Machine Learning with Python Peer-graded Assignment Solutions

Get All Machine Learning with Python Peer-graded Assignment Solutions

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

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Peer-graded Assignment: The best classifier Instruction

Now that you have been equipped with the skills to use different Machine Learning algorithms, over the course of five weeks, you will have the opportunity to practice and apply it on a dataset. In this project, you will complete a notebook where you will build a classifier to predict whether a loan case will be paid off or not.

You load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models:

• k-Nearest Neighbour
• Decision Tree
• Support Vector Machine
• Logistic Regression

The results is reported as the accuracy of each classifier, using the following metrics when these are applicable:

• Jaccard index
• F1-score
• LogLoass

Peer-graded Assignment: The best classifier Solution

Classification with Python

In this notebook we try to practice all the classification algorithms that we learned in this course.

We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.

Lets first load required libraries:

In [1]:

import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import pandas as pd
import numpy as np
import matplotlib.ticker as ticker
from sklearn import preprocessing
%matplotlib inline

About dataset

This dataset is about past loans. The Loan_train.csv data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:

Field	Description
Loan_status	Whether a loan is paid off on in collection
Principal	Basic principal loan amount at the
Terms	Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule
Effective_date	When the loan got originated and took effects
Due_date	Since it’s one-time payoff schedule, each loan has one single due date
Age	Age of applicant
Education	Education of applicant
Gender	The gender of applicant

Lets download the dataset

In [2]:

!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv

--2019-05-26 01:24:50--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23101 (23K) [text/csv]
Saving to: ‘loan_train.csv’

100%[======================================>] 23,101      --.-K/s   in 0.002s  

2019-05-26 01:24:50 (12.7 MB/s) - ‘loan_train.csv’ saved [23101/23101]

Load Data From CSV File

In [3]:

df = pd.read_csv('loan_train.csv')
df.head(10)

Out[3]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender
0	0	0	PAIDOFF	1000	30	9/8/2016	10/7/2016	45	High School or Below	male
1	2	2	PAIDOFF	1000	30	9/8/2016	10/7/2016	33	Bechalor	female
2	3	3	PAIDOFF	1000	15	9/8/2016	9/22/2016	27	college	male
3	4	4	PAIDOFF	1000	30	9/9/2016	10/8/2016	28	college	female
4	6	6	PAIDOFF	1000	30	9/9/2016	10/8/2016	29	college	male
5	7	7	PAIDOFF	1000	30	9/9/2016	10/8/2016	36	college	male
6	8	8	PAIDOFF	1000	30	9/9/2016	10/8/2016	28	college	male
7	9	9	PAIDOFF	800	15	9/10/2016	9/24/2016	26	college	male
8	10	10	PAIDOFF	300	7	9/10/2016	9/16/2016	29	college	male
9	11	11	PAIDOFF	1000	15	9/10/2016	10/9/2016	39	High School or Below	male

In [4]:

df.shape

Out[4]:

(346, 10)

Convert to date time object

In [5]:

df['due_date'] = pd.to_datetime(df['due_date'])
df['effective_date'] = pd.to_datetime(df['effective_date'])
df.head()

Out[5]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender
0	0	0	PAIDOFF	1000	30	2016-09-08	2016-10-07	45	High School or Below	male
1	2	2	PAIDOFF	1000	30	2016-09-08	2016-10-07	33	Bechalor	female
2	3	3	PAIDOFF	1000	15	2016-09-08	2016-09-22	27	college	male
3	4	4	PAIDOFF	1000	30	2016-09-09	2016-10-08	28	college	female
4	6	6	PAIDOFF	1000	30	2016-09-09	2016-10-08	29	college	male

Data visualization and pre-processing

Let’s see how many of each class is in our data set

In [6]:

df['loan_status'].value_counts()

Out[6]:

PAIDOFF       260
COLLECTION     86
Name: loan_status, dtype: int64

260 people have paid off the loan on time while 86 have gone into collection

Lets plot some columns to underestand data better:

In [7]:

# notice: installing seaborn might takes a few minutes
# !conda install -c anaconda seaborn -y

In [8]:

import seaborn as sns

bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)
g = sns.FacetGrid(df, col="Gender", hue="loan_status", palette="Set1", col_wrap=2)
g.map(plt.hist, 'Principal', bins=bins, ec="k")

g.axes[-1].legend()
plt.show()

