Kumar S

Motivated and detail-oriented recent graduate with a strong academic foundation in data science. Adept at leveraging statistical analysis and machine learning algorithms to extract meaningful insights from complex datasets. Possesses a solid understanding of programming languages such as Python and R, coupled with a passion for applying data-driven solutions to real-world problems. I am eager to contribute analytical skills, a fresh perspective, and a commitment to continuous learning in a dynamic data science role. Hardworking and passionate job seeker with strong organizational skills eager to secure a junior-level data science and data analyst position.

• Advance Excel
• Python
• R Programming
• SQL
• Tableau & Power BI

• Pinnacle Performer, November 2022—recognized for outstanding performance in November 2022.
• Pinnacle Performer, December 2022:Consistently demonstrated excellence, earning the Pinnacle Performer award for December 2022.
• Topper of the Month: January 2023—acknowledged as the top performer in January 2023.
• Topper of the Month: March 2023:Maintained a high level of performance, securing the Topper of the Month title in March 2023.
• Manager Spotlight Award (6/27/23): Received the Manager Spotlight Award on June 27, 2023, in recognition of exceptional leadership.
• Topper of the Month: July 2023:Attained the highest rank and performance metrics among 240 team members in July 2023.
• Highest Productivity Achievement (Nov 2022–July 2023): Demonstrated consistent excellence by achieving the highest productivity levels from November 2022 to July 2023.
• Excel Training and Process Training: conducted comprehensive Excel training for over 50 team members, contributing to their skill enhancement. Additionally, we provided training in various processes to enhance overall team performance.
• First Rank: July 2023-Secured the 1st Rank for the month of July 2023, showcasing unparalleled dedication and proficiency.
• Overall Excellence: Consistently delivered the highest productivity with 100% quality, establishing a remarkable standard for the team.

• Python Projects:
• Predictive Maintenance for Manufacturing Equipment:Objective: Developed a predictive maintenance model using machine learning algorithms to anticipate equipment failures in a manufacturing plant.
  Methodology: Collected sensor data from machinery and utilized time-series analysis techniques to detect patterns indicative of impending failures. Employed classification algorithms such as Random Forest and Gradient Boosting to predict maintenance needs.
  Results: Achieved a significant reduction in unplanned downtime, leading to cost savings and increased productivity for the manufacturing facility.
  Technologies Used: Python (Pandas, NumPy, scikit-learn), Time-series Analysis, Machine Learning Algorithms.
  
• Customer Segmentation and Churn Prediction for E-commerce Platform:Objective: Segmented customers based on their behavior and demographics, and developed a churn prediction model to identify customers at risk of leaving the platform.
  Methodology: Utilized clustering algorithms like K-means to segment customers into distinct groups. Employed classification algorithms such as Logistic Regression and XGBoost to predict customer churn based on historical data.
  Results: Identified key factors contributing to customer churn and implemented targeted retention strategies, resulting in a reduction in churn rate by X%.
  Technologies Used: Python (Pandas, NumPy, scikit-learn), Machine Learning Algorithms, Customer Segmentation Techniques.
  Customer Churn Prediction for Telecom Company:Objective: Developed a predictive model to identify customers at risk of churn for a telecom company, aiming to improve customer retention efforts.
  Methodology: Utilized historical customer data including usage patterns, account information, and customer interactions. Employed machine learning algorithms such as Logistic Regression, Random Forest, and Gradient Boosting to predict churn probability.
  Results: Achieved a churn prediction accuracy of over X%, enabling the company to implement targeted retention strategies and reduce customer attrition.
  Technologies Used: Python (Pandas, NumPy, scikit-learn), Machine learning Algorithms, Customer churn prediction.
  
• Flight Booking Price Prediction:Objective: Build a machine learning model to predict flight ticket prices for a travel booking platform, aiding customers in making informed booking decisions.
  Methodology: Gathered historical flight data, including routes, airlines, departure times, and ticket prices. Performed feature engineering to extract relevant features such as seasonality, route popularity, and advance booking trends. Utilized regression algorithms such as linear regression, decision Trees, and random forests to predict ticket prices.
  Results: Developed a user-friendly interface for customers to input their travel preferences and receive accurate price predictions. Enhanced user experience and increased customer satisfaction.
  Technologies Used: Python (Pandas, NumPy, scikit-learn), regression algorithms, feature engineering.
  
• SQL Projects:
• Employee Management System with SQL Server:Objective: Develop an employee management system using SQL Server to streamline HR operations for a small to medium-sized company.
  Description: Designed a database schema to store employee information such as personal details, job roles, attendance records, and performance evaluations. Implemented SQL queries and stored procedures to manage employee data, including adding new employees, updating records, and generating reports for payroll processing and performance reviews. Developed a user-friendly interface for HR staff to input and retrieve employee information efficiently.
  Technologies Used: SQL Server, T-SQL, Stored Procedures, Database Design.
  
• Online Bookstore Database:Objective: Create a database for an online bookstore using SQL Server to manage inventory, orders, and customer information.
  Description: Designed a relational database schema to store book details, inventory levels, customer accounts, and order history. Implemented SQL queries to handle online transactions, including searching for books, adding items to the shopping cart, and processing orders. Developed stored procedures to handle inventory updates, order processing, and customer account management. Integrated the database with a web application for online book browsing and purchasing.
  Technologies Used: SQL Server, T-SQL, Database Design, E-Commerce Concepts .