
  • ifa2020
  • ihome
  • ihome deals
  • imac
  • images
  • imitate
  • immunity
  • immuno-acceptance
  • immunotherapy
  • impacts
  • important
  • improved
  • Improving
  • in-app purchases
  • includes
  • income
  • incorrect
  • increase
  • increased
  • India
  • Indian
  • indie
  • individuals
  • indoor
  • inexpensive
  • Infants
  • infection
  • infections
  • infinity ward
  • Inflammation
  • influencer
  • influencers
  • Informing
  • informs
  • infotainment
  • Ingenious
  • initiation
  • injunction
  • Inkjet
  • Insect
  • Insight
  • Insights
  • insta360
  • insta360 one r
  • Instagram
  • instagram reels
  • instagram stories
  • installation
  • Instant
  • instant pot
  • instant pot smart wifi
  • instruments
  • insulin
  • integrated graphics
  • intel
  • intel core i9
  • intel deals
  • intel evo
  • intel xe graphics
  • intelevo
  • interact
  • interior
  • intermediate-mass black hole
  • intermittent computing
  • international
  • internet
  • internet archive
  • internet balloons
  • internet culture
  • internet research agency
  • interventions
  • interview
  • introduce
  • introduces
  • introducing
  • intrusive
  • invest
  • Investigational
  • investment
  • invests
  • invoice
  • ios
  • ios 13
  • ios 13.7
  • ios 14
  • ios13
  • ios14
  • iot
  • ip54
  • ipad
  • ipad air
  • ipad os 14
  • ipados14
  • iPhone
  • ipod
  • Islanders
  • isotope
  • israel
  • Italian
  • italy
  • items
  • its business time
  • japan
  • jason scott lee
  • jaxjox
  • jbl
  • jbl clip 4
  • jbl go 3
  • jbl partybox 310
  • jbl partybox on-the-go
  • jbl xtreme 3
  • JBL's
  • jeans
  • jedi
  • jeff bezos
  • jeff bond
  • jennifer jason leigh
  • jenny slate
  • jet li
  • jetpacks
  • jim butcher
  • JioFiber
  • jj abrams
  • joe biden
  • johnson johnson
  • jon favreau
  • jonathan majors
  • jordan eldredge
  • jordan peele
  • josh boone
  • josh guillory
  • journalism
  • juicer
  • july 4th
  • Jumping'
  • jumpstarts
  • jurassic world dominion
  • jurnee smollett
  • just transition
  • Justice
  • juul
  • jw nijman
  • jw rinzler
  • kamala harris
  • Karaoke
  • karate kid
  • kate bishop
  • kate bush
  • keanu reeves
  • Keeping
  • kenosha
  • kevin conway
  • keyboards
  • keystep
  • keystep pro
  • kick stage
  • Kidneys
  • kids
  • killer
  • king of sweden
  • kinja deals
  • konami
  • koofr
  • kotaku core
  • kotakucore
  • lab-grown
  • Labor
  • lafayette police chief scott morgan
  • laika
  • Lakers
  • lana wachowski
  • landlords
  • laptop
  • laptops
  • large attachments
  • largest
  • laser
  • laser tv
  • latest
  • launch
  • launch complex 2
  • launched
  • launches
  • laura ingraham
  • laurencefishburne
  • lawsuit
  • lawsuits
  • layout
  • leader
  • leading
  • leading-edge
  • League
  • league of legends
  • league of legends championship series
  • leak
  • leakages
  • Leaked
  • leaks
  • leaky buckets
  • learn
  • Legends
  • legion
  • legion slim 7i
  • Leinster
  • Lemonade
  • lenovo
  • lenovo legion 7
  • lenovo legion slim 7i
  • lenovo smart clock
  • lenovo smart clock essential
  • lenovo tab m10 hd gen 2
  • lenovo tab p11 pro
  • lenovo yoga
  • lenovo yoga 9i
  • leopard
  • lessen
  • letting
  • lev grossman
  • level
  • lewis hamilton
  • lg
  • lg deals
  • lg wing
  • lgbtq
  • license
  • licensing
  • lidar
  • lifestyle
  • light
  • Lightning
  • lightsabers
  • lightstrips
  • lightweight
  • ligo
  • linked
  • Links
  • Linux
