Ghanashyam Panda

Data Aspirant

[email protected]

Resume

Hello, I am

Ghanashyam Panda.

1

Python
Project

2

Machine Learning
Projects

About Me

I'm an aspiring Data Scientist driven by a passion for transforming raw data into meaningful insights. Pursuing my B.Tech in Computer Engineering at IIIT Bhubaneswar, I discovered a natural flair for data science where logic meets real-world impact.
Skilled in Python, machine learning, deep learning, and data analysis, I’ve built several end-to-end projects that tackle real business problems, focusing on both technical accuracy and practical relevance.
Though I haven’t worked in a formal role yet, my hands-on experience has equipped me to handle the full data science pipeline from data cleaning to model deployment with a problem-solving mindset.
I'm eager to bring my energy and skills to a team where data drives decisions. Let’s connect if you're looking for someone who learns fast and delivers with purpose.

Key Skills

Python

Scikit-learn

Machine Learning

Data Preprocessing

Model Evaluation

Model Deployment

Streamlit

Pandas

NumPy

Matplotlib

Seaborn

HTML

CSS

JavaScript

SQL

REST APIs

Git

Data Visualization

End-to-End ML Pipelines

My Projects

Credit Risk Model

Domain/Function: FinTech (Banking & Financial Services)

Health Insurance Premium Predictor

Domain/Function: Healthcare Analytics

Expense Tracker plus

Domain/Function: Expense Management

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

Machine Learning Workshop by AWS

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.

Mail Me

[email protected]

https://www.linkedin.com/in/ghanashyam-panda-57a6aa255/

GitHub

https://github.com/ghanashyam9348

Machine Learning Project

Credit Risk Model

Domain/Function: FinTech (Banking & Financial Services)

Credit-Risk-Model is a Python-based credit risk prediction pipeline. It features data cleaning, EDA, feature engineering, SMOTE-Tomek oversampling, model training with XGBoost (plus Logistic Regression and Random Forest), hyperparameter tuning via Optuna, and a Streamlit app for real-time

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

Jupyter Notebook

pandas

numpy

matplotlib

seaborn

scikit-learn

XGBoost

imbalanced-learn (SMOTE-Tomek)

Optuna

Streamlit

joblib

Git

GitHub

Streamlit UI of CrediSure app capturing borrower details and credit behavior to calculate loan default risk using ML-based credit scoring.

The app predicts a 65.68% loan default risk, shows a credit score of 505, and rates the applicant as “Average” based on entered financial metrics.

Project Details

Credit Risk Model is a Python-based machine learning pipeline designed to predict borrower default risk using demographic, loan and credit bureau data.
It begins with data cleaning—imputing missing values, Winsorizing outliers, and scaling features—followed by exploratory analysis with visual charts to uncover key patterns.
Feature engineering generates metrics such as debt-to-income ratios and target-encoded categories. Class imbalance is addressed with SMOTE-Tomek sampling.
Models including Logistic Regression, Random Forest and XGBoost are trained and hyperparameter-tuned via Optuna, then evaluated on recall, precision, F1-score and AUC. The chosen model is serialized with joblib and deployed in a Streamlit app that accepts user inputs and displays real-time risk scores with SHAP explanations.
The code leverages pandas, scikit-learn, imbalanced-learn, XGBoost, Optuna, joblib and Streamlit, with version control via Git.

Machine Learning Project

Health Insurance Premium Predictor

Domain/Function: Healthcare Analytics

Insurance-Premium-Predictor-App is a Python-based ML pipeline that cleans and analyzes insurance data, trains regression models with scikit-learn, and deploys the best model via Streamlit for real-time premium estimates using inputs like age, BMI, and smoker status.

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

Jupyter Notebook

pandas

numpy

matplotlib

seaborn

scikit-learn

joblib

Streamlit

Git

GitHub

Streamlit UI for predicting health insurance premiums using user inputs like age, income, BMI, habits, and medical history.

Displays predicted health insurance premium instantly after user submits personal and medical info via Streamlit form.

Project Details

Insurance Premium Predictor App is a Python-based machine learning pipeline delivering real-time health insurance cost estimates through an interactive web interface.
It loads raw customer and charge data, cleans and transforms it by imputing missing values, handling outliers, encoding categorical fields, and scaling numerical features.
Exploratory analysis with pandas, matplotlib, and seaborn uncovers correlations that guide feature selection.
The data is split into training and test sets to train linear regression, random forest, and gradient boosting models with grid search cross-validation for hyperparameter tuning.
Models are evaluated using mean absolute error, mean squared error, and R-squared. The best model is serialized with joblib. A Streamlit app provides a form for user inputs to deliver instant premium predictions.
Dependencies are managed via requirements.txt, and version control is maintained with Git and GitHub, demonstrating a full end-to-end workflow .

Python Project

Expense Tracker plus

Domain/Function: Expense Management

ExpenseTracker-Plus is a full-stack web app using FastAPI, Streamlit, and MySQL. Log, edit, and categorize income/expenses; filter by date, category, or amount; visualize interactive spending charts and generate monthly reports for real-time financial insights.

View Project
on GitHUB

Other Tools

Python 3

FastAPI

Uvicorn

Streamlit

MySQL

mysql-connector-python

Pytest

Git & GitHub

A responsive expense tracking app built with Streamlit, allowing users to log daily spending with categories, notes, and date-based summaries.

Visualizes monthly spending with bar charts and summaries, helping users track expenses clearly over time in an interactive Streamlit app.

Project Details

An end-to-end, full-stack web application that transforms expense tracking into an intuitive, data-rich experience.
Built with a FastAPI RESTful backend, a dynamic Streamlit frontend, and a MySQL database, ExpenseTracker-Plus empowers you to:
Capture & Organize entries by logging, editing, or deleting income and expense records. Assign them to default or custom categories, and add tags or notes for context.
Slice & Dice your data using versatile date ranges, categories, amounts, or custom labels to pinpoint exact transactions and trends.
Visualize spending trends with interactive bar charts for monthly totals and category breakdowns, plus generate concise monthly summaries for quick insights.
Tech Highlights: FastAPI-driven RESTful API; MySQL storage via a Python connector; responsive Streamlit interface with live updates; real-time analytics.
this project showcases full-stack Python mastery combining user-focused UI design, a robust backend, and practical finance management.