This repository contains a Jupyter notebook that demonstrates the classification of breast tumors into malignant or benign categories using the Support Vector Machines (SVM) algorithm. The project covers various stages of a machine learning pipeline, including data exploration, visualization, model training, and evaluation.
The dataset used in this project is sourced from the UCI Machine Learning Repository. It consists of 569 samples with 30 feature variables each. These features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass, describing characteristics of the cell nuclei present in the image.
- Problem Statement: Understanding the objective of the project.
- Data Importing: Loading necessary libraries and the dataset.
- Data Visualization: Exploring data distributions and relationships through plots.
- Model Training: Using the SVM algorithm to train the model on the dataset.
- Model Evaluation: Assessing the model's accuracy and performance on test data.
- Model Improvement: Suggestions and steps to improve the model's accuracy.
- Python: The project is entirely written in Python.
- Pandas: For data manipulation and analysis.
- NumPy: For numerical operations.
- Matplotlib & Seaborn: For data visualization.
- Scikit-learn: For implementing the SVM algorithm and other related machine learning operations.
- Clone this repository to your local machine.
- Ensure you have the required libraries installed. You can install them using
pip:pip install pandas numpy matplotlib seaborn scikit-learn - Open the Jupyter notebook to view and run the project.
- Implement other classification algorithms to compare performance.
- Deep dive into feature engineering for better accuracy.
- Use techniques like cross-validation for more robust model evaluation.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.