Welcome to the Breast Cancer Prediction repository! This project leverages the Breast Cancer Wisconsin (Diagnostic) Dataset to build a classification model that predicts whether a breast tumor is benign or malignant. Using Logistic Regression, we aim to aid early detection of breast cancer, which is crucial for improving patient outcomes.
This project includes:
- Exploratory Data Analysis (EDA) to understand the dataset and visualize feature correlations.
- Data Preprocessing steps to clean the data and prepare it for model training.
- Building a Logistic Regression model to classify tumors.
- Evaluating the model using accuracy and a confusion matrix.
- Testing the model on a sample input to predict tumor malignancy.
The dataset used is the Breast Cancer Wisconsin (Diagnostic) Dataset, which contains features computed from digitized images of fine needle aspirate (FNA) of breast masses, such as the radius, texture, perimeter, area, and smoothness of the cell nuclei.
- Python 🐍
- Pandas for data manipulation
- NumPy for numerical computations
- Matplotlib and Seaborn for data visualization
- Scikit-learn for model building and evaluation
git clone https://github.com/smebad/breast-cancer-prediction.git
cd breast-cancer-predictionWe begin by exploring the dataset to understand its structure and key features:
- Missing data is handled by removing unnecessary columns (id, Unnamed: 32).
- The target variable (diagnosis) is converted into numerical values:
- 0 for Benign
- 1 for Malignant
A heatmap visualizes correlations between features, helping us identify relationships between diagnostic measurements.
Using Logistic Regression, we train the model on 80% of the data and test it on the remaining 20%. The model achieves:
- Training Accuracy: 96.92%
- Test Accuracy: 95.61%
- Confusion Matrix The confusion matrix gives us insights into the model's performance, showing how well it distinguishes between benign and malignant tumors.
You can test the model by providing custom input data, and it will predict whether the tumor is benign or malignant. Here's an example input:
0.2505, 1.025, 1.74, 19.68, 0.004854, 0.01819, 0.01826, 0.007965, 0.01386, 0.002304,
14.11, 23.21, 89.71, 611.1, 0.1176, 0.1843, 0.1703, 0.0866, 0.2618, 0.07609)
This project successfully built a Logistic Regression model that accurately predicts whether a tumor is benign or malignant. With over 95% accuracy, this model can aid in the early detection of breast cancer, potentially saving lives through timely diagnosis.