📧 Email & SMS Spam Classifier

A machine learning project that classifies messages (Email/SMS) as Spam or Not Spam.
The project involves EDA, feature engineering, model building, hyperparameter tuning, and a Streamlit-based UI for interactive testing.

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🚀 Features

• Data Preprocessing

  • Text normalization (lowercasing, tokenization, removing punctuation & stopwords).
  • Stemming using NLTK.
  • Feature extraction using TF-IDF and CountVectorizer.

• Exploratory Data Analysis (EDA)

  • Spam vs Ham distribution.
  • Word frequency visualization.
  • WordCloud generation.

• Modeling & Evaluation

  • Implemented multiple classifiers:
    • SVC, K-Nearest Neighbors, Naive Bayes, Decision Tree, Logistic Regression, Random Forest
    • AdaBoost, Bagging, Extra Trees
  • Compared performance across models.
  • Hyperparameter tuning for optimal accuracy.
  • Implemented Stacking Classifier for ensemble learning.

• User Interface

  • Built with Streamlit.
  • User-friendly input box to test custom messages.

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🛠️ Tech Stack

• Languages: Python
• Libraries: NLTK, Scikit-learn, XGBoost, Pandas, NumPy, Matplotlib, Seaborn
• Deployment/UI: Streamlit

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