Problem: To predict Whether a patient has diabetes or not.
Platform: Python Jupyter
Dataset: Diabetes dataset : https://www.kaggle.com/uciml/pima-indians-diabetes-database

Features:Pragnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, DiabetesPedigreeFunction, Age.
Outcome: in binary 1/0  either a person has diabetes or not.
Algorithms: Logistic Regression, Random Forest and Gaussian Algorithm

Description:
-Dataset: Patient Diabetes
-Predicting diabetes result by Logistic Regreesion.

Steps:
1-First Load The dataset from the path.

2-Showing Co-Realation Features.

3-Finding number of true and False Cases.

4-Next tab shows the partition of dataset for training data and test data and shows percentage of how much data is for training data and how much is for test data.

5-Next showing the percentage of training data and test data according to the true and false cases.

6-Finding Modle Accurasy by Gaussian algorithm from Naive Bayes.

7-Finding Modle Accurasy by Random Forest Algorithm.

8-Showing Accuraccy for training and test data by each algorithm.

OUTPUT Accuraccy:
Training Accuracy: 0.7635    // Gaussian algorithm from Naive Bayes 
Test Accuracy: 0.7446

Model Trainind Accuracy: 0.98  // Random Forest
Model Test Accuracy: 0.76

Model Training Accuracy: 0.7635   // Sklearn metrices
Model Test Accuracy: 0.7446

Model Test Accuracy: 0.77       // Logistic Regression
Model Training Accuracy: 0.74