📩 Spam SMS Detection ML Project

• Deployed link✨ Here

📌 Project Objective

The goal of this project is to build and deploy a machine learning model that can classify SMS messages as Spam or Ham (Not Spam).
The model is trained using a labeled dataset and deployed for real-world testing.

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🛠️ Tech Stack

• Python
• Scikit-Learn
• Pandas, Numpy
• Natural Language Processing (NLP)
• NLTK
• Streamlit (for deployment)

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📚 Dataset

• Dataset Source: Kaggle - SMS Spam Collection Dataset
• Description: 5,500 SMS messages labeled as Spam or Not Spam.

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📊 Project Stages

1. Data Cleaning
2. Exploratory Data Analysis (EDA)
3. Text Preprocessing (tokenization, stemming, etc.)
4. Model Building (Naive Bayes, Logistic Regression, etc.)
5. Vectorization (TF-IDF, GridSearchCV)
6. Model Evaluation (Accuracy, Precision, Recall, F1 Score)
7. PyCharm App Development (Over Streamlit)

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📊 Model Performance

Metric	Score
Accuracy	97.9%
Precision	97.5%
Recall	96%

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⚙️ Steps to Run the Project

1. Clone the repository:

git clone https://github.com/BleeGleeWee/Spam-SMS-Detection.git
cd Spam-SMS-Detection

2. Install dependencies:

pip install -r requirements.txt

3. Run the Jupyter Notebook:

jupyter notebook spam_sms_detection.ipynb

4. For deployed app:

streamlit run app.py

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🌟 FINAL SHOWDOWN:

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Email/SMS-spam-classifier
│
├── data/
│   └── spam.csv                         # Original dataset (or link to download in README)
│
├── notebooks/
│   ├── 01_data_cleaning.ipynb           # Handling nulls, duplicates, formatting
│   ├── 02_eda.ipynb                     # Visualizations and exploratory analysis
│   ├── 03_text_preprocessing.ipynb      # Tokenization, stemming, stopword removal
│   ├── 04_model_building.ipynb          # Naive Bayes, Logistic Regression, etc.
│   └── 05_model_improvement.ipynb       # TF-IDF, hyperparameter tuning, evaluation
│
├── models/
│   ├── model.pkl                        # Serialized trained model (pickle)
|   └── vectorizer.pkl                   # Trained model then vectorized
│
├── app/
│   ├── app.py                           # App entry point
│   ├── main.py                  # Utility to load the model
│   └── train_model.py                   # Training model before testing   
│
├── .gitignore                           # Ignore notebooks checkpoints, model files, etc.
├── requirements.txt                     # All dependencies (Flask/FastAPI, sklearn, etc.)
├── nltk.txt                             # NLTK dependencies (stopwords, punkt)
├── README.md                            # Full documentation 
└── LICENSE                              # MIT or any preferred open-source license

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