https://github.com/ahmedabdalkreem/grammer-auto-correct

In this project work to make classification between the phase is correct or wrong if phase is right print the correct phase if phase is wrong be input of Transfer Learning and print the phase begore correct.
https://github.com/ahmedabdalkreem/grammer-auto-correct

decision-trees logistic-regression machine-learning matplotlib-pyplot naive-bayes-classifier nlp nltk-library pandas-library python random-forest sklearn spacy svm-model transfer-learning

Last synced: 2 months ago
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In this project work to make classification between the phase is correct or wrong if phase is right print the correct phase if phase is wrong be input of Transfer Learning and print the phase begore correct.

• Host: GitHub
• URL: https://github.com/ahmedabdalkreem/grammer-auto-correct
• Owner: AhmedAbdAlkreem
• Created: 2023-05-20T20:07:47.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-21T14:15:39.000Z (almost 2 years ago)
• Last Synced: 2025-01-16T18:35:17.637Z (4 months ago)
• Topics: decision-trees, logistic-regression, machine-learning, matplotlib-pyplot, naive-bayes-classifier, nlp, nltk-library, pandas-library, python, random-forest, sklearn, spacy, svm-model, transfer-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 338 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Grammer-Auto-Correct

In this project work to make classification between the phase is correct or wrong if phase is right print the correct phase if phase is wrong be input of Transfer Learning and print the phase before correct.

# In this project include four parts:

## 1) First part is preprocessing of dataset.

## 2) Second part is Machine Learning model.

## 3) 3th part is Transfer Learning.

## 4) 4th part is test unseen data with Machine Learning and Transfer Learning.

# First Part:

  we work in text dataset so that we use two library NLTK and Spacy the first one NLTK use to make tokeniziation, stemming, stopword, punctoutaion and transform data to lowercase

  the second one use Spacy to represent dataset and make visualization to dataset using displacy and to use parrsing and NER and before this dataset is ready to encoding it 

  using TF-IDF to be data ready to be input of model.

  

# Second Part:

  we try to train a lot of model to search a bout the best model to test it and can use it in real life the example of model train it 

  decision tree, SVM, Naive_bayes, Logistic Regression and Random Forest the best model make best accuracy is SVM and this model test it

  and be ready to use unseen data to check it.

  

# 3th Part:

  we use tranfer learning to grammer auto correct the wrong phase all things doing model take phase and correct it the transfer model

  using it called happytransformer.

  

# 4th Part:

  To check a bout phase is right or wrong a bout best_model save from Machine Learning model that use if the phase is right print the right phase 

  but if wrong be input of transfer learning and correct grammer of phase and print the phase but before correct.

  

# What you will Learn before this project?

1) Learn how to read text dataset.

2) Deal with preprocessing of text dataset.

3) Know how to use NLTK and Spacy library.

4) Learn how to visualizaion dataset using parsing.

5) Train a lot of Machine learning Model.

6) Test the best model make the best accuracy of train and validation.

7) Using transfer learning model.

8) Make function to know the phase is right or wrong.

9) Know how to mix between Machine Learning model and Trainsfer Learning.

At last you know how to deal with NLP project import of Grammer auto correct that i try to show in simple way.