MOHAMMED KAIF

Dynamic Data Scientist Consultant with hands-on experience at Rubixe.com, excelling in machine learning and data visualization. Successfully tackled real-world challenges through innovative proof of concept projects, showcasing strong analytical skills and a passion for problem-solving. Proficient in Python and MS Excel, I thrive in collaborative environments to deliver impactful solutions.

1.Handwritten Digits Image Processing.

 This project focused on developing and implementing a system for the automatic recognition of handwritten digits. Leveraging a publicly available dataset of labeled digit images, the project explored key areas like image preprocessing, feature extraction, model training, and evaluation to achieve accurate classification of digits from 0 to 9. 

2.Heart Disease Prediction.

 Problem Domain: Heart disease is a leading cause of death globally. Prediction models aim to identify individuals at high risk, enabling early intervention and potentially saving lives. This highlights the real-world impact and significance of the project. Data Sources: These projects often involve working with complex and diverse datasets, which could include Electronic Health Records (EHR), Physiological Measurements, Lifestyle Factors, and Genomic Data. Flight Fare Prediction, Project description not provided. Earthquake Damage Prediction, Project description not provided.

3.Sales Data Analysis

Designed and developed an interactive Power BI Sales Dashboard to visualize key performance metrics such as total sales, profit margins, regional performance, and product-wise trends. Utilized data modeling techniques, DAX measures, and dynamic visuals to enable real-time insights for stakeholders. Implemented filters and slicers for enhanced data interactivity and created drill-through pages for detailed analysis. The project improved data-driven decision-making and sales tracking efficiency across departments.

4. Insurance Data Analysis

Developed a comprehensive Power BI dashboard for insurance data analysis, providing deep insights into policy performance, claim trends, customer demographics, and risk factors. Utilized DAX for dynamic KPIs including total premiums, claim ratios, and loss severity. Implemented interactive filters and drill-through features for segment-wise and time-series analysis. The dashboard enabled data-driven decision-making, enhanced underwriting strategies, and improved operational visibility across business units.

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• Playing Cricket
• Playing Kabaddi