Snehdeep Kaur

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Employee Attrition Prediction

This project uses Machine Learning and Data Science techniques to predict whether an employee will stay or leave a company. I worked with a real Kaggle HR dataset to build a predictive analytics model that helps HR teams identify employees at risk of leaving and take early action.

Steps I Followed:

• Performed data cleaning and preprocessing (removed duplicates, encoded categorical data).
• Applied Logistic Regression (a supervised Machine Learning algorithm).
• Evaluated the model using Accuracy, Precision, Recall, and F1-Score.
• Used Data Visualisation to show clear insights for HR decision-making.

Main Features:

• Predicts employee attrition using historical HR data.
• Train-Test Split: 70% training, 30% testing.
• Provides insights to improve employee retention strategies.

Technologies Used:

Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Google Colab

What I Learned:

• How to apply Machine Learning for real-world business problems.
• The importance of Recall in identifying employees likely to leave (critical for HR planning).
• Strong skills in Data Cleaning, Feature Engineering, Model Building, and Visualisation.

This project highlights how AI and Data Analytics can support business decisions.

It demonstrates my ability to use predictive modeling to turn raw data into actionable insights - key skills for a Data Analyst or Data Scientist role.

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Robotic Nurse

At Innovation Fest 2025, our team Care Squad built a smart, AI-powered Robotic Nurse - a healthcare assistant robot designed to reduce NHS staff workload, promote hygiene, and automate medicine delivery in hospitals.

My Contributions:

• Designed a 3D-printed frame with a sleek, modular structure and team branding.
• Created impactful visual posters to showcase features and impact at the event.
• Programmed Arduino for automated cleaning brushes and servo-controlled medicine box.

Core Features:

• Dual rotating brushes + dust collector for hospital floor cleaning.
• Servo-controlled smart medicine box triggered by pushbutton.
• Autonomous navigation using ultrasonic sensors for safe movement.
• Dual Arduino boards for multitasking & efficiency.
• 6 DC motors, 2 motor drivers for smooth drive mechanism.

Technologies Used:

Arduino Uno (x2), Ultrasonic Sensors, DC Motors (x6), Motor Drivers, Servo Motors, Pushbutton, Breadboard, 3D Printed Frame.

Impact Goals:

• Reduce manual workload for NHS staff.
• Improve hygiene and patient safety.
• Ensure timely medicine delivery during peak hours.
• Contribute to UN SDGs for Health and Well-being.

This project proves how student-led innovation can solve real-world problems. With empathy and engineering, we created a functional prototype ready to make a difference in healthcare.

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Space Based Solar Power

As one of the Top 8 in the Viasat: Space for Good UK University Challenge, we proposed a cutting-edge solution to global energy poverty: Space-Based Solar Power (SBSP) enhanced by AI. Our idea addressed the issue of 750+ million people lacking electricity and aimed to offer 24/7, clean, weather-proof energy from space using microwave beam transmission.

The system captures solar energy via satellites in Low Earth Orbit (LEO), converts it to microwaves, and beams it safely to Earth-based rectennas. Unlike traditional renewables, SBSP works continuously, unaffected by night or weather.

My Contributions:

• Led research on LEO vs GEO satellite systems and proposed a low-cost, MVP-first approach.
• Created risk mitigation plans (cybersecurity, cost, regulation, space debris).
• Conducted data analysis using Google Colab, leveraging Pandas and Seaborn to explore and visualize key metrics.
• Used Canva for 3D visual design, graphs, and functional diagrams.
• Produced a compelling video pitch using visual storytelling and video editing tools.
• Analyzed financial models (£1.5B+ full scale vs MVP cost of £170M–500M).

Innovation:

What set my idea apart was the integration of AI algorithms to optimize satellite beam targeting, position control, and energy demand forecasting in real time—improving reliability, efficiency, and scalability.

Impact:

This project aligns with UN Sustainable Development Goal 7 (Affordable and Clean Energy) and aims to improve access to energy for 100M+ people by 2035.

What I Gained:

I gained deep insights into the intersection of aerospace, AI, energy policy, and sustainability. It strengthened my skills in presenting high-stakes ideas and building tech solutions with real-world impact.