Feature Engineering Bussiness Problem It is desired to develop a machine learning model that can predict whether people have diabetes when their characteristics are specified. You are expected to perform the necessary data analysis and feature engineering steps before developing the model. Dataset Story The dataset is part of the large dataset held at the National Institutes of Diabetes-Digestive-Kidney Diseases in the USA. Data used for diabetes research on Pima Indian women aged 21 and over living in Phoenix, the 5th largest city of the State of Arizona in the USA. The target variable is specified as "outcome"; 1 indicates positive diabetes test result, 0 indicates negative. Pregnancies : Number of pregnancies Glucose : 2-hour plasma glucose concentration in the oral glucose tolerance test Blood Pressure : Blood Pressure (Low blood pressure) (mmHg) SkinThickness : Skin Thickness Insulin : 2-hour serum insulin (mu U/ml) DiabetesPedigreeFunction : Function (2 hour plasma glucose concentration in oral glucose tolerance test) BMI : Body Mass Index Age : Age(year) Outcome : Have the disease (1) or not (0) Project Tasks Step 1 : Examine the overall picture. Step 2 : Capture the numeric and categorical variables. Step 3 : Analyze the numerical and categorical variables. Step 4 : Perform target variable analysis. (The mean of the target variable according to the categorical variables, the mean of the numeric variables according to the target variable) Step 5 : Analyze the outlier observation. Step 6 : Perform missing observation analysis. Step 7 : Perform correlation analysis. Task 2 : Feature Engineering Step 1 : Take necessary actions for missing and outlier values. There are no missing observations in the data set, but Glucose, Insulin etc. Observation units containing 0 in the variables may represent the missing value. E.g; a person's glucose or insulin value will not be 0. Considering this situation, you can assign the zero values to the relevant values as NaN and then apply the operations to the missing values. Step 2 : Create new variables. Step 3 : Perform the encoding operations. Step 4 : Standardize for numeric variables. Step 5 : Create the model.