SMS Spam Detection Using NLP

[pranaydeep1]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : SMS Spam Detection Using NLP

🔴 Aim : Develop an NLP-based model to classify SMS messages as either "ham" (legitimate) or "spam."

🔴 Dataset : https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset/data

🔴 Approach : 1.Exploratory Data Analysis (EDA):
Perform initial data inspection, missing value analysis, and data distribution checks.
Visualize data to understand the frequency of ham vs. spam messages and other relevant patterns.
2. Preprocessing:
Text normalization, including case conversion, stop word removal, punctuation removal, and stemming/lemmatization as needed.
3.Model Building:
Implement multiple algorithms to compare their performance:
Naive Bayes
Support Vector Machine (SVM)
Random Forest
Logistic Regression or any other suitable algorithm
Evaluate models using accuracy scores and select the best-fit algorithm for this dataset.
4. Evaluation & Comparison:
Document accuracy and performance metrics for each model to identify the optimal algorithm for this problem.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name : Pranay Deep Korada
• GitHub Profile Link : https://github.com/pranaydeep1
• Email ID : pranaydeep591@gmail.com
• Participant ID (if applicable):
• Approach for this Project : 1.Exploratory Data Analysis (EDA):
  Perform initial data inspection, missing value analysis, and data distribution checks.
  Visualize data to understand the frequency of ham vs. spam messages and other relevant patterns.

2. Preprocessing:
   Text normalization, including case conversion, stop word removal, punctuation removal, and stemming/lemmatization as needed.
   3.Model Building:
   Implement multiple algorithms to compare their performance:
   Naive Bayes
   Support Vector Machine (SVM)
   Random Forest
   Logistic Regression or any other suitable algorithm
   Evaluate models using accuracy scores and select the best-fit algorithm for this dataset.
3. Evaluation & Comparison:
   Document accuracy and performance metrics for each model to identify the optimal algorithm for this problem.

• What is your participant role? (Mention the Open Source program) GSSOC ext- Participant

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Happy Contributing 🚀

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

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[pranaydeep1]

@abhisheks008 can you assign this issue to me ?

[abhisheks008]

Similar problem statement is already present in this repository hence closing this issue.