# BEST代写-线上编程学术专家

Best代写-最专业靠谱代写IT | CS | 留学生作业 | 编程代写Java | Python |C/C++ | PHP | Matlab | Assignment Project Homework代写

# python代写 | Assignment 1: HELOC Data Analysis

### python代写 | Assignment 1: HELOC Data Analysis

Python数据分析

Assignment 1: HELOC Data Analysis
Date: September 20, 2019
Sumbit (in the ipynb format) via Moodle before 11:59pm October 9, 2019.
Consider an anonymized data of Home Equity Line of Credit (HELOC) loans, with the
dataset URLs provided by the STAT3612 course website. The target variable RiskFlag
indicates whether the loan is ever 90-day delinquent over a two-year period. The feature
variables are pulled from the credit bureau and their descriptions can be referred to the data
dictionary file. Some special values are also described in the data dictionary.
In this assignment you are required to perform the exploratory data analysis and generalized
linear modeling. You will need to submit your works in the Python notebook format, with
reproducible Python codes and adequate description. Indicate your name and UID in the
first cell of the notebook.
(1). (20%) The negative values (-7, -8, -9) for each variable can be be regarded as missing
information. Calculate the missing value frequencies separately for each feature, then visualize
the result by a bar chart.
(2). (20%) Use np.random.seed toset your UID as the random seed, then split the data
into training (80%) and testing (20%) sets. For each feature variable in the training data,
impute the missing values by the mean of observed values.
(3). (20%) For the training data, draw the boxplot for each feature variable as grouped
by RiskFlag. Lay out all the plots appropriately and annotate each box plot with the
corresponding feature name.
(4). (20%) Select top-5 features based on the boxplots and elaborate your reasons of such
choices. Fit a logistic model on the training data with selected features. Rank-order the
variable importance based on the Wald tests.
(5). (20%) Interpret the fitted logistic model in (4). Then, test its performance on the
testing data and report the prediction accuracy.
1