3. Given the following data for attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 Use all types of binning method for data smoothing in order to solve the above data.
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- 1. Suppose that the data for analysis include the attribute age. The age values for the data tuples are 6, 11, 12, 13, 13, 25, 47, 55, 72, 81, 87, 90. Sort it in increasing order then Smooth the data using following methods using a bin depth of 3. (i)smoothing by bin means (ii)smoothing by bin medians (iii)smoothing by bin mode (iv)smoothing by bin boundariesAnswer the following questions: Assume that in cone-shaped structures, the measurements for the height and radius of 6 cones are given as 8.28, 8.04, 9.06, 8.70, 7.58, 8.34 and 2.27, 1.98, 1.69, 1.88, 1.64, 2.14 respectively. Write R program for the scenario given below. (a). Make vectors with the given values. (b) The volume of a cone with radius R and height H is given by (1/ 3) TIR²H. Make a vector with the volumes of the 6 cones. (c). Compute the mean, median and standard deviation of the cone volumes. (d). Compute also the mean of volume for the cones with a height less than 8.5.1. Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70. Using the data above, please answer the following questions: b. Use "Normalization with decimal scaling" to change the age value to 25.
- 1. Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 7o. Using the data above, please answer the following questions: a. Use “min-max Normalization" to change the age value of 25 to range [0.0 , 1.0].Question p .Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45,46, 52, 70. Show a boxplot of the data? Full explain this question and text typing work only We should answer our question within 2 hours takes more time then we will reduce Rating Dont ignore this lineQuestion 7: Build a table to show the frequency of each offence category. Arrange the frequency in descending order. Which two categories have the most offences issued q7 * COffence category) *>* q7 The highest two frequency of offences are and Question 8: Draw a suitable plot to show the same information as in Question 7 but just restricted it to the highest 5 categories. Look at the lasi line of coding, what this coding is trying to do? It is just to make the label more visible by rotating the label. q7 *>* top_n(5) %>* ggplot (aes (x --- y = theme (axis. text.x - element_text (angle=45, hjust = 1))
- Given 3-way BTree created by these data: 3, 7,9, 23, 45, 1, 5, 14, 25, 24, 13, 11 1. Draw the final BTree. 2. Draw the final BTree after adding these further keys (from your answer in #1) 2, 6,12 3. Draw the final BTree after deleting these keys using successor (from your answer in #2) 5, 7, 23First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes hereAnswer the following questions : In real-world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem. The following data (in increasing order) represent the attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 Use smoothing by bin means to smooth these data, using a bin depth of 3. Illustrate your steps. Comment on the effect of this technique for the given data. How might you determine outliers in the data? What other methods are there for data smoothing?
- Step 1 Build a train/test split using Amazon data. Dataset name is amzn. Training Data starts in 2016. Testing Data is last 10 observations in dataset. Both should be pandas series type indexed by date. The last date in train should be '2017-12-14' Train = ?? Test = ?? Step 2 Adjust the ARIMA instance below to build an autoregression model using your training data from Step 1. This model should work on the first difference of the original closing price, and use one prior term or an order 1 AR model. from statsmodels.tsa.arima_model import ARIMA ar = ARIMA(train, order = (0, 0, 0)) model = ?? Python ProgrammingThe recurrence relation of the divide phase of merge function is T(n)=2T(n/2) and merge as O(n). The illustration for the divide phase is shown below. If the recurrence relation is to be made as T(n)=4T(n/2) and merge as O(logn), would this be better or worse than the original merge function? Why(please explainin detail)? Please also provide the illustration for the divide phase of the modified merge function.Use a time series data set to do visualizations and model diagnostics to build the best model possible of the following model structures. Include ACF, PACF, and manual differencing plots in your submission. Do not use any functions that estimate model degree, estimate model degree manually. Fit each model to your data and print model diagnostics. Transform your time series as needed before modeling. A pure autoregressive model, AR(p) A pure moving average model, MA(q) An autoregressive moving average model, ARMA(p,q) A ARIMA(p,d,q) model Use any functions to estimate model degree, such as forecast::auto.arima(). Fit the model. Make a paragraph on which model is best and why.