Introduction The goal of this project is to apply the statistical concepts that we have learned so far to real-world data both to practice and better understand how these concepts are useful.
Getting Started
This project consists of two parts: Data cleaning & Exploratory data analysis. You should begin with '1.-Data Cleaning.ipynb', and then move on to '2.-EDA.ipynb' as you will want to use the clean dataset you build in part 1 to complete part 2. After these 2 parts are finished, you need to create 8 charts using Matplotlib and 8 charts using Seaborn. Each set should have the distinct chart types including linear, bar, scatter, boxplot, pie, radar, multiple series chart and 3D charts. It would be nice to organize the charts as a subplot. The charts should have titles, labels, grids and other elements that allows to read the information properly. The charts should have proper dimentions and scales. The charts should have reasonable color shcheme. The charts should reflect the given dataset and be, at least, potentially useful in terms of descriptive analytics.
Expectations Write clean, well-commented code Fully explain your responses where necessary Refer to the lecture notes if you have questions Deliverables
'1.-Data Cleaning.ipynb' with your responses '2.-EDA.ipynb' with your responses '3. -Charts_{Name}.ipynd with your charts