initial commit- working on a case study to predict health outcome

[duttashi]

Things to do

• separate the categorical and continuous variables
• recode the categorical variables to numeric for fast computation. But maintain them as factor variables.
• perform multivariate visualizations
• check and remove outliers from continuous variables
• check for other issues like collinearity, multicollinearity etc.
• impute the missing values
• join the processed categorical and continuous data frames
• apply PCA and MCA to determine relevant variables
• split the processed data frame into train and test
• apply several models to determine high accuracy.

[duttashi]

• [x] separate the categorical and continuous variables - complete

• [x] recode the categorical variables to numeric for fast computation. But maintain them as factor variables.- complete

• [ ] perform multivariate visualizations - not done

• [ ] check and remove outliers from continuous variables - not done

• [ ] check for other issues like collinearity, multicollinearity etc. - not done

• [x] impute the missing values - complete

• [x] join the processed categorical and continuous data frames - complete

• [x] apply PCA and MCA to determine relevant variables - complete

• [x] split the processed data frame into train and test - complete

• [x] apply several models to determine high accuracy - complete

[duttashi]

no conflicts with the base branch. Okay to merge.