python jupyter notebook logistic regression dataset can be found on kaggle compas scores data pd

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Python Jupyter Notebook Logistic Regression The dataset can be found on KAGGLE (COMPAS SCORES). data = pd.read_csv('compas-scores-two-years.csv') data = data.query('days_b_screening_arrest <= 30 & days_b_screening_arrest >= -30') data 1A is not complete and needs an explanation for 1c. Question 1. Logistic Regression: What are the odds of getting a high-risk score? A higher (i.e., more "risky") score. COMPAS labels scores 1-4 as low, 5-7 as medium, and 8-10 as high scores. For the purposes of their analysis, ProPublica labeled any score above a low score as high. Question 1a (i) Create a logistic regression model to predict the score of defendants based on their sex, age, race, previous arrests, seriousness of the crime, and future criminal behavior. Please be sure to: - Standard scale your inputs - One-hot encode categorical inputs (hint: pd.get_dummies() is a useful function!) For the purposes of this notebook, do not worry about cross-validation or parameter selection. Please use the default LogisticRegression object (already imported at the top). from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler feature_columns = select_data[["sex", "age", "race", "priors_count", "c_charge_degree", "two_year_recid"]] target_column = select_data["score_text"] Question 1a (ii) Print out the coefficients paired with the corresponding feature names # Pair the coefficients with feature names # Student Code Here Question 1c Are Black defendants more likely to get a high-risk score opposed to white defendants? If so, by how much? Show your calculations. # Student Calculations Here

          Python Jupyter Notebook Logistic Regression
The dataset can be found on KAGGLE (COMPAS SCORES).
data = pd.read_csv('compas-scores-two-years.csv')
data = data.query('days_b_screening_arrest <= 30 & days_b_screening_arrest >= -30')
data
1A is not complete and needs an explanation for 1c.
Question 1. Logistic Regression: What are the odds of getting a high-risk score?
A higher (i.e., more "risky") score. COMPAS labels scores 1-4 as low, 5-7 as medium, and 8-10 as high scores. For the purposes of their analysis, ProPublica labeled any score above a low score as high.
Question 1a (i)
Create a logistic regression model to predict the score of defendants based on their sex, age, race, previous arrests, seriousness of the crime, and future criminal behavior. Please be sure to:
- Standard scale your inputs
- One-hot encode categorical inputs (hint: pd.get_dummies() is a useful function!)

For the purposes of this notebook, do not worry about cross-validation or parameter selection. Please use the default LogisticRegression object (already imported at the top).

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

feature_columns = select_data[["sex", "age", "race", "priors_count", "c_charge_degree", "two_year_recid"]]
target_column = select_data["score_text"]

Question 1a (ii)
Print out the coefficients paired with the corresponding feature names

# Pair the coefficients with feature names
# Student Code Here

Question 1c
Are Black defendants more likely to get a high-risk score opposed to white defendants? If so, by how much? Show your calculations.

# Student Calculations Here

python jupyter notebook logistic regression dataset can be found on kaggle compas scores data pdreadcsvcompas scores two yearscsv data dataquerydaysbscreeningarrest 30 daysbscreeninga 55317

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