Portfolio - Abhinay Angadi

Abhinay Angadi

Data Scientist

Resume

2

Machine Learning Projects

2

Deep Learning Projects

About Me

Software R&D Engineer with 1.5+ years of experience building and deploying machine learning solutions using Python and SQL. Delivered measurable improvements in retention, reporting efficiency, and predictive accuracy through structured analytics and stakeholder collaboration.

Key Skills

PYTHON

SQL

PANDAS

MATPLOTLIB

NUMPY

MACHINE LEARNING

DEEP LEARNING

NLP

STATISTICS

My Projects

Health Insurance Premium Prediction

Domain/Function: Insurance

Credit Risk Modelling

Domain/Function: Finance

HealthGuard AI: Intelligent Mask Detection for Public Safety

Domain/Function: Healthcare Industry

Car Damage Detection

Domain/Function: Image Classification

My Experience

Software R&D engineer - YuviPep Pvt. Ltd, Bangalore

Nov 2024 - Present

Engineered end-to-end machine learning workflows including data cleaning, feature engineering, model training, and evaluation using Python and Scikit-learn, improving prediction accuracy by 15% and reducing reporting inconsistencies by 25%.

Performed exploratory data analysis on 12K+ records using SQL and Pandas to identify churn predictors, enabling targeted retention initiatives that increased quarterly retention from 68% to 75%.

Deployed automated reporting dashboards and model monitoring pipelines using Streamlit and MLflow, reducing reporting time by 45% and lowering production incidents by 20%.

Skills: Python, Machine Learning, Pandas, Excel

Awards & Certificate

Python: Beginner to Advanced For Data Professionals

SQL for Data Science

Math and Statistics For AI, Data Science

Master Machine Learning for Data Science & AI: Beginner to Advanced

Deep Learning: Beginner to Advanced

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

Download Resume

Resume

Mail Me

[email protected]

https://www.linkedin.com/in/abhinay-angadi-541004159/

Call Me

+91 7997608258

GitHub

https://github.com/AngadiAbhinay01

Machine Learning Project

Health Insurance Premium Prediction

Domain/Function: Insurance

SHIELD Insurance faced declining market share due to Inadequate insurance pricing. Tasked with predicting the insurance cost for individuals, Used Machine Learning models for predicting insurance premium with 98% accuracy. ML models helped to gain 25% market share by accurate cost predictions.

View Project
on GitHUB

View Project
on Streamlit

Feature importance analysis revealed that insurance plan, genetic risk, and risk score have the highest impact on predicting insurance costs.

At least 95% of prediction errors have a percentage difference of less than 10% between the predicted and actual values.

Project Details

SHIELD Insurance faced a declining market share due to inaccurate premium pricing, causing customer loss and financial inefficiencies. The company needed a data-driven solution to optimize pricing and stay competitive.

The goal was to develop a Machine Learning model to predict insurance costs accurately based on customer demographics and health factors, ensuring competitive and risk-adjusted pricing.

Collected and cleaned historical insurance data, performed EDA to identify key factors, and built Linear Regression and XGBoost models. Tuned hyperparameters and evaluated models using R² Score achieving 98% accuracy. Deployed model using streamlit for real-time premium predictions.

The ML model improved pricing accuracy, leading to a 25% increase in market share. Automated premium predictions enhanced efficiency, reducing manual errors and boosting customer retention.

Machine Learning Project

Credit Risk Modelling

Domain/Function: Finance

Lauki Finance, an NBFC, faced rising loan defaults and approached me to build a credit risk model to predict default probability, credit score, and rating. Built various ML models(Logistic Regression, Random Forest, and XGBoost), LR achieved 95% accuracy, 96% recall, and reduced defaults by 15%.

View Project
on GitHUB

View Project
on Streamlit

Picture depicting KDE curve of Age feature

Graphs showing feature distribution of default and non-default.

Project Details

Lauki Finance, an NBFC, faced rising loan defaults, causing financial losses. They needed a credit risk model to assess default probability, credit scores, and ratings using factors like age, income, loan amount, and loan-to-income ratio.

The goal was to develop a Machine Learning model that could accurately predict default risk, helping the company make informed lending decisions and reduce defaults.

Collected and cleaned historical loan data, performed EDA to identify key risk factors, and built Logistic Regression, Random Forest, and XGBoost models. Tuned hyperparameters, achieving 95% accuracy and 96% recall. Integrated the model into Lauki Finance’s system via streamlit for real-time risk assessment.

The ML model led to a 15% decrease in loan defaults, improved credit risk evaluation, and enhanced lending efficiency, boosting financial stability.

Deep Learning Project

HealthGuard AI: Intelligent Mask Detection for Public Safety

Domain/Function: Healthcare Industry

MaskGuard AI is a deep learning–based computer vision system that detects whether a person is wearing a face mask in real time. The model uses a Convolutional Neural Network (CNN) for binary image classification and integrates OpenCV for face detection and live video processing.

View Project
on GitHUB

Graph depicting training and validation accuracy

Implementing neural network from scratch

Project Details

Developed a real-time Face Mask Detection system using Convolutional Neural Networks (CNN) to classify individuals as “Mask” or “No Mask.” The model was trained on a labeled image dataset and integrated with OpenCV for face detection and live video stream processing. The system performs real-time inference on webcam input, drawing bounding boxes and displaying prediction confidence scores. The model was optimized for accuracy and latency to ensure efficient performance in surveillance and public safety applications.

Deep Learning Project

Car Damage Detection

Domain/Function: Image Classification

Developed an AI model for Vrooms to classify car damage into 6 categories (e.g., Front Crushed). Used CNN, then improved accuracy with transfer learning (EfficientNet, ResNet50) and Optuna tuning (80% accuracy). Deployed via Streamlit, enabling fast damage assessment for pricing and insurance claims

View Project
on GitHUB

View Project
on Streamlit

User uploads a Front Breakage image, and the model correctly classifies it as Front Breakage.

User uploads a Rear Crushed image, and the AI model processes it, analyzes features, and accurately classifies it as Rear Crushed.

Project Details

Vrooms, a car company, sought an automated system to detect and classify exterior car damage accurately. Manual inspections were time-consuming and prone to inconsistencies, impacting used car pricing and insurance claim processing.

Developed a model to classify car images into six categories (Front/Rear Normal, Crushed, Breakage).

Built a CNN model, improved accuracy using regularization. To improve accuracy applied transfer learning with EfficientNet & ResNet50, then optimized hyperparameters using Optuna, achieving 80% accuracy. Deployed the model as a Streamlit web app, allowing users to upload images and get instant damage classification.

Automated damage assessment, eliminating manual inspections by 75%. This accelerated used car pricing and improved efficiency for P&C insurers by enabling faster claim settlements by 45%, enhancing customer satisfaction.