- Created new features using Python from raw data that influence price negotiations while buying a house.
- Utilized Feature Selection to choose the most significant influences from 79 numerical and categorical features..
- Aggregated and visualized the data by using pandas, matplotlib and seaborn to compile a professional reportn.
- Preformd Data Wrangling & Cleaning with panda on data to prepare Cardiovascular reports and utilize data in perdition models..
- Prepared Exploratory Data Analysis reports with Python concerning the distribution of Cardiovascular disease and related factors.
- Created Machine Learning Models to predict risk of heart attack using logistic regression & random forest comparing the results..
- Created a dashboard with Tableau that compares income, life insurance ratio of reinsurance acceptance, & retention..
- Created an interactive geographic map showing various countries according to income and data from the world bank webpage..
- Created KPI Tools to report by specified year and market share.
- Data Exploration: Utilized Python and SMOTE to prepare, display, and understand the data for perdition models.
- Data Modeling: Utilized PyCaret and Logistic Regression to build predictive model sfor the causes of diabetes..
- Data Reporting: Utilized Tableau to create a dashboard with appropriate chart and metrics visualizing the causes of diabetes.