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Siva Dandem

Data Analyst • Bengaluru • s**********@gmail.com • +91*******747 • linkedin.com/••••• • github.com/•••••

Career Objective

Aspiring Data Analyst seeking an opportunity to apply my skills in Python, SQL, Excel, Power BI, and data analytics to solve business problems through data-driven insights. Eager to contribute to AI-assisted analytics while continuously learning and growing in a collaborative environment.

Education

Lovely Professional University
Bachelor of Technology in Computer Science and Engineering • Punjab, India • 2019 – 2023

Technical Skills

Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Matplotlib,Seaborn,Scikit-learn,NLTK,XGBoost
Databases: MySQL,SQLite
Data Analytics: Exploratory Data Analysis,Data Cleaning,Data Validation,Feature Engineering,ETL,Statistical Analysis,Business Intelligence
Machine Learning: Supervised Learning,Logistic Regression,Random Forest,XGBoost,Classification,Model Evaluation
AI & NLP: LLM Response Evaluation,Prompt Engineering,Natural Language Processing (NLP),Text Preprocessing,Tokenization,Lemmatization,TF-IDF,Topic Modeling (LDA)
Visualization: Power BI,Microsoft Excel,Matplotlib

Projects

AI-Powered Manufacturing Quality Analytics | Jun 2026 – Jul 2026
Tools Used: Python, SQL, SQLite, Pandas, NumPy, Matplotlib, Power BI, Scikit-learn, XGBoost, NLTK
  • Developed an end-to-end manufacturing quality analytics solution by analyzing 271,000+ NHTSA vehicle complaints using Python, SQL, and Power BI.
  • Cleaned, validated, and transformed large manufacturing datasets using Python, Pandas, SQL, and feature engineering techniques to support analytics and modeling.
  • Applied NLP techniques including tokenization, lemmatization, TF-IDF, bigram analysis, and topic modeling to identify recurring quality issues from customer complaints.
  • Built Logistic Regression, Random Forest, and XGBoost models to predict complaint severity, with XGBoost achieving 96.13% classification accuracy.
  • Designed an interactive Power BI dashboard to monitor complaint trends, manufacturer performance, component failures, and predictive quality insights.
Customer Shopping Behavior Analysis | Apr 2026 – May 2026
Tools Used: Python, SQL Server, Power BI, Pandas, NumPy, Matplotlib
  • Analyzed 3,900+ customer transaction records using Python, SQL Server, and Power BI to identify purchasing patterns and customer behavior.
  • Performed data cleaning, duplicate removal, feature engineering, and data validation to improve data quality for business analysis.
  • Developed SQL queries to analyze customer segmentation, product performance, and revenue trends for business decision-making.
  • Built interactive Power BI dashboards visualizing KPIs, customer segments, sales performance, and revenue insights.
  • Presented analytical findings through dashboards and reports to support data-driven business decisions and performance monitoring.

Certifications

Complete Data Analyst Bootcamp From Basics To Advanced — Udemy • May 2026
The Complete Python Course — Udemy • Jul 2023

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