Simulation of Predictive Analytics Gaming Project in C++

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3 months ago

#include <iostream>
#include <vector>
#include <numeric>
#include <cmath>

// Function to calculate the mean of a vector
double mean(const std::vector<double>& data) {
    return std::accumulate(data.begin(), data.end(), 0.0) / data.size();
}

// Function to calculate the slope (m) and intercept (b) of the linear regression line
void linearRegression(const std::vector<double>& x, const std::vector<double>& y, double& slope, double& intercept) {
    size_t n = x.size();
    double x_mean = mean(x);
    double y_mean = mean(y);

    double numerator = 0.0;
    double denominator = 0.0;

    for (size_t i = 0; i < n; ++i) {
        numerator += (x[i] - x_mean) * (y[i] - y_mean);
        denominator += (x[i] - x_mean) * (x[i] - x_mean);
    }

    slope = numerator / denominator;
    intercept = y_mean - slope * x_mean;
}

// Function to predict a value based on the linear regression model
double predict(double x, double slope, double intercept) {
    return slope * x + intercept;
}

int main() {
    // Example data: x values (e.g., months) and y values (e.g., sales)
    std::vector<double> x = {1, 2, 3, 4, 5};  // Example x values
    std::vector<double> y = {2, 3, 5, 7, 11}; // Example y values (corresponding to x values)

    double slope, intercept;
    linearRegression(x, y, slope, intercept);

    std::cout << "Linear Regression Model:\n";
    std::cout << "Slope (m): " << slope << std::endl;
    std::cout << "Intercept (b): " << intercept << std::endl;

    double x_predict;
    std::cout << "Enter a value for x to predict its corresponding y: ";
    std::cin >> x_predict;

    double y_predict = predict(x_predict, slope, intercept);
    std::cout << "Predicted y for x = " << x_predict << " is " << y_predict << std::endl;

    return 0;
}

#include <iostream>

#include <vector>

#include <numeric>

#include <cmath>

// Function to calculate the mean of a vector

double mean(const std::vector<double>& data) {

return std::accumulate(data.begin(), data.end(), 0.0) / data.size();

}

// Function to calculate the slope (m) and intercept (b) of the linear regression line

void linearRegression(const std::vector<double>& x, const std::vector<double>& y, double& slope, double& intercept) {

size_t n = x.size();

double x_mean = mean(x);

double y_mean = mean(y);

double numerator = 0.0;

double denominator = 0.0;

for (size_t i = 0; i < n; ++i) {

numerator += (x[i] - x_mean) * (y[i] - y_mean);

denominator += (x[i] - x_mean) * (x[i] - x_mean);

}

slope = numerator / denominator;

intercept = y_mean - slope * x_mean;

}

// Function to predict a value based on the linear regression model

double predict(double x, double slope, double intercept) {

return slope * x + intercept;

}

int main() {

// Example data: x values (e.g., months) and y values (e.g., sales)

std::vector<double> x = {1, 2, 3, 4, 5}; // Example x values

std::vector<double> y = {2, 3, 5, 7, 11}; // Example y values (corresponding to x values)

double slope, intercept;

linearRegression(x, y, slope, intercept);

std::cout << "Linear Regression Model:\n";

std::cout << "Slope (m): " << slope << std::endl;

std::cout << "Intercept (b): " << intercept << std::endl;

double x_predict;

std::cout << "Enter a value for x to predict its corresponding y: ";

std::cin >> x_predict;

double y_predict = predict(x_predict, slope, intercept);

std::cout << "Predicted y for x = " << x_predict << " is " << y_predict << std::endl;

return 0;

}

Explanation

Function mean(const std::vector<double>& data):
- Purpose: Computes the mean of a dataset.
- Implementation: Uses std::accumulate to sum the data and divides by the number of elements.
Function linearRegression(const std::vector<double>& x, const std::vector<double>& y, double& slope, double& intercept):
- Purpose: Calculates the slope and intercept of the linear regression line.
- Parameters:
  - x: Vector of independent variable values.
  - y: Vector of dependent variable values.
  - slope: Reference to store the calculated slope of the line.
  - intercept: Reference to store the calculated intercept of the line.
- Implementation:
  - Computes the mean of x and y.
  - Calculates the numerator and denominator for the slope formula.
  - Computes the slope and intercept of the regression line.
Function predict(double x, double slope, double intercept):
- Purpose: Predicts the value of y for a given x based on the linear regression model.
- Implementation: Applies the linear regression formula $y = m x + b$ .
Main Function:
- Setup: Initializes example data for x and y values.
- Calculation:
  - Calls linearRegression to compute the model parameters.
  - Displays the slope and intercept.
- Prediction:
  - Prompts the user for a value of x
    
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    .
  - Uses predict to estimate the corresponding y value and displays the result.

Usage

Predictive Analytics: Demonstrates a basic predictive analytics approach using linear regression.
Forecasting: Allows users to input future values and see predictions based on past data trends.

Explanation

Usage

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