# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument.

# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument. please fast
You have a dataset that contains some patient medical records. This dataset is messy, full of incomplete/missing and erroneous data. Your task is to build a classification model on these records but you are concerned about the quality of the data handed to you. After consulting your colleagues you get an advice to chain two models: 1. the first model will de-noise the data and transform it into a better and complete representation of itself 2. the second model will take the output of the first model and classify the data What type of artificial neural network would you consider for your first model? Group of answer choices Autoencoder Convolutional neural network (CNN) Generative adversarial network (GAN) Multi-layer perceptron (MLP) Recurrent neural network (RNN)
Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi
Modify the codes 06_MNIST_binary_classification.jpynb and 07_MNIST_10_classes_classification.jpynb 1. Use the SGD method and mini-batch (MB) method with different batch sizes (you need to try different batch sizes) 2. Apply your trained models to the test data and display the accuracies. 3. Plot iterations (x-axis) vs. loss function for BG and MB at one figure
Consider a dataset Name Mood Scale Jim 2 Ben 3 How do we redefine the values of mood scale to be 1=Very Sad, 2=Sad, 3=Neutral, 4=Happy, 5=Very Happy on RStudio? What are the steps and code for that?
implement a D-i-D in this problem. Load the dataset on STATA: use http://www.stata.com/data/jwooldridge/eacsap/jtrain1 This has data on firms and the amount of job training they get. Only use the data from 1987 and 1988. Carefully study the data before you proceed. Construct the D-i-D estimator in different ways: (a) Run the regressionhrsempit = β0 + β1 grant it + β21( year = 1988) + β3Ei + uit where Ei is a dummy variable for being a treatment (i.e. someone who would receive the grant in 1988). (b) Run the fixed effect regression with firm fixed effects θi: hrsempit = θi + β1 grant it + β21( year = 1988) + uit (c) Construct the 4 means of controls and treatments, before and after, and es- timate the difference in difference with means. (d) Do you get exactly the same answer, why or why not? (e) Now include other controls to estimate the D-i-D regression. Justify what- ever you include and interpret. Provide line by line code for STATA and the solution
Implement a D-i-D in this problem. Load the dataset on STATA: use http://www.stata.com/data/jwooldridge/eacsap/jtrain1 This has data on firms and the amount of job training they get. Only use the data from 1987 and 1988. Carefully study the data before you proceed. Construct the D-i-D estimator in different ways: (a) Run the regressionhrsempit = β0 + β1 grant it + β21( year = 1988) + β3Ei + uit where Ei is a dummy variable for being a treatment (i.e. someone who would receive the grant in 1988). (b) Run the fixed effect regression with firm fixed effects θi: hrsempit = θi + β1 grant it + β21( year = 1988) + uit (c) Construct the 4 means of controls and treatments, before and after, and es- timate the difference in difference with means. (d) Do you get exactly the same answer, why or why not? (e) Now include other controls to estimate the D-i-D regression. Justify what- ever you include and interpret.
Load & check the data: breat_cancer dataset8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with linear kernel 9. Train an SVM classifier using the training data, set the kernel to linear and set the regularizationparameter to C= 0.1. Name the classifier clf_linear_firstname.10. Print out two accuracy score one for the model on the training set i.e. X_train, y_train and theother on the testing set i.e. X_test, y_test. Record both results in your written response.11. Generate the accuracy matrix. Record the results in your written response.Support vector machine classifier with “rbf” kernel12. Repeat steps 9 to 11, in step 9 change the kernel to “rbf” and do not set any value for C.Support vector machine classifier with “poly” kernel13. Repeat steps 9 to 11, in step 9 change the kernel to “poly” and do not set any value for C.Support vector machine…
4.4 You estimated a regression with the following output. Source | SS df MS Number of obs = 422 -------------+---------------------------------- F(1, 420) = 65.26 Model | 835000.255 1 835000.255 Prob > F = 0.0000 Residual | 5374272.98 420 12795.888 R-squared = 0.1345 -------------+---------------------------------- Adj R-squared = 0.1324 Total | 6209273.24 421 14748.8675 Root MSE = 113.12 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 20.01443 2.477622 8.08 0.000 15.14434 24.88451 _cons | -20.23479 242.2109 -0.08 0.933 -496.3314 455.8619…
A dataset has a large class imbalance. Class 1 has 80% of all of the elements. Class 2 has 9% of all of the elements. Class 3 has 7% of all of the elements. Class 4 has the remaining 4% of all of the elements. Does it make sense to perform a kNN machine learning modelling on that dataset? Explain why or why not?
When we buy a packaged data model, we get what we pay for.
# CG Q6a # Customer 27 in our dataset did not end up subscribing to the term deposit. ########## Let's use our fitted model to see what it would have predicted for this customer. ########## First, create an object called "nd" that that you will pass to the predict() ########## function in the newdata argument.