In [9]:

bins = np.linspace(df.age.min(), df.age.max(), 10)
g = sns.FacetGrid(df, col="Gender", hue="loan_status", palette="Set1", col_wrap=2)
g.map(plt.hist, 'age', bins=bins, ec="k")

g.axes[-1].legend()
plt.show()

Pre-processing: Feature selection/extraction

Lets look at the day of the week people get the loan

In [10]:

df['dayofweek'] = df['effective_date'].dt.dayofweek
bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)
g = sns.FacetGrid(df, col="Gender", hue="loan_status", palette="Set1", col_wrap=2)
g.map(plt.hist, 'dayofweek', bins=bins, ec="k")
g.axes[-1].legend()
plt.show()

We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4

In [11]:

df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)
df.head()

Out[11]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender	dayofweek	weekend
0	0	0	PAIDOFF	1000	30	2016-09-08	2016-10-07	45	High School or Below	male	3	0
1	2	2	PAIDOFF	1000	30	2016-09-08	2016-10-07	33	Bechalor	female	3	0
2	3	3	PAIDOFF	1000	15	2016-09-08	2016-09-22	27	college	male	3	0
3	4	4	PAIDOFF	1000	30	2016-09-09	2016-10-08	28	college	female	4	1
4	6	6	PAIDOFF	1000	30	2016-09-09	2016-10-08	29	college	male	4	1

Convert Categorical features to numerical values

Lets look at gender:

In [12]:

df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)

Out[12]:

Gender  loan_status
female  PAIDOFF        0.865385
        COLLECTION     0.134615
male    PAIDOFF        0.731293
        COLLECTION     0.268707
Name: loan_status, dtype: float64

86 % of female pay there loans while only 73 % of males pay there loan

Lets convert male to 0 and female to 1:

In [13]:

df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)
df.head()

Out[13]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender	dayofweek	weekend
0	0	0	PAIDOFF	1000	30	2016-09-08	2016-10-07	45	High School or Below	0	3	0
1	2	2	PAIDOFF	1000	30	2016-09-08	2016-10-07	33	Bechalor	1	3	0
2	3	3	PAIDOFF	1000	15	2016-09-08	2016-09-22	27	college	0	3	0
3	4	4	PAIDOFF	1000	30	2016-09-09	2016-10-08	28	college	1	4	1
4	6	6	PAIDOFF	1000	30	2016-09-09	2016-10-08	29	college	0	4	1

One Hot Encoding

How about education?

In [14]:

df.groupby(['education'])['loan_status'].value_counts(normalize=True)

Out[14]:

education             loan_status
Bechalor              PAIDOFF        0.750000
                      COLLECTION     0.250000
High School or Below  PAIDOFF        0.741722
                      COLLECTION     0.258278
Master or Above       COLLECTION     0.500000
                      PAIDOFF        0.500000
college               PAIDOFF        0.765101
                      COLLECTION     0.234899
Name: loan_status, dtype: float64

Feature befor One Hot Encoding

In [15]:

df[['Principal','terms','age','Gender','education']].head()

Out[15]:

	Principal	terms	age	Gender	education
0	1000	30	45	0	High School or Below
1	1000	30	33	1	Bechalor
2	1000	15	27	0	college
3	1000	30	28	1	college
4	1000	30	29	0	college

Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame

In [16]:

Feature = df[['Principal','terms','age','Gender','weekend']]
Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)
Feature.drop(['Master or Above'], axis = 1,inplace=True)
Feature.head()

Out[16]:

	Principal	terms	age	Gender	weekend	Bechalor	High School or Below	college
0	1000	30	45	0	0	0	1	0
1	1000	30	33	1	0	1	0	0
2	1000	15	27	0	0	0	0	1
3	1000	30	28	1	1	0	0	1
4	1000	30	29	0	1	0	0	1

Feature selection

Lets defind feature sets, X:

In [17]:

X = Feature
X[0:5]

Out[17]:

	Principal	terms	age	Gender	weekend	Bechalor	High School or Below	college
0	1000	30	45	0	0	0	1	0
1	1000	30	33	1	0	1	0	0
2	1000	15	27	0	0	0	0	1
3	1000	30	28	1	1	0	0	1
4	1000	30	29	0	1	0	0	1

What are our lables?

