Maximizing revenue for drivers

PROBLEM STATEMENT

In fast paced taxi booking sector, maing the most of revenue is essential for long term success and driver happiness. Our goal is to use data-driver insights to maximise revenue streams for taxi drivers in order to meet this need. Our research aims to determine whether payment methods have an impact on fare pricing by focusing on the relationship between payment type and fare amount.

BRIEF

1. Performed Data Exploration and Segmentation to study the key characteristic features of Payment Type and Trip fare using Python.
2. Executed Hypothesis Testing, Statistical Analysis, Anomaly Detection and Trend Analysis, and Feature Engineering to identify relationship between total fare amount and payment type Customer Behavior.
3. Designed Interactive dashboards using PowerBIh to analyze the trends, give recommendations and publish the findings.

DATA DESCRIPTION

The dataset is collected from NYC.GOV - https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page

1. VendorID: A code indicating the TPEP provider that provided the record. 1= Creative Mobile Technologies, LLC; 2= VeriFone Inc.

2. tpep_pickup_datetime: The date and time when the meter was engaged.

3. tpep_dropoff_datetime: The date and time when the meter was disengaged.

4. Passenger_count: The number of passengers in the vehicle. This is a driver-entered value.

5. Trip_distance: The elapsed trip distance in miles reported by the taximeter.

6. PULo cationID: TLC Taxi Zone in which the taximeter was engaged

7. DOLocationID: TLC Taxi Zone in which the taximeter was disengaged

8. RateCodeID: The final rate code in effect at the end of the trip. 1= Standard rate 2=JFK 3=Newark 4=Nassau or Westchester 5=Negotiated fare 6=Group ride Store_and_fwd_flag This flag indicates whether the trip record was held in vehicle memory before sending to the vendor, aka “store and forward,” because the vehicle did not have a connection to the server. Y= store and forward trip N= not a store and forward trip

9. Payment_type: A numeric code signifying how the passenger paid for the trip. 1= Credit card 2= Cash 3= No charge 4= Dispute 5= Unknown 6= Voided trip

10. Fare_amount: The time-and-distance fare calculated by the meter.Extra Miscellaneous extras and surcharges. Currently, this only includes the $0.50 and $1 rush hour and overnight charges.

11. MTA_tax: $0.50 MTA tax that is automatically triggered based on the metered rate in use.

12. Improvement_surcharge: $0.30 improvement surcharge assessed trips at the flag drop. The improvement surcharge began being levied in 2015.

13. Tip_amount: Tip amount – This field is automatically populated for credit card tips. Cash tips are not included.

14. Tolls_amount: Total amount of all tolls paid in trip.

15. Total_amount: The total amount charged to passengers. Does not include cash tips.

16. Congestion_Surcharge: Total amount collected in trip for NYS congestion surcharge.

17. Airport_fee: $1.25 for pick up only at LaGuardia and John F. Kennedy Airports

METHODOLOGY

1. Marketing Analysis.ipynb -

A) Data Cleaning:

• Renamning Columns

• String Manipulations

• Missing values (Dropping the NaN as they are <1%)

• DataType Conversion

B) Exploratory Data Analysis (EDA):

• Outlier Analysis (Replacing with median)

• Feature Engineering

• Anomaly Detection and Trend Analysis

• Correlation Analysis

C) Performing Statistical Analysis:

• Calculating T static and P-values

• Feature Importance (Using Random Forest)

Finding:

1. The proportion of customers paying with cards is significantly higher than those paying with cash, with card payments accounting for 77% of all transactions compared to cash payments at 23%.
2. This indicates a strong preference among customers for using card payments over cash, potentially due to convenience, security, or incentives offered for card transactions.

- Amount of customers prefer to pay with card vs cash

• T static (r): 152.9609345425821
• p-value: 0.00

We got a T-test of 152.96 and a p-value of almost zero, which states that they are statistically significant and have a positive correlation. (If the p-value is > 0.05, we will fail to reject the null hypothesis, where they do not correlate.)

Recommendations

Patterns:

1. Encourage customers to pay with credit cards to capitalize on the potential for generating more revenue for taxi cab drivers.

2. Implement strategies such as offering incentives or discounts for credit card transactions to incentivize customers to chooset this payment method.

3. Provide seamless and secure credit card payment options to enhance customer convenience and encourage adoption of this preferred payment method.