// Available for opportunities
Nikhil Tiwary

Nikhil
Tiwary

2+ML Projects
2Research Papers
6Certifications
B.TechCSE Graduate
01

About Me

Who I Am

B.Tech CSE (Data Science) graduate from KCC Institute of Technology & Management, Greater Noida. Passionate about Python, Machine Learning, and Deep Learning with a focus on research and innovation.

What I Do

I am a Data Science student specializing in Machine Learning and Deep Learning, with experience in building predictive and interpretable AI models using Python. I work on data analysis, exploratory data analysis (EDA), healthcare analytics, and machine learning research projects, including Explainable AI (XAI)-based cardiovascular disease prediction systems. I am passionate about solving real-world problems using AI-driven solutions and continuously improving my skills in Data Science, Machine Learning, and Deep Learning.

My Approach

My approach focuses on solving real-world problems using Data Science, Machine Learning, and Deep Learning techniques. I believe in combining data analysis, predictive modeling, and interpretable AI to build intelligent and reliable solutions. I work with a research-driven mindset, emphasizing data preprocessing, exploratory data analysis (EDA), model optimization, and Explainable AI (XAI) to create transparent and impactful machine learning systems.

Currently

Freshly graduated and actively seeking roles in Data Science & ML Engineering. Published researcher at an international Springer-associated conference. Open to industry roles, research positions, and impactful collaborations in healthcare AI.

02

Education

B.Tech โ€” Computer Science & Engineering (Data Science)
KCC Institute of Technology & Management, Greater Noida
2022 โ€“ 2026 โœ“ Completed

Specialization: Data Science. Coursework in Machine Learning, Deep Learning, EDA, Predictive Modeling, Feature Engineering, and Statistical Analysis. Final year research project presented at ICMCE-2026 (Springer).

Class 12 โ€” Mathematics (CBSE)
Aditya Birla Public School
2020 โ€“ 2022 68%
Class 10 (CBSE)
RK Public School, Garhwa
2008 โ€“ 2020 75%
03

Projects

ML Project ยท 2024โ€“2025
RandomForest-CVD-Prediction
  • Developed a Random Forest ensemble model with hyperparameter tuning via cross-validation, achieving optimized precision-recall balance across imbalanced clinical classes.
  • Identified 8+ statistically significant risk factors and reduced feature dimensionality by 25% through correlation analysis and feature importance ranking.
  • Produced interpretable risk-assessment visualizations translating model outputs into actionable healthcare insights for disease prevention.
PythonScikit-learnRandom Forest PandasNumPyJupyter Notebook
View Code
Coming Soon
Next Project

Currently in development. Building something in the healthcare AI space with deep learning components.

In Progress
04

Research Papers

01
โœ“ Presented Springer ICMCE-2026
XAI-Driven Gradient Boosting Model for Interpretable Cardiovascular Disease Prediction
2nd International Conference on Modern Trends in Computers & Electronics (ICMCE-2026) ยท Teerthanker Mahaveer University, Moradabad ยท 1โ€“2 May 2026
Proceedings to be published by Springer ยท Certificate ID: ICMCE2026/ID/363
Nikhil Tiwary ยท Aman Kumar ยท Ankur Pal ยท Saranya Raj
XGBoost SHAP / XAI Cardiovascular Disease Survey-Based Data Stratified K-Fold CV Healthcare Analytics
Read Abstract
Cardiovascular disease (CVD) is one of the leading causes of death in India. This study proposes an XGBoost-based predictive framework trained on primary survey data collected from the general population โ€” capturing lifestyle, physical activity, dietary intake, sleep, and general health โ€” to reflect a more realistic clinical environment. SHAP (SHapley Additive exPlanations) values are employed to ensure model interpretability, making predictions transparent and actionable for clinicians. Stratified K-Fold cross-validation (k=5) was used to mitigate overfitting on the dataset. The proposed model outperforms baseline methods including Logistic Regression, Random Forest, and ANN across all key metrics.
Model Performance Comparison
Model Accuracy Precision Recall F1-Score
Logistic Regression
0.85
0.830.840.83
Random Forest
0.88
0.870.860.86
Artificial Neural Network
0.89
0.880.870.87
โญ XGBoost (Proposed)
0.91
0.920.910.91
Download Paper View Code
02
โœ“ Published
Addressing Cardiovascular Health Disparities: From Modifiable Risks to Primary Care Interventions
DOI: 10.64388/IREV9I6-1712914
Nikhil Tiwary ยท 2025
Healthcare Analytics Cardiovascular Disparities Modifiable Risk Factors Primary Care Evidence-Based Medicine
Read Abstract
A data-driven analysis examining cardiovascular healthcare disparities, modifiable risk factors, and evidence-based primary care interventions. Applies healthcare analytics methodologies to surface actionable insights for disease prevention, early intervention, and equitable care delivery.
View Paper
05

Certifications

โ˜๏ธ
AWS โ€“ Solutions Architecture Job Simulation
Amazon Web Services (Forage)
ยท Cloud Architecture ยท Job Simulation
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๐Ÿ™
Career Essentials in GitHub Professional Certificate
GitHub
ยท Version Control ยท Collaboration ยท DevOps
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๐Ÿ“Š
Data Analytics Job Simulation
Deloitte Australia (Forage)
ยท Data Analysis ยท Business Intelligence
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๐Ÿ”ข
Data Analytics Job Simulation
Quantium (Forage)
ยท Statistical Analysis ยท Python ยท Retail Analytics
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๐Ÿ’ป
Software Engineering Job Simulation
JPMorgan Chase (Forage)
ยท Software Engineering ยท Financial Tech
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๐Ÿ†
Certificate of Participation โ€“ ICMCE 2026
Springer ยท Teerthanker Mahaveer University
ยท 1โ€“2 May 2026 ยท ID: ICMCE2026/ID/363
View Certificate
06

Skills

ML / AI
Gradient Boosting
Random Forest
Predictive Modeling
Deep Learning
Explainable AI (SHAP)
Data Science
Python
EDA
Feature Engineering
Pandas / NumPy
Cross-Validation
Tools & Platforms
Jupyter Notebook
Git / GitHub
Matplotlib / Seaborn
Scikit-learn
Neural Networks
07

Resume

Nikhil Tiwary โ€” Data Science & ML Engineer
B.Tech CSE (Data Science)
Download Resume
08

Contact

Let's build something intelligent together.

I'm actively looking for AI/ML opportunities โ€” whether it's a full-time role, research collaboration, or a side project worth building. Drop me a message.

Say Hello