README.md

Taxi Fare Prediction

ML.NET version	API type	Status	App Type	Data type	Scenario	ML Task	Algorithms
v0.10	Dynamic API	Up-to-date	Console app	.csv files	Price prediction	Regression	Sdca Regression

In this introductory sample, you'll see how to use ML.NET to predict taxi fares. In the world of machine learning, this type of prediction is known as regression.

Problem

This problem is centered around predicting the fare of a taxi trip in New York City. At first glance, it may seem to depend simply on the distance traveled. However, taxi vendors in New York charge varying amounts for other factors such as additional passengers, paying with a credit card instead of cash and so on. This prediction can be used in application for taxi providers to give users and drivers an estimate on ride fares.

To solve this problem, we will build an ML model that takes as inputs:

• vendor ID
• rate code
• passenger count
• trip time
• trip distance
• payment type

and predicts the fare of the ride.

ML task - Regression

The generalized problem of regression is to predict some continuous value for given parameters, for example:

• predict a house prise based on number of rooms, location, year built, etc.
• predict a car fuel consumption based on fuel type and car parameters.
• predict a time estimate for fixing an issue based on issue attributes.

The common feature for all those examples is that the parameter we want to predict can take any numeric value in certain range. In other words, this value is represented by integer or float/double, not by enum or boolean types.

Solution

To solve this problem, first we will build an ML model. Then we will train the model on existing data, evaluate how good it is, and lastly we'll consume the model to predict taxi fares.

1. Build model's pipeline

Building a model includes: uploading data (taxi-fare-train.csv with TextLoader), transforming the data so it can be used effectively by an ML algorithm (StochasticDualCoordinateAscent in this case):

    // STEP 1: Common data loading configuration
    let textLoader = 
        mlContext.Data.CreateTextLoader(
            separatorChar = ',',
            hasHeader = true,
            columns = 
                [|
                    TextLoader.Column("VendorId", Nullable DataKind.Text, 0)
                    TextLoader.Column("RateCode", Nullable DataKind.Text, 1)
                    TextLoader.Column("PassengerCount", Nullable DataKind.R4, 2)
                    TextLoader.Column("TripTime", Nullable DataKind.R4, 3)
                    TextLoader.Column("TripDistance", Nullable DataKind.R4, 4)
                    TextLoader.Column("PaymentType", Nullable DataKind.Text, 5)
                    TextLoader.Column("FareAmount", Nullable DataKind.R4, 6)
                |]
            )

    let baseTrainingDataView = textLoader.Read trainDataPath
    let testDataView = textLoader.Read testDataPath

    //Sample code of removing extreme data like "outliers" for FareAmounts higher than $150 and lower than $1 which can be error-data 
    //let cnt = baseTrainingDataView.GetColumn<decimal>(mlContext, "FareAmount").Count()
    let trainingDataView = mlContext.Data.FilterByColumn(baseTrainingDataView, "FareAmount", lowerBound = 1., upperBound = 150.)
    //let cnt2 = trainingDataView.GetColumn<float>(mlContext, "FareAmount").Count()

    // STEP 2: Common data process configuration with pipeline data transformations
    let dataProcessPipeline =
        EstimatorChain()
            .Append(mlContext.Transforms.CopyColumns("Label", "FareAmount"))
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("VendorIdEncoded", "VendorId"))
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("RateCodeEncoded", "RateCode"))
            .Append(mlContext.Transforms.Categorical.OneHotEncoding("PaymentTypeEncoded", "PaymentType"))
            .Append(mlContext.Transforms.Normalize("PassengerCount", "PassengerCount", NormalizingEstimator.NormalizerMode.MeanVariance))
            .Append(mlContext.Transforms.Normalize("TripTime", "TripTime", NormalizingEstimator.NormalizerMode.MeanVariance))
            .Append(mlContext.Transforms.Normalize("TripDistance", "TripDistance", NormalizingEstimator.NormalizerMode.MeanVariance))
            .Append(mlContext.Transforms.Concatenate("Features", "VendorIdEncoded", "RateCodeEncoded", "PaymentTypeEncoded", "PassengerCount", "TripTime", "TripDistance"))
            .AppendCacheCheckpoint(mlContext)
            |> downcastPipeline

    // (OPTIONAL) Peek data (such as 5 records) in training DataView after applying the ProcessPipeline's transformations into "Features" 
    Common.ConsoleHelper.peekDataViewInConsole<TaxiTrip> mlContext trainingDataView dataProcessPipeline 5 |> ignore
    Common.ConsoleHelper.peekVectorColumnDataInConsole mlContext "Features" trainingDataView dataProcessPipeline 5 |> ignore

    // STEP 3: Set the training algorithm, then create and config the modelBuilder - Selected Trainer (SDCA Regression algorithm)                            
    let trainer = mlContext.Regression.Trainers.StochasticDualCoordinateAscent(labelColumn = "Label", featureColumn = "Features")

    let modelBuilder = 
        Common.ModelBuilder.create mlContext dataProcessPipeline
        |> Common.ModelBuilder.addTrainer trainer

2. Train model

Training the model is a process of running the chosen algorithm on a training data (with known fare values) to tune the parameters of the model. It is implemented in the Fit() API. To perform training we just call the method while providing the DataView.

    let trainedModel = 
        modelBuilder
        |> Common.ModelBuilder.train trainingDataView

3. Evaluate model

We need this step to conclude how accurate our model operates on new data. To do so, the model from the previous step is run against another dataset that was not used in training (taxi-fare-test.csv). This dataset also contains known fares. Regression.Evaluate() calculates the difference between known fares and values predicted by the model in various metrics.

    let metrics = 
        (trainedModel, modelBuilder)
        |> Common.ModelBuilder.evaluateRegressionModel testDataView "Label" "Score"

    Common.ConsoleHelper.printRegressionMetrics (trainer.ToString()) metrics

To learn more on how to understand the metrics, check out the Machine Learning glossary from the ML.NET Guide or use any available materials on data science and machine learning.

If you are not satisfied with the quality of the model, there are a variety of ways to improve it, which will be covered in the examples category.

Keep in mind that for this sample the quality is lower than it could be because the datasets were reduced in size for performance purposes. You can use the original datasets to significantly improve the quality (Original datasets are referenced in datasets README).

4. Consume model

After the model is trained, we can use the Predict() API to predict the fare amount for specified trip.

   //Sample: 
    //vendor_id,rate_code,passenger_count,trip_time_in_secs,trip_distance,payment_type,fare_amount
    //VTS,1,1,1140,3.75,CRD,15.5
    let taxiTripSample = 
        {
            VendorId = "VTS"
            RateCode = "1"
            PassengerCount = 1.0f
            TripTime = 1140.0f
            TripDistance = 3.75f
            PaymentType = "CRD"
            FareAmount = 0.0f // To predict. Actual/Observed = 15.5
        };

    let resultprediction = 
        Common.ModelScorer.create mlContext
        |> Common.ModelScorer.loadModelFromZipFile modelPath
        |> Common.ModelScorer.predictSingle taxiTripSample

    printfn "=============== Single Prediction  ==============="
    printfn "Predicted fare: %.4f, actual fare: 15.5" resultprediction.FareAmount
    printfn "=================================================="

Finally, you can plot in a chart how the tested predictions are distributed and how the regression is performing with the implemented method PlotRegressionChart() as in the following screenshot: