• Aspiring Data Scientist 📊 | MSc. Physics 🌌
• Email: shirsh10malll@gmail.com , shirshmall10work@gmail.com | LinkedIn Profile: www.linkedin.com/in/shirsh10mall

• Master of Science (MSc) in Physics, Indian Institute of Technology (IIT Delhi) 🏛️, 2021-2023
• Bachelor of Science (BSc) in Physics Honours, Dyal Singh College, Delhi University 🏫, 2018-2021

1. Programming Languages: Python 🐍 • Elementary C++
2. Database: MySQL.
3. Machine Learning 📚: Supervised Learning • Unsupervised Learning • Model Deployment 🚀 • Model Optimization ⚙️ • Hyperparameter Tuning • Model Interpretability (SHAP) 🧐 • AutoML (H2O) 🤖 • Scikit-Learn • Classical Algorithms • Tree-Based Algorithms 🌲 • Ensemble-based Algorithms.
4. Natural Language Processing (NLP) 📝: Recurrent Neural Network (RNN) • Hugging Face Transformers 🤗 • Text Classification • Text Generation • Text Summarization 📑 • Topic Modeling • Machine Translation 🌍 • Question Answering 💬 • NLP Data Preprocessing 🧹 • Generative AI with LLMs • Image-Text Multi-models 🖼️📝 • Semantic Search 🔍 • Vector Database • Named Entity Extraction.
5. Computer Vision 🖼️: Image Processing • Convolutional Neural Networks (CNN) 🧠📷 • Image Segmentation • Object Detection 🕵️ • Image Classification • Transfer Learning 🔄 • Feature Extraction • Image Semantic Search 🔍🖼️.
6. Model Deployment Tools 🌐: Streamlit • Flask • FastAPI 🚀 • Gradio 📡 • Heroku • Docker Containerization 🐳 • Elementary HTML and CSS 🎨.
7. Additional Skills : Time Series Forecasting ⏳📈• Web Scraping (BeautifulSoup, Selenium, Requests) 🕸️ • Linux.
8. Deep Learning Tools/Framework : NumPy • Pandas 🐼 • Scikit-Learn • TensorFlow • PyTorch • Keras • Hugging Face Transformers 🤗 • NLTK • SpaCy • Gensim • Word2Vec • GloVe .
9. Data Analysis and Visualization 📊: Data Wrangling 🧹 • Tableau • Plotly • Seaborn • Matplotlib .

Dedicated to securing a data science role where I can apply my skills and contribute to data-driven decision-making.

1. Initial Years: Seeking to master data science skills, achieve continuous learning, and gain hands-on experience through collaborative projects.
2. Future Aspiration: Aspiring to secure a Data Science Management position, leveraging my technical expertise and leadership skills.

Team Player 🤝 | Collaborative 🤝

I transitioned from a background in Physics to Data Science due to my strong affinity for computational skills, coding, and numerical analysis. Encouraged by professors' endorsements for my computational abilities during graduation and master's academic years, I discovered my passion for data science and machine learning, ultimately leading me to pursue a career in this dynamic field.

I have diligently undertaken a range of data science courses and successfully executed diverse projects. My journey began with foundational linear regression and simple machine learning projects, eventually progressing to creating interactive dashboards, advanced machine learning and deep learning endeavours, and foraying into intricate realms such as Computer Vision and advanced Natural Language Processing.

1. Road Traffic Accidents Severity Prediction

   • Objective: Develop a machine learning model to predict the severity of road traffic accidents using multi-class classification algorithms.
   • Link: https://github.com/shirsh10mall/Road-Traffic-Severity-Classification

2. Patient Survival Prediction

   • Objective: Develop a predictive model for patient survival using medical data to aid urgent healthcare decisions.
   • Link: https://github.com/shirsh10mall/Patient-Survival-Prediction

3. Site Energy Intensity Prediction

• Objective: Develop a machine learning model, primarily using CatBoost, to predict Site Energy Usage Intensity (Site EUI) for buildings, aiming to reduce energy consumption.
• Link: https://github.com/shirsh10mall/Site_Energy_Intensity_Prediction

4. Particle Precipitate Flux-Prediction Mesoscale

   • Objective: Enhance electron particle precipitation prediction accuracy using curated DMSP data and advanced deep learning models.
   • Link: https://github.com/shirsh10mall/Defense-Meteorological-Satellite-Program-DMSP---Particle-Precipitate-Flux-Prediction-Mesoscale-

5. Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines

   • Objective: Develop a predictive model to determine the likelihood of individuals receiving H1N1 and seasonal flu vaccines based on demographic, opinion, and health behaviour data.
   • Link: https://github.com/shirsh10mall/Flu-Shot-Learning---Classification-Data-Science-Projects

6. Revenue Forecasting in Dynamic Price System:

   • Objective: Develop a revenue forecasting model for dynamic pricing optimization in e-commerce by analyzing historical data to predict revenue per user action and enable dynamic price adjustments.
   • Link: https://github.com/shirsh10mall/Revenue-Forecast-for-Dynamic-Pricing

