Breast-cancer-detection-using-logistic-regression

This Python project demonstrates a basic machine learning pipeline for predicting breast cancer diagnosis (benign or malignant) using the Wisconsin Breast Cancer dataset. The code implements both Logistic Regression and K-Nearest Neighbors (KNN) classification models, allowing for comparison of their performance.

Overview

This project covers the fundamental steps involved in a typical machine learning workflow, including data loading, preprocessing, model training, evaluation, and visualization. It provides a clear and concise example of how to build a classification model for a binary classification problem.

Features

• Data Loading and Preprocessing: Loads the Wisconsin Breast Cancer dataset from a CSV file (breast_cancer.csv). Handles label encoding to convert categorical target variables into numerical representations.
• Train-Test Split: Divides the dataset into training and testing sets to evaluate model performance on unseen data.
• Model Training: Trains either a Logistic Regression or a K-Nearest Neighbors (KNN) classifier. The code includes implementations for both. Note: You can easily switch between the models by commenting/uncommenting the relevant code blocks.
• Model Evaluation: Evaluates the trained model using a confusion matrix, k-fold cross-validation, ROC curve, and accuracy comparison between training and testing sets.
• Visualization: Generates visualizations of the confusion matrix, ROC curve, and train/test accuracy comparison using matplotlib and seaborn.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Dataset

The project uses the Wisconsin Breast Cancer dataset, which is assumed to be in a CSV file named breast_cancer.csv. This file should be placed in the same directory as the Python script. The dataset should contain features relevant to breast cancer diagnosis and a target variable indicating the diagnosis (benign or malignant). Ensure the target variable is encoded as 2 and 4, which the code then maps to 0 and 1.

How to Run

1. Clone the repository: git clone https://github.com/SubhajitMondal95/Breast-Cancer-Prediction.git
2. Install the required libraries: pip install pandas numpy scikit-learn matplotlib seaborn
3. Place the breast_cancer.csv file in the same directory as the Python script.
4. Run the script: python your_script_name.py (Replace your_script_name.py with the name of your Python file).

Model Selection

By default, the code runs Logistic Regression. To switch to KNN, comment out the Logistic Regression code block and uncomment the KNN code block. Remember to scale your data if you are using KNN.

Evaluation Metrics

The project uses the following evaluation metrics:

• Confusion Matrix: Visualizes the model's performance in terms of true positives, true negatives, false positives, and false negatives.
• K-Fold Cross-Validation: Provides a more robust estimate of the model's performance by averaging the accuracy across multiple folds of the data.
• ROC Curve and AUC: Illustrates the trade-off between true positive rate and false positive rate and provides a measure of the model's ability to distinguish between classes.
• Train/Test Accuracy: Compares the model's accuracy on the training and testing sets to assess potential overfitting.

Future Enhancements (Optional)

• Feature Engineering: Explore techniques to create new features from the existing ones to potentially improve model performance.
• Model Tuning: Implement hyperparameter tuning using techniques like GridSearchCV or RandomizedSearchCV to optimize the model's parameters.
• Advanced Classification Models: Experiment with other classification algorithms, such as Support Vector Machines, Random Forests, or Gradient Boosting.
• Deployment: Deploy the trained model as a web application or using a framework like Flask or Streamlit.