What is a vectorizer in nlp

This recipe explains what is a vectorizer in nlp

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

Learn to Implement Customer Churn Prediction Using Machine Learning in Python

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

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

A/B Testing Approach for Comparing Performance of ML Models
The objective of this project is to compare the performance of BERT and DistilBERT models for building an efficient Question and Answering system. Using A/B testing approach, we explore the effectiveness and efficiency of both models and determine which one is better suited for Q&A tasks.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

Learn How to Build PyTorch Neural Networks from Scratch
In this deep learning project, you will learn how to build PyTorch neural networks from scratch.

Image Classification Model using Transfer Learning in PyTorch
In this PyTorch Project, you will build an image classification model in PyTorch using the ResNet pre-trained model.

Time Series Project to Build a Multiple Linear Regression Model
Learn to build a Multiple linear regression model in Python on Time Series Data

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.

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification