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- from sklearn.linear_model import LinearRegression
- from sklearn.datasets import make_regression
- #generate regression dataset
- X,y = make_regression(n_samples=100, n_features=2, noise=0.1)
- print(X)
- print(y)
- model=LinearRegression()
- model.fit(X,y)
- Xnew= [[-1.07296862, -0.52817175]]
- ynew= model.predict(Xnew)
- # print("X=%s, Predicted=%" (Xnew[0], ynew[0]))
- print("X={}, Predicted={}".format(Xnew[0],ynew[0]))
- print (model.intercept_, model.coef_)
- my = model.intercept_ + model.coef_[0]* Xnew[0][0] + model.coef_[1]*Xnew[0][1]
- print(my)
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