✅ Question 1 of 4

Description

You are provided with datasets containing information about taxi drivers and their rides. Your task is to perform some basic data analysis and save the results to a CSV file.

The data is located in the following CSV files:

drivers.csv:

• driver_id (type: int): Unique driver identifier.
• age (type: int): Driver’s age.
• second_language (type: str): Driver’s second language. If a driver doesn’t have a second language, the value is "no".
• rating (type: float): Driver’s average rating.

rides_{i}.csv, split into 4 files (rides_1.csv to rides_4.csv):

• ride_id (type: int): Unique ride identifier.
• driver_id (type: int): Driver’s identifier.
• passenger_id (type: int): Passenger’s identifier.
• date (type: str): Date of the ride.
• status (type: str): Status of the ride; one of ["Rejected by the driver", "Cancelled by the passenger", "Success"].

Your tasks are as follows:

1. Calculate the average driver rating

• Compute the average of the rating column from the drivers.csv file.

2. Calculate the percentage of drivers with a second language

• Determine the percentage of drivers who have a second language (i.e., where second_language is not "no").
• Store the result as:
  • insight_type: "percentage_drivers_with_second_language"
  • value: The calculated percentage.

3. Calculate the ride success rate

• Combine the ride data from all rides_{i}.csv files into a single dataset.
• Calculate the percentage of rides that were successful (i.e., where status is "Success").
• Store the result as:
  • insight_type: "ride_success_rate"
  • value: The calculated percentage.

Output Requirements:

• Save the results in a CSV file named analysis_results.csv.
• The CSV file should have two columns: insight_type and value.
• Each row corresponds to one of the tasks above.
• All numeric values will be considered correct if they match the expected values up to two decimal places.

Notes:

• You are allowed to use any Python libraries you want, including pandas, numpy, and scikit-learn.
• Remember to combine the ride data from all four rides_{i}.csv files before performing the analysis.
• tests/data_analysis_tests_data/expected.csv demonstrates the expected format of the output file and the expected value of average_driver_rating. Note that the values of percentage_drivers_with_second_language and ride_success_rate are shown as zeroes in that file — this is just a placeholder and not the actual expected result.

✅ Question 2 of 4

Description

You are given access to the data containing information about taxi drivers and their rides, created by April 15th, 2023. When calculating any time features, consider April 15th, 2023 as today.

The data is distributed across 6 different files:

drivers.csv:

• driver_id (type: int): Unique identifier of the driver
• car_id (type: int)
• age (type: int)
• started_driving_year (type: int)
• second_language (type: str): If a driver doesn’t have a second language, the value is "no"
• rating (type: float)
• net_worth_of_tips (type: float)
• driver_class (type: str): One of the following: ["A class", "B class"]

rides_{i}.csv, split into 4 files:

• ride_id (type: int)
• driver_id (type: int)
• passenger_id (type: int)
• date (type: str)
• status (type: str): One of the following: ["Rejected by the driver", "Cancelled by the passenger", "Success"]
• car_clearness_upvote_given (type: bool)
• politeness_upvote_given (type: bool)
• communication_upvote_given (type: bool)
• punctuality_upvote_given (type: bool)
• complaint_given (type: bool)

cars.csv:

• car_id (type: int)
• model (type: str)
• manufacture_year (type: int)
• last_inspection_date (type: str)

Your task:

Retrieve the needed information from the data about each driver and store it in the collected.csv file.

Your goal is to obtain a table with the following columns:

• driver_id (int)
• car_model (str)
• car_manufacture_year (int)
• days_since_inspection (int): number of days passed since the last inspection
• age (int)
• experience (int): calculated as 2023 - started_driving_year
• second_language (str)
• rating (float)
• net_worth_of_tips (float)
• number_of_upvotes (int)
• driver_class (str)

You may order rows and columns in any way you find comfortable to work with. Tests are designed to be order-agnostic.

✅ Question 3 of 4

Description

You are given a dataset containing information about taxi drivers and their performance metrics. The dataset includes various attributes for each driver.

The dataset has the following columns:

• driver_id (int)
• car_model (str)
• car_manufacture_year (int)
• days_since_inspection (int)
• age (int)
• experience (int)
• second_language (str)
• rating (float)
• net_worth_of_tips (float)
• number_of_rejected_rides (int): number of rides with status = "Rejected by the driver"
• number_of_upvotes (int)
• number_of_complaints (int)
• number_of_incidents (int)
• driver_class (str)

The dataset is divided into train and test sets:

• Train set: 70% — located at data/train.csv
• Test set: 30% — located at data/test.csv

Perform the following data preparation steps:

a. Fill missing values in the age column with the mean age of the drivers, rounded to the nearest integer.

b. Convert the second_language and car_model columns into numerical values using ordinal encoding. Encoding should:

• Start from 0
• Be consecutive integers

✅ Correct mapping example:

• "Nissan Altima" → 3
• "Ford Fusion" → 1
• "Honda Accord" → 0
• "Hyundai Sonata" → 2
  => Set: (0, 1, 2, 3)

❌ Incorrect mapping example:

• "Nissan Altima" → 5
• "Ford Fusion" → 4
• "Honda Accord" → 2
• "Hyundai Sonata" → 1
  => Set: (1, 2, 4, 5)

c. Normalize the net_worth_of_tips column using Standard Scaling.

d. Convert the driver_class column into numerical values:

• "A class" → 0
• "B class" → 1

Note: Please ensure not to cause data leakage from the test set into the train set.

After completing all steps, save the processed data to:

• processed_train.csv
• processed_test.csv

Ensure that:

• Values in net_worth_of_tips are written with exactly 5 digits after the decimal point.

Execution Constraints:

• Time limit: 8 seconds
• Memory limit: 4 GB

✅ Question 4 of 4

Description

Using the cleaned dataset from the prior question, your goal is to train a classifier that can predict whether the driver is of:

• A class (0)
• B class (1)

This is a free-form task — use any machine learning model or Python libraries.

Dataset:

The test set from the previous task is split into:

• Training set: 70% (data/train.csv)
• Validation set: 15% (data/val.csv)
• Test set: 15% (data/test.csv)

Your task:

Predict classes of drivers from test.csv with the lowest possible error.

Use these metrics:

• precision
• recall
  ✅ B class is the positive class.

Goal:

Maximize recall, while keeping precision relatively high

Once satisfied with validation set performance, submit predictions for test set in:

• predictions.csv
  With the format:

driver_class
0
1
0
...

Scoring the solution:

• Only the first 10 rows of test data are scored immediately
• Submit to check score on the full dataset

Execution Constraints:

• Time limit: 8 seconds
• Memory limit: 4 GB

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