Podcast
Questions and Answers
What is the purpose of the 'chisq.test(a$target,a$sex)' function in the provided code snippet?
What is the purpose of the 'chisq.test(a$target,a$sex)' function in the provided code snippet?
- To calculate the mean value of the target variable for different genders.
- To test the relationship between the target variable and gender. (correct)
- To create a scatter plot between the target variable and gender.
- To impute missing values in the 'sex' column.
What does the 'anov()' function with 'target~(resting.bp.s)' as an argument do?
What does the 'anov()' function with 'target~(resting.bp.s)' as an argument do?
- Calculates the correlation coefficient between the target variable and resting blood pressure.
- Filters out observations in the dataset based on resting blood pressure values.
- Creates a new dataset named 'resting.bp.s'.
- Performs analysis of variance on the target variable and resting blood pressure data. (correct)
In the given code, what is the purpose of 'ifelse(testset$predicted_probability>0.50,1,0)'?
In the given code, what is the purpose of 'ifelse(testset$predicted_probability>0.50,1,0)'?
- Setting all predicted probabilities to 0.50 in the test set.
- Classifying test data based on predicted probabilities. (correct)
- Calculating the mean probability for the target variable greater than 0.50.
- Calculating the median of predicted probabilities.
What information does the 'table(testset$target,testset$binary)' provide?
What information does the 'table(testset$target,testset$binary)' provide?
Why is 'sample.split(a$target,SplitRatio = 0.80)' used in the code?
Why is 'sample.split(a$target,SplitRatio = 0.80)' used in the code?
What does 'model=randomForest(target~.,data=trainingset)' achieve in the code snippet?
What does 'model=randomForest(target~.,data=trainingset)' achieve in the code snippet?
What does 'sum(is.na(a))' in the code snippet do?
What does 'sum(is.na(a))' in the code snippet do?
What is the purpose of 'chisq.test(a$target,a$chest.pain.type)' in the code snippet?
What is the purpose of 'chisq.test(a$target,a$chest.pain.type)' in the code snippet?
What does 'testset$predicted_probability>0.50' determine in the code snippet?
What does 'testset$predicted_probability>0.50' determine in the code snippet?
What information does 'table(a$resting.ecg)' provide in the code snippet?
What information does 'table(a$resting.ecg)' provide in the code snippet?
What is the purpose of 'anova=aov(target~(age),data=a)' in the code snippet?
What is the purpose of 'anova=aov(target~(age),data=a)' in the code snippet?
What does 'model=randomForest(target~,.,data=trainingset)' achieve in the code snippet?
What does 'model=randomForest(target~,.,data=trainingset)' achieve in the code snippet?
Study Notes
R Script Steps
- Set working directory to
C:/Users/nishitha1/Desktop/231BCADA23
- Read CSV file
newppt.csv
intoa
withna.strings
set to empty string - Calculate sum of NA values in
a
usingsum(is.na(a))
- Create frequency table for
a$resting.ecg
usingtable(a$resting.ecg)
Handling Missing Values
- Replace NA values in
a$resting.ecg
with 0
ANOVA Models
- Create ANOVA model for
target
vsage
usingaov
function - Summarize ANOVA model using
summary
function - Create ANOVA models for
target
vsresting.bp.s
,cholesterol
,max.heart.rate
, andoldpeak
Chi-Square Tests
- Perform chi-square tests for
target
vssex
,chest.pain.type
,fasting.blood.sugar
,resting.ecg
,exercise.angina
, andST.slope
Random Forest Model
- Split data into training set (80%) and test set (20%) using
sample.split
function - Create random forest model using
randomForest
function withtarget
as response variable - Summarize random forest model using
summary
function - Predict probabilities for test set using
predict
function - Create binary predictions using
ifelse
function
Confusion Matrix
- Create confusion matrix for test set using
table
function
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Description
Learn how to perform statistical analysis using R commands such as reading CSV files, handling missing values, conducting ANOVA tests, and performing chi-squared tests. Explore different variables like age, resting blood pressure, cholesterol levels, and heart rate in a dataset.