  • lite
  • lithography
  • Little
  • liu cixin
  • liu yifei
  • liucixin
  • live
  • live sports
  • livestream
  • livestreaming
  • lo-fi
  • lo-fi player
  • local news
  • Locating
  • location
  • lockhart
  • lockheed martin
  • Loggerhead
  • logitech
  • logo
  • longread
  • looks
  • loon
  • loses
  • louisiana
  • lovecraft country
  • lovecraft country recaps
  • low-cost
  • Lowe's
  • lower ninth ward
  • lpddr5
  • lsc
  • lucasfilm
  • Lucid
  • lucid air
  • lucid motors
  • LucidLink
  • lucifer
  • Lumix
  • lutron
  • m night shyamalan
  • macbook air
  • macbook pro
  • mach 5
  • mach-e
  • machine learning
  • magenta
  • Magenta's
  • magicbook pro 16
  • mail
  • mail in ballots
  • mail-in voting
  • Mail's
  • maintain
  • maisie williams
  • makes
  • Making
  • malaria
  • males
  • Managing
  • Mandalorian
  • Mandalorian's
  • mandy patinkin
  • manipulated media
  • map
  • mapping
  • marijuana
  • marine
  • Mario
  • mario kart
  • mario kart live
  • mario kart live home circuit
  • mark zuckerberg
  • market
  • Marketing
  • martial arts
  • marvel
  • marvel cinematic universe
  • marvel comics
  • marvel studios
  • Marvel's
  • marvelentertainment
  • marvels avengers
  • masks
  • massive entertainment
  • massiveentertainment
  • mastodon
  • mastodons
  • MatePad
  • material
  • mathematical
  • Matric
  • matt ruff
  • matter
  • matterport
  • mattress
  • mattresses
  • mauritius
  • max-q
  • meat
  • mechanical
  • media
  • MediaTek
  • mediatonic
  • medicine
  • mega city one
  • mega-shark
  • meghan markle
  • meghanmarkle
  • meh deals
  • members
  • memes
  • memory
  • mental health
  • mentality
  • mergers and acquistions
  • messages
  • messenger
  • metadata
  • metal gear solid
  • Meteorite
  • method
  • metroid
  • miami
  • michael k williams
  • Microbes
  • microfiber
  • Microgel
  • Microsoft
  • microsoft edge
  • Microsoft's
  • mid-range
  • Middle
  • midi
  • midi controller
  • migrations
  • miir deals
  • military technology
  • militias
  • Millions
  • Minecraft–
  • Miniature
  • minimize
  • mining
  • mirrorless
  • mirrorless cameras
  • misha green
  • misinformation
  • mistakes
  • mite-y
  • mixed reality
  • mixes
  • mobil
  • mobile
  • Mobile's
  • model
  • model 3
  • model s
  • model x
  • model y
  • moderna
  • modification
  • mods
  • modular synthesizer
  • mojang
  • molecular
  • Molecule
  • monique candelaria
  • monitor
  • Monitoring
  • Monsters
  • months
  • moon
  • morally bankrupt exploitative shitbags
  • more oled laptops please
  • mortality
  • motherandroid
  • Motorola
  • motorola one
  • motorola one 5g
  • Motorola's
  • Motors
  • mouse
  • moveaudio s200
  • movie
  • movie theaters
  • movies
  • movies anywhere
  • mozilla firefox
  • mq direct deals
  • mr carey
  • msi
  • msi summit
  • msi summit series
  • MSI's
  • mulan
  • multiverses
  • Munster
  • Murray
  • museum
  • museums
  • music
  • music making
  • music quiz
  • musical instruments
  • Musk's
  • mustang
  • mustang mach-e
  • mutations
  • myneato
  • mystery
  • mystery jetpack
  • myths
  • naked
  • naming
  • Nanoearthquakes
  • nanomachine
  • nasa
  • national security agency
  • Nations
  • Natural
  • Nature
  • naughty dog
  • Neanderthals
  • neato
  • neato d10
  • neato d8
  • neato d9
  • nebraska
  • needs
  • Neglected
  • nemesis
  • neon
  • nest
  • nest hello
  • netflix
  • networks
  • neuralink
  • neurons
  • new mutants
