Reference_Notebook_Milestone_1_Classification+FINAL
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Rutgers University *
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Course
700
Subject
Economics
Date
Apr 30, 2024
Type
html
Pages
37
Uploaded by DeanQuetzalPerson1017 on coursehero.com
Milestone 1
¶
Problem Definition
¶
The context:
Why is this problem important to solve?
The objectives:
What is the intended goal?
The key questions:
What are the key questions that need to be answered?
The problem formulation:
What is it that we are trying to solve using data science?
Data Description:
¶
The Home Equity dataset (HMEQ) contains baseline and loan performance information for 5,960 recent home equity loans. The target (BAD) is a binary variable that indicates
whether an applicant has ultimately defaulted or has been severely delinquent. This adverse outcome occurred in 1,189 cases (20 percent). 12 input variables were registered for each applicant.
•
BAD:
1 = Client defaulted on loan, 0 = loan repaid
•
LOAN:
Amount of loan approved.
•
MORTDUE:
Amount due on the existing mortgage.
•
VALUE:
Current value of the property.
•
REASON:
Reason for the loan request. (HomeImp = home improvement, DebtCon= debt consolidation which means taking out a new loan to pay off other
liabilities and consumer debts)
•
JOB:
The type of job that loan applicant has such as manager, self, etc.
•
YOJ:
Years at present job.
•
DEROG:
Number of major derogatory reports (which indicates a serious delinquency or late payments).
•
DELINQ:
Number of delinquent credit lines (a line of credit becomes delinquent when a borrower does not make the minimum required payments 30 to 60 days past the day on which the payments were due).
•
CLAGE:
Age of the oldest credit line in months.
•
NINQ:
Number of recent credit inquiries.
•
CLNO:
Number of existing credit lines.
•
DEBTINC:
Debt-to-income ratio (all your monthly debt payments divided by your
gross monthly income. This number is one way lenders measure your ability to manage the monthly payments to repay the money you plan to borrow.
Important Notes
¶
•
This notebook can be considered a guide to refer to while solving the problem. The evaluation will be as per the Rubric shared for each Milestone. Unlike previous courses, it does not follow the pattern of the graded questions in different sections. This notebook would give you a direction on what steps need to be taken in order to get a viable solution to the problem. Please note that this is just one way of doing this. There can be other 'creative' ways to solve the problem and we urge you to feel free and explore them as an 'optional' exercise.
•
In the notebook, there are markdowns cells called - Observations and Insights. It is a good practice to provide observations and extract insights from the outputs.
•
The naming convention for different variables can vary. Please consider the code
provided in this notebook as a sample code.
•
All the outputs in the notebook are just for reference and can be different if you follow a different approach.
•
There are sections called Think About It
in the notebook that will help you get a
better understanding of the reasoning behind a particular technique/step. Interested learners can take alternative approaches if they want to explore different techniques.
Import the necessary libraries
¶
In [109]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score,precision_score,recall_score,f1_score
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import RandomForestClassifier
import scipy.stats as stats
from sklearn.model_selection import GridSearchCV
import warnings
warnings.filterwarnings('ignore')
Read the dataset
¶
In [5]:
hm=pd.read_csv("hmeq.csv")
In [6]:
# Copying data to another variable to avoid any changes to original data
data=hm.copy()
Print the first and last 5 rows of the dataset
¶
In [7]:
# Display first five rows
# Remove ___________ and complete the code
hm.head()
Out[7]:
BAD LOAN MORTDUE
VALUE
REASON
JOB
YOJ DEROG DELINQ
CLAGE
N
0
1
1100
25860.0
39025.0
HomeImp Other 10.5 0.0
0.0
94.366667
1.
1
1
1300
70053.0
68400.0
HomeImp Other 7.0
0.0
2.0
121.833333 0.
2
1
1500
13500.0
16700.0
HomeImp Other 4.0
0.0
0.0
149.466667 1.
3
1
1500
NaN
NaN
NaN
NaN
NaN NaN
NaN
NaN
N
BAD LOAN MORTDUE
VALUE
REASON
JOB
YOJ DEROG DELINQ
CLAGE
N
4
0
1700
97800.0
112000.0 HomeImp Office 3.0
0.0
0.0
93.333333
0.
