Open to opportunities

Ayush Parwal

@ayushparwal

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Aspiring data scientist with expertise in machine learning and AI.

India

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What I'm looking for

I am looking for a dynamic role that fosters innovation and growth in AI and machine learning.

I am currently pursuing a BTech in Computer Science with a focus on Artificial Intelligence and Machine Learning at the Indian Institute of Information Technology, Nagpur. My academic journey has been marked by a strong GPA of 7.73 and a deep engagement with relevant coursework, including Machine Learning, Computer Vision, and Deep Learning. I thrive on challenges and have participated in various competitions, where I have honed my skills in developing innovative solutions.

One of my notable achievements includes developing an XGBoost-based machine learning model for predicting agricultural crop yields, which achieved an impressive accuracy of 90.41%. Additionally, I secured Rank 1 in a Kaggle competition focused on academic success classification, showcasing my ability to leverage advanced machine learning techniques. My projects, such as Speech Emotion Detection and Plant Disease Classification, reflect my commitment to applying deep learning in real-world scenarios, enhancing usability and accessibility through interactive web interfaces.

Experience

Work history, roles, and key accomplishments

Machine Learning Engineer

Innovative AI Challenge

Developed an XGBoost-based machine learning model for predicting agricultural crop yields with an accuracy of 90.41%, leveraging 5-fold cross-validation and Optuna for hyperparameter tuning. This scalable, data-driven solution aimed to boost agricultural productivity, empowering farmers and policymakers with actionable insights.

XGBoost Machine Learning Optuna Hyperparameter Tuning Data Analysis Cross Validation

Data Scientist

Kaggle

Achieved Rank 1 in Kaggle’s “Classification with an Academic Success Dataset” competition with strong Private and Public LB scores. Utilized XGBoost, CatBoost, and LightGBM models, optimized with Optuna, and combined using ensemble learning for optimal performance on Kaggle GPU.

XGBoost CatBoost LightGBM Optuna Data Classification Machine Learning Kaggle

Deep Learning Engineer

Self-Employed

Designed and implemented a deep learning model using LSTM, achieving an impressive 86.48% accuracy in detecting and classifying speech emotions from audio inputs. Built an interactive web interface with Streamlit, allowing real-time testing of audio files with SpeechRecognition for transcription and analysis.

Python Streamlit Speech Recognition Deep Learning

Full Stack AI Developer

Self-Employed

Developed an interactive interface using TypeScript and CSS for styling, providing a seamless and creative user experience. Utilized the Meta LLaMA model through the Groq API to analyze and generate insights about an Ayurveda dataset, enabling natural language understanding and interaction.

GROQ Vite TypeScript NLP CSS Vercel API Integration Natural Language Understanding

Computer Vision Engineer

Self-Employed

Utilized pretrained models like ResNet and Inception, achieving 84.61% accuracy in plant disease classification. Designed a dynamic and user-friendly web interface using Streamlit for instant disease classification from uploaded leaf images, recognizing 38 classes.

ResNet Inception Streamlit Image Processing OpenCV Plant Disease Classification

Education

Degrees, certifications, and relevant coursework

Indian Institute of Information Technology, Nagpur

Bachelor of Technology, Computer Science with Artificial Intelligence and Machine Learning

Grade: 7.73 GPA

Activities and societies: Actively participated in hackathons and Kaggle competitions, achieving Rank 1 in a classification competition. Developed several AI/ML-focused projects including Speech Emotion Detection and Plant Disease Classification. Earned NVIDIA certifications in AI for Anomaly Detection and Building Transformers-Based NLP Applications.

Pursuing a Bachelor of Technology in Computer Science with a specialization in Artificial Intelligence and Machine Learning. Engaged with coursework covering Machine Learning, Deep Learning, Large Language Models, and Data Structures and Algorithms. Developed a strong foundation in core computer science principles and advanced AI/ML techniques.