linear regression is the most basic type of regression commonly used for predictive analysis the idea is pretty simple we have a dataset and we have features associated with it features should be chosen very cautiously as they determine how much our model will be able to make future predictions we try to set the weight of these features over many iterations so that they best fit our dataset in this particular code i had used a csgo dataset adr vs rating we try to best fit a line through dataset and estimate the parameters collect dataset of csgo the dataset contains adr vs rating of a player return dataset obtained from the link as matrix run steep gradient descent and updates the feature vector accordingly param datax contains the dataset param datay contains the output associated with each dataentry param lendata length of the data param alpha learning rate of the model param theta feature vector weight s for our model param return updated feature s using currfeatures alpha gradientw r t feature return sum of square error for error calculation param datax contains our dataset param datay contains the output result vector param lendata len of the dataset param theta contains the feature vector return sum of square error computed from given feature s implement linear regression over the dataset param datax contains our dataset param datay contains the output result vector return feature for line of best fit feature vector return sum of square error for error calculation param predictedy contains the output of prediction result vector param originaly contains values of expected outcome return mean absolute error computed from given feature s driver function data collectdataset lendata data shape0 datax np cnp oneslendata data 1 astypefloat datay data 1 astypefloat theta runlinearregressiondatax datay lenresult theta shape1 printresultant feature vector for i in rangelenresult printftheta0 i 5f if name main main collect dataset of csgo the dataset contains adr vs rating of a player return dataset obtained from the link as matrix this is for removing the labels from the list run steep gradient descent and updates the feature vector accordingly_ param data_x contains the dataset param data_y contains the output associated with each data entry param len_data length of the data_ param alpha learning rate of the model param theta feature vector weight s for our model param return updated feature s using curr_features alpha_ gradient w r t feature return sum of square error for error calculation param data_x contains our dataset param data_y contains the output result vector param len_data len of the dataset param theta contains the feature vector return sum of square error computed from given feature s implement linear regression over the dataset param data_x contains our dataset param data_y contains the output result vector return feature for line of best fit feature vector return sum of square error for error calculation param predicted_y contains the output of prediction result vector param original_y contains values of expected outcome return mean absolute error computed from given feature s driver function
import numpy as np
import requests


def collect_dataset():
    
    response = requests.get(
        "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
        "master/Week1/ADRvsRating.csv"
    )
    lines = response.text.splitlines()
    data = []
    for item in lines:
        item = item.split(",")
        data.append(item)
    data.pop(0)  
    dataset = np.matrix(data)
    return dataset


def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
    
    n = len_data

    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_grad = np.dot(prod, data_x)
    theta = theta - (alpha / n) * sum_grad
    return theta


def sum_of_square_error(data_x, data_y, len_data, theta):
    
    prod = np.dot(theta, data_x.transpose())
    prod -= data_y.transpose()
    sum_elem = np.sum(np.square(prod))
    error = sum_elem / (2 * len_data)
    return error


def run_linear_regression(data_x, data_y):
    
    iterations = 100000
    alpha = 0.0001550

    no_features = data_x.shape[1]
    len_data = data_x.shape[0] - 1

    theta = np.zeros((1, no_features))

    for i in range(iterations):
        theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
        error = sum_of_square_error(data_x, data_y, len_data, theta)
        print(f"At Iteration {i + 1} - Error is {error:.5f}")

    return theta


def mean_absolute_error(predicted_y, original_y):
    
    total = sum(abs(y - predicted_y[i]) for i, y in enumerate(original_y))
    return total / len(original_y)


def main():
    
    data = collect_dataset()

    len_data = data.shape[0]
    data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
    data_y = data[:, -1].astype(float)

    theta = run_linear_regression(data_x, data_y)
    len_result = theta.shape[1]
    print("Resultant Feature vector : ")
    for i in range(len_result):
        print(f"{theta[0, i]:.5f}")


if __name__ == "__main__":
    main()