Denniskamau/IBM-Research-Africa-Technical-Challenge

# Summary of the solution ## Tools -Language: Python3 -Liblary: Pandas ## Approach - I decided to use OOP because this way I can reuse methods already defined and implements for a given solution - Example of this methods are reading data and checking for Empty value - For working with data I decided to use pandas liblary because it provide simplicity while working with large dataset ## First Question - First I had to load the data and factor in for Input/Output Error - Next check to see if there are any missing values in the dataset - Understand how the dataset is or have a look at it before sorting - Since we only want data for Sunday then we hav to filter out the dataset and drop the rows that contains the data where the day is not Sunday - In the already filtered dataset find the top seven most traveled routesand rank them accordingly in descending order. - Finaly calculate the average of the top seven routes ## Second Question - Make use of the methods defined in solution one to load the dataset and check if there are any empty values - If everything is OK filter the dataset according to the route and drop all the rows where the column of travel_from is not Kijauri - Next filter out the remaining dataset and drop all the rows where the travel_time was after 0730hrs - The resultant dataset will only contain the values for travel duration that occured before 7:30 - Calculate the probability of the mean of travel being a shuttle because the dataset has both bus and shuttle ## Third Question - Make use of methods in solution one for loading the dataset and checking if any of the fields are empty - Get all transaction receipt that have letters MK in then in that order - Get the index of letter M and add 2 more index to get the next letter after index of K - store this letters in a separate list - Loop through the list and hold the letter that appears the most times - Due to the fact that it is appearing most times then it is the most probale letter in the t …