This project involves detecting breast cancer using the Naive Bayes classifier in Jupyter Notebook. Breast cancer detection is a crucial task in healthcare, as it aids in the early diagnosis and treatment of the disease. Through this project, we aim to explore and understand how the Naive Bayes classifier can be used for breast cancer detection.

The dataset used for this project is the Breast Cancer Wisconsin (Diagnostic) dataset. It contains various features extracted from digitized images of breast mass, and the task is to classify the samples as either malignant (cancerous) or benign (non-cancerous). Make sure to preprocess and clean the dataset before using it for modeling.

To get started with the project, follow the steps below:

1. Clone the repository:

git clone https://github.com/shaadclt/Breast-Cancer-Detection-NaiveBayesClassifier.git

2. Change into the project directory:

cd Breast-Cancer-Detection-NaiveBayesClassifier

3. Install the required dependencies:

4. Run Jupyter Notebook:

jupyter notebook

5. Open the Breast Cancer Detection.ipynb notebook in Jupyter.

6. Follow the instructions in the notebook to load the dataset, preprocess the data, train the Naive Bayes classifier, and make predictions.

The notebook provides an overview of the steps involved in breast cancer detection using the Naive Bayes classifier. The steps include:

1. Data Loading: Loading the dataset into a pandas DataFrame.
2. Data Preprocessing: Handling missing values, encoding categorical variables (if any), and splitting the dataset into training and testing sets.
3. Naive Bayes Classifier: Training the Naive Bayes classifier on the preprocessed dataset.
4. Model Evaluation: Assessing the model performance using evaluation metrics such as accuracy, precision, recall, or F1-score.
5. Prediction: Using the trained model to make predictions on new breast cancer samples.

The notebook includes explanations, code snippets, and visualizations to aid in understanding the breast cancer detection process using the Naive Bayes classifier.

The project aims to detect breast cancer using the Naive Bayes classifier. The results and insights gained from this project include:

• Evaluating the performance of the Naive Bayes classifier in terms of accuracy and other evaluation metrics.
• Understanding the important features or factors that contribute to the classification of breast cancer samples.
• Applying the trained model to make predictions on new, unseen breast cancer samples.

The insights gained from this project can aid in the early detection and diagnosis of breast cancer, leading to better treatment outcomes.

You can customize the project by modifying the dataset, experimenting with different preprocessing techniques, trying other classification algorithms, or exploring additional features for breast cancer detection. This project serves as a starting point for breast cancer detection using the Naive Bayes classifier, and you can extend it further to suit your needs.

This project is licensed under the MIT License. See the LICENSE file for more information.

• This project is created for the purpose of exploring breast cancer detection using the Naive Bayes classifier in Jupyter Notebook.

Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more features, please open an issue or submit a pull request.