Data Science: Diabetes Prediction Project with Python [2023]

Data Science: Diabetes Prediction Project with Python [2023], available at $49.99, has an average rating of 3.17, with 17 lectures, based on 3 reviews, and has 23 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. or Data scientists: Data scientists and analysts who want to develop their skills in machine learning and Python programming. or Python developers: Python developers who want to learn how to use their skills for diabetes prediction and data analysis in the field of healthcare. or 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. or 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. or 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. It is particularly useful 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. or Data scientists: Data scientists and analysts who want to develop their skills in machine learning and Python programming. or Python developers: Python developers who want to learn how to use their skills for diabetes prediction and data analysis in the field of healthcare. or 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. or 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. or 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.

Enroll now: Data Science: Diabetes Prediction Project with Python [2023]

Summary

Title: Data Science: Diabetes Prediction Project with Python [2023]

Price: $49.99

Average Rating: 3.17

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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.

Thank you for your interest in this course…

I will see you in the course…

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Learn the machine learning algorithm and its basics

Lecture 1: Machine learning algorithm

Chapter 3: Project Steps

Lecture 1: Step by step process

Chapter 4: Dataset downloading and starting google colab

Lecture 1: Dataset downloading and starting google colab

Chapter 5: Importing required libraries

Lecture 1: Importing required libraries

Chapter 6: Import data set and get number of rows and columns in google colab

Lecture 1: Import data set and get number of rows and columns in google colab

Chapter 7: Analyse the dataset with mean values

Lecture 1: Analyse the dataset with mean values

Chapter 8: Splitting the dataset by data and labels

Lecture 1: Splitting the dataset by data and labels

Chapter 9: Standardize the dataset

Lecture 1: Standardize the dataset

Chapter 10: Train and test split the dataset

Lecture 1: Train and test split the dataset

Chapter 11: Training the model

Lecture 1: Training the model

Chapter 12: Create a diabetic predictive system

Lecture 1: Testing the predictive system with random data

Lecture 2: Create a diabetic predictive system

Chapter 13: Step – by – step explanation

Lecture 1: Step 1 : Importing libraries

Lecture 2: Step 2 : Import dataset, Analyse and Splitting the data, Creating SVM

Lecture 3: Step 3 : Training and testing the model, Testing with random data

Chapter 14: Download the source code

Lecture 1: Download the source code in .py file and .ipynb file format

Instructors

Muthu Manavandi
Teacher in Sanyu academy from 2006 & trained 1000+ students

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 0 votes
5 stars: 1 votes

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