Spam Email Detection

In this project, I developed a complete end-to-end machine learning system for detecting spam emails, starting from data analysis to model evaluation and performance optimization.

? **Project Workflow:**

1. **Data Analysis:**

* Understanding the dataset

* Exploring spam vs. ham distribution

* Extracting most frequent words

2. **Data Preprocessing:**

* Text cleaning (removing punctuation, lowercasing)

* Stopwords removal

* Tokenization

* Text vectorization using:

* CountVectorizer

* TF-IDF

3. **Modeling:**

* Logistic Regression

* Linear Support Vector Classifier (Linear SVC)

* K-Nearest Neighbors (KNN)

4. **Fine Tuning:**

* Hyperparameter optimization using GridSearchCV

5. **Evaluation:**

* Accuracy

* Precision

* Recall

* F1-Score

* ROC Curve & AUC Score

* Test set performance

? Models were compared and evaluated to select the best-performing model based on real test data.

? **Use Cases:**

* Email spam filtering systems

* Fraud detection

* Text classification tasks

?️ **Technologies Used:**

Python – Pandas – NumPy – Scikit-learn – NLP

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? I focus on building efficient, data-driven solutions to solve real-world problems using machine learning.