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hridya sudhakaran

Mathematics postgraduate · Data Science trainee

Kozhikode, Kerala, India

@hridya_sudhakaran

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About

Mathematics postgraduate and Data Science trainee with hands-on experience in Python, SQL, data analysis, statistical reasoning, and machine learning. Contributed to AI and predictive modeling projects involving data cleaning, preprocessing, validation, reporting, and automation.

Experience

  • Data Science Intern
    Curvelogics Advanced Technology Solutions Pvt. Ltd.
    Jul 2026 – Present

Education

  • Advanced AI & Applied Data Science with Python
    Data Science Academy
    Data Science · 2025 – 2026
  • M.Sc. Mathematics
    Pondicherry University
    Mathematics · 2023 – 2025
  • B.Sc. Mathematics
    Maharaja's College
    Mathematics · 2020 – 2023

Skills

<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
SQL NumPy Excel Python Pandas FastAPI PyTorch Seaborn XGBoost Power BI Matplotlib TensorFlow scikit-learn Data Cleaning Google Gemini Data validation Data Preprocessing Prompt Engineering Agentic AI Systems Feature Engineering Statistical Analysis API Integration Understanding Generative AI (ChatGPT, Copilot, Gemini)

Tools / apps / platforms

<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
Git GitHub Jupyter Notebook

Projects

  • Used Car Price Prediction
    Python, Pandas, Scikit-learn, XGBoost, Matplotlib

    Developed a regression workflow using 15K+ used-car listings with data inspection, cleaning, exploratory analysis, feature preprocessing, and model comparison. Compared Linear Regression, Ridge, Random Forest, and XGBoost, with XGBoost achieving R² = 0.891 and MAE ≈ ₹66K.

  • Customer Churn Prediction
    Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

    Built an end-to-end classification workflow covering data cleaning, preprocessing, categorical encoding, exploratory analysis, feature preparation, and model evaluation. Compared classification performance using accuracy, precision, recall, and F1-score, achieving 79% accuracy.

Courses & certifications

  • Advanced AI & Applied Data Science with Python · Data Science Academy · 2026

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