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<speak> Welcome to the lesson on importing libraries and data set.<break strength="x-strong"/> So let's start bulling the machine learning model.<break strength="strong"/> I will provide you with the link to this folder.<break strength="x-strong"/> You can download this folder as a zip file. <break strength="x-strong"/>You can use this file path to import the data set, and you can start working on it. <break strength="x-strong"/>Let's go to our Jupyter notebook.<break strength="x-strong"/> Let's start solving this machine learning problem. <break strength="x-strong"/>So, here this data set contains several medical predictor variables and<break strength="strong"/> one target variable. <break strength="x-strong"/>Let's see the description.<break strength="x-strong"/> The goal is to build a machine learning model to predict whether a patient has diabetes or not.<break strength="x-strong"/> What is Machine Learning Model?<break strength="x-strong"/> A model in machine learning is the output of a machine learning algorithm run on data.<break strength="x-strong"/> A model represents what was learned by a machine learning algorithm.<break strength="x-strong"/> First, we have the pregnancies column, which is the number of times a person has become pregnant.<break strength="x-strong"/> I mean the person is female then, how many times she has become pregnant in her life.<break strength="x-strong"/> Next, we will check the plasma glucose concentration.<break strength="x-strong"/> Then we will check diastolic blood pressure, which is in m m. <break strength="x-strong"/> And then, we will check triceps skinfold thickness, which is skin thickness in m m<break strength="x-strong"/> Then, we will check insulin, which is 2- hours of serum insulin.<break strength="x-strong"/> Then, we will check the body mass index B M I, which is in kg's.<break strength="x-strong"/> And then, we will check the Diabetes Pedigree Function.<break strength="x-strong"/> And after that, we will check the age of that patient in years.<break strength="x-strong"/> Finally, we will check the outcome, whether the person has diabetes or not. <break strength="x-strong"/> Here in the outcome, we have two variables zero and 1. <break strength="x-strong"/> </speak>