Readmitted-Rate

Introduction

Patients readmitted rate, which is a supervised learning classification problem.

Using a combination of models (KNN, SVM, Decision Tree, Perceptron and Naïve Bayes algorithms） to deal with the real-world datasets from UCI machine learning lab.

Including:

EDA
Data Cleaning
Feature Seletion
Models tuning
Ensemble
and get the best accuracy about the readmitted rate.

Implementation:

Put these file { Smote_BackwardWrapper_Pipeline.py,

ForwardWrapper_Smote.py,

Results for the test set.py,

GridSearch Main Version Updated.py,

Base_Scoring_Algorithm (Corr, Chi2, Mutual_Info, C4_5 Importance).py,

New_train_set.csv,

New_test_set.csv } in the same location.

Filter Method:

Base_Scoring_Algorithm

(Corr, Chi2, Mutual_Info, C4_5 Importance).py is for feature selection:

use to calculate correlation, chi-square, mutual information between variables and target label

GridSearch Main Version Updated.py:

Using the results of Scoring to do feature selection with filter method; Tuning different models with selected features using gridsearch

###Results for the test set.py: Compute different accuracy score of test set with best tuned parameters

Wrapper Method:

Fordward wrapper to find the global best collection of features.

ForwardWrapper_Smote.py:

Input training set data and return selected features as csv

Smote_BackwardWrapper_Pipeline.py

Take advantages of pipeline to do SMOTE, backward wrapper and tuning;

Compute different accuracy score of test set with best tuned parameters;

Save best parameters, test scores, and feature selected as csv