CHARUNLAL V

[email protected] • +91-6384210606 • Tirupattur, Tamil Nadu, India
LinkedIn: linkedin.com/in/charunlal-venkatesan • GitHub: github.com/charunlal

PROFESSIONAL SUMMARY

Computer Science undergraduate (CGPA 7.90/10) with hands-on experience across three internships in full-stack web development and data analytics. Proficient in Python, Flask, JavaScript, MySQL, and data analysis libraries (Pandas, NumPy, Matplotlib, Seaborn). Built and deployed real-time dashboards and predictive models that converted raw datasets into actionable, decision-ready insights. Strong record of fast learning, clean code, and on-time delivery in agile, deadline-driven environments.

WORK EXPERIENCE

Web Development Intern
05/2026 - 07/2026
Diyan Tech Solutions
• Designed and developed [X]+ full-stack web application modules using Flask and JavaScript, covering both front-end UI and back-end business logic.
• Integrated MySQL databases to support real-time data updates, improving data refresh accuracy across live project modules.
• Collaborated with a cross-functional team of [X] members through the full software development lifecycle, from requirements gathering to deployment.

EDUCATION

B.E., Computer Science and Engineering
01/2023 - 01/2027
P.S.V. College of Engineering and Technology, Krishnagiri, Tamil Nadu GPA: 7.90 / 10
HSC (Class 12)
Linganna Mani Matric. Higher Secondary School, Tiruppattur GPA: 75%
SSLC (Class 10)
Linganna Mani Matric. Higher Secondary School, Tiruppattur GPA: 84%

SKILLS

PROJECTS

Student Performance Analysis — Web Dashboard
Technologies: Python, Flask, MySQL
• Built a real-time dashboard tracking [X] students across [Y] performance metrics, with interactive charts for instructors to monitor academic progress.
• Designed a normalized MySQL schema to store and serve live student data efficiently to the Flask front end, cutting query response time.
Healthcare Readmission Analysis
Technologies: Python, Pandas, NumPy, Matplotlib
• Analyzed a healthcare dataset of [X]+ patient records to identify key factors driving readmissions.
• Built a predictive model achieving [Y]% accuracy using machine learning techniques, generating actionable clinical insights for care teams.

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