In this repository, I am added a 10 machine learning classification model for breast cancer dataset classification.
- Decision Trees Classifier
- MLP Classifier
- K-Nearest Neighbours Classifier
- Logistic Regression Classifier
- Naïve Bayes Classifier
- Random Forest Classifier
- Stochastic Gradient Descent Classifier
- SVM Classifier
- XGBoost Classifier
This is the dataset I used for developing our classification model, the dataset description is followings:
Data Set Characteristics: Multivariate
Number of Instances: 569
Area: Life
Attribute Characteristics: Real
Number of Attributes: 32
Date Donated: 1995-11-01
Associated Tasks: Classification
Missing Values? : No
workflow used in a developing model are followings:
- Import required libraries
- Step 1 - Dataset Selection
- Step 2 - Data Normlization
- Step 3 - Train and Test Split
- Step 4 - Algorithem Selection
- Step 5 - Model Evalution (Training)
- Step 5 - Model Evalution (Testing)
- Accuracy
- Sensitivity/Recall
- Specificity
- Precision
- F-Score
- Mens Squre Error
- Root Mens Squre Error
- ROC_AUC scores (Plot)