Many people say they want to buy the most expensive car when they get rich, but while they are not they have to take cheaper ones. This kind of situation makes us think that a low price is a driving factor while deciding a car. Using this context as motivation we are going to investigate the characteristics of most popular brands on eBay. If the theory that a lower price is determinant the most popular cars will be the ones with a lower price.
The dataset consists of vehicles classifieds section on the German eBay website for vehicles. The main areas of this analysis are data cleaning and data visualization. The data set was download from Kaggle https://www.kaggle.com/orgesleka/used-cars-database.
Steps:
Import Packages,
Read dataset,
Explore data,
Detecting missing values
,Detecting anomalies,Imputing for missing values,Merging issues,Joining multiples datasets
What I learned:
1-The method describes shows the number of unique values, but in some situations you can have no unique values, but it does not mean the values are the same.
e.g.: in a class with students from many countries such as Brazil, Argentina, Mexico, and Japan. If you use the method described in the unique value you can have null values, because you can have 2 from Argentina, 2 from Mexico and 2 from Japan.
A more accurate way would be using the described method and for the columns that have null unique values, you applied the value_counts method because it separates in blocks using our example it would say we have 2 from Argentina, 2 from Mexico and 2 from Japan.
2-It is safer to create a list of columns you want to delete, instead delete one-by-one.
3- Sometimes you can fix missing values with values in other rows.In this project there missing values for the category of a car model, but checking another row with the same model you can just find out the category. the procedure it explained in the project.