Heart Disease Predictor via Phonocardiogram (PCG)
Budget: ₹1,500 – ₹12,500 INR
Heart Disease Prediction Using Heart Sounds (PCG)
Description:
I am looking for an experienced freelancer to build a complete end-to-end project on Heart Disease Prediction using Heart Sounds (Phonocardiogram – PCG data). This project is for academic use, and I require everything including the final working model, clean code, dataset, and a complete explanation.
Project Requirements:
Objective:
Create a Machine Learning / Deep Learning model to detect heart disease from heart sound recordings (PCG signals).
Dataset:
You are required to find and use any suitable publicly available dataset (e.g., from PhysioNet, Pascal Heart Sound Challenge, etc.)
I do not have the dataset, so please include data preprocessing in the pipeline.
What You Need to Deliver:
a. Full Working Project Folder:
Well-commented Python code (can use libraries like TensorFlow, Keras, PyTorch, scikit-learn, librosa, etc.)
Data preprocessing and feature extraction pipeline
Trained model with evaluation metrics
Clear directory structure and README file
b. Explanation Documents:
Full Project Report (PDF or DOC) covering:
Introduction to the problem
Dataset description
Preprocessing steps
Feature extraction & model design
Training, validation, and testing process
Evaluation metrics (accuracy, confusion matrix, etc.)
Result discussion & future scope
Presentation Slides (PPT) for academic submission
c. Optional (Bonus):
Simple demo (CLI or basic GUI using Streamlit/Tkinter)
Plots: waveforms, spectrograms, confusion matrix, accuracy/loss curves
Preferred Tech Stack:
Python (Mandatory)
ML/DL libraries (TensorFlow, PyTorch, Keras, etc.)
Signal processing tools like Librosa or SciPy (optional)
Timeline:
Deadline: 15–20 days
Budget:
₹6000 – ₹8000 INR (Fixed)
Best quality at a fair price expected
Important Notes:
This is a complete academic-level project. I need everything ready to use.
No dataset will be provided from my side – you must find and use a suitable one.
Please apply only if you can deliver everything end-to-end with clear explanations and working code.
Description:
I am looking for an experienced freelancer to build a complete end-to-end project on Heart Disease Prediction using Heart Sounds (Phonocardiogram – PCG data). This project is for academic use, and I require everything including the final working model, clean code, dataset, and a complete explanation.
Project Requirements:
Objective:
Create a Machine Learning / Deep Learning model to detect heart disease from heart sound recordings (PCG signals).
Dataset:
You are required to find and use any suitable publicly available dataset (e.g., from PhysioNet, Pascal Heart Sound Challenge, etc.)
I do not have the dataset, so please include data preprocessing in the pipeline.
What You Need to Deliver:
a. Full Working Project Folder:
Well-commented Python code (can use libraries like TensorFlow, Keras, PyTorch, scikit-learn, librosa, etc.)
Data preprocessing and feature extraction pipeline
Trained model with evaluation metrics
Clear directory structure and README file
b. Explanation Documents:
Full Project Report (PDF or DOC) covering:
Introduction to the problem
Dataset description
Preprocessing steps
Feature extraction & model design
Training, validation, and testing process
Evaluation metrics (accuracy, confusion matrix, etc.)
Result discussion & future scope
Presentation Slides (PPT) for academic submission
c. Optional (Bonus):
Simple demo (CLI or basic GUI using Streamlit/Tkinter)
Plots: waveforms, spectrograms, confusion matrix, accuracy/loss curves
Preferred Tech Stack:
Python (Mandatory)
ML/DL libraries (TensorFlow, PyTorch, Keras, etc.)
Signal processing tools like Librosa or SciPy (optional)
Timeline:
Deadline: 15–20 days
Budget:
₹6000 – ₹8000 INR (Fixed)
Best quality at a fair price expected
Important Notes:
This is a complete academic-level project. I need everything ready to use.
No dataset will be provided from my side – you must find and use a suitable one.
Please apply only if you can deliver everything end-to-end with clear explanations and working code.