Project Overview

The projects objective is to examine a dataset which consists of car sale information over the last few decades. We seek to examine this data and give actionable insights to car manufacturers to increase their sales in the future.

We use pandas profiling before and after we process our data to have a clear picture of the data that we will finally be using for the EDA.

After processing our data, we look to find correlations between our input features and try to understand how they correlate to car sales.



Correlation between quantitative input features


Features such as car body are examined in order to find out the distribution of cars sold with respect to their body. This along with other similar insights are inspected in order to understand how they affect car sales. These insights will help us provide better suggestions to car manufactureres.



Pie chart representing sales with respect to car body type

After answering various questions about our data, we look into a few assumptions in order to see if they are valid or not. This will also give us a slightly deeper insight into our data.

Lastly, we provide insights that would be helpful to car manufacturers in order to increase their sales. These insights take into account the correlation of input features with car sales. Head over to the link below to check out the entire python notebook which contains a more in-depth exploration of the data along with the steps involved in training our model and evaluating its performance.

• Repository