You will get Heart Disease Detection Model Using Ensemble Machine Learning
Project details
I offer a comprehensive heart disease detection model powered by machine learning. The model uses a variety of algorithms, such as Random Forest, XGBoost, and stacked models, to accurately predict the likelihood of heart disease based on a set of features like age, sex, cholesterol levels, and blood pressure. I specialize in optimizing models to ensure high performance and reliability, with an emphasis on real-world applications for healthcare professionals.
What sets this project apart is its focus on delivering a highly accurate, well-documented, and easy-to-implement solution that can be used in healthcare or research settings. My goal is to help healthcare providers improve early diagnosis and decision-making by providing a model with over 85% accuracy. Whether you're a hospital looking to integrate machine learning or a research team analyzing heart disease data, this solution will fit seamlessly into your workflow.
What sets this project apart is its focus on delivering a highly accurate, well-documented, and easy-to-implement solution that can be used in healthcare or research settings. My goal is to help healthcare providers improve early diagnosis and decision-making by providing a model with over 85% accuracy. Whether you're a hospital looking to integrate machine learning or a research team analyzing heart disease data, this solution will fit seamlessly into your workflow.
Machine Learning Tools
GitHub Copilot, Microsoft Excel, NumPy, pandas, Python, Python Scikit-Learn, scikit-learnWhat's included
| Service Tiers |
Starter
$15
|
Standard
$35
|
Advanced
$55
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 10 days |
Number of Revisions | 1 | 2 | 4 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 4 | 6 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Source Code
(+ 2 Days)
+$10Frequently asked questions
About Kunal
Full Stack Development | Machine Learning | Redis | Docker | Postgres
Pune, India - 1:50 am local time
I work with React, Next.js, Angular, Node.js, Express, FastAPI, MongoDB, PostgreSQL, Redis, RabbitMQ, Docker, Kubernetes, and WebSockets to build reliable products that handle real-time communication and production-scale workloads.
I have built:
real-time robotics control systems
industrial telemetry dashboards
live quiz engines with WebSocket synchronization
anomaly detection systems using BERT + BiLSTM
full-stack analytics platforms
I focus on writing clean, maintainable code and understanding client requirements clearly before starting, so delivery is fast and aligned with business goals...
If you need someone who can build full-stack products, backend systems, APIs, dashboards, or real-time features, I can help.
Steps for completing your project
After purchasing the project, send requirements so Kunal can start the project.
Delivery time starts when Kunal receives requirements from you.
Kunal works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preprocessing: Clean and preprocess the provided dataset
The first step is to ensure the dataset is clean and ready for modeling. This includes handling missing values, converting categorical data into numerical format, and standardizing.
Model Development: Train and evaluate ML models as Random Forest, XGBoost.
nce the data is ready, I'll build and train machine learning models, evaluating their performance using appropriate metrics like accuracy, precision, recall, and ROC curves.