Graduate Engineer Trainee
Tata Technologies
January 2025 - Present
Copyright © Adi
About Me
Age: 22
Place: Bengaluru, Karnataka
Phone: 8072050055
Email: adityajai243@gmail.com
My Thoughts
I’m not particularly eager to define myself by my work. I define myself by the work I want to do. Skills can be taught, but personality is inherent. I prefer to keep learning, continue challenging myself, and do interesting things that matter. I'm Always curious, humble, and courageous.
My Goal
My abundant energy and boundless enthusiasm fuels me in the pursuit of many interests, hobbies, areas of study. I’m a fast learner, able to pick up new skills and juggle different projects and roles with relative ease. I like to work on the public issues which can be solved using technology.
What I’m good at?
Technical Skills
Python, HTML, CSS, JavaScript, React, Node, C/C++, Machine learning, Kotlin, DSA, MySQL, PHP
Familiar With
Java, Express, Firebase
Soft Skills
I prioritize efficient time management, collaboration, curiosity, adaptability, and strategic thinking. These qualities ensure effective problem-solving and sustained success in all endeavors.
Tools known
VS Code, Android Studio, Pycharm, Jupyter Notebook, MongoDB Atlas, Git, Github, Postman, Insomnia
Things I Love
Learning new things, Travelling, Solving sudoku, Spending time outdoors, Cooking and Trying out new Food
My Work
QuickChat
Developed QuickChat, a real-time chat application using React.js and Firebase. Implemented user authentication, message synchronization, and database management with Firebase Firestore. Ensured a responsive design for seamless user experience across devices, showcasing expertise in modern web development and real-time data handling.
Driver Drowsiness Detection
This project utilizes deep learning and Haar cascade files for driver drowsiness detection. Analyzing real-time facial expressions and eye movements, it alerts drivers to signs of drowsiness, enhancing road safety by issuing timely warnings and reducing the risk of accidents caused by fatigue.
Stock market forecasting
Implemented a time series forecasting model for stock prices using a stacked Long Short-Term Memory (LSTM) network. The project involved data collection, preprocessing, and feature engineering, followed by the design and training of the LSTM model. Achieved accurate predictions of stock market trends, showcasing the model's capability to capture temporal dependencies.
Youtube Video Downloader
Developed an android application that helps to download youtube videos from youtube by just providing the URL of the video/shorts, used pytube, python kivy to create the application and used bulidozer to convert it to an apk.
Soil Type Classification
Developed a deep learning model for soil type classification using the AlexNet architecture. The project involved data preprocessing, model training, and evaluation on a dataset of soil images.
Diabetes Prediction
Developed a machine learning model to predict diabetes based on patient data. The project involved data preprocessing, feature selection, and model training using algorithms like Logistic Regression and Random Forest. Achieved high accuracy and provided insights into key risk factors. Used Flask for implementation.
Sepsis Disease Prediction
Developed a machine learning model to predict sepsis disease based on patient data. The project involved data preprocessing, feature selection, and model training using algorithm Random Forest.
Customer Segmentation
Conducted a customer segmentation project using R, employing K-means clustering to group customers based on characteristics like gender, age, annual income, and spending patterns. The project involved data preprocessing, cluster analysis, and visualization to identify distinct customer segments.My Experience
Data Science Virtual Intern
LetsGrowMore
April 2023 - May 2023
Google Cloud
Participant | Google CloudReady Facilitator Program
April 2022 - July 2022
Teacher
Invent Institute of Mathematics & Science
April 2020 - May 2023