gaurav

data scientist

·      We got the data set having 150000 observations.

·      The project life cycle includes the following steps: Data processing, cleaning, EDA, Model building, deployment.

·      The programming language used is python and we uploaded the data from CSV.

·      The data cleaning consists of dropping of NA values and resetting of index.

·      In EDA part we compared the relationship between our outcome variable with independent variables using various plots and checked the collinearity among the independent variables. 

·      In model building we tried the models: Logistic Regression, Decision Tree, Random Forest and XGB and we got the highest accuracy with XB boost with train accuracy: 95% and test accuracy: 93%.

We deployed the model using python flask and Heroku