https://github.com/samamasaleem/breast-cancer-detection-using-logistic-regression
This project utilizes Logistic Regression for breast cancer classification, incorporating data visualization to enhance understanding. It covers the entire workflow, from data import to model evaluation, ensuring a comprehensive analysis of the classification process.
https://github.com/samamasaleem/breast-cancer-detection-using-logistic-regression
breast-cancer-wisconsin classification logistic-regression machine-learning
Last synced: about 2 months ago
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This project utilizes Logistic Regression for breast cancer classification, incorporating data visualization to enhance understanding. It covers the entire workflow, from data import to model evaluation, ensuring a comprehensive analysis of the classification process.
- Host: GitHub
- URL: https://github.com/samamasaleem/breast-cancer-detection-using-logistic-regression
- Owner: SamamaSaleem
- Created: 2024-06-04T08:02:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-11T08:06:35.000Z (over 1 year ago)
- Last Synced: 2025-08-01T17:10:06.483Z (2 months ago)
- Topics: breast-cancer-wisconsin, classification, logistic-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 164 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Logistic Regression for Breast Cancer Classification
This project demonstrates the implementation of a Logistic Regression model to classify breast cancer data. The steps below detail the process from importing libraries and data, to training the model and evaluating its performance.
## Table of Contents
- [Installation](#installation)
- [Dataset](#dataset)
- [Implementation](#implementation)
- [Importing the Libraries](#importing-the-libraries)
- [Importing the Dataset](#importing-the-dataset)
- [Splitting the Dataset](#splitting-the-dataset)
- [Training the Model](#training-the-model)
- [Predicting Test Results](#predicting-test-results)
- [Visualizing the Dataset](#visualizing-the-dataset)
- [Evaluating the Model](#evaluating-the-model)
- [Confusion Matrix](#confusion-matrix)
- [Accuracy](#accuracy)
- [k-Fold Cross Validation](#k-fold-cross-validation)
- [Results](#results)
## Installation
Ensure you have Python and the following libraries installed:
- pandas
- scikit-learn
- seaborn
- matplotlib
You can install the required libraries using pip:
```sh
pip install pandas scikit-learn seaborn matplotlib
```
## Dataset
The dataset used is `breast_cancer.csv`. Make sure this file is in the same directory as your script or provide the correct path to the file.
## Implementation
### Importing the Libraries
```python
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score
```
### Importing the Dataset
```python
dataset = pd.read_csv("breast_cancer.csv")
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
dataset.head()
```
### Splitting the Dataset
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
```
### Training the Model
```python
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
```
### Predicting Test Results
```python
y_pred = classifier.predict(X_test)
```
### Visualizing the Dataset
```python
sns.countplot(x='Clump Thickness', data=dataset)
plt.title('Distribution of Clump Thickness')
plt.show()
# Uncomment the lines below to see the pairplot (note: this may take some time for large datasets)
# sns.pairplot(dataset, hue='Class')
# plt.title('Pairplot of Dataset')
# plt.show()
```
*The graph shows the frequency distribution of Clump Thickness in the dataset.*
### Evaluating the Model
#### Confusion Matrix
```python
cm = confusion_matrix(y_test, y_pred)
print(cm)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()
```
*The confusion matrix visualizes the model's performance by comparing predicted and actual classifications.*
#### Accuracy
```python
print(accuracy_score(y_test, y_pred))
```
Output:
```
0.9562043795620438 (95.62%)
```
#### k-Fold Cross Validation
```python
accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)
print("Accuracy: {:0.2f} %".format(accuracies.mean() * 100))
print("Standard Deviation: {:0.2f} %".format(accuracies.std() * 100))
sns.boxplot(data=accuracies)
plt.title('k-Fold Cross Validation Accuracies')
plt.xlabel('Accuracy')
plt.show()
```
Output:
```
Accuracy: 96.70 %
Standard Deviation: 1.97 %
```
*The boxplot illustrates the distribution of accuracies across 10-fold cross-validation, showing the model's robustness and consistency.*
## Results
This Logistic Regression model demonstrates a high accuracy of approximately 95.62% on the test set and an average cross-validation accuracy of 96.70% with a standard deviation of 1.97%, indicating a reliable model with consistent performance.