Machine Learning Disease Prediction And Drug Recommendation

Name: Machine Learning Disease Prediction And Drug Recommendation
Rating: 3.7 (65 reviews)

Supervised Machine Learning, Django, JQuery And Ajax

Created byNoel Moses Mwadende

Last updated 3/2024

English

English [Auto],

What you'll learn

Use Pandas in data science for loading dataset and operations on data
Create list in Python and appending to a list
Create Numpy array, one dimensional Array and to convert one dimensional Array to two dimensional Array
Preprocessing of data using Pandas and scikit learn
Use train test split function to divide dataset into training and testing set
To create model using supervised machine learning algorithms
Learn the relationship between Views, URL and templates in Django
How to create a project and an application in Django
Intergrate the concept of JQuery, Ajax, Django and machine learning
Create Django web application for deploying machine learning model
Learn how to deploy machine learning model on Django

Course content

10 sections • 30 lectures • 6h 28m total length

Introduction And Project Demonstration23:12
Introduction of Machine Learning6:20
How to Run a Project Step By Step17:21
Execute a step-by-step guide to run a disease prediction and drug recommendation project, featuring logistic regression, a decision tree model, and Django deployment.

Python Introduction And Hello World6:45
Variable Declaration, Initialization And Data Types7:05
Learn how to declare and initialize Python variables, reserve memory, and work with basic data types such as integers and strings.
Python Data Structure23:47
Control Structure16:49
Python Functions12:42
Learn to create Python functions with and without parameters, explore function calls in a Django context, use dictionaries for user data, and handle parameter-driven greetings.

Loading Dataset6:44
Traing.csv is the training dataset for training disease prediction model
Exploratory Data Analysis - EDA27:56
Data Convertion, Feature Selection and Train Test Split9:59
Convert disease names into numbers as part of data preprocessing. Select features, assign X and y (prognosis), and perform a train-test split to prepare for model training.
Training The model14:20
Train and compare decision tree, random forest, and logistic regression models using data preprocessing and train-test split, then measure accuracy scores and prepare deployment by saving the model with Joblib.
Dump The Model and Making Prediction15:13

Create Django Project and Application6:52
Create a Django project and application for a health care app, configure the base settings and database, and run the development server with static and media files.
Creating Model4:21
Create a machine learning model by building user and medical tables, migrating schemas with Python, and enabling disease prediction and drug recommendations in a MySQL database.
Creating Users4:12
Home Page5:52

Requirements

Python Basics, Python Flask basics and Django web framework basics
Machine Learning Basics and Python Pandas
Javascript basics and Ajax
Computer with minimum of 4 RAM and 250 HDD.

Description

This is Supervised machine learning full course. It covers all basic concepts from Python, Pandas, Django, Ajax and Scikit Learn. The course start on Jupyter notebook where different operations will performed on data. Learn Python basic, mostly control structure and Django. The end goal of this course is to teach how to deploy machine learning model on Django Python web framework. Actually, that is the purpose of machine learning. We should learn how to put machine learning model into application and it must solve people's problems in the community.

The methodology used in teaching are very easy and understood-able. In Python Basics we learn the concepts which are needed in model deployment in Django. This include learning list, array, tuple,dictionary, if statement, for loop,while loop and functions.

The created model which is disease prediction model and drug name recommendation model are deployed on different dashboard of Django web application.disease prediction model is deployed on patient dashboard and drug name recommendation model is deployed on doctor dashboard.

If there is any mis-understanding based on disease prediction result and drug name recommendation, patient can ask for appointment with doctor. Doctor usually use machine learning model to recommend for drug names and schedule for appointment with patient.

Who this course is for:

Beginner to Python
Beginner to machine learning
Beginner to Predictive Analytics
Beginner to Data science
Beginner to Artificial intelligence

Machine Learning Disease Prediction And Drug Recommendation

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 47min

Environment Set3 lectures • 35min

Python Basics5 lectures • 1hr 7min

Disease Prediction5 lectures • 1hr 14min

Drug Recommendation2 lectures • 13min

Create Django Project4 lectures • 21min

User Registration And Log In2 lectures • 24min

Patient Dashboard3 lectures • 1hr 12min

Doctor Dashboard2 lectures • 34min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: