If you've been studying data science for a while, you might know that in order to learn data science you need to learn math, statistics, and programming. This is a good start for anyone interested in data science, but do you know how to get even more exposure to data science? A project will help you put into practice all the knowledge you've acquired from math, statistics, and programming. So far you might've seen each of them individually, but after you finish a project, the concepts you've learned in each field will make more sense. In this article, I listed some end-to-end data science projects you can do with Python.

I used Jupyter Notebook as the Integrated Development Environment (IDE). The libraries required are; numpy, pandas, matplotlib, pickle or joblib and scikit-learn. These are pre-installed in the latest version of Anaconda. If you don't have any of these libraries you can pip install them or update conda. The dataset used for this model is the Pima Indians Diabetes dataset which consists of several medical predictor variables and one target variable, Outcome.