from sklearn.linear_model import LinearRegression # line.fit(X_train,y_train) regressor = LinearRegression() regressor.fit(X_train, y_train) #training the algorithm print("The intercept: \n", regressor.intercept_) print("The coefs :\n", regressor.coef_) X1 = np.concatenate((np.ones((...
It's not yet needed in this project, but it can be the case, for example you might want an output layer with a softmax classification and also some other regression in there, with some other activation function (the alternative being having a single 'sublayer' with more complex activation...
This Kaggle competition requires you to fit/train a model to the providedtrain.csvtraining set to make predictions of house prices in the providedtest.csvtest set. We present an application of theget_regression_points()function allowing students to participate in this Kaggle competition. It will:...
lr_chilledw = LinearRegression() lr_chilledw.fit(XX_chilledw_train,YY_chilledw_train)print"The test score R2: ", lr_chilledw.score(XX_chilledw_test, YY_chilledw_test)print"The Linear Regression coefficients are"pd.DataFrame(zip(XX_chilledw_train.columns, lr_chilledw.coef_), columns = ...
1. Logistic regression (logistic.py) 2. Perceptron (perceptr on.py) 3. SVM (svm.py) 4. Softmax (softmax.py) For the logistic regression classifier, multi-class prediction is difficult, as it requires a one-vs-one or one-vs-rest classifier for every class. Therefore, you only need ...
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1-https://www.kaggle.com/code/soylevbeytullah/sim-linear-reg-tv-sales 2-https://www.kaggle.com/code/soylevbeytullah/multi-linear-regression-advertising 3-https://www.kaggle.com/code/soylevbeytullah/fuel-economy-linear-regression 4-https://www.kaggle.com/code/soylevbeytullah/multiple-linear-re...
The study contains an overview of using machine-learning algorithms, namely support vector machine (SVM) and linear regression models (LR), for real estate price forecasting, starting with the Kaggle dataset. Notably, the data are carefully cleaned, anomalies are eliminated, and SVM and linear ...
I downloaded the dataset from Kaggle. You can click here to download the dataset here. Download the data and open it with excel. Here we have the features and the medv column is the target for us. Linear Regression with Excel Now that we have the dataset with us and the packages ...
Kernel regression is a great way to find out non-linear relationships between variables; bootstrapping and calculating confidence intervals are also great ways to give insight to how confident are our non-linear estimations. Before actually building any models that assembles all the predictor variable...