  • new orleans
  • new swift 5 and swift 3 from acer
  • new tab page
  • new years eve
  • newegg
  • newegg deals
  • newest
  • Newly
  • news
  • newsletter
  • newyork
  • next-gen
  • nfl
  • nfl network
  • nfl redzone
  • ngo
  • nhra nationals
  • nhtsa
  • nick antosca
  • nickelodeon
  • nicolas cage
  • nike
  • nike deals
  • niki caro
  • ninebot
  • ninja
  • nintendo
  • nintendo switch
  • nintendo switch deals
  • no man's sky
  • no time to die
  • noah ringer
  • noise
  • noise cancelling
  • noise-canceling
  • Nokia
  • nokia 3310
  • Nominet
  • north korea
  • north pole
  • northern
  • nos4a2
  • nostalgia
  • not the fun jedi saga
  • notebook
  • notice
  • Novak
  • Novel
  • novels
  • nsa
  • nsa scandal
  • nubia watch
  • nubia watch review
  • Nuclear
  • Nuggets
  • nuke
  • Nurses
  • nvidai
  • nvidia
  • nvidia geforce
  • nvidia rtx 3070
  • nvidia rtx 3080
  • nvidia rtx 3090
  • Nvidia’s
  • nvidiageforce
  • nxtpaper
  • nyc
  • nypd
  • Ocean
  • oceans
  • oculus quest
  • offer
  • offered
  • offering
  • offers
  • official
  • oil and gas
  • oil spill
  • older
  • Olufsen's
  • olympics
  • on demand
  • oneplus
  • oneplus 7t
  • oneplus watch
  • online
  • OnlyFans
  • onmail
  • open the flood gates
  • opens
  • operating
  • Operation
  • opioids
  • Oracle
  • orbit
  • oregon trail
  • origami
  • origin
  • Orion
  • orion pictures
  • our garbage president
  • outage
  • outages
  • Outbreak
  • Overcast's
  • overheating
  • OVHcloud
  • oxygen
  • P-Series
  • pacemakers
  • packages
  • packs
  • paleontology
  • panasonic
  • panasonic lumix s5
  • Panasonic's
  • Pandemic
  • Panther
  • paper
  • paper based electronics
  • paramount
  • participate
  • partybox
  • pascal
  • patch
  • patent
  • Pattinson
  • pavement
  • paying
  • payments
  • paypal
  • pbug
  • pc
  • pc gaming
  • pco
  • peacock
  • Peculiar
  • peddling to nowhere
  • pedro pascal
  • peloton
  • penguin random house
  • pens
  • Pentagon
  • People
  • permafrost
  • permanent
  • permanently
  • permuted press
  • Personal
  • personal computing
  • personal data
  • personalization
  • petrochemicals
  • pfizer
  • Philips
  • philips hue
  • phone
  • phone cases
  • phone trees
  • phones
  • Photography
  • photon
  • Photos
  • pictures
  • pilot
  • pins
  • Pinterest
  • pinterest today
  • pique your interest
  • Pixel
  • plague rallies
  • planetary
  • planetary science
  • plans
  • Plant
  • plants
  • Plasmin
  • plastic
  • plastic pollution
  • platforms
  • play store
  • playstation
  • playstation 4
  • playstation 5
  • playstation vr
  • playstation4
  • playstationvr
  • please help my brain its very sick
  • please no
  • pleasure
  • plugin
  • poaching
  • poco x3
  • pocox3
  • podcast
  • podcasts
  • point-of-care
  • pokemon go
  • polar orbit
  • Polestar
  • polestar 2
  • police
  • police shootings
  • policy
  • Political
  • political ads
  • politics
  • Pollination
  • populations
  • porsche
  • Portable
  • portable speaker
  • portable speakers
  • portfolios
  • Portugal
  • possessor
  • Possible
  • Post-COVID
  • postal apocalypse
  • postal service
  • potential
  • powerful
  • powertrain
  • practical magic
  • pre-order
  • Predator
  • predator x25
  • predict
  • predictions
  • pregnancy
  • pregnancy tests
  • prehistoric
  • premier access
  • Premiere
  • premium
  • preorder
  • preorders
  • prepared
  • presents
  • president
  • president trump
  • presige 14 evo
