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𝐁𝐑𝐄𝐀𝐒𝐓 𝐂𝐀𝐍𝐂𝐄𝐑 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 𝐔𝐒𝐈𝐍𝐆 𝐌𝐀𝐂𝐇𝐈𝐍𝐄 𝐋𝐄𝐀𝐍𝐈𝐍𝐆 𝐂𝐋𝐀𝐒𝐒𝐈𝐅𝐈𝐄𝐑

INTRODUCTION :

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.

STRUCTURE THE PROJECT INTO A SERIES OF STEPS :

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

GOAL OF PROJECT:

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.

IMPORTING LIBRARIES

LOAD THE DATASET

Load data in python using panda’s library

BASIC CHECKS

DOMAIN ANALYSIS

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.