Create linear-regression-without-libraries.py

Author: Narra_Venkata_Raghu_Charan

linear-regression-without-libraries.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+# Sample Data (Height vs. Weight, for example)
+# X represents height in cm, Y represents weight in kg
+X = np.array([150, 160, 170, 180, 190])
+Y = np.array([50, 60, 65, 70, 80])
+n = len(X)
+
+# Mean of X and Y
+mean_x = np.mean(X)
+mean_y = np.mean(Y)
+
+# Calculating the coefficients for Linear Regression
+numerator = np.sum((X - mean_x) * (Y - mean_y))  # Σ((Xi - X̄) * (Yi - Ȳ))
+denominator = np.sum((X - mean_x) ** 2)  # Σ((Xi - X̄)^2)
+
+# Slope (m) and Intercept (b) for y = mx + b
+m = numerator / denominator  # Slope
+b = mean_y - (m * mean_x)    # Intercept
+
+print(f"Slope (m): {m}")
+print(f"Intercept (b): {b}")
+
+# Prediction using the regression model
+def predict(x):
+    return m * x + b
+
+# Predicted values for X
+Y_pred = predict(X)
+
+# Plotting the results
+plt.scatter(X, Y, color="blue", label="Data Points")  # Plot original data points
+plt.plot(X, Y_pred, color="red", label="Regression Line")  # Plot regression line
+
+plt.xlabel("Height (cm)")
+plt.ylabel("Weight (kg)")
+plt.title("Linear Regression Example")
+plt.legend()
+
+# Save the plot as an image file
+plt.savefig("linear_regression_plot.png")
+print("Plot saved as 'linear_regression_plot.png'")