Data Science: Diabetes Prediction Project with Python [2023]

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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