Uploaded both csv's and notebook-Team All Stars

[naman-1801]

Team Members:

• Roll No. B22164: Naman Singhania
• Roll No. B22244: Toshan Gupta
• Roll No. B22245: Tushar Mittal
  In order to pre-process the data, we started by removing any rows that had NaN values for the attributes lifestyle, diastolic BP, and systolic BP.
  After that, we interpolated the numerical characteristics and substituted the mode for the missing values in the categorical data.Next, we eliminated the anomalies from the data and employed one hot encoding to transform the categorical data into numerical data.

Then, using a regression model for multivariate distribution, we calculated the dagger and utilised regret to forecast the two features, namely the diastolic and systolic blood pressure.
We pre-processed the data for the classification task by interpolating, eliminating the outlier, and then one-hot encoding the gender into numerical data.
The data was then divided into train and test, we used the multivariate Bayes Classifier to train the model.
After that, we divided the test data and computed accuracy; the result was 76%.
Next, we used the test file to apply the model.