  • pressure cooker
  • pressure-lowering
  • presumably
  • Preventing
  • preview
  • price
  • price drop
  • prices
  • primal
  • Prime
  • prime air
  • prime deliveries
  • prime gaming
  • prime video
  • prince harry
  • princeharry
  • principles
  • print
  • printer
  • Prior
  • prison phone app
  • privacy
  • privacy and security
  • problems
  • processor
  • processors
  • product
  • Products
  • Program
  • programs
  • prohibited
  • project
  • project 10 million
  • project athena
  • projector
  • projectors
  • proof
  • Proposed
  • props
  • propulsion
  • prosthetics
  • protein
  • protests
  • prototype
  • provide
  • ps plus
  • ps vr
  • ps1
  • ps2
  • ps3
  • ps4
  • ps5
  • psvr
  • pubg
  • pubg corporation
  • pubg mobile
  • pubg mobile nordic map
  • pubgmsg
  • purchase
  • purchased
  • purdue university
  • putting
  • pxo
  • qanon
  • qopy notes
  • quadruple
  • Qualcomm
  • qualcomm snapdragon
  • qualcomm snapdragon 8cx gen 2
  • Qualcomm's
  • quantum
  • quarter mile
  • quicker
  • quickly
  • quoll
  • quote
  • quote tweet
  • race
  • race car
  • racing
  • racism
  • Radiocarbon
  • Radiologists
  • Raised
  • ralph macchio
  • ram
  • rami ismail
  • RAMPOW
  • randomised
  • Raptors
  • rare earth metals
  • ray-tracing
  • raytheon
  • raytracing
  • razer
  • razer blade 15
  • razer deals
  • Razer's
  • razr
  • razr 2
  • reaches
  • readily
  • real estate
  • reality
  • Realme
  • realtor
  • recent
  • recipe
  • recommended reading
  • record
  • recreading
  • redesign
  • Redmi
  • reels
  • reface
  • reflex
  • reflex latency analyzer
  • refresh rate
  • Regional
  • regulates
  • regulating
  • regulation
  • reinfection
  • release
  • release date
  • released
  • releasedate
  • releases
  • releasing
  • relic
  • reliever
  • relocation
  • remain
  • remote
  • remote learning
  • remote vehicle setup
  • remove
  • removed
  • renewable energy
  • rental
  • repair
  • Report
  • reportedly
  • reporting
  • representation
  • reproductive health
  • reproductive justice
  • Republican
  • republicans
  • Research
  • Researchers
  • resembles
  • reset
  • resignation
  • resolution
  • Resource
  • respiratory
  • response
  • restriction
  • retail
  • Retest
  • retro
  • retro gaming
  • return
  • return of the jedi
  • retweet
  • retweet with comment
  • reunite
  • reusable
  • reusable spacecraft
  • revealed
  • reveals
  • Revel
  • reverse engineering
  • review
  • reviews
  • Revolt
  • reweaving
  • rexlex
  • rhythm
  • rian johnson
  • rianjohnson
  • richard branson
  • richard donner
  • rick snyder
  • right
  • right wing extremism
  • ring
  • riot games
  • rip
  • risks
  • rival
  • riverdale
  • rmit university
  • roadmap
  • roads
  • roav
  • roav deals
  • Robert
  • robert pattinson
  • robert reiner
  • robin wright
  • robot
  • robotic
  • robotic vacuum
  • robots
  • rocket
  • rocket lab
  • rocket league
  • rockets
  • room
  • room 104
  • rosamund pike
  • rosamundpike
  • rough
  • routes
  • royal family
  • royalfamily
  • rtx
  • rtx 30 series
  • rtx 3000
  • rtx 3070
  • rtx 3080
  • rtx 3090
  • rumor
  • rumors
  • running
  • rupert murdoch
  • rural
  • russia
  • s1
  • safety
  • sales
  • samara weaving
  • samsung
  • samsung deals
  • samsung galaxy fit2
  • samsung unpacked
  • Samsung's
  • san francisco
  • sandragon 8cx
  • Santana
  • sars cov 2
  • satechi
  • satellite
  • satellites
  • saucy nugs
