About Me

Hi, I’m Arpita Rout, a final-year Computer Science and Engineering student with a deep interest in Machine Learning, Artificial Intelligence, and using technology to solve real-world problems.

I’m passionate about building intelligent systems, whether it’s training models, working with data, or exploring how machines can learn, adapt, and improve over time. I enjoy the full process: from understanding a problem and preprocessing data, to experimenting with different models and fine-tuning them to get meaningful results.

I’ve worked with popular ML frameworks and tools, and I’m always looking for ways to apply them creatively. I believe in continuous learning, clean code, and building solutions that are not only functional but thoughtful and impactful.

When I’m not coding or exploring new AI models, you’ll usually find me designing little creative ideas, or going on a refreshing jog to clear my mind. I enjoy learning things outside tech too, like psychology or just watching a good suspense-thriler series. I believe a curious mind needs balance, and these moments help me recharge and stay inspired.

Tech Stack

Data Science & ML

NumPy

Pandas

Matplotlib

Seaborn

TensorFlow

Keras

PyTorch

Scikit-learn

Frontend

HTML

CSS

JavaScript

Bootstrap

Programming Languages

Python

C/C++

Java

Tools & Platforms

VS Code

Git

GitHub

Jupyter Notebook

Google Colab

Streamlit

Power BI

Databases & Shell

MySQL

SQL

Linux Shell

Amazon Review Sentiment Analysis

This project analyzes Amazon product reviews to detect sentiment using NLP and a fine-tuned BERT model. Built with Python, Hugging Face Transformers, and Pandas, it helped me understand pre-trained language models, tokenization, and sentiment classification. I also learned to evaluate NLP pipelines using confusion matrices.

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Plant Disease Detection

I built a CNN-based model to classify plant leaf images into 38 disease categories, aimed at supporting early diagnosis in agriculture. Using TensorFlow/Keras and a Kaggle dataset, I learned image preprocessing, model evaluation, and techniques to improve accuracy and reduce overfitting for real-world reliability. It strengthened my understanding of deep learning workflows.

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Breast Cancer Prediction (Know it Early)

This Streamlit app predicts breast cancer likelihood using a logistic regression model. Built with Python, Scikit-learn, and Pandas, it helped me understand end-to-end ML pipelines, from preprocessing to deployment, and taught me how to balance data and interpret metrics.

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Credit Card Fraud Detection

This model detects fraudulent credit card transactions using machine learning classifiers like SVM and Random Forest. Built with Scikit-learn and Python, it taught me about class imbalance, precision-recall trade-offs, and the practical role of AI in financial fraud prevention. It also improved my skills in evaluating models.

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Sales Performance Dashboard

This interactive Power BI dashboard visualizes sales trends, regional performance, and top products. It helped me understand data modeling, DAX, and visual storytelling, while learning to clean data, define KPIs, and use dynamic filters for business insights. It also enhanced my ability to communicate data-driven strategies clearly and effectively.

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Library Management System

A full-stack library management system built with Python and MySQL to handle books, members, and transactions. This early backend project taught me CRUD operations, schema design, user authentication, and integrating front-end logic with persistent storage. It gave me a strong foundation in building scalable backend systems.

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About Me

Tech Stack

Data Science & ML

Frontend

Programming Languages

Tools & Platforms

Databases & Shell

A Glimpse of My Work

Amazon Review Sentiment Analysis

Plant Disease Detection

Breast Cancer Prediction (Know it Early)

Credit Card Fraud Detection

Sales Performance Dashboard

Library Management System

Connect with Me