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

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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

Data

Breast Cancer Wisconsin (Diagnostic) Data Set

Look at the dataset

difference between Malignant and Benignt

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