Data Science: Diabetes Prediction- Model Building Deployment

Data Science: Diabetes Prediction- Model Building Deployment udemy course

A practical hands on Machine Learning Project on Diabetes Prediction - Model Building and Deployment

This course is about predicting whether or not the person has diabetes using Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.

This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building, evaluation and deployment techniques. We will explore multiple ML algorithms to create our model and finally zoom into one which performs the best on the given dataset.

At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1  :  Installing Packages

Task 2  :  Importing Libraries.

Task 3  :  Loading the data from source.

Task 4  :  Pandas Profiling

Task 5  :  Understanding the data

Task 6  :  Data Cleaning and Imputation

Task 7  :  Train Test Split

Task 8  :  Scaling using StandardScaler

Task 9  :  About Confusion Matrix

Task 10 :  About Classification Report

Task 11 :  About AUC-ROC

Task 12 :  Checking for model performance across a wide range of models

Task 13 :  Creating Random Forest model with default parameters

Task 14 :  Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Task 15 :  Hyperparameter Tuning using RandomizedSearchCV

Task 16 :  Building RandomForestClassifier model with the selected hyperparameters

Task 17 :  Final Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Task 18 :  Final Inference

Task 19 :  Loading the saved model and scaler objects

Task 20 :  Testing the model on random data

Task 21 :  What is Streamlit and Installation steps.

Task 22 :  Creating an user interface to interact with our created model.

Task 23 :  Running your notebook on Streamlit Server in your local machine.

Task 24 :  Pushing your project to GitHub repository.

Task 25 :  Project Deployment on Heroku Platform for free.

Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. All the datasets used in the course are in the resources section.

3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

Music : bensound