Introduction to Machine Learning Algorithms in Tamil
Simple Linear regression
Multiple Linear Regression
இயந்திர வழிக் கற்றல் நெறிமுறைகள் அறிமுகம்
மேலும் அறிய, பின் வரும் இணைப்புகள், நிரல்களைக் காண்க.
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| import matplotlib.pyplot as plt | |
| x=[[6],[8],[10],[14],[18],[21]] | |
| y=[[7],[9],[13],[17.5],[18],[24]] | |
| plt.figure() | |
| plt.title('Pizza price statistics') | |
| plt.xlabel('Diameter (inches)') | |
| plt.ylabel('Price (dollars)') | |
| plt.plot(x,y,'.') | |
| plt.axis([0,25,0,25]) | |
| plt.grid(True) | |
| plt.show() |
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| import matplotlib.pyplot as plt | |
| from sklearn.linear_model import LinearRegression | |
| x = [[6], [8], [10], [14], [18]] | |
| y = [[7], [9], [13], [17.5], [18]] | |
| model = LinearRegression() | |
| model.fit(x,y) | |
| plt.figure() | |
| plt.title('Pizza price statistics') | |
| plt.xlabel('Diameter (inches)') | |
| plt.ylabel('Price (dollars)') | |
| plt.plot(x,y,'.') | |
| plt.plot(x,model.predict(x),'–') | |
| plt.axis([0,25,0,25]) | |
| plt.grid(True) | |
| plt.show() | |
| print ("Predicted price = ",model.predict([[21]])) |
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| from sklearn.linear_model import LinearRegression | |
| import numpy as np | |
| x = [[6], [8], [10], [14], [18]] | |
| y = [[7], [9], [13], [17.5], [18]] | |
| model = LinearRegression() | |
| model.fit(x,y) | |
| print ("Residual sum of squares = ",np.mean((model.predict(x)– y) ** 2)) | |
| print ("Variance = ",np.var([6, 8, 10, 14, 18], ddof=1)) | |
| print ("Co-variance = ",np.cov([6, 8, 10, 14, 18], [7, 9, 13, 17.5, 18])[0][1]) | |
| print ("X_Mean = ",np.mean(x)) | |
| print ("Y_Mean = ",np.mean(y)) |
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| from sklearn.linear_model import LinearRegression | |
| import numpy as np | |
| from numpy.linalg import inv,lstsq | |
| from numpy import dot, transpose | |
| x = [[6], [8], [10], [14], [18]] | |
| y = [[7], [9], [13], [17.5], [18]] | |
| model = LinearRegression() | |
| model.fit(x,y) | |
| x_test = [[8], [9], [11], [16], [12]] | |
| y_test = [[11], [8.5], [15], [18], [11]] | |
| print ("Score = ",model.score(x_test, y_test)) |
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| from sklearn.linear_model import LinearRegression | |
| from numpy.linalg import lstsq | |
| import numpy as np | |
| x = [[6, 2], [8, 1], [10, 0], [14, 2], [18, 0]] | |
| y = [[7], [9], [13], [17.5], [18]] | |
| model = LinearRegression() | |
| model.fit(x,y) | |
| x1 = [[8, 2], [9, 0], [11, 2], [16, 2], [12, 0]] | |
| y1 = [[11], [8.5], [15], [18], [11]] | |
| predictions = model.predict([[8, 2], [9, 0], [12, 0]]) | |
| print ("values of Predictions: ",predictions) | |
| print ("values of β1, β2: ",lstsq(x, y, rcond=None)[0]) | |
| print ("Score = ",model.score(x1, y1)) |