Portfolio - Moksh Jain | Codebasics

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Hello, I am

Moksh Jain

Aspiring Data Scientist

Resume

2

Machine Learning
Projects

About

Me

Hello! I'm Moksh Jain, an aspiring Data Scientist proficient in Python, SQL and Machine Learning, with experience turning data into practical solutions. Building real-world machine learning projects that connect technical skills with meaningful outcomes.

Tools and Technologies:
✔️ I use Python and SQL to write code, handle data and create models.
✔️I work with Jupyter notebook, Pandas, Numpy, Matplotlib and Seaborn for data handling and visualization.
✔️Experienced with Sickit learn and XGBoost for building predictive models.
✔️Hands-on with Streamlit, FastAPI for building backend and frontend, and Git for version control system.
Strong foundation in descriptive and inferential statistics, including hypothesis testing for data-driven decision-making.

🔃I'm currently exploring deep learning after building some models in statistical machine learning.
I love sharing my experiences and journey with others. Looking forward to connect with you all.

Key

Skills

Python

Data Visualization

Machine Learning

My SQL

Exploratory Data Analysis

My

Projects

Credit Risk Modelling

Credit Risk Modelling

Domain/Function: Finance/Credit Risk Modelling

Built a Streamlit app that automates the process of credit risk evaluation using machine learning. This app is designed for a loan processing officer to check an applicant's creditworthiness for the applied loan by providing the default probability, credit score and rating associated with it.

Machine Learning

Health Care Premium Prediction

Health Care Premium Prediction

Domain/Function: Health Care

This Streamlit web application predicts the expected health insurance premium based on a user’s personal, health, and lifestyle details. It helps users estimate their annual premium by leveraging a machine learning model trained on real-world data.

Machine Learning

Awards

& Certificate

CB Cert

Python: Beginner to Advanced For Data Professionals

CB Cert

Math and Statistics For AI, Data Science

CB Cert

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

Contact Me

Email

[email protected]

Github

https://github.com/itsmoksh

Linkedin

https://www.linkedin.com/in/itsmoksh/

Moksh Jain

Aspiring Data Scientist

Download Resume

Resume

Machine Learning Project

Credit Risk Modelling

Domain/Function: Finance/Credit Risk Modelling

Built a Streamlit app that automates the process of credit risk evaluation using machine learning. This app is designed for a loan processing officer to check an applicant's creditworthiness for the applied loan by providing the default probability, credit score and rating associated with it.

View Project
Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

Scikit Learn

XGBoost

Streamlit

FastAPI

Credit Risk Modelling

Increased Loan-to-income ratio and higher delinquent months are more prone to becoming a defaulter

Credit Risk Modelling

Significantly low Loan-to-income ratio resulting in less default probability.

Project Details

This app enables a finance company to quickly assess the credit risk of loan applicants, reducing decision-making time while improving accuracy. It predicts the probability of default, generates a credit score (300–400) with corresponding ratings, and provides clear insights for decision-making.
The major contributors to finding the default probabilities are: Loan-to-income ratio, Credit utilization ratio and Delinquency ratio.
Features:
• Streamlit-based interactive web app
• Probability of the applicant being a defaulter
• Generates credit score & rating for easy interpretation
• Achieved >90% accuracy and 93% recall for identifying defaulters
• Strong model performance with AUC 98% and KS Statistic 85.9
• Built with Logistic Regression, XGBoost, SMOTE Tomek, Optuna

Machine Learning Project

Health Care Premium Prediction

Domain/Function: Health Care

This Streamlit web application predicts the expected health insurance premium based on a user’s personal, health, and lifestyle details. It helps users estimate their annual premium by leveraging a machine learning model trained on real-world data.

View Project
Engagement

View Project
on GitHUB

View Project
on Streamlit

Other Tools

Python

Sickit Learn

XGBoost

Streamlit

FastAPI

Health Care Premium Prediction

A patient is having a lower age, no medical history does not have much impact on the premium, but bmi category of Overweight increases some premium.

Health Care Premium Prediction

In this case a patient has relatively more age, with a severe medical history, and a gold plan increases the premium amount.

Project Details

Manually finalizing health premiums is time-consuming; for that, I have built an application that provides healthcare premium for each patient based on certain parameters, reducing decision-making time with low error. These major parameters include: Premium plan, Age, Medical history, and BMI category.

Features:
• Based on medical history, generates a risk score.
• Stremlit UI for the interface.
• Two different models for age groups(>18,>25)
• High accuracy of 97%.
• Error percentage of less than 10%.