📧 Spam Mail Prediction using Logistic Regression

This project predicts whether an incoming email is Spam or Ham (not spam) using a Logistic Regression model. The workflow includes preprocessing the dataset, splitting into training/testing sets, and training the model for accurate predictions.

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🚀 Workflow

1. Mail Data Collect email datasets containing labeled examples of spam and ham messages.

2. Data Preprocessing

   • Cleaning the text (removing stop words, punctuation, and numbers).
   • Converting text into numerical form using techniques like TF-IDF.
   • Handling missing values.

3. Train-Test Split Split the processed dataset into training and testing subsets to evaluate model performance.

4. Model Training Train a Logistic Regression model using the training dataset.

5. Prediction

   • Input: New email text.
   • Output: Spam or Ham.

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🛠️ Tech Stack

• Language: Python

• Libraries:

  • pandas – Data manipulation
  • numpy – Numerical computations
  • scikit-learn – Machine learning algorithms and preprocessing
  • matplotlib / seaborn – Visualization

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📊 Model Evaluation

The trained Logistic Regression model is evaluated using:

• Accuracy
• Precision
• Recall
• F1 Score

📊 Classification Report

This report summarizes the performance of the classification model. The model was evaluated on a test set of 15 samples, focusing on its ability to distinguish between "Ham" and "Spam" messages.

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Detailed Metrics

Class	Precision	Recall	F1-Score	Support
Ham	0.64	1.00	0.78	7
Spam	1.00	0.50	0.67	8

• Precision: The percentage of positive predictions that were actually correct.
• Recall: The percentage of actual positives that were correctly identified.
• F1-Score: A measure that balances precision and recall.
• Support: The number of occurrences of each class in the test set.

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Overall Performance

Metric	Score
Accuracy	0.73
Macro Average	0.72
Weighted Average	0.72

• Accuracy: The percentage of total predictions that were correct.
• Macro Average: The unweighted average of the metrics across all classes.
• Weighted Average: The average of the metrics, weighted by the number of samples in each class.

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📌 Future Improvements

• Experiment with advanced models like Naive Bayes or Random Forest.
• Implement real-time email scanning.
• Create a web-based UI for user interaction.

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🖊️ Author

• Husnain Khaliq