🌟 Email Spam Detection

🎯 AIM

To classify emails as spam or ham using machine learning models, ensuring better email filtering and security.

📊 DATASET LINK

Email Spam Detection Dataset

📚 KAGGLE NOTEBOOK

Notebook Link

Kaggle Notebook

⚙️ TECH STACK

Category	Technologies
Languages	Python
Libraries/Frameworks	Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn
Databases	NOT USED
Tools	Kaggle, Jupyter Notebook
Deployment	NOT USED

📝 DESCRIPTION

What is the requirement of the project?

To efficiently classify emails as spam or ham.
To improve email security by filtering out spam messages.

How is it beneficial and used?

Helps in reducing unwanted spam emails in user inboxes.
Enhances productivity by filtering out irrelevant emails.
Can be integrated into email service providers for automatic filtering.

How did you start approaching this project? (Initial thoughts and planning)

Collected and preprocessed the dataset.
Explored various machine learning models.
Evaluated models based on performance metrics.
Visualized results for better understanding.

Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).

Scikit-learn documentation.
Various Kaggle notebooks related to spam detection.

🔍 PROJECT EXPLANATION

🧩 DATASET OVERVIEW & FEATURE DETAILS

📂 spam.csv

The dataset contains the following features:

Feature Name	Description	Datatype
Category	Spam or Ham	object
Text	Email text	object
Length	Length of email	int64

🛠 Developed Features from spam.csv

Feature Name	Description	Reason	Datatype
Length	Email text length	Helps in spam detection	int64

🛤 PROJECT WORKFLOW

Project workflow

  graph LR
    A[Start] --> B[Load Dataset]
    B --> C[Preprocess Data]
    C --> D[Vectorize Text]
    D --> E[Train Models]
    E --> F[Evaluate Models]
    F --> G[Visualize Results]

Step 1Step 2Step 3Step 4Step 5Step 6

Load the dataset and clean unnecessary columns.

Preprocess text and convert categorical labels.

Convert text into numerical features using CountVectorizer.

Train machine learning models.

Evaluate models using accuracy, precision, recall, and F1 score.

Visualize performance using confusion matrices and heatmaps.

🖥 CODE EXPLANATION

Section 1Section 2Section 3Section 4Section 5

Data loading and preprocessing.

Text vectorization using CountVectorizer.

Training models (MLP Classifier, MultinomialNB, BernoulliNB).

Evaluating models using various metrics.

Visualizing confusion matrices and metric comparisons.

⚖️ PROJECT TRADE-OFFS AND SOLUTIONS

Trade Off 1Trade Off 2

Balancing accuracy and computational efficiency.
Used Naive Bayes for speed and MLP for improved accuracy.

Handling false positives vs. false negatives.
Tuned models to improve precision for spam detection.

🎮 SCREENSHOTS

Visualizations and EDA of different features

Confusion Matrix comparision

Model performance graphs

Meteric comparison

📉 MODELS USED AND THEIR EVALUATION METRICS

Model	Accuracy	Precision	Recall	F1 Score
MLP Classifier	95%	0.94	0.90	0.92
Multinomial NB	93%	0.91	0.88	0.89
Bernoulli NB	92%	0.89	0.85	0.87

✅ CONCLUSION

🔑 KEY LEARNINGS

Insights gained from the data

Text length plays a role in spam detection.
Certain words appear more frequently in spam emails.

Improvements in understanding machine learning concepts

Gained insights into text vectorization techniques.
Understood trade-offs between different classification models.

🌍 USE CASES

Email Filtering SystemsSMS Spam Detection

Can be integrated into email services like Gmail and Outlook.

Used in mobile networks to block spam messages.