Predicting that whether the patient has diabetes or not on the basis of the features we will provide to our machine learning model, and for that, we will be using the famous Pima Indians Diabetes Database.
1.Data analysis: Here one will get to know about how the data analysis part is done in a data science life cycle.
2.Exploratory data analysis: EDA is one of the most important steps in the data science project life cycle and here one will need to know that how to make inferences from the visualizations and data analysis.
3.Model building: Here we will be using ML model.
4.Saving model: Saving the best model using pickle to make the prediction from real data.
1.Get the code from the repository
(https://github.com/csoren66/Diabetics_Prediction.git)
2.Download the dataset that will be used to train a transaction classifier. Unzip it and put the content (diabetics.csv) under main folder (Diabetics_Prediction)
3.Install required python packages if previously not installed.
4.Finally run on Jupyter Notebook or Google Colab and enjoy 😉