Kanishk Raj

I'm Kanishk Raj, a driven and self-directed professional with 2 years of experience, currently expanding my global perspective as a Global Immersion Program Participant at The University of Tulsa, USA. I hold myself and my work to a high standard, balancing bold ambition with a grounded, pragmatic approach that gets things done — while remaining genuinely open and approachable to the people and ideas around me. Whether navigating new environments or taking ownership of challenging goals, I thrive on leading with accountability and the confidence to act with purpose.

Experience

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Global Immersion Program Participant

The University of Tulsa, USA · Oct 2025 – Nov 2025

Not yet confirmed

• Collaborated in cross-cultural teams and engaged with international faculty, strengthening communication and leadership skills.
• Participated in workshops, classroom observations, and group activities focused on problem-solving and global leadership.
• Gained international exposure through professional interactions, discussions, and reflective learning activities.

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AI-ML Intern

National Institute of Technology, Jamshedpur · Jun 2024 – Jul 2024

Not yet confirmed

• Worked on the Sonar Mines vs Rocks dataset using Logistic Regression, Decision Tree, LDA, Gradient Boosting, and Random Forest.
• Evaluated models using accuracy, precision, recall, and ROC-AUC.
• Applied feature engineering, preprocessing, hyperparameter tuning, and cross-validation to improve model performance.
• Gained hands-on experience with Scikit-learn, Pandas, NumPy, and model evaluation techniques.
• Completed Machine Learning Mastery with Python by Jason Brownlee.

IoT Intern

Pantechelearning · Apr 2024 – Jul 2024

Not yet confirmed

• Responsibilities:
• • Participated in the IoT Masterclass, organized by APSSDC in association with Pantech e Learning Pvt Ltd.
• • Engaged primarily in the Python for Machine Learning course component of the program.
• Key Achievements:
• • Successfully completed the one-month IoT Masterclass internship program with a focus on Python for Machine Learning.
• • Applied machine learning techniques using Python in various IoT-related projects.
• • Developed and evaluated machine learning models using Python libraries such as scikit-learn and pandas.
• • Gained insights into integrating machine learning models with IoT systems for data analysis and decision-making.
• Learning and Development:
• • Acquired practical knowledge in applying machine learning to IoT data.
• • Enhanced programming skills in Python, particularly for machine learning applications.
• • Learned best practices for data preprocessing, model training, and performance evaluation.

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Machine Learning Intern

National Institute of Technology Jamshedpur · May 2024 – Jun 2024

Not yet confirmed

• Responsibilities:
• • Engaged in a machine learning project using the Sonar Mines vs Rocks dataset.
• • Applied various classification algorithms including logistic regression, decision tree classifier, linear discriminant analysis, and ensemble methods such as gradient boosting and random forest classifier.
• • Analyzed model performance using metrics like accuracy, precision, recall, and ROC-AUC.
• • Conducted feature engineering and data preprocessing to enhance model accuracy.
• Key Achievements:
• • Successfully developed and evaluated multiple machine learning models for binary classification tasks.
• • Improved model performance by fine-tuning hyperparameters and employing cross-validation techniques.
• • Gained hands-on experience with Python libraries such as scikit-learn, pandas, and numpy.
• Learning and Development:
• • Completed “Machine Learning Mastery with Python” by Jason Brownlee, enhancing practical knowledge in machine learning and Python programming.
• • Learned key machine learning concepts including data preparation, model evaluation, and algorithm selection.
• • Implemented various machine learning algorithms from scratch and using scikit-learn.
• • Developed a strong foundation in advanced techniques such as ensemble methods and hyperparameter tuning.
• • Gained insights into best practices for model deployment and performance optimization.