Predicted the likelihood of approval of credit card customer application with 80?curacy by building and evaluating logistic regression and random forest model.

Key Achievement: Developed a model with an auc score 0.70 with a credit rating score .

Designed a machine learning model using SVM and tuned hyper parameter to predict the handwritten digit

Developed a model with an accuracy score of 0.94

Objective: Identify the most potential leads in web marketing for an education company

Tech Stack: Python, Jupyter Notebook, Logistic Regression

Solution: Created a Logistic regression model to identify the most potential leads

Key Achievement: Developed a model with an accuracy of 79%, sensitivity of 78% and specificity of 78% and determined the optimal cut-off using ROC curve