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Data Analyst with less than a year in Python, SQL & Power BI
B.Tech Computer Science graduate (2024) with hands-on experience in data analysis, predictive modeling, and data visualization. Proficient in Python (Pandas, NumPy, Scikit-learn), SQL, and Power BI for end-to-end data workflows — from data cleaning and feature engineering to insight generation and reporting. Built and evaluated machine learning models on real-world agricultural datasets, achieving measurable performance improvements through comparative algorithm analysis. Certified in Power BI (Simplilearn) and SQL (HackerRank) with AWS cloud exposure. Eager to leverage analytical skills to deliver data-driven business insights.
ISTS Women's Engineering College
B.Tech · Computer Science & Engineering
January 1, 2020 – January 1, 2024
Akshara Junior College, Jaggampeta
Intermediate · MPC (Mathematics, Physics, Chemistry)
January 1, 2018 – January 1, 2020
Z.P.P High School, Kamarjupeta
SSC · Secondary School Certificate
January 1, 2017 – January 1, 2018
XPLORE
Cloud Computing Intern
January 1, 2023 – December 31, 2023
India
Multicriteria Agriculture Recommendation System
January 1, 2023 – January 1, 2024
Designed and developed a crop yield prediction system applying Decision Tree, Random Forest, and Logistic Regression algorithms to multi-variable agricultural datasets Processed and cleaned structured datasets containing 10+ environmental and soil parameters, ensuring data integrity and removing inconsistencies prior to model training Engineered features through selective preprocessing and transformation, optimizing model inputs to improve prediction accuracy Benchmarked three ML algorithms through comparative performance evaluation, identifying the highest-accuracy model for crop yield prediction Delivered interpretable crop yield predictions by translating model outputs into actionable agricultural insights
Power BI Certification
Simplilearn
January 1, 2023 – Present
SQL (Basic) Certification
HackerRank
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's academic project on agriculture recommendation system shows an interest in applying data science to real-world problems. The internship in cloud computing indicates a willingness to explore diverse technical areas. The listed soft skills suggest a self-motivated and analytical individual. The project and internship, while academic/entry-level, show a breadth of exposure relevant to a data-driven environment. However, the overall experience is limited to one academic project and one internship, which restricts a deeper assessment of cultural fit in a professional team setting.
Soft Skills & Operational Fit
The candidate demonstrates strong academic performance and analytical aptitude, as evidenced by high GPAs and being a school topper. Soft skills like data storytelling, analytical thinking, time management, self-motivation, and being a fast learner are listed, which are beneficial for operational fit. However, these are self-reported and not validated by specific assessment data.