🧭 Introduction

Smart Text Classifier is a machine learning project focused on classifying SMS text messages as spam or ham (not spam). It leverages classical ML algorithms—Logistic Regression, Random Forest, and Multinomial Naive Bayes—to compare performance and identify the most effective method for spam detection. The project addresses challenges in automated text classification and showcases clear evaluation metrics for each model.

🛠️ Project Type

Backend

🚀 Deployed App

• Frontend: Not applicable
• Backend: Jupyter Notebook / Python Script
• Database: Static CSV

📁 Directory Structure

smart-text-classifier/
├── data/
│ ├── spam.csv
├── notebooks/
│ ├── Smart Text Classifier.ipynb
├── Visuals/
├── README.md

🎥 Video Walkthrough of the project

🎥 Video Walkthrough of the codebase

✨ Features

• Spam vs. ham classification using multiple ML models
• Data preprocessing, label encoding, and train-test splitting
• Performance visualization (precision, recall, F1-score, confusion matrix)
• Results comparison across algorithms

🎯 Design Decisions or Assumptions

• Selected classical ML algorithms for interpretability and simplicity
• Used default hyperparameters for baseline model performance
• Applied standard text preprocessing (lowercasing, punctuation removal)
• Focused on model evaluation instead of app deployment
• Assumed labeled dataset with "spam" and "ham" categories

🧪 Installation & Getting Started

Install dependencies and launch the notebook:

git clone https://github.com/Shaileshahire06/Smart-Text-Classifier.git
cd smart-text-classifier
pip install -r requirements.txt
jupyter notebook

📌 Usage

Step-by-step:

1. Load and preprocess the data
2. Train models using provided notebook
3. Evaluate and visualize model performance

🔐 Credentials

*No login or credentials

🌐 APIs Used

None – analysis is entirely local using SQL + Pandas

📮 API Endpoints

Not applicable – this is a non-service-based analytical project

🧰 Technology Stack

• Python: Data analysis with Pandas
• NumPy / Pandas — Data manipulation and preprocessing
• Scikit-learn — Machine learning algorithms and model evaluation
• Matplotlib / Seaborn — Data visualization and analysis
• Jupyter Notebook: Code, commentary, and charts