Diabetes-Prediction

• This Notebook will help predict the chances of a women having Diabetes based on input provided.

Data Information:

• Downloaded from Kaggle
• Pregnancies: Number of times pregnant
• Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skin fold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index (weight in kg/(height in m)2)
• DiabetesPedigreeFunction: Diabetes pedigree function (a function which scores likelihood of diabetes based on family history)
• Age: Age (years)
• Outcome: Class variable (0 if non-diabetic, 1 if diabetic)

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EDA(Exploratory Data Analysis)

• Skinthichkness was replaced from 0 to 5mm, as minimal checkpoint
• All the values with pregency value greater than 10 was coverted to 10.
• Only Glucode value greater than 0 was taken into account
• BMI values were changed to 10 as minimal value since many had BMI=0
• Insulin Values was converted to mean, as many values were between 0-10

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Observation

• Relation between various parameters of the dataset.

• Women with lesser number of pregrenancy have less chances of getting Diabetes

• Women who have high DiabetesPedigreeFunction are more likely to have Diabetes
• With less DiabetesPedigreeFunction, the chances are very low (i.e when the value is less than 0.4)

• Elderly woman are more likely to have Diabetes.

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Machine Learning Algorithm

• Train and Test sizes were 70% and 30% each.
• ML model Multiple Logistic Regression gives us an accuracy of 82.09%
• Decision Tree Classifier gives an accuracy_score of 71.16%
• RandomForestClassifier gives us an accuracy_score of 76.84%

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Prediction

• Prediction model was built and deployed.

• The output '1' predicts the chances of having diabtes and '0' predicts the chances are very less