This device is not compatible.

You will learn to:

Perform various preprocessing steps such as handling missing values and data skewness, scaling, etc.

Perform exploratory data analysis and create meaningful visualizations.

Build simple neural networks with Keras and TensorFlow.

Train and evaluate models using various metrics and visualizations.

Skills

Data Science

Data Visualisation

Deep Learning

Prerequisites

Hands-on experience with Python

Basic understanding of Keras

Basic understanding of deep learning fundamentals

Technologies

Keras

Python

seaborn

TensorFlow

Project Description

In this project, weâ€™ll use the Python Keras library to create neural networks and use them to predict diabetes with patient health data.

Weâ€™ll load and analyze the data using pandas, visualize it using matplotlib and seaborn. Weâ€™ll perform data cleaning, preprocessing and feature selection, and then split it into training, validation and testing datasets using NumPy. After the data is ready, we will train simple deep learning models using Keras. We will then plot the training curves and perform model evaluation using scikit-learn.

Project Tasks

1

Getting Started

Task 0: Introduction

Task 1: Import the Libraries

Task 2: Load the Dataset

2

Exploratory Data Analysis

Task 3: List the Missing Values and Display Dataset Description

Task 4: Create a Pairplot

3

Preprocessing

Task 5: Handle Zero Values

Task 6: Handle Skewness and Feature Scaling

Task 7: Create Dataset Splits

4

Model Training

Task 8: Create the Model

Task 9: Train the Model

Task 10: Print the Training Curves

5

Performance Evaluation

Task 11: Create a Confusion Matrix

Task 12: Compute the Classification Metrics

Congratulations!