README.md

Diabetes Prediction Using Classification Method

Final Year Project

Project Domain / Category

Machine learning , Data science

Tools:

Language:

Python

Framework:

Anaconda

Introduction:

This model will help patient as well as doctors to diagnose that a patient has a diabetes or not. It will be predicting the data on the basis of dataset. A detailed informative data is given which will be used to train the model for prediction.

Display Data:

Display the summary statistics, trends, patterns and insights on the data visually by performing the EDA (Exploratory Data Analysis).

Pre-process the data:

Split the data into train (70% of given data set) and test (30% of given data set). Train the model using Neural Network (machine learning algorithm).

Testing:

Apply test data on trained model for evaluation.

Training:

Train the data set for prediction.

Apply models:

Apply SVM, Decision tree, Logistic regression on the train data.

Results:

Predict a patient has diabetics or not. Generate a confusion matrix to assess the Accuracy, Precision, Recall, F1 score in the trained model.

Accuracy:

Show which model give the high accuracy for prediction.

Save Model:

Save the model for future use.