city of ABC, the price of a house is dependent to other variables such as lotsize, number of bedrooms, ive way, recreation room, air condition, etc., as it is shown in table "Housing_Modified.csv". proximate a model of price vs. It's independent variables on the table. Discuss you finding. The lousing_Modified.csv" data is given by the invigilator.
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- We created a model (Im) using LinearRegression from sklearn library. We got the data from the cars dataframe used in class and we fit the model using the data. Now we have several new values for the independent variable X, called X_new and we want to estimate the price for these list of new cars. What command should we use for this task? price_hat=X_new.predict(lm) price_hat=lm.predict(X_new) price_hat=X_new.predict() O d. price_hat=lm.estimate(X_new) a. b. C.We need to clean the price change data to get daily prices. For a game-date combination, if the game_price_changes # data has an observation on that day, the $price variable in that data measures the price on that day. If there is no # price data on a day, we know that price did not change on that day, so its takes the previous value. For example, for # game 730 (Counter-Strike: Global Offensive). The price was changed to $9.99 on 2012-11-01. Then it was changed again # to $11.24 on 2012-11-22. This means that before the change on 2012-11-22, the price stayed at $9.99. # By the end of this step, you should have a price data at the level of game-date. There should be no gap in dates unless # for specific reasons that lead to missing data. You should then be able to merge the players data with the price data.Make the 4 variables you have identified (chk_acct, product, sav_acct, job) in the previous question into factors. Since the categories are unordered, turn these variables into nominal factors. This is easily accomplished with factor(): variable <- factor(variable). The levels will be assigned automatically.
- A6. Suppose a farmer decided to keep individual records for the new cattle. His records show 4 bred heifers valued at $900; 5 cows valued at $1,000, and 1 bull valued at $2,000. Using the chart below, setup the individual records for each animal for year 1. Make sure that your animal ID for each type of animal is different, for example, bred heifers can start at 22-1, etc.; cows can start at 22- 01, and the bull can be 22-001.You are a Data Scientist at United Health. You want to check if a patient will develop cancer based on smoking habits. Please write the R code to generate confusion matrix. Use the following details. After splitting the dataframe, we have test_cancer, and training_cancer. The outcome variable is develop_cancer.MatLab Load the data flu.mat (you can do this by typing load flu in your script). This data is the flu trends seen in the United States 2005-2006, divided by region. We will use regressions to look at the data during flu season in the Pacific region. Create your x data: have x equal to 1:30. These represent 30 weeks between Oct. 2005 and May 2006. Create your y data: have y equal to flu.Pac(1:30)’. This is the flu trend for each week. Make sure you have an apostrophe after the last parenthesis. Fit the data below with a straight line and with a 2nd order polynomial. Use least-squares regression. Calculate the coefficient of determination (r^2) and the correlation coefficient (r) for each regression. Plot the two regression curves against the data. Which regression is better? Is there a polynomial you think would work better? Describe the data – what does it mean to you?
- Model evaluation Create a predictions variable using your fitted model and the test dataset; call it y_pred. Then get the accuracy score of your predictions and save it in a variable called accuracy. Finally get the confusion matrix for your predictions and save it in a variable called confusion_mat. Code: y_pred = Noneaccuracy = Noneconfusion_mat = NoneImplement the following ER model and sample data using Oracle SQL Developer and your CDM Oracle database account. You will submit a SQL script file and its entire outputs. The table name should be CUSTOMERS (plural). Each CustomerID should be a 3-digit number. Names should be strings of up to thirty characters. The sample data should be: 101, John Doe 102, [Rachel Araya] Order The table name should be ORDERS (plural). Each OrderID should be a 4-digit number. Product names should be strings of up to thirty characters. The sample data should be: 1001, 01-JAN-22, Apple, 101 1002, [a date of your choice], [a product of your choice], 102 PLEASE FOLLOW BELOW The SQL script to (i) drop the two tables, (ii) create the two tables, (iii) insert the sample data, and (iv) show the full contents of the two tables. The header of your script should be something like:/*Your NameIS451 HW5*/ Make sure to start your script file with DROP TABLE commands for the two tables in order to avoid…Use the sacramento.csv file( I cant upload it its a excel file) to complete the following assignment.a file, sacramento.py, that loads the .csv file and runs a logistic The regression should predict whether or not a house has 1 or more than one bathroom based on beds, sqft, and price, in that order. You will need to create a new variable from baths, and it should make it such that those observations of 1 bath correspond to a value of 0, and those with more than 1 bath correspond to a 1. Make sure to add a constant using sm.add_constant(X) Your file should print the results in this way: print(mod.params.round(2)) print(mod.pvalues.round(2)) print('The smallest p-value is for sqft')
- To run any stats you must first import statistics True False 2. For relatively low-level stats, it generally doesn't matter if you run them in statsmodels, the statistics library, or the other ways we saw True False 3. By default, scikit-learn and statsmodels create equivalent logistic regression models True False 4. which of these might you want to add to the default statmodels regression model? Constant Multiple comparisons Scatterplot 5.Which of the following is how we would create a logistic regression model? sm.OLS sm.Logit() sm.LogReg() sm.Logistic() 6.Which of the following are common ways of installing packages? conda pip install.py install.packages()…Produce a table of time poverty rates overall, by age category, and by sex (hint: use the time deficit variable, which is available only for adults). What is the code for this in STATA. How to produce a time poverty table in STATA.Models are useful for a wide range of reasons. Sort the models into their appropriate groupings.