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Project details
This project involves building a machine learning-based Health Disease Prediction Model that analyzes patient data to predict the likelihood of heart disease. The model utilizes Random Forest, SVM, and Logistic Regression for high-accuracy predictions.
Key features include data preprocessing (handling missing values, scaling, encoding), feature engineering (age groups, cholesterol-to-age ratio), and evaluation using accuracy, confusion matrix, and AUC-ROC.
The solution is implemented in Python with libraries like scikit-learn, pandas, NumPy, and matplotlib. The final output provides insightful predictions that can help in early disease detection and prevention.
Key features include data preprocessing (handling missing values, scaling, encoding), feature engineering (age groups, cholesterol-to-age ratio), and evaluation using accuracy, confusion matrix, and AUC-ROC.
The solution is implemented in Python with libraries like scikit-learn, pandas, NumPy, and matplotlib. The final output provides insightful predictions that can help in early disease detection and prevention.
AI Development Type
Deep Learning, Model TuningAI Tools
Azure Machine Learning, MATLAB, Open Neural Network Exchange, OpenCV, PyBrain, PyTorch, TensorFlowAI Development Language
PythonWhat's included $150
These options are included with the project scope.
$150
- Delivery Time 10 days
- Number of Revisions 4
- AI Model Integration
- Detailed Code Comments
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
Fast 8 Days Delivery
+$200
Additional Revision
+$10About Sree
Data Entry & Transcription Services | Artificial Intelligence, C++
Pileru, India - 2:15 am local time
Machine Learning enthusiast with a strong foundation in building intelligent models, data-driven solutions, and
AI-powered applications. Passionate about applying ML techniques in real-world scenarios, from data preprocessing
to model deployment. Additionally, experienced in full-stack web development (MERN), enabling seamless
integration of ML models into web applications and bridging the gap between AI and user-centric software
development.
Steps for completing your project
After purchasing the project, send requirements so Sree can start the project.
Delivery time starts when Sree receives requirements from you.
Sree works on your project following the steps below.
Revisions may occur after the delivery date.
Project Planning & Dataset Selection
Identified the problem: Predicting heart disease using medical data. Selected a relevant dataset with features like age, cholesterol levels, blood pressure, and more.
Data Preprocessing & Feature Engineering
Handled missing values, scaled numerical features, and one-hot encoded categorical variables. Created new features (e.g., cholesterol-to-age ratio, blood pressure categories, age groups).