df.shape
Creating a list type variable called **col_remove**, in which the features that are not important for our goal will be added
col_remove = ['id']
## Analysis of Missing Values
df.isna().sum()
Loading a class called Utils, this class helps to vizualize the data
utils = Utils()
utils.plot_variables_nan(df)
utils.df_nan
Removing the **riesgo** variable since it has more than 99% the NaN
df = df.drop(columns=['riesgo'])
df.shape
## Analysis of target value
df[['client']].hist()
plt.ylabel('Count')
plt.show()
