Portfolio - YOGESH SENGAR | Codebasics

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YOGESH SENGAR

Data Aspirant

EMAIL

[email protected]

PHONE

+91 8222842109

Download CV

About Me

Hello, I am Yogesh Sengar, born in Hathras, Uttar Pradesh, and brought-up in Rewari, Haryana. I completed my postgraduation in Master of Computer Applications from GL Bajaj Institute of Technology and Management in Greater Noida. Prior to that, I earned my Bachelor of Computer Applications from IIMT College of Management, also in Greater Noida. My schooling was completed at Suraj School in Rewari.

I am a results-driven data analyst with a solid foundation in data visualization, programming, and statistical analysis. I have proficiency in leveraging Python and its libraries to extract insights from complex datasets. I have experience creating compelling visualizations using Matplotlib and Seaborn, and I utilize Scikit-learn for machine learning applications. Additionally, I am adept at processing data with Pandas and NumPy, and I possess skills in SQL for effective database management.

My expertise includes:

Data Visualization: Utilizing Matplotlib and Seaborn for insightful data pre

1

Machine Learning Project

Key Skills

SQL:QUERIES,RETRIEVAL

MACHINE LEARNING:DATA,ALGORITHMS,MODELS

TOOLS USED:JUPYTER NOTEBOOK,PYCHARM

DATA VISULIZATION:EXCEL,MATPLOTLIB,SEABORN

PROGRAMMING LANGUAGE:PYTHON,NUMPY,PANDAS

My Projects

My Experience

Data Visualization: Utilizing Matplotlib and Seaborn for insightful data presentations, along with proficiency in Excel for data analysis.

Programming Language: Strong command of Python for developing robust data processing and analysis solutions.

DBMS: Experience in SQL for effective database management and data retrieval.

Data Processing: Skilled in using Pandas and NumPy for data manipulation and analysis.

Machine Learning: Familiarity with Scikit-learn for implementing machine learning models and pipelines.

NLP & Statistics: Knowledgeable in natural language processing techniques and statistical methods to derive meaningful conclusions from data.

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

Let's Work Together!

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.

EMAIL

[email protected]

Call Me

+91 8222842109

LinkedIn

https://www.linkedin.com/in/yogesh-sengar-8427a9169

GitHub

https://github.com/yogeshsengar17

Atliq_Bank_Project

Domain/Function: IT Services & Consulting, Banking, Healthcare, Finance

This project analyzes customer data to understand spending habits, credit usage, and preferences across age groups. It includes insights on income, credit limits, payment types, and product categories to help design targeted banking products and offers.

View Project
on GitHUB

Atliq_Bank_Project

Charts show age group distribution, income, credit stats, payment preferences, and product choices—26–48 age group dominates in most metrics.

Atliq_Bank_Project

Bar charts showing average annual income by gender, location, occupation, and marital status, with business owners earning the highest.

Project Details

The Atliqo Bank project is a data analysis initiative aimed at understanding customer behavior across different demographics to support the launch of a new credit card product. By analyzing key variables such as age group, gender, occupation, income, credit score, payment preferences, and product purchase patterns, the project uncovers valuable insights into spending habits and financial behavior. Visualizations highlight trends like higher incomes among the 49–65 age group, dominant use of debit cards, and preferred product categories across age segments. These insights are intended to help Atliqo Bank design personalized credit card offerings tailored to different customer profiles, ultimately enhancing customer satisfaction and driving product adoption. The project leverages Python, Pandas, and visualization libraries to conduct EDA and deliver data-driven recommendations.

Machine Learning Project

Premium_prediction

Domain/Function: IT Services & Consulting, Banking, Healthcare, Finance

This project is a Machine Learning-based web app that predicts healthcare insurance premiums using user input like age, BMI, smoking status, etc. Built with Python, Streamlit, and regression models, it helps users estimate insurance costs quickly and accurately.

View Project
on GitHUB

Premium_prediction

This image shows the main.py file of a Streamlit app for health insurance cost prediction, displaying categorized input options in the GitHub repo.

Premium_prediction

This image shows Streamlit UI input layout in main.py, capturing number inputs and dropdowns for user health and demographic data for prediction.

Project Details

This project, Healthcare Premium Prediction, leverages Machine Learning to predict individual medical insurance premiums based on user inputs such as age, sex, BMI, number of children, smoking status, and region. The solution uses regression algorithms to model the relationship between these factors and premium cost. Built using Python and deployed with Streamlit, the web application offers an interactive and user-friendly interface for real-time predictions. Users can input their personal health and demographic details to instantly estimate their insurance premiums. The model is trained on a publicly available dataset, and evaluation metrics such as R² score and RMSE are used to validate its accuracy. This project demonstrates the practical application of ML in the insurance sector, providing users with actionable insights and enabling insurance companies to offer better, data-driven pricing strategies.