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Heart-Disease-Prediction-with-lask-App-using-Machine-Learning

Heart Disease Prediction using Machine Learning with Flask App Project

Original price was: ₹2,499.00.Current price is: ₹799.00.

The following contents are Downloadable immediately after the successful payment

  • Source Code

  • Dataset

  • Project Report (Both PDF and Word files) 

  • Instructions to install the necessary Software and Libraries

  • Step-by-step instructions to execute the project

Project Description

According to World Health Organization statistics, cardiovascular disease is the leading cause of death in the world. CVDs were responsible for 32% of all global deaths in 2019, as estimated by the World Health Organization. Heart attacks and strokes were responsible for 85% of these deaths. Low- and middle-income countries account for more than three-quarters of all CVD deaths.

We have created a web application and a prediction model based on machine learning using which a patient can fill in basic details like age, gender, Chest Pain Types, Cholesterol Level, etc. Based on these data, the model is able to predict heart disease. We have used various machine learning algorithms like Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and KNN for prediction. The user is also able to print the report for tracking the disease.

Video Demonstration of Heart Disease Prediction with Flask App using Machine Learning project

Steps in Implementing Heart Disease Prediction with Flask App using Machine Learning project:-

The steps are broadly divided into the below steps. The sub-steps are also listed while we approach each of the steps.
  • Reading, understanding, and visualizing the data
  • Preparing the data for modeling
  • Building the model
  • Evaluate the model

We have used a total of 5 algorithms in our project

  1. Logistic Regression
  2. Support Vector Machine
  3. Decision Tree
  4. Random Forest, and
  5. KNN

Installation of software, libraries, and execution

1. Install the Anaconda Python Package
2. Move to the downloaded project directory (Heart Disease Prediction)
3. Create the virtual environment using the below command
>>conda create -n hdp
4. Activate the virtual environment using the command
>>conda activate hdp
5. Now install the required libaries using the below command
>>pip install -r requirements.txt
6. Next to train the model open the Jupyter Notebook using the below command
>>jupyter notebook
7. Open the Heart-Disease-Prediction.ipynb and run all cells
8. Once the training is completed the trained model models.pkl will be stored in the current working directory
9. To run the app open the Anaconda prompt and type the following command
>>python app.py

Programming Languages and Libraries used

Language:  Python, Javascript,  CSS, HTML
Algorithms: Logistic Regression, SVM, Decision Tree, Random Forest, KNN
Framework: Flask
Tools: Anaconda, jupyter notebook
Libraries: NumPy, Pandas, Matplotlib

Happy Learning

Still need help to set up and execute the project

  • Setup and modification are paid services based on requirements.
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