# Simulation of Predictive Analytics Gaming Project in C++

### Explanation

1. 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.
2. 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.
3. 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=mx+by = mx + b.
4. 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.
• 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.
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