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karan shelke

Data Scientist

6
Projects
9
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Education

Sanjivani College of Engineering Kopargaon â€ĸ 2026

About Me

Aspiring Data Scientist with a strong foundation in Mechatronics Engineering, IoT, and Automation. Skilled in Python, SQL, Power BI, and PLC with hands-on experience in predictive analytics, data vi

Projects

Showcase of Work

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Autonomous Obstacle-Avoidance Robot with Real-Time Navigation

Developed an Arduino-based autonomous robot with ultrasonic sensors and DC motors for real-time obstacle detection and dynamic navigation. Enabled collision-free movement, demonstrating skills in robotics, automation, and intelligent mobility systems.

Files (4)

PLC-Based Automated Elevator Control System

Developed a PLC-based automated elevator system using Siemens LOGO! and Allen Bradley, integrating IR sensors for precise floor detection, safety features, and efficient control logic for scalable industrial automation.

Files (4)

The Orders Dashboard – Business Insights using Power BI

Designed an interactive Power BI dashboard on Tata Group’s 50K+ orders dataset to analyze sales, profit, customer behavior, and regional performance. Leveraged Power Query for data cleaning, data modeling for relationships, and DAX for KPIs. The dashboard offers interactive visuals, drill-through, and geographic insights, enabling actionable business decisions and optimized sales and profitability strategies.

Files (4)

IoT-Based Smart Health Monitoring Belt for Farm Animals

Designed and developed a smart IoT-based health monitoring belt for livestock using ESP32 and multiple sensors. The system continuously tracks animal vital signs and sends real-time data to a cloud dashboard for health prediction, stress detection, and disease alerts.

Files (4)

Startup Growth Analytics: Exploring Patterns of Success in Indian Startups

Analyze real-world Indian startup funding data to uncover factors that drive startup growth and early success.

Files (4)

Startup Growth Analytics: Exploring Patterns of Success in Indian Startups

Objective: Analyze real-world Indian startup funding data to uncover factors that drive startup growth and early success. Dataset Used: Indian Startups Funding Dataset (2015–2024) – includes information on startup name, industry, city, funding amount, investors, and investment type. Tools & Techniques: Python, Pandas, Plotly (interactive visualizations), Scikit-Learn (for predictive modeling), Data Cleaning, EDA, Feature Engineering. Key Insights: Top Funding Cities: Bengaluru, Mumbai, and Gurgaon lead in total funding raised. Industry Trends: E-Tech, FinTech, and E-commerce startups attract the highest investments. Investment Types: Seed rounds and Series A/B dominate early-stage funding. Successful Startup Patterns: Startups with funding > $1M and multiple founders have higher success probability. Yearly Trends: Total funding has steadily increased over recent years, indicating a growing startup ecosystem. Impact: Provides data-driven recommendations for aspiring entrepreneurs and investors on where and in which sector to focus for maximum growth potential.

Files (4)