🧠 Spam email Classification

📋 OVERVIEW

This project involves building and evaluating a K-Nearest Neighbors (KNN) model for classification tasks. The notebook includes:

• 📊 Data Preprocessing
• 🏋️‍♂️ Model Training
• 🧪 Performance Evaluation
• 📈 Visualization of Results

🔍 DESCRIPTION

The main steps include data processing and data understanding, then performing tf-idf normalization into vector data, and then performing testing on many popular classification models. Decision Tree, XGBClassifier, RandomForest, AdaBoost, KNeighbors, GradientBoosting, Stacking, Voting

💡 KNN (K-Nearest Neighbors)

• Supervised learning algorithm used for classification and regression.
• It predicts based on the majority vote of its k-nearest data points.
• Simple but sensitive to noisy data and requires proper scaling.

💡 Decision Tree

• Uses a tree-like model of decisions, splitting data based on features.
• Easy to interpret, but can overfit if not pruned properly.
• Works well with small to medium-sized datasets.

💡 Random Forest

• Ensemble model that combines multiple Decision Trees.
• Reduces overfitting by averaging the predictions of individual trees.
• Robust, accurate, and effective for a wide range of data.

💡 XGBoost (Extreme Gradient Boosting)

• Powerful ensemble model using boosting with Decision Trees.
• Optimized for speed and performance, often used in competitions.
• Needs careful tuning to avoid overfitting.

💡 AdaBoost (Adaptive Boosting)

• Focuses on correcting errors made by previous models in the ensemble.
• Adjusts the weights of misclassified samples in each iteration.
• Effective but sensitive to noisy data.

💡 Gradient Boosting

• Boosting algorithm that minimizes prediction error using Gradient Descent.
• Builds trees sequentially, each correcting the errors of the previous.
• High accuracy but can be computationally expensive.

💡 Stacking Classifier

• Ensemble method that combines multiple models using a meta-learner.
• Each base model's predictions are used as input features for the final model.
• Can leverage the strengths of diverse models for improved accuracy.

💡 Voting Classifier

• Combines predictions from different models using majority voting.
• Supports "hard voting" (majority class) and "soft voting" (probability-based).
• Simple yet effective ensemble approach for boosting model performance. Open the Jupyter Notebook and execute the cells sequentially.

🛠 DEPENDENCIES

This project requires the following Python packages:

• 🐍 Python 3.x
• 📒 Jupyter Notebook
• 🗃️ Pandas - Data manipulation
• 🔢 NumPy - Numerical operations
• 🖼️ Matplotlib - Basic plotting
• 📊 Seaborn - Enhanced visualizations
• 📚 NLTK - Natural Language Processing
• 📝 Regex - Text preprocessing
• 💬 WordCloud - Text visualization

Machine Learning

• 🌳 DecisionTree - Tree-based model
• 🤖 RandomForest - Ensemble of trees
• 📈 LogisticRegression - Linear classifier
• 🏘️ KNN - Nearest neighbors
• 🔥 XGBoost - Gradient boosting
• ⚡ AdaBoost - Adaptive boosting
• 🚀 GradientBoosting - Sequential boosting
• 🗳️ VotingClassifier - Majority voting
• 🔗 StackingClassifier - Meta-ensemble

Preprocessing

• 🔠 TfidfVectorizer - Text vectorization
• 🔢 LabelEncoder - Encoding labels
• 📏 MinMaxScaler - Feature scaling

Evaluation

• 📊 ConfusionMatrix - Error analysis
• 📈 ROC Curve - Model performance
• 📋 Classification Report - Metrics summary

📝 USAGE

To explore and use this project, follow the link below:

• 📈 View and Run the Notebook on Kaggle:

  Spam Message Classification Notebook on Kaggle

Simply click the link above to access the full Jupyter Notebook directly on Kaggle, where you can view the code, run it, and interact with the data.

📊 RESULTS

The KNN model's performance is evaluated using metrics such as:

• Accuracy
• Precision
• Confusion Matrix

The results are visualized with plots, and a detailed discussion is included in the notebook.

🌐 REFERENCES

• K-Nearest Neighbors Algorithm Explained
• Decision Tree Algorithm Explained
• Random Forest Algorithm Explained
• XGBoost Documentation
• AdaBoost Algorithm Explained
• Gradient Boosting Algorithm Explained
• Stacking Classifier in Scikit-Learn
• Voting Classifier in Scikit-Learn
• Logistic Regression Algorithm Explained
• Confusion Matrix and Performance Metrics
• TF-IDF Vectorization Explained