Question

Linear regression and Print MSE and R-square for the training and test data. Of csv file in python

          Linear regression and Print MSE and R-square for the training and test data. Of csv file in python
        

Added by Jose Ramon C.

Horngren’s Cost Accounting
Horngren’s Cost Accounting
Srikant M. Datar, Madhav V. Rajan 16th Edition
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Linear regression and Print MSE and R-square for the training and test data. Of csv file in python
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6. Linear Regression with L2 (Ridge) Regularization def ridge_regression(X, Y, iterations=1000, learning_rate=.01, L2_penalty=1): no_of_training_examples, no_of_features = X.shape np.zeros(no_of_features) for _ in range(iterations): b, W = update_weights(X, Y, no_of_training_examples, learning_rate, L2_penalty) 7. Split the dataset into training and test sets def train_test_split(dataset, split): ### YOUR CODE HERE ### return X_train, Y_train, X_test, Y_test 8. Perform regression algorithm on dataset def evaluate_ridge_regression(dataset, split): X_train, Y_train, X_test, Y_test = functions.train_test_split(dataset, split) # Train the model b, W = functions.ridge_regression(X_train, Y_train, iterations=10000, L2_penalty=0.01) # Make a prediction with the model yhat = functions.predict(X_test, b, W) print("Predicted values:", np.round(yhat[:3], 2)) print("Real values:", Y_test[3]) print("Trained W:", round(W[0], 2)) print("Trained b:", round(b, 2)) visualise(X_test, Y_test, yhat)

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Using the same datasets as in problem 2, utilize numpy's polyfit function to find the slope and intercept of the regression line. (the data sets are x = [2.3, 3.1, 4.4, 5.5, 6.1, 7.7, 8.8] and y = [3.2, 4.4, 4.0, 5.9, 6.5, 7.1, 8.0])

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Write a Python function that performs linear regression given the x-data and y-data, with x-data in one list and y-data in another list. There should be an option to force the fitted equation through the origin. The output is/are the coefficient(s) and r^2, the coefficient of determination. Call this function "linear_regression".

Akash M.


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Transcript

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00:01 First, we need to import the necessary packages.
00:04 So, import numpy as and p, ok.
00:10 Pandas as pd and import matplotlib .aplotat .plt.
00:17 So, split the datasets into training and test sets and define train, test, split.
00:34 Ok, so dataset and split as xtrain, ytrain, xtest and ytest...
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