Title

Data Science: Diabetes Prediction- Model Building Deployment

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

3.83 (3 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Data Analysis and Understanding

Data Cleaning and Imputation

Data Preparation

Model Building for Diabetes Prediction

Hyperparameter Tuning

Classification Metrics

Model Evaluation

Running the model on a local Streamlit Server

Pushing your notebooks and project files to GitHub repository

Deploying the project on Heroku Cloud Platform

Why take this course?

🎓 Data Science: Diabetes Prediction - Model Building and Deployment 🚀

Course Headline: A Practical, Hands-On Machine Learning Project on Diabetes Prediction

Dive deep into the world of Data Science with this comprehensive course that takes you from initial data analysis to deploying your model on the cloud. This is a real-world project where you'll learn to predict diabetes using Machine Learning Models, evaluate their performance, and make them available for users through a sleek interface.

Course Overview:

This course will guide you step by step through the entire Data Science pipeline. You'll start with understanding the data, move on to cleaning it, selecting the right model, tuning hyperparameters, and finally deploying your model for real-world applications. With a focus on practical application, you'll learn:

Tasks Outline:

Installing Packages - Set up your environment with all the necessary tools and libraries.
Importing Libraries - Get familiar with the libraries that will be used throughout the course.
Loading the Data - Bring your data into a usable format with pandas.
Pandas Profiling - Gain insights into your dataset.
Understanding the Data - Analyze and visualize data to make informed decisions.
Data Cleaning and Imputation - Handle missing values, outliers, and prepare the dataset for modeling.
Train Test Split - Learn how to divide your data into training and test sets properly.
Scaling using StandardScaler - Prepare your data for model building with proper scaling techniques.
About Confusion Matrix - Understand the metrics used to evaluate classification models.
About Classification Report - Analyze the performance of your model from various angles.
About AUC-ROC - Learn how to evaluate models for imbalanced datasets.
Checking for Model Performance Across Models - Compare different algorithms and their performance.
Creating Random Forest Model with Default Parameters - Build a baseline model to start with.
Model Evaluation – Classification Report, Confusion Matrix, AUC-ROC - Evaluate your model's performance rigorously.
Hyperparameter Tuning using RandomizedSearchCV - Optimize your model to perform its best.
Building RandomForestClassifier Model with Selected Hyperparameters - Implement the hyperparameters that give you the best results.
Final Model Evaluation – Classification Report, Confusion Matrix, AUC-ROC - Test your optimized model to ensure it's ready for deployment.
Final Inference - Draw conclusions from your evaluations.
Loading the Saved Model and Scaler Objects - Prepare your model for real-world use.
Testing the Model on Random Data - Validate your model's robustness with new, unseen data.
What is Streamlit? - Discover how to build interactive web apps with Streamlit.
Creating a User Interface to Interact with Our Created Model - Build an intuitive and user-friendly interface for your model.
Running Your Notebook on Streamlit Server Locally - Learn how to run your project locally without any server issues.
Pushing Your Project to GitHub Repository - Version control and share your project with the world.
Project Deployment on Heroku Platform for Free - Deploy your model so users can interact with it online.

What You Will Receive:

Certificate of Completion: Showcase your new skills with a certificate to prove your expertise in Data Science.
Educational Resources: Access to all the resources, including the dataset and code, used throughout the course.
Lifetime Access: Learn at your own pace, with lifetime access to the course material.

Course Requirements:

Basic knowledge of Python programming.
Familiarity with machine learning concepts (highly recommended but not mandatory).

Enroll Now and Start Your Data Science Journey!

Grab your coffee, click on the 'ENROLL NOW' button, and embark on a journey to master Data Science through a real-world project. Whether you're a beginner or an advanced learner, this course will equip you with the skills needed to predict diabetes and deploy your models using cutting-edge tools like Streamlit and Heroku.

Happy Learning! 📚✨

Note: This course and its related contents are for educational purposes only. The project aims to promote understanding and awareness of diabetes through data science. Always ensure that you comply with ethical guidelines, data privacy laws, and regulations when working with real-world datasets.

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