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💡[Feature]: Enhance and merge email spam detection notebook with EDA and NLP improvements #1455
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Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊 Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue. You can also check our CONTRIBUTING.md for guidelines on contributing to this project. |
its already there |
Hello @Niraj1608! Your issue #1455 has been closed. Thank you for your contribution! |
@sanjay-kv |
@sanjay-kv Can it be alright to either replace an existing file with this or add it to the current one? Let me know what you prefer! |
add it to the current one |
[Feature]: Enhance and merge email spam detection notebook with EDA and NLP improvements #1455
@sanjay-kv its level 2 issue and merged pr but got 10 points can you check pls :) |
share ss |
updated |
Is there an existing issue for this?
Feature Description
Hi, I am Niraj. I've been reviewing the Email Spam Detection with Machine Learning project and noticed several areas where improvements can be made. Specifically, I propose:
Enhanced EDA: Adding more detailed charts and visualizations using Python libraries like Seaborn or Matplotlib will help in better understanding the data distribution and correlation between features. This could include heatmaps, pair plots, and distribution plots to visualize relationships and patterns in the data.
Advanced NLP Techniques: Incorporating more Natural Language Processing (NLP) techniques, such as advanced tokenization, lemmatization, and more sophisticated vectorization techniques like TF-IDF.
Data Cleaning: Introducing robust data cleaning methods to remove noisy data, handle missing values, and preprocess text data more efficiently will improve the model's accuracy.
This would enhance the overall performance of the spam detection model by making it more interpretable and efficient through better visualizations and data processing.
Dataset Issue: The project is missing the dataset required for running the notebook. I propose including a well-structured dataset to ensure reproducibility and ease of use for others.
If you like this idea, please assign this task to me, and I will add the corresponding improvements and charts to it.
Thank you for your time and consideration!
Use Case
Incorporating the enhanced EDA and advanced NLP techniques from the Spam Mail Predictor notebook will provide better insights into the dataset, leading to more accurate model training and predictions. This is crucial for users looking for deeper analysis and improved model performance.
Benefits
Improved EDA: The Spam Mail Predictor notebook features more detailed EDA, including additional visualizations and insights.
Enhanced NLP: It also includes more advanced NLP techniques, such as TF-IDF and more extensive text preprocessing steps.
Dataset Integration: Adding a clear, usable dataset to the notebook will ensure reproducibility and ease of use for others.
Add ScreenShots
@sanjay-kv once you assign me work i will create it .
Priority
High
Record
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