Pandas, Numpy, Matplotlib, Seaborn, Sklearn, Flask.
Jupyer Notebook, Python Idle(3.9 64 bit).
PIMA Indian Diabetes dataset
Step1: Import required libraries, Import diabetes dataset.
Step2: Pre-process the data to remove all the null values and missing data.
Step3: Perform a percentage split of 80% to divide the dataset as Training set and 20% to Test set.
Step4: Select the machine learning algorithm i.e. KNearestNeighbour, Support Vector Machine, Decision Tree, Logistic regression, Random Forest, SVM, Naïve Bayes algorithm.
Step5: Build the classifier model for the mentioned machine learning algorithm based on the training set.
Step6: Test the Classifier model for the mentioned machine learning algorithm based on the test set.
Step7: Perform Comparison Evaluation of the experimental performance results obtained for each classifier.
Step8: After analyzing based on various measures conclude the best performing algorithm.
Step9: Finally build and Host a Flask Web app.
Step10: A user has to put details like Pregnancies, Insulin Level, Age, BMI, DiabetesPedigree Function, Skin Thickness, Glucose Level etc