🧑‍💻: Deep_Learning/Spam Vs Ham Mail Classification [With Streamlit GUI]/Model/model1 Enhancement problem

[Sakeebhasan123456]

hii @UTSAVS26 i analyzed the deep learning model and these problems come into the picture i would like to work on these problems please assign this project to me

SMS Spam Classification Project

Hidden Problems & Solutions

Identifying and addressing these problems is crucial for enhancing the performance, reliability, and usability of the SMS Spam Classification model.

#	Problem	Description	Solution
1	Class Imbalance Not Addressed	The dataset contains approximately 87.4% ham and 12.6% spam messages without balancing techniques.	- Apply SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic spam samples. - Use class weighting in algorithms to give more importance to the spam class.
2	Limited Evaluation Metrics	Evaluation primarily relies on accuracy and precision.	- Incorporate recall, F1-score, and ROC AUC Score to get a more comprehensive evaluation of model performance. - Use confusion matrix visualizations.
3	Single Train-Test Split Without Cross-Validation	Only a single train-test split is used for model evaluation.	- Implement Stratified K-Fold Cross-Validation to ensure reliable and generalizable performance estimates. - Utilize cross_val_score for robust metrics.
4	Suboptimal Model Selection and Lack of Hyperparameter Tuning	Multiple models are used without thorough hyperparameter optimization.	- Perform GridSearchCV or RandomizedSearchCV for hyperparameter tuning. - Explore advanced models like Logistic Regression, XGBoost, or LightGBM.
5	Use of GaussianNB with High-Dimensional Sparse Data	Gaussian Naive Bayes is applied to TF-IDF vectors, which are high-dimensional and sparse.	- Prefer models like MultinomialNB or BernoulliNB for high-dimensional sparse data. - Alternatively, use models that handle sparsity better, such as SVM or Random Forest.
6	Lack of Pipeline Integration	Preprocessing, feature extraction, and modeling steps are handled separately.	- Utilize sklearn.pipeline to chain preprocessing and modeling steps, preventing data leakage and enhancing workflow maintainability.
7	Insufficient Text Preprocessing	Basic preprocessing steps are applied, but advanced techniques like handling contractions are missing.	- Implement contraction expansion to maintain semantic integrity. - Apply spelling correction and Named Entity Recognition (NER) to enhance text quality.
8	Potential Overfitting with Multinomial and BernoulliNB	These models can overfit if the feature space is too large or not properly regularized.	- Apply regularization techniques such as adjusting the alpha parameter. - Reduce feature dimensionality using TruncatedSVD or SelectKBest.
9	Streamlit App Dependencies Not Managed Properly	The Streamlit app comments out loading the vectorizer and model, indicating potential deployment issues.	- Ensure proper saving and loading of the TF-IDF vectorizer and trained model using pickle or joblib. - Verify file paths and manage dependencies correctly.
10	No Handling of Rare Words or Enhanced Feature Selection	TF-IDF is used without additional feature selection or handling of rare/high-frequency terms.	- Enhance TF-IDF parameters (max_df, min_df, ngram_range) to better capture important features. - Implement feature selection methods like Chi-Squared or Mutual Information.

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✅ To be Mentioned while taking the issue :

• Full name : Sakeeb hasan
• Open Source Program name:-GSocs Extended

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

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[Sakeebhasan123456]

hii @UTSAVS26 please check i send the pull request

[github-actions]

✅ This issue has been closed. Thank you for your contribution! If you have any further questions or issues, feel free to join our community on Discord to discuss more!