This project focuses on classifying emails into Spam or Not Spam categories using Machine Learning techniques. It is implemented in a Jupyter Notebook and provides a step-by-step approach to building and evaluating the classification model.
Key Features
Preprocessing of raw email data (e.g., cleaning, tokenization, and vectorization).
Implementation of multiple classification algorithms like Naive Bayes, Logistic Regression, or SVM.
Performance evaluation using metrics like accuracy, precision, recall, and F1-score.
Visualization of results through plots and charts for better understanding.
Tech Stack
Python
Jupyter Notebook
Libraries Used:
scikit-learn pandas numpy matplotlib seaborn
Data Preprocessing:
Removal of special characters, stopwords, and unwanted symbols. Conversion of text into numerical features using techniques like TF-IDF or Bag of Words.
Model Training:
Multiple models are trained on the processed data to classify emails into Spam or Not Spam categories.
Evaluation:
Models are evaluated using metrics and confusion matrix for performance analysis.
Visualization:
Insights are visualized with charts to show data distribution, feature importance, and model accuracy.