Classify SMS or email messages as spam or not spam using text content, word frequency, and message m...

Problem Description

Predict spam using text content, word frequency, message metadata

Source: direct_input
Original URL: N/A

Problem Understanding

The system analyzed the problem statement and identified it as a classification task with 80% confidence.

Problem Analysis:

• Task Type: Classification
• Confidence Level: 80%
• Key Features Identified: word frequency, message metadata

Dataset

• Name: Spambase
• Source: uci
• URL: https://archive.ics.uci.edu/dataset/148/spambase

ML Solution Approach

The machine learning model solves this problem by:

1. Problem Formulation: Treats this as a classification task where the model learns to predict categories/classes based on input features.

2. Model Selection: After evaluating multiple algorithms, Unknown was selected as the best-performing model.

3. How It Works:

   • The model learns patterns from historical data
   • It maps input features to output classes/categories
   • Given new data, it predicts the most likely class
   • The model uses learned decision boundaries to make predictions

4. Solution Capability: The model can automatically classify new instances into the appropriate category based on the patterns it learned during training.

Model Performance

• Accuracy: 0.996
• Precision: 0.998
• Recall: 0.996
• F1 Score: 0.997

Model Interpretability (SHAP)

SHAP is enabled in pipeline configuration, but explanations were not generated for this run. This can happen when dependency/runtime constraints prevent SHAP computation.

Model Details

• Best Model: Unknown
• Framework: SCIKIT-LEARN
• Training Date: 2026-04-02
• Model File: classification_model_20260402_201023.pkl

Installation

pip install -r requirements.txt

Usage

Training

1. Place your dataset as data.csv
2. Update the target column name in train.py
3. Run:

python train.py

Prediction

1. Place new data as new_data.csv
2. Run:

python predict.py

Predictions will be saved to predictions.csv.

Limitations

• This is an automated solution and may require manual tuning
• Model performance depends on data quality
• Additional feature engineering may improve results

License

This project is generated by an automated ML pipeline. Use at your own discretion.

Generated By

Autonomous ML Automation Pipeline Generated on: 2026-04-02 20:10:23