Replacing “PAIDOFF” and “COLLECTION” strings with 0 and 1 integers because some models can only work with integers or floats

In [18]:

df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)
df.head()

Out[18]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender	dayofweek	weekend
0	0	0	0	1000	30	2016-09-08	2016-10-07	45	High School or Below	0	3	0
1	2	2	0	1000	30	2016-09-08	2016-10-07	33	Bechalor	1	3	0
2	3	3	0	1000	15	2016-09-08	2016-09-22	27	college	0	3	0
3	4	4	0	1000	30	2016-09-09	2016-10-08	28	college	1	4	1
4	6	6	0	1000	30	2016-09-09	2016-10-08	29	college	0	4	1

In [19]:

y = df['loan_status'].values
y[0:20]

Out[19]:

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Doing Train test split –

In [20]:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.40, random_state=5)

In [21]:

print ('Train set:', X_train.shape,  y_train.shape)
print ('Test set:', X_test.shape,  y_test.shape)

Train set: (207, 8) (207,)
Test set: (139, 8) (139,)

In [22]:

display(X_train.shape, y_train.shape)
display(X_test.shape, y_test.shape)

(207, 8)

(207,)

(139, 8)

(139,)

Normalize Data

Data Standardization give data zero mean and unit variance (technically should be done after train test split )

In [23]:

# (technically should be done after train test split )
X_train = preprocessing.StandardScaler().fit(X_train).transform(X_train)
X_test = preprocessing.StandardScaler().fit(X_test).transform(X_test)
X_train[0:5]

Out[23]:

array([[ 0.52533066, -0.97207061,  0.53495582, -0.45109685,  0.85993942,
         2.76134025, -0.86846836, -0.91206272],
       [ 0.52533066,  0.91317564, -0.47326137,  2.21681883,  0.85993942,
        -0.36214298, -0.86846836,  1.09641582],
       [ 0.52533066,  0.91317564, -0.13718897, -0.45109685,  0.85993942,
        -0.36214298,  1.15145243, -0.91206272],
       [ 0.52533066,  0.91317564, -0.64129757, -0.45109685,  0.85993942,
        -0.36214298, -0.86846836,  1.09641582],
       [ 0.52533066, -0.97207061, -0.47326137,  2.21681883, -1.16287262,
        -0.36214298, -0.86846836,  1.09641582]])

Classification

Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model You should use the following algorithm:

• K Nearest Neighbor(KNN)
• Decision Tree
• Support Vector Machine
• Logistic Regression

Notice:

• You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.
• You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.
• You should include the code of the algorithm in the following cells.

K Nearest Neighbor(KNN)

Notice: You should find the best k to build the model with the best accuracy.
warning: You should not use the loan_test.csv for finding the best k, however, you can split your train_loan.csv into train and test to find the best k.

In [24]:

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

In [25]:

# We will be checking for value of K from 0 to 25 so
n = 25
accuracy = np.zeros(n)
for i in range(1,n+1):
    clf = KNeighborsClassifier(n_neighbors = i).fit(X_train, y_train)
    y_test_predicted = clf.predict(X_test)
    accuracy[i-1] = (accuracy_score(y_test, y_test_predicted))
accuracy

Out[25]:

array([ 0.65467626,  0.72661871,  0.67625899,  0.72661871,  0.69064748,
        0.69064748,  0.64748201,  0.69064748,  0.63309353,  0.69784173,
        0.69064748,  0.69784173,  0.69064748,  0.69784173,  0.6618705 ,
        0.69064748,  0.6618705 ,  0.67625899,  0.67625899,  0.67625899,
        0.70503597,  0.71223022,  0.72661871,  0.74100719,  0.74100719])

In [26]:

plt.plot(range(1,n+1),accuracy,'g')
plt.ylabel('Accuracy')
plt.xlabel('Number of Neighbors (K)')
plt.show()

accuracy = pd.DataFrame(accuracy)
print("Maximum Accuracy Got is - " )
accuracy.sort_values(by = 0, ascending = False)[0:1]

Maximum Accuracy Got is - 

Out[26]:

	0
24	0.741007

In [27]:

clf_KNN = KNeighborsClassifier(n_neighbors = 24).fit(X_train, y_train)
# y_test_pred_KNN = clf_KNN.predict(X_test)