In [8]:
# Display last 5 rows
# Remove ___________ and complete the code
hm.tail()
Out[8]:
BAD LOAN MORTDUE VALUE REASON
JOB
YOJ DEROG DELINQ
CLAGE
5955
0
88900 57264.0
90185.0 DebtCon
Other 16.0 0.0
0.0
221.808718
5956
0
89000 54576.0
92937.0 DebtCon
Other 16.0 0.0
0.0
208.692070
5957
0
89200 54045.0
92924.0 DebtCon
Other 15.0 0.0
0.0
212.279697
5958
0
89800 50370.0
91861.0 DebtCon
Other 14.0 0.0
0.0
213.892709
5959
0
89900 48811.0
88934.0 DebtCon
Other 15.0 0.0
0.0
219.601002
Understand the shape of the dataset
¶
In [9]:
# Check the shape of the data
# Remove ___________ and complete the code
print(hm.shape)
(5960, 13)
Insights dataset has 5901 rows and 13 columns
Check the data types of the columns
¶
In [10]:
# Check info of the data
# Remove ___________ and complete the code
hm.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5960 entries, 0 to 5959
Data columns (total 13 columns):
# Column Non-Null Count Dtype --- ------ -------------- ----- 0 BAD 5960 non-null int64 1 LOAN 5960 non-null int64 2 MORTDUE 5442 non-null float64
3 VALUE 5848 non-null float64
4 REASON 5708 non-null object 5 JOB 5681 non-null object 6 YOJ 5445 non-null float64
7 DEROG 5252 non-null float64
8 DELINQ 5380 non-null float64
9 CLAGE 5652 non-null float64
10 NINQ 5450 non-null float64
11 CLNO 5738 non-null float64
12 DEBTINC 4693 non-null float64
dtypes: float64(9), int64(2), object(2)
memory usage: 605.4+ KB
Insights __
bad and loan are int, value and reason are obj while rest are float
Check for missing values
¶
In [11]:
# Analyse missing values - Hint: use isnull() function
# Remove ___________ and complete the code
#percent_missing = hm.isnull().sum() * 100 / len(hm)
print(hm.isnull().sum())
BAD 0
LOAN 0
MORTDUE 518
VALUE 112
REASON 252
JOB 279
YOJ 515
DEROG 708
DELINQ 580
CLAGE 308
NINQ 510
CLNO 222
DEBTINC 1267
dtype: int64
In [12]:
#Check the percentage of missing values in the each column.
# Hint: divide the result from the previous code by the number of rows in the dataset
# Remove ___________ and complete the code
percent_missing = hm.isnull().sum() * 100 / len(hm)
missing_value_hm = pd.DataFrame({'column_name': hm.columns,
'percent_missing': percent_missing})
print(missing_value_hm)
column_name percent_missing
BAD BAD 0.000000
LOAN LOAN 0.000000
MORTDUE MORTDUE 8.691275
VALUE VALUE 1.879195
REASON REASON 4.228188
JOB JOB 4.681208
YOJ YOJ 8.640940
DEROG DEROG 11.879195
DELINQ DELINQ 9.731544
CLAGE CLAGE 5.167785
NINQ NINQ 8.557047
CLNO CLNO 3.724832
DEBTINC DEBTINC 21.258389
Insights __
reason and job are sigificant information, DEBTINC, DEROG have the most null values., debtinc passing over the thereshold
Think about it:
¶
•
We found the total number of missing values and the percentage of missing values, which is better to consider? •
What can be the limit for % missing values in a column in order to avoid it and what are the challenges associated with filling them and avoiding them? We can convert the object type columns to categories
converting "objects" to "category" reduces the data space required to store the dataframe
Convert the data types
¶
In [13]:
cols = data.select_dtypes(['object']).columns.tolist()
#adding target variable to this list as this is an classification problem and the target variable is categorical
cols.append('BAD')
In [14]:
cols
Out[14]:
['REASON', 'JOB', 'BAD']
In [15]:
# Changing the data type of object type column to category. hint use astype() function
# remove ___________ and complete the code
hm= hm.astype({"BAD":'category', "REASON":'category',"JOB":'category'})
In [16]:
# Checking the info again and the datatype of different variable
# remove ___________ and complete the code
print (hm.dtypes)
BAD category
LOAN int64
MORTDUE float64
VALUE float64
REASON category
JOB category
YOJ float64
DEROG float64
DELINQ float64
CLAGE float64
NINQ float64
CLNO float64
DEBTINC float64
dtype: object
Analyze Summary Statistics of the dataset
¶
In [17]:
# Analyze the summary statistics for numerical variables
# Remove ___________ and complete the code
hm.describe().T
Out[17]:
count
mean
std
min
25%
50%
LOAN
5960.0 18607.969799
11207.480417 1100.000000 11100.000000 16300.0000
MORTDUE
5442.0 73760.817200
44457.609458 2063.000000 46276.000000 65019.0000
VALUE
5848.0 101776.048741 57385.775334 8000.000000 66075.500000 89235.5000
YOJ
5445.0 8.922268
7.573982
0.000000
3.000000
7.000000
DEROG
5252.0 0.254570
0.846047
0.000000
0.000000
0.000000
DELINQ
5380.0 0.449442
1.127266
0.000000
0.000000
0.000000
CLAGE
5652.0 179.766275
85.810092
0.000000
115.116702
173.466667
NINQ
5450.0 1.186055
1.728675
0.000000
0.000000
1.000000
CLNO
5738.0 21.296096
10.138933
0.000000
15.000000
20.000000
DEBTINC
4693.0 33.779915
8.601746
0.524499
29.140031
34.818262
Insights __
mean looks reasonable for most categories, with the lean of a loan being $18000 on average. value has a higher standard deviation, there much be a greater range in the value of their houses. the data looka to be maybe skewed to the right or normal, because the mean is higher than the median for almost all catetories but DEBTINC. There seem to be dome outliers in morgage due and value and debtinc ans well as perhaps in loan.
In [18]:
# Check summary for categorical data - Hint: inside describe function you can use the argument include=['category']
# Remove ___________ and complete the code
hm.describe(include=['category']).T
Out[18]:
count unique
top
freq
BAD
5960
2
0
4771
REASON
5708
2
DebtCon 3928
JOB
5681
6
Other
2388
Insights _
there seem to be more 0s for bad, and the most popular reason is DEbtcon,
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