Sachin Patil

Knowledgeable about business analysis principles and data interpretation. Strong problem-solving abilities and effective communication skills. Quick learner with enthusiasm for understanding business processes and improving efficiency. Ready to use and develop analytical thinking, teamwork, and data management skills.

• Microsoft Azure Fundamentals
• AWS Certified Solutions Architect
• Java
• Python
• Introduction to Data Analyst
• Business Analysis & Process Management
• Front-End Web Development
• Data Structure Prerequisite Course

• Reading books, articles, Traveling to different places, Engaging in brain games, puzzles.

1. Energy Saving Measures of National Health Services (NHS)

• Conducted a comprehensive study on energy-saving measures in the NHS, analysing real-time energy monitoring data and developing strategies for staff and patient engagement.
• Utilized regression analysis and statistical models to predict utility costs and assess the impact of energy-efficient technologies.
• Delivered actionable recommendations contributing to cost reductions and alignment with NHS sustainability goals, including achieving net-zero emissions by 2040.

2. Server Load Prediction Using Deep Learning

As part of my Bachelor of Engineering (Information Technology) journey at NBN Sinhgad School of Engineering, Pune, I worked on a project that leverages deep learning for accurate server load prediction. By implementing LSTM, CNN, and RNN models, we aimed to optimize resource allocation, reduce downtime, and enhance server efficiency.

Key Highlights:

• Implemented advanced neural network architectures for real-time load prediction. Achieved high accuracy in forecasting server workloads using historical data.
• Enhanced system performance through intelligent resource management.
• This project was a fantastic opportunity to apply AI in real-world IT infrastructure. Excited to explore more innovations in machine learning and cloud computing!

Customer Segmentation using K-Means Clustering:
Technologies: Python (Pandas, NumPy), Scikit-learn (K-Means Clustering)

• Collected and preprocessed a customer dataset (including age, income, product preferences) for segmentation analysis.
• Applied K-Means clustering to segment customers into distinct groups based on purchasing behavior and demographics.
• Visualized customer segments using scatter plots to identify patterns and outliers.
• Analyzed the segments to provide actionable insights, such as targeted marketing strategies and product recommendations for each group.