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R mini project using Data Science and ML model

R 100.00%
caret classification data-preprocessing data-visualization decision-tree feature-extraction fetal-health ggplot2 machine-learning naive-bayes prediction r-language r-programming random-forest rose xgboost

fetal_health_prediction's Introduction

Fetal_Health_Prediction

build status

Overview

  • This project deals with a 3 class problem for predicting the fetal health into categories like i. Normal ii. Suspect iii. Pathological
  • The Data undergoes pre-processing to remove outliers, then cross validated for better results. Models used include Decision Tree, Random Forest, XGBoost and Naive Bayes.

Dataset

Reproduction Child-healthcare Classification

  • This dataset has over 2100 records
  • Fetal_health is the target variable
  • It has 22 input variables including: - Baseline value - Baseline Fetal Heart Rate (FHR) - Accelerations - Number of accelerations per second - Fetal_movement - Number of fetal movements per second - Uterine_contractions - Number of uterine contractions per second - Light_decelerations - Number of LDs per second - Severe_decelerations - Number of SDs per second - Prolongued_decelerations - Number of PDs per second - Abnormal_short_term_variability - Percentage of time with abnormal short term variability - Mean_value_of_short_term_variability - Mean value of short term variability - Percentage_of_time_with_abnormal_long_term_variability - Percentage of time with abnormal long term variability

Accuracy

So, the following are algorithm implemented along with their accuracy,

  • Naïve Bayes (Accuracy:0.81)
  • Decision Tree (Accuracy:0.82)
  • XGBoost (Accuracy:0.84)
  • Random Forest (Accuracy: 0.87)

Pre-requisites Required

R studio and R Programming to be installed using their official documentation.

  • Install both simultaneously & ensure version compatability. If either is already downloaded, upgrade it to latest version.

    install.packages("ggplot2")
    
    install.packages("ROSE")
    
install.packages("rpart")
install.packages("rpart.plot")
install.packages("randomForest")
install.packages("e1071")
install.packages("xgboost")
install.packages("caret")

Execution

  • Run the given code by downloading the file and ensure respective csv file is also in same directory
  • Set the directory using setwd command.
  • Install the required packages
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