Tripti Kharole

Tripti Kharole

Data Scientist, AI Engineer, Full Stack Developer

Data Science and Artificial Intelligence professional

Personal Details
Born	13 Sep 2004 Indore
Nationality	Indian
Career & Education
Occupation	Data Scientist, AI Engineer, Full Stack Developer
Education	B.Tech in Information Technology, Shri G.S. Institute of Technology & Science, Indore; Diploma in Computer Science Engineering, Government Polytechnic College, Balaghat
Recognition & Links
Website	Official Website

Tripti Kharole is an Indian technology professional and Information Technology graduate whose work focuses on data science, machine learning, artificial intelligence, and full-stack application development. Her technical work combines machine learning models with web applications and application programming interfaces, with interests spanning computer vision, natural language processing, predictive analytics, and AI-powered software systems.

Overview

Kharole's technical background includes Python, JavaScript, SQL, TensorFlow, Scikit-learn, Pandas, NumPy, React.js, FastAPI, Flask, Node.js, MySQL, and MongoDB. Her project work has focused on applying artificial intelligence and machine learning to practical problems, including disease detection, logistics prediction, route intelligence, and agricultural assistance.

Rather than focusing exclusively on machine learning model development, her projects commonly combine trained models with backend services, APIs, databases, and user-facing web applications. This approach has resulted in several full-stack AI prototypes involving machine learning, deep learning, computer vision, and predictive analytics.

Education

Tripti Kharole studied for a Diploma in Computer Science Engineering at Government Polytechnic College, Balaghat, from 2020 to 2023, graduating with a CGPA of 8.4.

She subsequently pursued a Bachelor of Technology in Information Technology at Shri G.S. Institute of Technology & Science, Indore, from 2023 to 2026, completing the program with a CGPA of 7.0.

Her relevant academic coursework includes machine learning, data structures and algorithms, database systems, and statistics. Her education has provided a foundation in software development, data analysis, database technologies, and artificial intelligence.

Professional career

From December 2025 to February 2026, Kharole worked as a Full Stack Web Developer Intern at Digital One Box in Balaghat.

During her internship, she developed and deployed responsive full-stack web applications using React.js, Node.js, and REST APIs. Her responsibilities included integrating JWT authentication, implementing database operations, and developing automated data-loading pipelines.

She also worked on CI/CD deployment workflows and communicated technical performance metrics to non-technical stakeholders. According to her professional profile, these development and workflow improvements contributed to reductions in release-cycle time, manual data-entry effort, and deployment turnaround time.

Artificial intelligence and machine learning

Kharole's project portfolio includes applications involving deep learning, computer vision, predictive analytics, and natural language processing. Her work demonstrates an emphasis on connecting machine learning models with practical software applications.

LumpiScan

LumpiScan is a deep learning-based disease detection system developed using Python, TensorFlow, convolutional neural networks, Flask, and React.js.

The system uses MobileNetV2 for image classification and achieved approximately 90–95% classification accuracy on a held-out test set. The project implements a two-stage artificial intelligence pipeline in which cow validation is performed before disease prediction.

The system was tested using more than 10,800 labelled images and incorporated location-based veterinary recommendations. Kharole also implemented JWT authentication and multilingual support as part of the application.

LogisAI

LogisAI is an AI-based logistics platform designed for delivery prediction, predictive analytics, and route intelligence. It was developed using Python, Scikit-learn, FastAPI, and React.js.

The system uses traffic and weather-related information for route and delivery analysis. Kharole trained two Random Forest models using 5,000 records to predict delivery time and delivery-delay risk across eight simulated metropolitan zones.

The project also includes a responsive analytics dashboard with role-based access. Route intelligence was implemented using external services and APIs including Open-Meteo, OSRM, Nominatim, and Carto.

AgriSmart

AgriSmart is an intelligent agriculture assistance platform combining machine learning, computer vision, natural language processing, and web application technologies.

The platform uses TensorFlow-based convolutional neural network models and achieved a reported accuracy of 96% across 38 plant disease classes. It provides a visual workflow for agricultural assistance and incorporates automated weather-based insights.

AgriSmart also includes a multilingual AI voice assistant supporting 10 languages through speech recognition and text-to-speech technologies. The project illustrates Kharole's interest in applying artificial intelligence to agricultural applications and accessible technology.

Technical expertise

Kharole's programming languages include Python, JavaScript, and SQL. Her data science and machine learning technologies include TensorFlow, Scikit-learn, Pandas, NumPy, convolutional neural networks, natural language processing, computer vision, deep learning, and MobileNetV2.

Her data analysis experience includes exploratory data analysis, data cleaning, feature engineering, model evaluation, precision and recall analysis, and data visualization.

For backend and application development, she has worked with Flask, FastAPI, Node.js, and REST APIs. Her database technologies include MySQL and MongoDB. Her development toolkit includes Git, GitHub, Postman, Jupyter Notebook, and Google Colab.

Her technical concepts include machine learning, data analysis, data visualization, feature engineering, model deployment, object-oriented programming, and database management systems.

Achievements

Kharole has independently designed and deployed three AI and machine learning full-stack prototypes combining MERN-stack technologies, deep learning models, and REST APIs.

Her computer vision work has involved training models using more than 10,800 images, with reported validation accuracy reaching up to 96% across disease-detection tasks.

She has also participated in national-level artificial intelligence and software development hackathons, presenting software and AI-based solutions under time constraints.

Areas of interest

Kharole's professional interests include Data Science, Machine Learning, and AI Engineering. Her project work covers practical applications of artificial intelligence in areas including agriculture, animal health, logistics, predictive analytics, and full-stack software development.

Her combination of machine learning, backend development, frontend development, databases, and API integration enables her to work across multiple stages of an AI application, from model development and evaluation to deployment and user-facing implementation.

See also

• Data science
• Machine learning
• Artificial intelligence
• Computer vision
• Deep learning
• Natural language processing

External links

• Tripti Kharole on LinkedIn