1. Object Detection for Traffic Vehicle Detection:

   • Objective: Develop an accurate object detection system for classifying various vehicle types in road traffic using the IITM-HeTra dataset, with a focus on enhancing traffic management and safety.
   • Link: https://github.com/shirsh10mall/Object-Detection-for-Traffic-Vehicle-Detection

2. Cotton Disease Prediction - Image Classification

   • Objective: Develop a robust image classification model using Convolutional Neural Networks (CNNs) to accurately predict diseases in cotton plants.
   • Link: https://github.com/shirsh10mall/Cotton-Disease-Prediction-Image-Classification-

3. Water Bodies Segmentation in Satellite Images

   • Objective: Precisely segment water bodies in Sentinel-2 satellite images using binary image segmentation techniques.
   • Link: https://github.com/shirsh10mall/Water-body-segmentation-in-Satellite-Images-Image-Segmentation-

1. Semantic Search-based News Recommendation and Wikipedia Articles Question

   • Objective: Develop an innovative Semantic Search-based News Recommendation and Wikipedia Articles Question Answering System to enable efficient exploration of news articles and accurate information retrieval.
   • Link: https://github.com/shirsh10mall/Semantic-News-Search-and-Question-Answering-Wiki-Articles

2. Fine-Tuning Google Flan T5 Large LLM for Instruction-Based Question Answering (Instruct QA) - LLM QLoRA PEFT - Open Orca Dataset

   • Objective: Fine-tune the Flan T5 Large model to answer questions from the Open Orca dataset with task-specific instructions, employing PEFT and Transfer Learning for enhanced performance.
   • Link: https://github.com/shirsh10mall/Instruct-QnA-Fine-Tuning-Google-Flan-T5-Large-LLM-QLoRA-PEFT-Open-Orca-Dataset

3. English-Hindi Neural Machine Translation

   • Objective: Develop a neural machine translation system to translate English sentences into Hindi using an Encoder-Decoder architecture based on Bidirectional GRU with Multi-head Attention Layers and Fine-tuned pre-trained model.
   • Link: https://github.com/shirsh10mall/English-to-Hindi-Text-Translation-

4. Topic Modelling on Customer Complaints

   • Objective: Apply topic modelling techniques (LSA, LDA, and BERTopic) to cluster customer complaints, revealing underlying topics to enhance understanding of consumer feedback and improve financial products and services.
   • Link: https://github.com/shirsh10mall/Topic-Modeling-BERTopic-LSI-LDA-Customer-Complaints

5. Text Classification - Cyberbullying Detection and E-commerce Product Classification:

   • Objective: Develop accurate text classification models to identify instances of cyberbullying content and classify E-commerce products based on their descriptions into multiple categories.
   • Link: https://github.com/shirsh10mall/Text-Classification-NLP-Binary-Multiclass

1. Image-Text Multi-Modal Deep Learning
   • Objective: Develop a multi-modal deep learning solution for Image Captioning (ViT-BERT) and Image Retrieval (CLIP Model).
   • Link: https://github.com/shirsh10mall/Image-Text-Multi-Models

1. Stocks and Financial Analysis Dashboard
   • Objective: Create an interactive Tableau dashboard for analyzing historical stocks and financial data of Infosys and TCS, providing investors with insights to make informed investment decisions.
   • Link: https://github.com/shirsh10mall/Stocks-and-Financial-Analysis-Dashboard

1. MSc Thesis - Advancing Computational Particle Physics through Data and Deep Learning

   • Objective: Employ data analysis, simulation, and machine learning to enhance our understanding of particle collisions at the LHC and contribute to advancements in particle physics beyond the Standard Model.
   • Link: https://github.com/shirsh10mall/MSc-Project---Studies-at-Large-Hadron-Collider

2. Course Assignments

   • Link: https://github.com/shirsh10mall/Deep-Learning-Course-IIT-Delhi-APL745-

   • Non-Linear Regression and Projectile Motion: Implemented numpy and scipy-based univariate and multivariate non-linear regression models for projectile motion prediction.

   • Classification Algorithms and Optimization Techniques: Developed Logistic Regression and explored techniques like Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent for binary and multi-class classification.

   • Artificial Neural Network (ANN) for Classification: Built numpy-based ANN, performed binary/multi-class tasks on MNIST data, grasped feedforward, backpropagation, and gradient descent.

   • Convolutional Neural Network (CNN) for Image Classification: Created numpy-powered CNN, trained on MNIST images, understood convolution, pooling, and fully connected layers.

   • Recurrent Neural Network (RNN) for Time Series Data: Implemented basic RNN from scratch, applied on time series, and grasped sequential data handling.

   • Solving Differential Equations with PINN: Used PyTorch-based PINN to solve static bar problems' differential equations under specific loads.

   • Elasticity Partial Differential Equations using PINN: Applied PINN to solve elasticity-related partial differential equations for 2D planes, merging physics with neural networks.

   • DeepONet Architecture for Integration Operator: Implemented DeepONet, trained to learn integration, showcasing neural network's mathematical concept mastery.