Decision Tree

In [28]:

from sklearn.tree import DecisionTreeClassifier

In [29]:

clf2 = DecisionTreeClassifier(criterion = 'gini').fit(X_train, y_train)
y_test_pred_KNN = clf.predict(X_test)
print("Accuracy using criterion as gini - ", accuracy_score(y_test, y_test_pred_KNN))
clf3 = DecisionTreeClassifier(criterion = 'entropy').fit(X_train, y_train)
y_test_pred_KNN = clf2.predict(X_test)
print("Accuracy using criterion as entropy - ", accuracy_score(y_test, y_test_pred_KNN))

Accuracy using criterion as gini -  0.741007194245
Accuracy using criterion as entropy -  0.654676258993

In [30]:

# using criterion as gini
clf_DT = DecisionTreeClassifier(criterion = 'gini').fit(X_train, y_train)

Support Vector Machine

In [31]:

from sklearn.svm import SVC

In [32]:

clf4 = SVC(kernel = 'poly').fit(X_train, y_train)
print("accuracy using polynomial kernel - ", accuracy_score(y_test, clf2.predict(X_test)))
clf5 = SVC(kernel = 'rbf').fit(X_train, y_train)
print("accuracy using Radial Basis function kernel - ", accuracy_score(y_test, clf3.predict(X_test)))

accuracy using polynomial kernel -  0.654676258993
accuracy using Radial Basis function kernel -  0.640287769784

In [33]:

# using linear for kernel in our SVM model
clf_SVM = SVC(kernel = 'poly', random_state = 4).fit(X_train, y_train)

Logistic Regression

In [34]:

from sklearn.linear_model import LogisticRegression

In [35]:

clf_LR = LogisticRegression(solver='lbfgs', warm_start = True)
clf_LR.fit(X_train, y_train)
#print("accuracy score - ", accuracy_score(y_test, clf_LR.predict(X_test)))

Out[35]:

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='lbfgs', tol=0.0001,
          verbose=0, warm_start=True)

Model Evaluation using Test set

In [36]:

from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import f1_score
from sklearn.metrics import log_loss

First, download and load the test set:

In [37]:

!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv

--2019-05-26 01:25:52--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3642 (3.6K) [text/csv]
Saving to: ‘loan_test.csv’

100%[======================================>] 3,642       --.-K/s   in 0s      

2019-05-26 01:25:52 (585 MB/s) - ‘loan_test.csv’ saved [3642/3642]

Load Test set for evaluation

In [38]:

test_df = pd.read_csv('loan_test.csv')
test_df.head()

Out[38]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender
0	1	1	PAIDOFF	1000	30	9/8/2016	10/7/2016	50	Bechalor	female
1	5	5	PAIDOFF	300	7	9/9/2016	9/15/2016	35	Master or Above	male
2	21	21	PAIDOFF	1000	30	9/10/2016	10/9/2016	43	High School or Below	female
3	24	24	PAIDOFF	1000	30	9/10/2016	10/9/2016	26	college	male
4	35	35	PAIDOFF	800	15	9/11/2016	9/25/2016	29	Bechalor	male

Test Data Pre-processing: Feature selection/extraction same as done with train data

In [39]:

# conversion to datetime object
test_df['due_date'] = pd.to_datetime(test_df['due_date'])
test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])
test_df.head()

Out[39]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender
0	1	1	PAIDOFF	1000	30	2016-09-08	2016-10-07	50	Bechalor	female
1	5	5	PAIDOFF	300	7	2016-09-09	2016-09-15	35	Master or Above	male
2	21	21	PAIDOFF	1000	30	2016-09-10	2016-10-09	43	High School or Below	female
3	24	24	PAIDOFF	1000	30	2016-09-10	2016-10-09	26	college	male
4	35	35	PAIDOFF	800	15	2016-09-11	2016-09-25	29	Bechalor	male

In [40]:

# creating weekend column
test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek
test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)
test_df.head()

Out[40]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender	dayofweek	weekend
0	1	1	PAIDOFF	1000	30	2016-09-08	2016-10-07	50	Bechalor	female	3	0
1	5	5	PAIDOFF	300	7	2016-09-09	2016-09-15	35	Master or Above	male	4	1
2	21	21	PAIDOFF	1000	30	2016-09-10	2016-10-09	43	High School or Below	female	5	1
3	24	24	PAIDOFF	1000	30	2016-09-10	2016-10-09	26	college	male	5	1
4	35	35	PAIDOFF	800	15	2016-09-11	2016-09-25	29	Bechalor	male	6	1

