what is wrong with this code

#include "src/XGBRegressor.h"  // to include the generated XGBReg model header file
#include <fstream>
#include <sstream>
#include <vector>
#include <string>
#include <random>
#include <algorithm>
#include <iostream>

struct DataRecord {
    int dayOfWeek;
    int season;
    double timeOfDay;
    int zoneID;
    double temperature;
    double humidity;
    double illuminance;
    double eCO2;
    double tvoc;
    double sound;
    double aqi;
    double overallScore;  // The actual score to compare with
};

// Function to read the CSV file and populate the data
std::vector<DataRecord> readCSV(const std::string& filename) {
    std::ifstream file(filename);
    std::vector<DataRecord> data;
    std::string line;

    // Skip the header
    std::getline(file, line);

    while (std::getline(file, line)) {
        std::stringstream ss(line);
        DataRecord record;
        std::string temp;

        // Parse each value from the CSV and store it in the record
        std::getline(ss, temp, ','); record.dayOfWeek = std::stoi(temp);
        std::getline(ss, temp, ','); record.season = std::stoi(temp);
        std::getline(ss, temp, ','); record.timeOfDay = std::stod(temp);
        std::getline(ss, temp, ','); record.zoneID = std::stoi(temp);
        std::getline(ss, temp, ','); record.temperature = std::stod(temp);
        std::getline(ss, temp, ','); record.humidity = std::stod(temp);
        std::getline(ss, temp, ','); record.illuminance = std::stod(temp);
        std::getline(ss, temp, ','); record.eCO2 = std::stod(temp);
        std::getline(ss, temp, ','); record.tvoc = std::stod(temp);
        std::getline(ss, temp, ','); record.sound = std::stod(temp);
        std::getline(ss, temp, ','); record.aqi = std::stod(temp);
        std::getline(ss, temp, ','); record.overallScore = std::stod(temp);

        data.push_back(record);
    }

    return data;
}

// Function to randomly sample 20% of the data
std::vector<DataRecord> sampleData(const std::vector<DataRecord>& data, double sampleFraction = 0.2) {
    std::vector<DataRecord> sampledData = data;
    std::random_device rd;
    std::mt19937 g(rd());
    std::shuffle(sampledData.begin(), sampledData.end(), g);

    size_t sampleSize = static_cast<size_t>(sampleFraction * data.size());
    sampledData.resize(sampleSize);
    return sampledData;
}

// Predict the overall score using the trained XGBoost model
float predictOverallScore(int dayOfWeek, int season, float timeOfDay, int zoneID,
                          float temperature, float humidity, float illuminance,
                          float eCO2, float tvoc, float sound, float aqi) {
    double input_features[] = {
        static_cast<double>(dayOfWeek), static_cast<double>(season), timeOfDay,
        static_cast<double>(zoneID), temperature, humidity, illuminance,
        eCO2, tvoc, sound, aqi
    };
    return score(input_features);  // Use the XGBRegressor model's score function
}

// Function to export prediction and actual results to a CSV for plotting
void exportData(const std::vector<double>& actual, const std::vector<double>& predicted) {
    std::ofstream outFile("predictions.csv");
    outFile << "Actual,Predicted\n";
    for (size_t i = 0; i < actual.size(); ++i) {
        outFile << actual[i] << "," << predicted[i] << "\n";
    }
    outFile.close();
}

// Function to test the model with the sampled data and store the predictions
void testModel(const std::vector<DataRecord>& sampledData) {
    std::vector<double> predictions;
    std::vector<double> actualScores;

    for (const auto& record : sampledData) {
        // Predict the overall score using the model
        double predictedScore = predictOverallScore(record.dayOfWeek, record.season, record.timeOfDay,
                                                    record.zoneID, record.temperature, record.humidity,
                                                    record.illuminance, record.eCO2, record.tvoc,
                                                    record.sound, record.aqi);

        // Store the prediction and actual score for comparison
        predictions.push_back(predictedScore);
        actualScores.push_back(record.overallScore);
    }

    // Export the data to a CSV file for plotting in Python
    exportData(actualScores, predictions);
}

int main() {
    // Read data from CSV
    std::string filename = "src/data.csv";
    std::vector<DataRecord> data = readCSV(filename);

    // Take a 20% random sample of the data
    std::vector<DataRecord> sampledData = sampleData(data);

    // Test the model with the sampled data
    testModel(sampledData);

    return 0;
}