Breast-Cancer-Predictions-With-SVM : Project Overview

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

• Building some plots and graphs to take an overview about what your data looks like.

• Machine Learning Algorithms used in this Notebook: Logistic Regression, Gradient Boosting Classifier, Random Forest Classifier, Decision Tree Classifier, Kneighbours Classifier, XGB Classifier, Supportr vector Classifier

• Evaluating the performance of SVM Classifier by Differents Metrics.

Code and Resources Used

• Python Version: 3.8.3

• Packages : Pandas, Numpy, Matplotlib, Seaborn, Sklearn.

• Understanding a Classification Report For Your Machine Learning Model.

• True Positive Rate

• How to plot an ROC curve in Python

Data

Breast Cancer Wisconsin (Diagnostic) Data Set

Look at the dataset

EDA

• Checking for the correlation

• plotting the highly correlated pairs

Model Building

• In this section, I tried different models and evaluate them using the Accuracy_Score:

• Logistic Regression
• Gradient Boosting Classifier
• Random Forest Classifier
• Decision Tree Classifier
• Kneighbours Classifier
• XGB Classifier
• Supportr vector Classifier

Model Performance

In this step, I evaluate the performance of the models using:

• Accuracy_Score
• Recall
• Precision
• Classification Report
• The ROC Curve