Hi, I'm Arun Jarupula

Data Scientist|Data Analyst| AI/ML Engineer | Problem Solver

Commited and excited about the opportunity to collabrate with brightest mind in industry with a strong work ethic and a thrist for knowledge. Aspring to launch a successful career as a Data Scientist.Where i can utilize my skills to make a meaningful impact. .

Get In Touch View My Work

About Me

Data Science professional with hands-on experience in machine learning and data analysis, supported by industry internship and multiple end-to-end projects. Proven track record of building and optimising predictive models, achieving up to 89% accuracy and delivering measurable business impact. Experienced in handling large datasets, developing data pipelines, and applying statistical techniques to solve complex problems. Proficient in Python, SQL, and data visualisation tools, with a strong foundation in AI and analytics. Adept at translating data insights into actionable outcomes to support decision-making. Seeking a Data Scientist or Data Analyst role to contribute to data-driven innovation.

Who I Am

A creative problem-solver focused on delivering innovative and efficient solutions.

My Goal

To develop innovative solutions that simplify and enhance everyday digital interactions.

Skills & Expertise

A comprehensive toolkit built through years of hands-on experience

Programming

Python R Language SQL DAX HTML CSS Java Script Responsive Website

Data Science

Machine Learning Data Analatics Deep Learning Predictive Modelling statistical Analysis Large Language Models NLP

Machine Learning & AI

Numpy Pandas Matplotlib OpenCV TensorFlow Deep Learning GenAI

visualization

Power Bi Tableau Matplotlib Seaborn

Tools & Libraries

Git VS Code Pandas Numpy Scikit-Learn> TensorFlow SHAP Jupyter SQLite

Frame Works

Flask Streamlit

Work Experience

Machine Learning And Data Analysis Intern

03/2024 - 05/2024

Cognifyz Technologies

Key Achievements:

Proficient in handling missing values, encoding categorical variables, and splitting datasets for machine learning tasks.
Experienced in selecting and training regression algorithms like linear regression and decision trees.
Skilled in evaluating model performance using regression metrics to ensure accuracy and generalization.
Capable of interpreting model results to extract meaningful insights for informed decision-making.
Spearheaded data analytics projects at Conifyz Technologies, leveraging data-driven insights to optimize decision-making processes
demonstrated proficiency in data analysis and visualization, contributing to enhanced business outcomes.

Featured Projects

A selection of my recent work showcasing diverse technical skills

Maternal Health Risk Prediction

Developed predictive models using Logistic Regression, Random Forest, and SVM to assess maternal health risks. Achieved 84.59% accuracy with Blood Sugar and Blood Pressure identified as key risk factors through feature importance analysis. Implemented comprehensive data preprocessing and model validation techniques.

Key Highlights: Multiple ML algorithms comparison. Feature importance analysis with SHAP. Cross-validation and hyperparameter tuning. Clinical insights for healthcare professionals.

R Logistic Regression Random Forest SVM

Code

COVID-19 Socioeconomic Analysis

Comprehensive study examining the effects of vaccination rates and socio-economic factors on COVID-19 death rates across different regions. Applied ANOVA, Chi-square tests, and regression analysis for robust statistical insights. Integrated SQLite for efficient data management and querying.

Key Highlights: Multi-dimensional statistical analysis. Database integration with SQLite. Regional comparative studies. Policy-relevant insights extraction.

R SQLite Regression Chi-square ANOVA

Code

Machine Learning for Early Detection of Heart Disease Using Multimodal Patient Data

Built a high-accuracy machine learning model using Random Forest algorithm to predict heart disease risk. Integrated SHAP (SHapley Additive exPlanations) for model explainability, providing transparent insights into prediction factors. Achieved 89% accuracy with comprehensive feature engineering.

Key Highlights : Explainable AI implementation. SHAP values for feature interpretation. High prediction accuracy (89%). Clinical decision support focus.

Python Random Forest SHAP Scikit-learn Logistic Regression SVM = SV Classifier XGBoost Classifier Multi-Layer Perceptron (MLP):

Code

Get In Touch

Have a project in mind or just want to chat? I'd love to hear from you

Let's work together

I'm always open to discussing new projects, creative ideas, or opportunities. Feel free to reach out!

jarupula.arun2001@gmail.com

+44 7887140291

London, Ilford, United Kingdom