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

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 .

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