Jaichandu

AI & ML undergraduate

Chennai, India 0+ yrs exp 81 · Excellent

About

AI & ML undergraduate at Saveetha School of Engineering (SIMATS) with hands-on internship experience building and evaluating machine learning models. Proficient in Python, Scikit-learn, Pandas, NumPy, and SQL. Skilled at end-to-end ML workflows: data collection, preprocessing, feature engineering, model training, and evaluation. Passionate about applying deep learning and NLP to real-world problems, with demonstrated ability to collaborate in cross-functional teams.

Skills & Expertise (27)

Python Advanced

8.1/10

0.5

Years Exp

scikit-learn Advanced

8.1/10

0.5

Years Exp

Pandas Advanced

8.1/10

0.5

Years Exp

NumPy Advanced

8.1/10

0.5

Years Exp

Jupyter Notebook Advanced

8.1/10

0.5

Years Exp

Hyperparameter Tuning Advanced

8.1/10

0.5

Years Exp

Supervised Learning Advanced

8.1/10

0.5

Years Exp

Model Evaluation Advanced

8.1/10

0.5

Years Exp

Feature Engineering Advanced

8.1/10

0.5

Years Exp

SQL Intermediate

7.4/10

0.5

Years Exp

Google Colab Intermediate

7.4/10

0.5

Years Exp

VS Code Intermediate

7.4/10

0.5

Years Exp

GitHub Intermediate

7.4/10

0.5

Years Exp

Git Intermediate

7.4/10

0.5

Years Exp

Unsupervised Learning Intermediate

7.0/10

0.5

Years Exp

Data Visualization Intermediate

7.0/10

0.5

Years Exp

Matplotlib Intermediate

7.0/10

0.5

Years Exp

NLP Intermediate

6.5/10

NLTK Intermediate

6.5/10

Keras Intermediate

6.5/10

TensorFlow Intermediate

6.5/10

MySql Intermediate

6.5/10

0.5

Years Exp

Seaborn Intermediate

6.0/10

0.5

Years Exp

JavaScript Beginner

4.5/10

C++ Beginner

4.5/10

HTML5 Beginner

4.5/10

CSS3 Beginner

4.5/10

Work Experience

Machine Learning Engineer Intern

Learn Flu

Aug 2025 - Nov 2025

Built and evaluated 4+ ML classification and regression models using Scikit-learn and Python, achieving an average model accuracy of 87% across projects. Collected, cleaned, and pre-processed 3 structured datasets (50K+ records each) using Pandas and NumPy, reducing data inconsistencies by ~30%. Applied feature engineering techniques (encoding, scaling, dimensionality reduction) to improve model performance by 12% on validation sets. Implemented end-to-end ML pipelines in Jupyter Notebook, from EDA and preprocessing through model training, evaluation, and performance reporting. Collaborated with a 5-member cross-functional team to translate data insights into actionable outcomes, presenting findings in weekly team reviews. Tracked all code and experiments using Git and GitHub, maintaining version control across the full internship project lifecycle.