  • savings
  • scam
  • scams
  • scandals
  • scanwatch
  • school
  • schools
  • sci fi
  • science
  • Scientist
  • scientists
  • scorched
  • score
  • scott pruitt
  • scream 5
  • screen
  • screen pass
  • sd-03
  • Seagate
  • sean bean
  • sean murray
  • seanan mcguire
  • season
  • section 702
  • security
  • sedan
  • seeds
  • sega
  • segway
  • segway es2
  • select
  • self-centered
  • self-driving
  • self-organizing
  • sells
  • semi-autonomous
  • Sennheiser
  • sensing
  • sensor
  • September
  • sequencer
  • sequencing
  • Serena
  • Serengeti
  • Series
  • series 3
  • services
  • Severe
  • Shade
  • Shadowlands
  • shares
  • sharing
  • shenmue
  • shenmue 3
  • shield
  • shopping
  • short-throw projector
  • shortcut
  • shortcuts
  • shows
  • shudder
  • shut up and take my money
  • siberia
  • sick days
  • side deal deals
  • sidedeals
  • sights
  • signs
  • Silicon
  • Silver
  • simply
  • simulating
  • simulation
  • singapore
  • singing
  • sinkholes
  • skin
  • skullcandy
  • skydrive
  • skyscraper
  • slack
  • sleep
  • small
  • smart
  • smart clock
  • smart glasses
  • smart home
  • smart homes
  • smart lighting
  • smart lights
  • smart lock
  • smart speakers
  • smarthome
  • smartlighting
  • smartlock
  • smartphone
  • smartphones
  • smartwatch
  • smartwatches
  • smic
  • smoker
  • smoking
  • snapdragon
  • snapdragon 732g
  • snapdragon 765
  • snapdragon 8cx
  • snapdragon 8cx gen 2
  • social distancing
  • social life
  • social media
  • social media mistakes
  • social network
  • social networking
  • sociology
  • software
  • solar
  • solo pro
  • solve
  • Songbirds
  • Sonos
  • sony
  • Sony's
  • soundbar
  • south korea
  • southern route
  • space
  • space race
  • spacecraft
  • spaceflight
  • spacelopnik
  • spaceshiptwo
  • SpaceX
  • Spain
  • sparks
  • speaker
  • speakers
  • Special
  • species
  • specifications
  • spectre x360 13
  • speed
  • spent
  • spike
  • split inbox
  • split-second
  • Splitting
  • sports
  • sports plus
  • Spotify-owned
  • spread
  • sputnik v
  • square enix
  • st patricks day
  • stadia
  • Stage
  • stanford university
  • star trek
  • star trek 4
  • star trek discovery
  • star trek the motion picture
  • star trek the motion pictureinside the art and visual effects
  • star wars
  • star wars galaxys edge
  • star wars rebels
  • star wars the high republic
  • star wars the last jedi
  • star wars the rise of skywalker
  • Starlink
  • starlink hits streaming milestone
  • starship
  • start
  • starts
  • starwars
  • state
  • states
  • stationary
  • stationary bike
  • statistics
  • steady
  • stealth 15m
  • Steam
  • steelseries
  • stephen hawking
  • steroids
  • steven spielberg
  • stick
  • stop-motion animation
  • store
  • stories
  • story
  • stranger things
  • stream
  • streaming
  • streaming video
  • streaming wars
  • strength
  • Stress
  • Strix
  • Strokes
  • Strong
  • Structural
  • Structure
  • student
  • Study
  • sturgis
  • sub-6
  • subscription codes
  • subsurface oceans
  • subterranean oceans
  • Subtypes
  • success
  • suffering
  • suicide
  • suicide prevention
  • suited
  • summit b
  • summit e
  • summit series
  • sunglasses
  • sunlight
  • sunrise movement
  • sunscreen
  • Super
  • super bomberman r
  • super bomberman r online
  • super mario
  • super mario 3d all-stars
  • super mario 3d world
  • super mario 64
  • super mario all-stars
  • super mario bros.