In [41]:

# replacing male and female string with 0 and 1
test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)
test_df.head()

Out[41]:

	Unnamed: 0	Unnamed: 0.1	loan_status	Principal	terms	effective_date	due_date	age	education	Gender	dayofweek	weekend
0	1	1	PAIDOFF	1000	30	2016-09-08	2016-10-07	50	Bechalor	1	3	0
1	5	5	PAIDOFF	300	7	2016-09-09	2016-09-15	35	Master or Above	0	4	1
2	21	21	PAIDOFF	1000	30	2016-09-10	2016-10-09	43	High School or Below	1	5	1
3	24	24	PAIDOFF	1000	30	2016-09-10	2016-10-09	26	college	0	5	1
4	35	35	PAIDOFF	800	15	2016-09-11	2016-09-25	29	Bechalor	0	6	1

In [42]:

# creaing dummies and dropping one column

X_t = test_df[['Principal','terms','age','Gender','weekend']]
X_t = pd.concat([X_t,pd.get_dummies(test_df['education'])], axis=1)
X_t.drop(['Master or Above'], axis = 1,inplace=True)
X_t.head()

Out[42]:

	Principal	terms	age	Gender	weekend	Bechalor	High School or Below	college
0	1000	30	50	1	0	1	0	0
1	300	7	35	0	1	0	0	0
2	1000	30	43	1	1	0	1	0
3	1000	30	26	0	1	0	0	1
4	800	15	29	0	1	1	0	0

In [43]:

# Feature Scaling
X_t = preprocessing.StandardScaler().fit(X_t).transform(X_t)
X_t[0:5]

Out[43]:

array([[ 0.49362588,  0.92844966,  3.05981865,  1.97714211, -1.30384048,
         2.39791576, -0.79772404, -0.86135677],
       [-3.56269116, -1.70427745,  0.53336288, -0.50578054,  0.76696499,
        -0.41702883, -0.79772404, -0.86135677],
       [ 0.49362588,  0.92844966,  1.88080596,  1.97714211,  0.76696499,
        -0.41702883,  1.25356634, -0.86135677],
       [ 0.49362588,  0.92844966, -0.98251057, -0.50578054,  0.76696499,
        -0.41702883, -0.79772404,  1.16095912],
       [-0.66532184, -0.78854628, -0.47721942, -0.50578054,  0.76696499,
         2.39791576, -0.79772404, -0.86135677]])

In [44]:

test_df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)

In [45]:

y_t = test_df['loan_status'].values
y_t[0:20]

Out[45]:

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

In [46]:

# np arrays to store intermediate result
Jaccard = np.full(4, np.nan)
F1_score = np.full(4, np.nan)
LogLoss = np.full(4, np.nan)
Algorithm = np.array(4)
Algorithm = ["KNN", "Decision Tree", "SVM", "LogisticRegression"]

In [47]:

Jaccard[0] = jaccard_similarity_score(y_t, clf_KNN.predict(X_t))
Jaccard[1] = jaccard_similarity_score(y_t, clf_DT.predict(X_t))
Jaccard[2] = jaccard_similarity_score(y_t, clf_SVM.predict(X_t))
Jaccard[3] = jaccard_similarity_score(y_t, clf_LR.predict(X_t))

In [48]:

F1_score[0] = f1_score(y_t, clf_KNN.predict(X_t))
F1_score[1] = f1_score(y_t, clf_DT.predict(X_t))
F1_score[2] = f1_score(y_t, clf_SVM.predict(X_t))
F1_score[3] = f1_score(y_t, clf_LR.predict(X_t))

In [49]:

LogLoss[3] = log_loss(y_t, clf_LR.predict_proba(X_t))

In [50]:

Report = pd.DataFrame({"Jaccard":Jaccard, "F1-score":F1_score, "LogLoss":LogLoss}, index=Algorithm)

Report

In [51]:

Report

Out[51]:

	F1-score	Jaccard	LogLoss
KNN	0.235294	0.759259	NaN
Decision Tree	0.300000	0.740741	NaN
SVM	0.352941	0.796296	NaN
LogisticRegression	0.250000	0.777778	0.470967

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