Category : predict

I watned to predict the income using text data(description) as a predictor. This is how my dataframe looks like: c_description 641 fierce roman commander marcus vinicius become … 645 melancholy poet reflect three woman love lose … 644 disturb blanche dubois move sister new orleans… 643 lonely woman recall first love thirteen year p… 642 ..

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My model seems to be fine, there are values for each parameters excerpt of model.summary here Everything is training well with an epoch of 60, accuracy at 95% and val_acc at 91% (a liiitle bit overfitting I think) Epoch 57/60 51/51 [==============================] – 20s 404ms/step – batch: 25.0000 – size: 59.7647 – loss: 0.1540 – ..

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I built a small learning program should produce some forecast values. So I did the following to predict with my test data, ok. from sklearn.tree import DecisionTreeRegressor DT_model = DecisionTreeRegressor(max_depth=5).fit(X_train,Y_train) DT_predict = DT_model.predict(X_test) #Predictions on Testing data X_test open low close 38 19183.13 18163.52 18316.43 2091 226.28 214.98 219.49 824 6201.23 6143.08 6183.00 But since ..

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I have .csv file like that: 1;Airedale terrier;2;3;2;2;3;2;2;2;1;2;2;2;2;2;3;3;3;2;2;2;3;2;3;2;2;1;56;61;17;24;11 It has a lot of data and has labels for each columns. I want to predict the best class name for input data. For Example: CSV file: 8;Appenzeller;2;3;2;3;1;3;1;1;2;3;1;3;3;3;2;2;3;2;3;3;3;2;3;2;3;2;50;56;20;25;13 9;Ariegeois;2;2;2;3;3;2;2;2;3;3;2;2;2;1;3;1;3;2;1;3;3;2;3;2;2;1;50;58;28;30;12 10;Australian cattle dog;2;3;1;3;1;3;1;1;1;1;1;2;2;3;1;1;3;2;2;2;3;1;3;3;3;2;43;51;20;30;14 11;Australian silky terrier;3;3;2;3;2;2;1;1;3;3;1;1;2;2;3;3;2;3;3;2;3;3;3;3;2;2;21;26;3;6;13 First column is id, second breed name, and rest are data I ..

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I am implementing a BG/NBD model and have plotted the probability of being alive matrix, which is the following: fig = plt.figure(figsize=(15,10)) plot_probability_alive_matrix(bgf) plt.show() Can someone help me interpret what the vertical yellow line at the left of the plot is? I know that bottom right means a customer is most likely "alive", and top ..

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I have a dataframe with 36540 rows, below is a dataset I picked up. the objective is to predict y. #data y=np.array([1,1,0,0,0,0,1,0,0,0.25,0,0,1,1.5,0,0,0,0.25,0.2,0],dtype=’float32′).reshape(-1,1) x=np.array([[ [0,5,1.000000,11080.000000,98,23,93691,113,98,11079.000000,5], [0,5,1.000000,11080.000000,103,23,93691,114,103,11079.000000,5], [4,5,161.000000,11080.000000,103,23,93691,121,103,10919.000000,5], [0,5,281.000000,11080.000000,103,23,93691,128,103,10799.000000,5], [0,5,281.000000,11080.000000,103,23,93691,135,103,10799.000000,5], [0,5,46.142857,10834.285714,103,23,93691,142,103,10799.000000,5], [0,6,169.000000,23680.000000,126,15,20093,100,126,23849.000000,6], [4,6,518.714286,23680.000000,126,15,20093,107,126,23306.142857,6], [0,6,714.333333,23613.333333,126,15,20093,114,126,22899.000000,6], [0,6,701.000000,23600.000000,126,15,20093,121,126,22899.000000,6], [6,6,563.857143,23548.571429,126,15,20093,128,126,22984.714286,6], [0,6,421.000000,23420.000000,126,15,20093,135,126,22999.000000,6], [0,6,2231.000000,16230.000000,115,15,7855,96,115,13999.000000,6], [4,6,2631.000000,16230.000000,115,15,7855,103,115,13599.000000,6], [0,7,49.000000,23360.000000,125,15,6512,80,125,23409.000000,7], [0,7,49.000000,23360.000000,125,15,6512,79,125,23409.000000,7], [0,7,49.000000,23360.000000,125,15,6512,87,125,23409.000000,7], [0,7,1.000000,22610.000000,125,15,6512,101,125,22609.000000,7], [0,7,17.571429,22592.857143,125,15,6512,108,125,22609.000000,7], [1,7,50.714286,22550.000000,125,15,6512,115,125,22600.428571,7]]]).reshape(20,-1) I am trying to train a non-linear regression model but model doesn’t ..

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