Diabetics Prediction
Diabetes prediction using machine learning utilizes patient data to identify individuals at risk of developing diabetes, enabling early intervention
40 Hrs. | Intermediate
INR 0
This Course Includes:
- 30 Days Access to Workspace
- Dedicated Mentor Support
- Project Resources & References
- Project Completion Certificate
Skills you will develop
- Python
- Machine Learning
- Numpy
- Pandas
- Matplotlib
- Seaborn
- HTML
- Python-Flask
Project Description
In this project, we aim to use machine learning algorithms to predict the onset of diabetes in individuals based on their health records and other relevant factors such as age, BMI, family history, and lifestyle habits. The dataset used in this project will include information on various clinical parameters such as blood pressure, BMI, Heart diseases and cholesterol levels.
Our goal is to develop a predictive model that can accurately identify individuals who are at high risk of developing diabetes, thereby allowing for early intervention and prevention of the disease. By using machine learning techniques to analyse large amounts of data, we can identify patterns and make accurate predictions that could potentially save lives.
Overall, this project has the potential to contribute to the field of healthcare by improving early detection and prevention of diabetes, ultimately leading to better health outcomes for individuals and communities.
Our goal is to develop a predictive model that can accurately identify individuals who are at high risk of developing diabetes, thereby allowing for early intervention and prevention of the disease. By using machine learning techniques to analyse large amounts of data, we can identify patterns and make accurate predictions that could potentially save lives.
Overall, this project has the potential to contribute to the field of healthcare by improving early detection and prevention of diabetes, ultimately leading to better health outcomes for individuals and communities.
Technical Architecture:
Project Flow:
? User interacts with the UI to enter the input.
? Entered input is analyzed by the model which is integrated.
? Once model analyses the input the prediction is showcased on the UI
To accomplish this, we have to complete all the activities listed below,
? Define Problem / Problem Understanding
o Specify the business problem
o Business requirements
o Literature Survey
o Social or Business Impact.
? Data Collection & Preparation
o Collect the dataset
o Data Preparation
? Exploratory Data Analysis
o Descriptive statistical
o Visual Analysis
? Model Building
o Training the model in multiple algorithms
o Testing the model
? Performance Testing
o Testing model with multiple evaluation metrics
? Model Deployment
o Save the best model
o Integrate with Web Framework
Prior Knowledge:
You must have prior knowledge of following topics to complete this project.
? ML Concepts
Supervised learning: https://www.javatpoint.com/supervised-machine-learning
Unsupervised learning: https://www.javatpoint.com/unsupervised-machine-learning
? Decision tree: https://www.javatpoint.com/machine-learning-decision-tree-classification- algorithm
? Random forest: https://www.javatpoint.com/machine-learning-random-forest-algorithm
? KNN: https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning
? Xgboost: https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to- understand-the-math-behind-xgboost/
? Evaluation metrics: https://www.analyticsvidhya.com/blog/2019/08/11-important-model- evaluation-error-metrics/
? NLP:-https://www.tutorialspoint.com/natural_language_processing/natural_language_processing_python.htm
? Flask Basics: https://www.youtube.com/watch?v=lj4I_CvBnt0
Project Structure:
? User interacts with the UI to enter the input.
? Entered input is analyzed by the model which is integrated.
? Once model analyses the input the prediction is showcased on the UI
To accomplish this, we have to complete all the activities listed below,
? Define Problem / Problem Understanding
o Specify the business problem
o Business requirements
o Literature Survey
o Social or Business Impact.
? Data Collection & Preparation
o Collect the dataset
o Data Preparation
? Exploratory Data Analysis
o Descriptive statistical
o Visual Analysis
? Model Building
o Training the model in multiple algorithms
o Testing the model
? Performance Testing
o Testing model with multiple evaluation metrics
? Model Deployment
o Save the best model
o Integrate with Web Framework
Prior Knowledge:
You must have prior knowledge of following topics to complete this project.
? ML Concepts
Supervised learning: https://www.javatpoint.com/supervised-machine-learning
Unsupervised learning: https://www.javatpoint.com/unsupervised-machine-learning
? Decision tree: https://www.javatpoint.com/machine-learning-decision-tree-classification- algorithm
? Random forest: https://www.javatpoint.com/machine-learning-random-forest-algorithm
? KNN: https://www.javatpoint.com/k-nearest-neighbor-algorithm-for-machine-learning
? Xgboost: https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to- understand-the-math-behind-xgboost/
? Evaluation metrics: https://www.analyticsvidhya.com/blog/2019/08/11-important-model- evaluation-error-metrics/
? NLP:-https://www.tutorialspoint.com/natural_language_processing/natural_language_processing_python.htm
? Flask Basics: https://www.youtube.com/watch?v=lj4I_CvBnt0
Project Structure:
Create the Project folder which contains files as shown below
? We are building a flask application which needs HTML pages stored in the templates folder and a python script app.py for scripting.
? Random.pkl is our saved model. Further we will use this model for flask integration.
? Training folder contains a model training file.
? We are building a flask application which needs HTML pages stored in the templates folder and a python script app.py for scripting.
? Random.pkl is our saved model. Further we will use this model for flask integration.
? Training folder contains a model training file.