Diabetes prediction with deep learning and Machine learning

Prerequisite :

• You must have python 3.7 or more installed on your system.

• You must have Spyder, jupyter notebook or pycharm installed on your system. Spyder or jupyter notebook come up with anaconda. you just need to launch them after installing anaconda.

• If you work on a colab no need to install python or Any other IDE, you just need to sign in with google colab.

What you’ll learn

• How to read the data using pandas dataframe

• Perform Basic Exploratory Data analysis

• How to build the deep neural network model for classification problems and evaluate the model.

• How to apply the various Machine learning algorithms on the dataset to classify the diebetic and normal patients. E.g Decision tree, naive bayes, random forest,support vector, KNeighborsClassifier, adaboost, gradient boosting and XGB classifier.
  

Problem Statement:

The objective of this project is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Key highlights of projects or Essence

• This project is about classification analysis.

• This project shows you how to read the data and perform some basic Exploratory data analysis.

• This project shows you how to perform data preprocessing.

• This project shows you how to build the classification model using deep neural networks.

• This project shows you how to use the data to build the diabetes prediction model by applying various machine learning classification algorithms.

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