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Data Science with less than a year in Python, Machine Learning & SQL
B. Tech Computer Science graduate (2025) with hands-on experience in in Python, Machine Learning, SQL, and statistical analysis, backed by real-world projects involving forecasting, classification, and interactive analytics dashboards. Skilled in building end-to-end data pipelines from data cleaning and feature engineering to model training, evaluation, and visualization. Strong problem-solver with a research-oriented mindset and experience presenting data-driven insights that influence decision-making. Recognized for clarity in communication, curiosity, and the ability to translate complex data into meaningful business outcomes. Highly motivated to contribute to data science teams by developing scalable models and intelligent systems.
Vignan's LARA Institute of Technology and Science
Bachelor of Technology (B. Tech) · Computer Science Engineering
August 1, 2021 – June 30, 2025
Sri Chaitanya Junior College
Intermediate (MPC)
June 1, 2019 – May 31, 2021
Dr. K.L.P Public School
CBSE (10th Class)
June 1, 2018 – May 31, 2019
Hospitality Revenue & Demand Intelligence Dashboard
December 1, 2025 – June 1, 2026
Designed and developed an interactive Hospitality Data Analysis Dashboard using Power BI to track key hotel performance metrics. The analysis of demand patterns and occupancy rates and booking volatility and cancellation cycles used SQL together with Power BI. The system produces multiple analytical dashboards which display booking peak times and location performance weaknesses. The system enables users to create interactive dashboards which display occupancy rates and cancellation statistics and revenue patterns and city-specific performance data. The system performs competitor analysis and demand pattern evaluation to create strategic recommendations which direct business operations. The system provides vital information which helps organizations create improved pricing strategies and boost their property occupancy rates. The system provided vital information which organizations applied to boost their revenue production and operational efficiency.
EV Sales Forecasting & Predictive Intelligence System
December 1, 2025 – June 1, 2026
Analyzed and visualized EV sales trends across Indian states using Python, Pandas, Seaborn, and Matplotlib. Built and deployed Random Forest and Prophet models to predict future EV sales and identify key growth factors. The analysis of EV sales data at the state level revealed patterns and seasonal patterns and growth factors. The system generates 12-month sales forecasts by running its forecasting models which use Prophet and Random Forest algorithms. The Streamlit dashboard enables users to access forecast data and regional performance metrics and trend deviation monitoring. The system generates reports which contain analytical results together with strategic advice for clients. The system showed which markets produced the most sales volume and which markets would drive future business growth.
View ProjectNPTEL-Joy of Computing using Python (Elite Certification in Python Programming)
NPTEL
June 1, 2026 – Present
Unified Mentor Data Analyst course completion certificate
Unified Mentor
June 1, 2026 – Present
NASSCOM Future Skills Prime-Data Analytics certification
NASSCOM Future Skills Prime
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a proactive approach to applying learned skills to real-world scenarios (hospitality revenue, EV sales forecasting). The diversity in project domains (hospitality, automotive) indicates adaptability and a broad interest in data applications. Their certifications further demonstrate a commitment to continuous learning and skill development, aligning with a culture of growth and innovation. The target role of Data Science aligns well with their demonstrated technical skills and project focus.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving, analytical thinking, and communication skills, which are crucial for a Data Science role. Their project descriptions highlight an ability to work with diverse datasets and present findings clearly. The mention of collaboration as a soft skill suggests potential for good teamwork. However, without actual work experience, the operational fit in a professional team setting is yet to be fully validated.