  • super mario bros. 35
  • super mario galaxy
  • super mario sunshine
  • super typhoons
  • superlist
  • superman and lois
  • superpowers
  • SuperTank
  • supplier
  • support
  • supposedly
  • Supra
  • Surface
  • surface duo
  • surprise
  • surprising
  • surveillance
  • susanna clarke
  • suv
  • swamp thing
  • sweden
  • Swift
  • swift 3
  • swift 5
  • swift3
  • switch
  • switch online
  • syfy
  • syndrome
  • synth
  • synthesizer
  • Synthetic
  • T-Mobile
  • T-Mobile's
  • tablet
  • tabletop games
  • tablets
  • take-two interactive
  • takes
  • taobao
  • tar
  • taser
  • tattoo
  • taxes
  • taycan
  • taycan cross turismo
  • tcl
  • tcl nxtpaper
  • TCL's
  • team joe
  • Team's
  • TeamGroup
  • tease
  • tech policy
  • technique
  • technology
  • TechRadar's
  • teenage engineering
  • Teenagers
  • telecoms
  • telemate
  • TELEVISION
  • telmate
  • Tencent
  • tencent games
  • Tenet
  • terms
  • terms of disservice
  • tesla
  • test flight
  • testbed
  • testing
  • tetris
  • texas
  • text-to-speech
  • textlies
  • thanks
  • that's
  • the 100
  • the amazon is burning at an alarming rate
  • the avengers
  • the batman
  • the best keyboards
  • the best of gizmodo
  • the best stories of the week
  • the best tech for remote learning
  • the boys
  • the descent
  • the division 2
  • the dream architects
  • the engadget podcast
  • the goonies
  • the host
  • the last campfire
  • the last of us
  • the last of us part ii
  • the magicians
  • the mandalorian
  • the matrix
  • the matrix 4
  • the multivorce
  • the new mutants
  • the premiere
  • the princess bride
  • the riddler
  • the silver arrow
  • the sims
  • the three-body problem
  • the walking dead
  • the witcher 3
  • thebuyersguide
  • thedivision2
  • theengadgetpodcast
  • themandalorian
  • theme partks
  • themorningafter
  • theory
  • Therapeutic
  • therapy
  • There
  • There's
  • These
  • thethreebodyproblem
  • they call it global warming for a reason
  • they cloned tyrone
  • things
  • think
  • third
  • this is not the future
  • thom browne
  • Thousands
  • thps
  • thq
  • thrawn
  • thrawn ascendancy chaos rising
  • threatening
  • Three
  • Throne
  • throwing
  • TicWatch
  • Tiger
  • tiger lake
  • tiktok
  • tim sweeney
  • Time's
  • timothy olyphant
  • timothy zahn
  • titan books
  • Today
  • Today's
  • toilets
  • tokyo olympics
  • tomorrow
  • tony hawk
  • tony hawk's pro skater
  • tools
  • totally
  • toyota
  • track
  • tracy deonn
  • trade
  • trade war
  • traffic
  • trailers
  • trainees
  • transfer
  • transit
  • transmission
  • transportation
  • trayford pellerin
  • tread
  • treadmill
  • treat
  • treatment
  • trees
  • trending topic
  • treyarch
  • trials
  • tricks
  • tripled
  • trivia
  • true wireless
  • true wireless earbuds
  • truestrike
  • trump
  • trump administration
  • trump rallies
  • Trump's
  • trumps america
  • tucker carlson
  • tumors
  • Tungsten
  • turing
  • turned
  • turntables
  • turtles
  • tv
  • tvs
  • tweets
  • twist
  • twitch
  • twitch sings
  • twitter
  • typhoons
  • typical
  • uber
  • Ubisoft
  • Ubisoft's
  • ufc
  • ufc 4
  • ula
  • Ulster
  • Ultra
  • ultra short throw projector
  • Ultrabooks
  • ultraportables
  • unboxing
  • Uncategorized @hi
  • Unconventional
  • uncover
  • Uncovering
  • under-display
  • understanding
  • unexpected
  • unfair
  • unfiltered
  • unintentionally
  • Unique
  • United
  • united launch alliance
  • united nations
  • unlock
  • unprecedented
  • unreal engine
  • unveils
  • upcoming
  • Update
  • upgrade
  • upper
