ML Project

Link: Taxi - Fare Prediction 

Dataset Overview:

This dataset presents an opportunity to construct predictive models aimed at estimating the total amount paid by travelers for taxi journeys. With access to a training set containing the target variable 'total_amount' along with various informative features, participants are challenged to create accurate predictive models.

Columns Description:

The dataset comprises various columns, each offering valuable insights into taxi rides. Notably:

• total_amount: The total amount paid by the traveler for the taxi ride.

• VendorID: An identifier for taxi vendors.

• tpep_pickup_datetime and tpep_dropoff_datetime: Timestamps indicating pickup and drop off times.

• passenger_count: The number of passengers during the ride.

• trip_distance: The distance traveled during the trip.

• RatecodeID: Rate code for the ride.

• store_and_fwd_flag: A flag indicating whether the trip data was stored and forwarded.

• PULocationID and DOLocationID: Pickup and drop off location identifiers.

• payment_type: Payment type used for the ride.

Models Used:

1. Multiple Linear Regression

2. SGD Regressor

3. k - Neighbors Regressor

4. Decision Tree Regressor

5. Random Forest Regression