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Ananthnath Erukulla

Data Analyst • India • a*****************@gmail.com • +91*******065 • linkedin.com/••••• • drivetube.ai/•••••

Career Objective

B.Tech Computer Science & Engineering graduate (Class of 2026) specializing in Data Analytics, with hands-on experience in Python, SQL, and Tableau. Skilled in cleaning and assessing data for accuracy, identifying anomalies, and generating actionable, quantified business insights. Comfortable working independently on end-to-end analysis projects and eager to apply problem-solving and analytical skills within a collaborative team environment.

Education

Lovely Professional University
Bachelor of Technology – Computer Science and Engineering • Phagwara, Punjab, India • August 2022 – May 2026
Sri Chaithanya Junior Kalashala
Intermediate in Math, Physics and Chemistry • Hyderabad, Telangana, India • June 2020 – March 2022
Delhi Public School
Matriculation • Hyderabad, Telangana, India • April 2019 – March 2020

Technical Skills

Programming Language: Python,SQL
Version Control & Development Tools: GitHub
Data Analysis & Visualization: Pandas,NumPy,Matplotlib,Tableau,Data Cleaning,Exploratory Data Analysis
Machine Learning & AI: Random Forest,Logistic Regression,Decision Trees,KNN,SVM,Model Evaluation
AI/ML Frameworks & Libraries: scikit-learn,XGBoost
MLOps: Feature Engineering
Project Management & Collaboration: Google Workspace,Microsoft Office

Projects

AgroIntel — Smart Agriculture Platform | November 2025 – April 2026
Tools Used: Python, Pandas, NumPy, Scikit-learn, XGBoost, Data Cleaning, Feature Engineering, Model Evaluation, Random Forest, GitHub
  • Built crop recommendation models using XGBoost and Scikit-learn and selected the top-performing model through cross-validation and hyperparameter tuning
  • Cleaned and preprocessed heterogeneous soil and climate records using Pandas to handle missing values and inconsistent formats
  • Engineered domain features (N, P, K ratios and aggregated rainfall windows) that increased model separability during validation
  • Compared model performance against Random Forest baselines and documented precision/recall trade-offs to guide production selection
  • Validated feature importance to identify top predictors and distilled findings for agronomic interpretation
Tableau Dashboard — Automotive Industry Trends Analysis | June 2025 – July 2025
Tools Used: Tableau, SQL, Excel, Data Visualization, Trend Analysis, Data Cleaning
  • Designed an interactive Tableau dashboard to analyze 50,000+ car sales records and enable drill-down by region, fuel type and transmission
  • Queried and aggregated raw sales data using SQL to produce dealer-level and segment summaries for visualization
  • Identified ₹625M in peak dealer sales using cohort aggregation and highlighted EVs as the highest-value segment for strategic focus
  • Created comparative visualizations of average kilometers and pricing by fuel type to inform inventory and pricing decisions
  • Transformed and normalized disparate sales files in Excel and Pandas prior to loading into Tableau for consistent reporting
Diabetes Prediction Using Machine Learning | June 2024 – July 2024
Tools Used: Python, Pandas, NumPy, Scikit-learn, Logistic Regression, Decision Trees, KNN, Exploratory Data Analysis, Data Cleaning
  • Developed predictive models (Logistic Regression, Decision Trees, KNN) on the Pima Indian Diabetes dataset using Scikit-learn to classify diabetes risk
  • Handled missing values and applied normalization with Pandas and NumPy to prepare features for modeling
  • Conducted EDA to surface three key predictors and used feature selection to improve model precision
  • Tuned model hyperparameters and compared classifiers on precision/recall to reduce false positives
  • Documented preprocessing pipelines and model performance metrics to support reproducible evaluation
Data Science Using Machine Learning — Summer Training (Capstone) | June 2024 – July 2024
Tools Used: Python, Pandas, Matplotlib, Scikit-learn, SVM, Model Evaluation
  • Applied supervised learning algorithms (KNN, Decision Trees, Logistic Regression, SVM) to a business-oriented capstone dataset
  • Processed and visualized datasets using Pandas and Matplotlib to inform feature selection and modeling strategy
  • Built and evaluated classification pipelines in Scikit-learn with cross-validation to measure generalization
  • Implemented model evaluation metrics and produced comparative reports to recommend the best-performing approach
  • Presented capstone findings that mapped technical results to a recommended business action plan

Certifications

Data Analytics Job Simulation — Deloitte | Forage • June 2025
Data Analysis with Tableau — Coursera • November 2024
Data Science and Machine Learning Certification — Allsoft Solutions | IBM • July 2024
Excel Fundamentals for Data Analysis — Coursera • April 2024

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