  • us air force
  • us military
  • usda
  • user data
  • user review
  • user review roundup
  • user reviews
  • userreview
  • userreviewroundup
  • userreviews
  • users
  • Using
  • usps
  • ust
  • vacation
  • vaccine
  • Vaccines
  • vacuum
  • valentines day
  • validates
  • valve
  • vanderbilt university
  • vantrue
  • vaping
  • variations
  • vava
  • vava deals
  • vehicle
  • vehicles
  • Velour
  • Venom
  • verizon
  • version 1.7.14.0
  • vertical
  • vesa
  • vfx
  • vibert thio
  • vicarious visions
  • victoria
  • victorian police
  • videgames
  • video
  • video authenticator
  • video cards
  • video games
  • video streaming
  • videocards
  • videos
  • vinyl
  • viral videos
  • virgin galactic
  • virginia
  • Virgo
  • virtual
  • virtual reality
  • virtual showroom
  • virtual tour
  • Viruses
  • visually impaired
  • Vitamin
  • Vizio
  • vlambeer
  • vlogging
  • vod
  • voice acting
  • voice assistant
  • Volkswagen
  • volta zero
  • voting
  • voting information center
  • vr
  • vr gaming
  • vrgaming
  • vss unity
  • vulnerable
  • wakanda
  • wallops island
  • wally wingert
  • Walmart
  • walmart is coming
  • wanted pinkertons
  • wants
  • Warcraft
  • warner bros
  • Warriors
  • Wasps
  • watch
  • watch es
  • watch gs pro
  • watch it nerds
  • watch parties
  • watches
  • water
  • water resistant
  • waze
  • wearable
  • wearables
  • weather
  • weather is happening
  • web
  • web browsers
  • web tracking
  • webcams
  • weber
  • weber smokefire ex4
  • weber smokefire ex4 review
  • website
  • weed
  • weeklydeals
  • weigh
  • Weight
  • Welcome
  • wernher von braun
  • West'
  • western
  • western digital
  • western digital deals
  • whales
  • What's
  • whatever
  • WhatsApp
  • Where
  • Which
  • white house
  • white privilege
  • whole foods market
  • why is it always florida
  • widescreen
  • wifi
  • wifi 6
  • wifi smart lock
  • wifi6
  • wikipedia
  • wildfire season is year round now
  • wildfires
  • wildleaks
  • wildlife
  • william zabka
  • Williams
  • winamp skin museum
  • windows
  • windows 10
  • windows 95
  • windows on arm
  • wing
  • winner
  • winning
  • wireless
  • wireless headphones
  • wisconsin
  • wishes
  • Witcher
  • withdraws
  • withings
  • withings scanwatch
  • Wolves
  • Women
  • won't
  • wonder woman 1984
  • woodpeckers
  • Wool-like
  • WordPress
  • working
  • workout
  • workplace
  • workstation
  • World
  • world health organization
  • world's
  • worsens
  • worst
  • worth
  • writing
  • Wrong-way'
  • wynonna earp
  • x men
  • X-ray
  • x3
  • x44
  • xbox
  • xbox deals
  • xbox live gold
  • xbox series s
  • xbox series x
  • Xiaomi
  • Xiaomi's
  • Xperia
  • xperia 5 ii
  • xps 13
  • Yahoo
  • years
  • Yellowstone
  • yoda
  • yoga
  • yoson an
  • you get a laptop and you get a laptop
  • you're
  • youku
  • Young
  • your news update
  • youtube
  • youtube tv
  • yu suzuki
  • yummy
  • yves maitre
  • z
  • zack snyder
  • zenbook 13
  • zenbook flip 13
  • zenbook flip s
  • zenbook s
  • Zendure
  • Zenfone
  • zimbabwe
  • zombies
  • Zooming
  • zte
  • zuko

Advertise

Contact us

Follow Us

Recent News

Poco C3 to Feature 13-Megapixel Triple Rear Camera Setup, Up to 4GB RAM

Poco C3 to Feature 13-Megapixel Triple Rear Camera Setup, Up to 4GB RAM

October 3, 2020
Know About Gandhi jayanti 2020: etihaas, mahatv

Know About Gandhi jayanti 2020: etihaas, mahatv

October 1, 2020

जिज्ञासा ज़रूरी है इसीलिए हम आपको देंगे जानकारी जो आपकी जिज्ञासा की प्यास को बुझा देगी
© JIGYAASA.IN

No Result
View All Result
  • Home

© 2020 JIGYAASA.IN