https://github.com/chandraprakash-bathula/keywords_prediction-machine-learning-integration

Keywords Prediction Model Built the Model By: Data Cleaning Removing Stopwords Constructing Word2vec Advancing to TF-IDF Weighted Word2vec.
https://github.com/chandraprakash-bathula/keywords_prediction-machine-learning-integration

algori artifici data machine-learning tf-idf weighted-word2vec word2vec

Last synced: 23 days ago
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Keywords Prediction Model Built the Model By: Data Cleaning Removing Stopwords Constructing Word2vec Advancing to TF-IDF Weighted Word2vec.

• Host: GitHub
• URL: https://github.com/chandraprakash-bathula/keywords_prediction-machine-learning-integration
• Owner: ChandraPrakash-Bathula
• Created: 2024-01-07T00:55:50.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-02-08T01:25:41.000Z (12 months ago)
• Last Synced: 2024-11-07T10:53:52.923Z (2 months ago)
• Topics: algori, artifici, data, machine-learning, tf-idf, weighted-word2vec, word2vec
• Language: Jupyter Notebook
• Homepage:
• Size: 1.17 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Keywords Prediction Model

Welcome to our Keyword Prediction model's GitHub repository! This project integrates a machine learning model for predicting keywords with a user-friendly interface, aiming to assist users in extracting relevant keywords from text data.

### Techniques Utilized :

1. **Data Cleaning :**

   - We perform thorough data cleaning processes to preprocess the input text data, ensuring consistency and improving the quality of predictions.

2. **Removing Stopwords :**

   - Stopwords, commonly occurring words in a language, are removed from the text data to focus on meaningful keywords and improve the accuracy of predictions.

3. **Constructing Word2vec :**

   - Word2vec, a popular word embedding technique, is utilized to represent words in a continuous vector space. This allows our model to capture semantic similarities between words and enhance keyword prediction performance.

4. **Advancing to TF-IDF Weighted Word2vec :**

   - TF-IDF (Term Frequency-Inverse Document Frequency) weighted Word2vec incorporates the importance of words based on their frequency and significance across documents. This advanced technique improves the precision of keyword predictions by considering the context and relevance of words.

### How to Use :

- Clone or download the repository to your local machine.

- Install the required dependencies specified in the `requirements.txt` file.

- Follow the instructions in the user interface to input your text data.

- Obtain predicted keywords based on the implemented machine learning model.

### Contributions:

We welcome contributions and feedback from the community to enhance the functionality and performance of our keyword prediction model. Please feel free to submit issues, pull requests, or suggestions via GitHub..!

### Acknowledgments:

We acknowledge the contributions of the open-source community and the libraries utilized in this project, enabling us to build an efficient and effective keyword prediction model.

Thank you for visiting the repository and exploring our Keyword Prediction Model! We hope it proves to be a valuable tool in your text analysis endeavors.