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DARSI RAVI CHANDU

Machine Learning Engineer • Guntur, AP, India • d**************@gmail.com • +91*******417 • drivetube.ai/•••••

Professional Summary

Machine Learning Engineer with 0 years of experience applying data preprocessing, feature engineering, and supervised learning to build predictive models and analytics solutions for business problems in educational and process-analytics contexts.

Technical Skills

Programming Languages: Python,Java,C,C++,JavaScript
Web Technologies: HTML,CSS
Frameworks and Libraries: Pandas,NumPy,scikit-learn
Databases: SQL,MySQL
Data and Analytics: Data preprocessing,Feature engineering,Regression models,Model evaluation,Cross-validation
Tools and Methodologies: Jupyter Notebook,VS Code,GitHub,Git
Concepts: Exploratory data analysis,Data cleaning,Process mining concepts,Business analytics

Work Experience

Smart Bridge Educational Services
AI & ML Intern
EdTech company — developed AI/ML solutions and analytics to support educational operations and business decision-making.
Tech Stack: Python, Pandas, NumPy, scikit-learn, Jupyter Notebook, GitHub
  • Collected, cleaned, and preprocessed student and operational datasets using Pandas and NumPy to prepare training sets and reduce noise for downstream modeling.
  • Engineered features and trained supervised models (regression and classification) in scikit-learn to address business problems such as student outcome prediction and resource allocation.
  • Implemented cross-validation, hyperparameter tuning, and model evaluation (RMSE, MAE, classification metrics) to select models with better generalization and robustness.
  • Packaged analysis and modeling workflows in Jupyter Notebooks and modular Python scripts; version-controlled code and collaborative changes through GitHub.
  • Translated business questions into ML tasks by collaborating with stakeholders, documented assumptions and modeling decisions, and presented findings with clear visualizations.
  • Applied best practices such as feature scaling, regularization, and pipeline construction to improve model stability and reproducibility across experiments.
Celonis (Virtual Intern)
Process Mining Virtual Intern
Process mining / business-analytics domain — analyzed event logs and workflows to identify inefficiencies and recommend process improvements.
Tech Stack: Celonis, SQL, Python, Jupyter Notebook
  • Performed process discovery and analyzed event logs using Celonis process-mining concepts to map end-to-end workflows and identify bottlenecks.
  • Extracted and preprocessed process datasets using SQL and Python to create clean inputs for process analytics and KPI computation.
  • Calculated and tracked key process metrics and variants; visualized process performance and deviations to support root-cause analysis.
  • Built Celonis dashboards and visual summaries to demonstrate workflow inefficiencies and proposed targeted process changes.
  • Documented process-mining methodology, findings, and recommended actions; produced reproducible notebooks and supporting SQL queries.
  • Collaborated with peers on case studies to simulate process optimization scenarios and translate analytics into actionable operational recommendations.
Information Technology

Projects

Care Path AI – Predictive Patient Journey & Automatic Doctor Allocation Platform
Tools Used: Python, Pandas, scikit-learn, Feature engineering, Regression models
  • Developed a predictive patient-journey model to forecast patient care pathways using regression techniques and structured healthcare data.
  • Implemented automatic doctor-allocation logic based on model outputs and engineered features to improve decision-making and resource utilization.
  • Conducted data preprocessing, feature selection, model training, and evaluation; documented model assumptions and performance for stakeholder review.
Advanced Traffic Volume Estimation
Tools Used: Python, Pandas, NumPy, scikit-learn, Model evaluation
  • Built a traffic-volume prediction system using historical traffic data, applying feature engineering to capture temporal patterns and trends.
  • Trained and compared regression models using scikit-learn with cross-validation; evaluated models using standard regression metrics to guide selection.
  • Prepared reproducible analysis in Jupyter Notebooks and used GitHub to track iterations and document model development steps.

Education

Kallam Haranadhareddy Institute of Technology
Bachelor of Technology - Information Technology • Guntur, AP, India • 2020 – 2026
Intermediate - MPC
Intermediate • 2020 – 2022
Secondary School - 10th
Secondary School • 2020

Certifications

Web Development — Internshala
Certified System Administrator (CSA) — ServiceNow
Enhancing Soft Skills and Personality — NPTEL
Software Testing — NPTEL
Microcontroller and Microprocessor — NPTEL

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