Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. 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)

Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. 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)

Np Ms Office 365/Excel 2016 I Ntermed

1st Edition

ISBN:9781337508841

Author:Carey

Publisher:Carey

Chapter3: Performing Calculations With Formulas And Functions

Section: Chapter Questions

Problem 3.15CP

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Use the sacramento.csv file to complete the following assignment. Create a file, sacramento.py, that loads the .csv file and runs a logistic regression. 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. Note: you will not need to upload the .csv to CodeGrade because I have pre-loaded it. 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')
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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()…
Modify the accompanying credit risk data using IF functions to include new columns, classifying the checking and savings account balances as low if the balance is less than $250, medium if at least $250 but less than $2000, and high otherwise.
We use the Breast Cancer Wisconsin dataset from UCI machine learning repository: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 Data File: breast-cancer-wisconsin.data (class: 2 for benign, 4 for malignant) Data Metafile: breast-cancer-wisconsin.names Please implement this algorithm for logistic regression (i.e., to minimize the cross-entropy loss as discussed in class), and run it over the Breast Cancer Wisconsin dataset. Please randomly sample 80% of the training instances to train a classifier and then testing it on the remaining 20%. Ten such random data splits should be performed and the average over these 10 trials is used to estimate the generalization performance. You are expected to do the implementation all by yourself so you will gain a better understanding of the method. Please submit: (1) your source code (or Jupyter notebook file) that TA should be able to (compile and) run, and the…
Use import sys. Use the german_credit_data.csv file to complete the following assignment. Create a file, german.py, that loads the .csv file and runs a regression predicting credit amount from age and duration, in that order. Add a constant using sm.add_constant(data). You will need to rename the column 'Credit amount' and should change it to 'Credit_amount'. Note: you will not need to upload the .csv to CodeGrade because I have pre-loaded it. Then, print the parameters and R-squared to 2 decimals using print(model.params.round(2)) print(model.rsquared.round(2))
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dataframe = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_train_raw.csv') df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_test_raw.csv') Write a function that takes in as input a dataframe and a column name, and returns the mean for numerical columns and the mode for non-numerical columns. Function Specifications: The function should take two inputs: (df, column_name), where df is a pandas DataFrame, column_name is a str. If the column_name does not exist in df, raise a ValueError. Should return as output the mean if the specified column is numerical and return a list of the mode(s) otherwise. The mean should be rounded to 2 decimal places. If there is more than one mode for a given non-numerical column, the fuction should return a list of all modes. def calc_mean_mode(df, column_name): # your code here return calc_mean_mode(df,'Age') Expected Outputs:…