Machine learning, Python, SQL

Yesh Damania

Jersey City, NJ +1-201-***-**** ********@*******.*** LinkedIn GitHub Medium Portfolio Skills

Languages: Python

Technologies: Machine Learning (Scikit-Learn, TensorFlow, PyTorch, Keras), Data Visualization (Matplotlib, Seaborn, Power BI, Tableau), Deep Learning, Statistics, Hierarchical Clustering, RFM Analysis, SQL Programming, Version Control (Git, GitHub) Applications: Jupyter Notebook, VS Code, Google Colab, MySQL, Microsoft OfficeSuite (Word, Excel, PowerPoint), Outlook, Adobe. Additional Skills: SharePoint, Basic Web Content Management, Editing & Proofreading, Experience with AI-generated content and optimization, Finding impactful graphics for web pages and reports, Writing and maintaining internal documentation for projects Experience

Intern Carrer, Mumbai, India Oct 2023 – Jul 2024

Power BI Developer

• Developed a Global Terrorism Analysis dashboard, visualizing attack hotspots, common weapons, and primary targets from 1970 to 2017, enabling better risk assessment and policy formulation.

• Created a COVID-19 Analysis dashboard to track daily cases, deaths, and testing data, offering insights on public health measures and improving pandemic response strategies.

• Maintained organized records and exported data visualizations for stakeholder reporting and archival use.

• Coordinated via email to deliver project updates and clarify documentation requirements. Cognifyz Technologies, Mumbai, India Dec 2022 - Sep 2023 Machine Learning Intern

• Developed machine learning models for restaurant rating prediction, recommendation systems, cuisine classification, and geographical analysis, leveraging regression and classification techniques for data-driven insights.

• Preprocessed large-scale restaurant datasets by handling missing values, encoding categorical variables, and performing geospatial visualizations, ensuring data quality, consistency, and interpretability for improved model performance.

• Implemented a restaurant recommendation system using content-based filtering, analyzing user preferences such as cuisine type and price range to deliver personalized suggestions and enhance decision-making.

• Managed internal documentation, organized project folders, and prepared reports for non-technical audiences.

• Communicated weekly progress through emails and maintained version control for collaborative tasks. Education

Stevens Institute of Technology, Hoboken, NJ Expected: May 2026 Master of Science – Data Science

Relevant coursework: Machine Learning, Statistical Methods, Augmented Intelligence and Generative AI. M.H. Saboo Siddik College of Engineering, (Mumbai University) May 2023 Bachelor of Engineering – Electronics & Telecommunication GPA: 8.68/10 Award: 3rd Place in Final Year Project Presentation at A.P. Shah Institute of Technology. Projects

Inventory Management Dashboard (Power BI)

● Designed an inventory management dashboard using Power BI and Excel, enabling Warehouse and In-plant Inventory Managers to optimize inventory levels and service efficiency.

● Integrated ABC and XYZ classification, inventory turnover ratio, safety stock levels, reorder point estimation, and demand forecasting, providing actionable insights for informed decision-making.

● Processed historical order data (33,920 records) and stock details (304 records) to enhance inventory control strategies through DAX functions and intuitive visualizations.

Customer Segmentation (K-Means Clustering)

● Processed customer data, including age, income, and spending score, to identify buying patterns and group customers based on similarities.

● Applied K-Means Clustering with the Elbow Method to segment customers into categories like Careless, Target, and Sensible, helping businesses tailor marketing strategies.

● Visualized customer clusters using scatter plots, exported insights into an Excel report, and provided businesses with data to target high- value customers for promotions.

Uber Ridership Analysis (Tableau)

● This project analyzes Uber’s ridership data to identify key trends in ride requests, peak demand hours, and factors affecting ride completion. The dataset includes details like request time, drop-off time, pickup location, driver ID, and ride status, providing insights into Uber’s operations.

● Using Tableau, interactive dashboards visualize ride demand fluctuations, incomplete requests, and supply-demand patterns. The charts highlight trends, outliers, and inefficiencies in Uber’s ride allocation system.

● The analysis helps identify gaps between ride requests and driver availability, enabling data-driven decisions for better resource allocation. These insights aim to enhance Uber’s service efficiency, improve customer satisfaction, and support demand forecasting. Certifications

● Data Science and Analytics with Advanced ML track from Imarticus Learning, Data Analytics and Visualization Job Simulation, Forage

(Accenture), Data Analytics and Visualization Job Simulation (Goldman Sachs), Google Analytics Certification (Skillshop), KPMG AU

- Data Analytics Job Simulation (Forage), Organize Image Data with PCA (Cognitive Class)

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