Breast cancer detection using machine learning classification is a project where you build a model to identify whether a given set of medical features indicates the presence of breast cancer. This project involves using a labeled dataset of medical records, where each record is classified as either indicating breast cancer or not.

Goal of the ML project

Import essential libraries

Load breast cancer dataset & explore

Create DataFrame

EDA

Pair plot of breast cancer data

• Counterplot

• Heatmap

• Heatmap of breast cancer DataFrame

• Heatmap of a correlation matrix

• Correlation barplot

Data Preprocessing

Split DataFrame in train and test

Feature Scaling

Model Building

• Support Vector Classifier

• Logistic Regression

• K – Nearest Neighbor Classifier

• Naive Bayes Classifier

• Decision Tree Classifier

• Random Forest Classifier

• Adaboost Classifier

• XGBoost Classifier

XGBoost Parameter Tuning Randomized Search

Confusion Matrix

Classification Report of Model

Cross-validation of the ML model

Save the Machine Learning model

Conclusion

We have extracted features of breast cancer patient cells and normal person cells. As a Machine learning engineer / Data Scientist has to create an ML model to classify malignant and benign tumor.

Load data in python using panda’s library

Mean Radius: This feature represents the average distance from the center to points on the perimeter of the tumor.

Mean Texture: It signifies the average variation in gray-scale intensities of the pixels in the image, which can correlate with the homogeneity of the tumor.

Mean Perimeter: This denotes the average length of the tumor boundary.

Mean Area: It indicates the average area of the tumor.

Mean Smoothness: This feature characterizes the variation in radius lengths in the tumor, providing insights into how smooth or irregular the tumor boundary is.

Mean Compactness: It combines the perimeter and area of the tumor to provide a measure of how compact the shape of the tumor is.

Mean Concavity: This represents the severity of concave portions of the contour of the tumor.

Mean Concave Points: It signifies the number of concave portions of the contour of the tumor.

Mean Symmetry: This feature quantifies the symmetry of the tumor shape.

Mean Fractal Dimension: It measures the complexity of the tumor shape at different scales.

Radius Error: This indicates the standard error of the mean of distances from the center to points on the perimeter.

Texture Error: It represents the standard error of variation in gray-scale intensities.

Perimeter Error: This denotes the standard error of the tumor perimeter.

Area Error: It indicates the standard error of the tumor area.

Smoothness Error: This represents the standard error of the variation in radius lengths.

Compactness Error: It denotes the standard error of the tumor compactness.

Concavity Error: This signifies the standard error of the severity of concave portions.

Concave Points Error: It represents the standard error of the number of concave portions.

Symmetry Error: This indicates the standard error of tumor symmetry.

Fractal Dimension Error: It represents the standard error of tumor shape complexity.

Worst Radius: This feature represents the largest distance from the center to points on the perimeter among all measurements.

Worst Texture: It signifies the highest variation in gray-scale intensities among all measurements.

Worst Perimeter: This denotes the longest tumor boundary among all measurements.

Worst Area: It indicates the largest tumor area among all measurements.

Worst Smoothness: This feature represents the smoothness of the largest tumor among all measurements.

Worst Compactness: It signifies the compactness of the largest tumor among all measurements.

Worst Concavity: This represents the severity of concave portions of the contour of the largest tumor among all measurements.

Worst Concave Points: It signifies the number of concave portions of the contour of the largest tumor among all measurements.

Worst Symmetry: This feature quantifies the symmetry of the largest tumor among all measurements.

Worst Fractal Dimension: It measures the complexity of the shape of the largest tumor among all measurements.

Target: This denotes the class label, where 0 indicates benign and 1 indicates malignant, which is the target variable for prediction.