Describing the profile of a bank’s coustomer: Cleaning & EDA

The code and datasets are available in my Github repository🤓

In this project, I used the public dataset that it is available in the youtube channel (see in the description box of this video).

🔎 The goal of this project is described or recognized the coustomer’s profile after a marketing campaign. However, I had to clean the dataset in order to implement the Exploratory Data Analysis. Let me explain here, a few steps that I followed in this project.

For cleanning process

1. I found some ‘Nan’ values inside the columns of the dataset.
2. Due these ‘Nan’ values are less the 1% of the all entries in dataset, I decided to eliminate these columns.
3. I explored the numerical values and I found some non-sense or outlier vaklues. So I removed them.
4. Then, I worked the categorical entries. There were duplicates with some differences on fontsize. I used some dictionary in order to unify all those values.
5. Save the cleanned dataset.

For Exploratory Data Analysis