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Identification of Abnormal Breast Growths as Potential Cancers Using Machine Learning : Comparison of performance of various classification algorithms

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Feature_selection_Logistic_Regression_F1_score.ipynb
Feature_selection_Logistic_Regression_F1_score.ipynb
 
 
Feature_selection_Random_Forest_F1_score.ipynb
Feature_selection_Random_Forest_F1_score.ipynb
 
 
Gaussian Naive Bayes Hyperparameter Tuning.ipynb
Gaussian Naive Bayes Hyperparameter Tuning.ipynb
 
 
Gaussian Naive Bayes Normalized.ipynb
Gaussian Naive Bayes Normalized.ipynb
 
 
Gaussian Naive Bayes.ipynb
Gaussian Naive Bayes.ipynb
 
 
K-NN.ipynb
K-NN.ipynb
 
 
K-NN_Normalized.ipynb
K-NN_Normalized.ipynb
 
 
KNN Feature Selection.ipynb
KNN Feature Selection.ipynb
 
 
KNN_Normalized_2.ipynb
KNN_Normalized_2.ipynb
 
 
Logistic Regression_normalized.ipynb
Logistic Regression_normalized.ipynb
 
 
Logistic Regression_unnormalized.ipynb
Logistic Regression_unnormalized.ipynb
 
 
ML Project Proposal.pdf
ML Project Proposal.pdf
 
 
README.md
README.md
 
 
Random_forest_normalized.ipynb
Random_forest_normalized.ipynb
 
 
Random_forest_unnormalized.ipynb
Random_forest_unnormalized.ipynb
 
 
Statistics of best feature combination using Logistic Regression.ipynb
Statistics of best feature combination using Logistic Regression.ipynb
 
 
Statistics of best feature combination using Random Forest.ipynb
Statistics of best feature combination using Random Forest.ipynb
 
 
wdbc.data
wdbc.data
 
 
wdbc.names
wdbc.names
 
 

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ML_Project

This repository contains the code of shallow ML classification algorithms for the project titled "Identification of Abnormal Breast Growths as Potential Cancers Using Machine Learning" done as a part of the course - Machine Learning and Artificial Intelligence for Engineers at CMU in Fall 2021.

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Identification of Abnormal Breast Growths as Potential Cancers Using Machine Learning : Comparison of performance of various classification algorithms

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