What is a vectorizer?
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# What is a vectorizer?

This recipe explains what is a vectorizer

## Recipe Objective

What is a Vectorizer? Vectorization is the process of converting words into numbers is called Vectorization, It is a methodology in NLP to map words or phrases from vocabulary to a corresponding vector of real numbers which is used to find word predictions, similarities etc.

The vectorization is used in use case like:

Text classification

Compute Similar words

Document Clustering / Grouping

Natural language Processing (NLP)

feature extraction in Text Classification.

lets see a example of vectorizer by using Count Vectorizer

## Step 1 - Import the necessary libraries

`import pandas as pd` `from sklearn.feature_extraction.text import CountVectorizer`

## Step 2 - Store the Count vectorizer in a variable

`Count_vect = CountVectorizer()`

## Step 3 - Take the Sample text

`text1 = "jack wants to play football"` `text2 = "Heena also loves to play football"`

## Step 4 - fit_transform the text and get the feature names

`vectors = Count_vect.fit_transform([text1, text2])` `feature_names = Count_vect.get_feature_names()`

## Step 5 - Convert the vectors to dense

`dense = vectors.todense()`

## Step 6 - Convert the dense to list and then to DataFrame

`denselist = dense.tolist()` `df = pd.DataFrame(denselist, columns=feature_names)`

## Step 7 - Print the output

`df`
```	also	football	heena	jack	loves	play	to	wants
0	0	1		0	1	0	1	1	1
1	1	1		1	0	1	1	1	0```

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