- Explored dataset - found many zero values in Cholesterol and one in RestingBP
- Applied decision tree and random forest on raw data
- Got an accuracy of around 85%
- Can replace Cholesterol values with mean retain info
- Another way to predict cholesterol values using other features
- Explored dataset to find trends in data
- Preprocess data to remove unrealistic values and didone hot encoding
- Tried various models like Random Forest, Decision Tree, KNN, Logistic regression, Naive Bayes
- Got an accuracy of 90 percent on logistic regression and naive bayes
- Would work on preprocessing and improving accuracy
- Explored data set and did EDA of data
- Cleaned the data by using data cleaning techniques
- Tried some machine learning models and found the appropriate model according to data
- Got an accuracy of 85.5 percent by KNN model
- Done with the creating input function for the user to input the data
- Finished basic exploration and preprocessing of dataset
- Implemented Several Models with accuracies:
- Logistic Regression : 91%
- Decision Tree : 79%
- Random Frorest : 89%
- Bagging Classifier : 87%
- Gradient Boost : 89%
- Adaboost : 89%
- Voting Classifier : 90%
- Omkar Prabhune
- Shreyash Deshmukh
- Isha Deshpande
- Manomay Jamble
- Devanshu Dalal
- Vaishnavi Pingat