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Data Science: Diabetes Prediction Project with Python [2025]
32 students
What you'll learn
- Students will learn how to use the Python programming language for data analysis and manipulation.
- Students will learn how to create numpy arrays to better understand and communicate their data.
- Machine learning algorithm: Students will learn how to use support vector machine learning model in this course.
- Diabetes prediction model: Students will learn how to build model to predict the onset of diabetes using svm.
- Model evaluation: Students will learn how to evaluate the performance of the models using test data accuracy score and training data accuracy score.
- Data preparation: Students will learn how to prepare data for analysis, including fitting, transforming and standardizing data.
- Early detection and prevention of diabetes: Students will learn about the early detection and prevention of diabetes using data science
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Course content
14 sections • 17 lectures • 1h 24m total length
Requirements
- Basic Python knowledge
- Interest to learn data science
- Laptop or desktop computer with internet connection
Description
Welcome to the course on "Diabetes Prediction Project with Python" - In this course You will learn to build and evaluate a machine learning model using python.
Introduction:
In this course, you will learn how to use the Support Vector Machine (SVM) algorithm for diabetes prediction. You will work with real-world diabetes data, perform train and test split, and build a predictive model to identify new cases of diabetes.
Data Collection and Preparation:
You will learn how to download and prepare real-world diabetes data, including calculating mean values and counting the number of people affected by diabetes and those who are not.
Train and Test Split:
You will learn how to perform train and test split, which is a critical step in evaluating the performance of predictive models.
Support Vector Machine (SVM) Algorithm:
This section will cover the basics of SVM, including its mathematical foundations and how it can be used for diabetes prediction.
Building the Predictive Model:
You will use the SVM algorithm to build a predictive model that can be used to identify new cases of diabetes. You will also learn how to evaluate the accuracy of the models and understand the factors that contribute to diabetes risk.
Evaluating the Model:
You will learn how to evaluate the performance of their models, including accuracy, precision score.
Conclusion:
By the end of the course, you will have a complete understanding of how to use SVM for diabetes prediction and the skills necessary to build a predictive system that can be used to identify new cases of diabetes. This course covers all the necessary skills and concepts for students to succeed in the field of data science and machine learning, including data collection and preparation, machine learning algorithms, model building and evaluation, and more. With its practical, hands-on approach, this course is an excellent resource for anyone looking to advance their skills in data science and machine learning and apply them to real-world problems.
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Who this course is for:
- Healthcare professionals: Doctors, nurses, and other healthcare professionals who want to learn how to use data science techniques for early detection and prevention of diabetes.
- Data scientists: Data scientists and analysts who want to develop their skills in machine learning and Python programming.
- Python developers: Python developers who want to learn how to use their skills for diabetes prediction and data analysis in the field of healthcare.
- Individuals interested in diabetes: People who are interested in learning more about diabetes and how data science can be used for its prevention and management.
- Students and recent graduates: Students and recent graduates in fields such as computer science, data science, and healthcare who want to gain hands-on experience in the application of data science to healthcare.
- Anyone interested in personal and professional growth: This course is suitable for anyone who wants to learn about the data science approach to diabetes prediction and expand their knowledge in this area.