代做R编程作业、代写R程序作业、代写R实验、代写R实验报告帮做R语言编程|代写留学生R
In the programming assignment 1, you are asked to perform data analysis and data
preprocessing using the following dataset. You can use built-in function in sklearn and
matplotlib for these tasks.
Dataset
● Bank Marketing Dataset [1]
The dataset is related with direct marketing campaigns of a Portuguese banking
institution. The
classification goal is to predict whether a client will subscribe a term deposit.
● Each data record, describing a client, contains the basic information of the client
and whether the client subscribed the term project. Please treat column 1 to 20 as
features and column 21 as the class.
Data Analysis
● Task 1. Plot the distribution of values in the class attribute of the dataset using a
bar chart. Please describe what you observe, e.g. whether the data distribution is
imbalanced.
● Task 2. Read the reference and answer the following questions.
a) Please summarize the characteristics and differences of chi-square
function (https://en.wikipedia.org/wiki/Chi-squared_test) and mutual
information functions (https://en.wikipedia.org/wiki/Mutual_information).
b) Can we simply apply chi-square function and mutual information function
on Bank Marketing Dataset for feature selection? Please explain. (hint: the
difference between categorical and numerical data)
c) Employ chi-square or mutual information as appropriate to obtain a
measure between values of each feature and the class. Rank features by
their measures of chi-square and mutual information.
Note: Please make two lists: one for chi-square and the other for mutual
information. An attribute only belongs to one list.
● Task 3. Based on the two ranked lists obtained in Task 2, plot the value
distribution of (i) the highest ranked three categorical features, (ii) the lowest
ranked three categorical features, (iii) the highest ranked three numerical features,
and (iv) the lowest ranked three numerical features. Describe what you observe
from these value distributions.
Note: Please plot a Bar chart and a Histogram for a categorical feature and a
numerical feature, correspondingly. See below for examples. For Histogram,
please evenly divide the overall value range into 10 intervals. For each bar and
interval, please color the portion of records/instances corresponding to different
classes and show the overall count.
Bar Chart
Histogram
Data preprocessing
? ● Task 3. Normalize the range of values of numerical features. If values are all
positive or all negative, normalize them into [0, 1] or [-1, 0], respectively.
Otherwise, normalize them into [-1, 1]. For each normalized numerical feature,
submit the ranges of its original and normalized values.
? ● Task 4. Encode categorical features using one-hot representation scheme. For
example, assuming that there is a ‘state’ feature with three categorical
values, ’PA’, ‘NY’ and ‘NJ’. Create three new binary features, namely
‘state_is_PA’, ‘state_is_NY’ and ‘state_is_NJ’ to replace ‘state’, where the
feature values are either 0 or 1. For each new binary feature, count and report the
number of value 1, e.g., “state_is_PA”: 15000, “state_is_NY”: 20000 and
“state_is_NJ”: 10000.
Packages
● sklearn (http://scikit-learn.org/). A machine learning framework in Python
● matplotlib (https://matplotlib.org/). Website provides tutorials on how to plot bar
chart and histogram in python.
● NumPy (http://scikit-learn.org/). A fundamental package for scientific computing
with Python.
Reference
[1] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of
Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.
[2] Please refer to http://scikit-learn.org/stable/modules/preprocessing.html#.
http://www.6daixie.com/contents/14/1793.html
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