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<speak> So, here I can see that only total stop has only one null value.<break strength="x-strong"/> And also route has one null value. <break strength="x-strong"/> So all other columns don't have null values. <break strength="x-strong"/> Let's drop these null values.<break strength="x-strong"/> To drop null values, we use train_df.drop n a.<break strength="x-strong"/> We will write train_df.drop n a in place = True. <break strength="x-strong"/>Let's run the cell.<break strength="x-strong"/> And also, we will check the duplicate value and remove them. <break strength="x-strong"/>Let's run the cell.<break strength="x-strong"/> So the important part of data analysis is analyzing the duplicate values, and removing them.<break strength="x-strong"/> The duplicated method helps in analyzing duplicate values only. <break strength="x-strong"/> This method returns Boolean Series denoting duplicate rows. <break strength="x-strong"/> Boolean series contains True or false<break strength="x-strong"/> Returns True only for unique elements. <break strength="x-strong"/> So, we will remove the duplicate values by using drop duplicates. <break strength="x-strong"/> Let's run the cell.<break strength="x-strong"/> Now, let's check the data frame.<break strength="x-strong"/> After removing null values and duplicate values, this is our data frame.<break strength="x-strong"/> Now, let's proceed to check the shape. <break strength="x-strong"/> Let's check the shape after removing the duplicate values. <break strength="x-strong"/> Here, we can see that now we have 10462 data points.<break strength="x-strong"/> Means, we have 10462 rows. <break strength="x-strong"/>Before removing null and duplicate values, it was 10683 rows. <break strength="x-strong"/>683 and here we have 462. <break strength="x-strong"/>Approximately 200 data points are removed after removing the duplicate values. <break strength="x-strong"/> Now, let's check the value counts of additional info columns. <break strength="x-strong"/> <break strength="x-strong"/> </speak>