Please help cross check my work if that was done correctly. see the sample data below and the code i did. If there is a modification kindly anoted it

import pandas as pd import mysecrets import requests from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split

# Load the data from the link data = pd.read_csv('bolttest.csv') data = data.fillna(data.mean()) # Extract the relevant columns and split into training and test sets X = data[["distance", "duration"]] y = data["upfront_price"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train a linear regression model model = LinearRegression() model.fit(X_train, y_train)

# Make predictions on the test data predictions = model.predict(X_test)

# Check the difference between the predicted and metered price for prediction, metered_price in zip(predictions, y_test): if abs(prediction - metered_price) > 0.2 * metered_price: # Return the metered price if the difference is too large print(metered_price) else: # Return the predicted price otherwise print(prediction)

Part I: Data Analysis The key aspect of ride-hailing is upfront pricing, which works as the following way: First, it predicts the price for a ride based on predicted distance and time. This price is what you see on the screen of the phone before ordering a ride. Second, if the metered price based on actual distance and time differs a lot from the predicted one, the upfront price switches to the metered price. 'A lot' means more than 20%. For example, suppose you want to make a ride that upfront price predicts a cost of 5 euros. If the metered price is between 4 and 6 euros, you will end up paying 5 euros. You will end up paying something else if the metered price is anything less than 4 or more than 6 euros. No customer likes surprises (especially when it comes to money!), thats why we always strive to improve our upfront pricing precision for our customers smooth journeys. In the attached file you will find a sample data test.csv. Your task is to analyze this data and identify the top 1 to 2 opportunities that can help us improve upfront pricing precision. The expected output

order_id_new

order_try_id_new

calc_created

metered_price

upfront_price

distance

duration

gps_confidence

entered_by

b_state

dest_change_number

prediction_price_type

predicted_distance

predicted_duration

change_reason_pricing

ticket_id_new

device_token

rider_app_version

order_state

order_try_state

driver_app_version

driver_device_uid_new

device_name

eu_indicator

overpaid_ride_ticket

fraud_score

22

22

2/2/2020 3:37

4.04

10

2839

700

1

client

finished

1

upfront

13384

1091

1376

CI.4.17

finished

finished

DA.4.37

1596

Xiaomi Redmi 6

1

0

-1383

618

618

2/8/2020 2:26

6.09

3.6

5698

1

client

finished

1

upfront

2286

360

2035

CA.5.43

finished

finished

DA.4.39

1578

Samsung SM-G965F

1

0