# importing the required libraries
import numpy as np
import pandas as pd
pd.set_option("display.precision", 2)
import seaborn as sns
sns.set_style('whitegrid')
import matplotlib.pyplot as plt
import re
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.sparse import hstack
from sklearn.multiclass import OneVsRestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics# importing and reading the .csv file
df = pd.read_csv('ResumeDataSet.csv')
print("The number of rows are", df.shape[0],"and the number of columns are", df.shape[1])
df.head()The number of rows are 962 and the number of columns are 2
| Category | Resume | |
|---|---|---|
| 0 | Data Science | Skills * Programming Languages: Python (pandas... |
| 1 | Data Science | Education Details \r\nMay 2013 to May 2017 B.E... |
| 2 | Data Science | Areas of Interest Deep Learning, Control Syste... |
| 3 | Data Science | Skills � R � Python � SAP HANA � Table... |
| 4 | Data Science | Education Details \r\n MCA YMCAUST, Faridab... |
# Checking the information of the dataframe(i.e the dataset)
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 962 entries, 0 to 961
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Category 962 non-null object
1 Resume 962 non-null object
dtypes: object(2)
memory usage: 15.2+ KB
# Checking all the different unique values
df.nunique()Category 25
Resume 166
dtype: int64
# Plotting the distribution of Categories as a Count Plot
plt.figure(figsize = (15,15))
sns.countplot(y = "Category", data = df)
df["Category"].value_counts()Java Developer 84
Testing 70
DevOps Engineer 55
Python Developer 48
Web Designing 45
HR 44
Hadoop 42
Data Science 40
Operations Manager 40
Mechanical Engineer 40
Sales 40
ETL Developer 40
Blockchain 40
Arts 36
Database 33
PMO 30
Electrical Engineering 30
Health and fitness 30
Business Analyst 28
DotNet Developer 28
Automation Testing 26
Network Security Engineer 25
Civil Engineer 24
SAP Developer 24
Advocate 20
Name: Category, dtype: int64
# Plotting the distribution of Categories as a Pie Plot
plt.figure(figsize = (18,18))
Category = df['Category'].value_counts().reset_index()['Category']
Labels = df['Category'].value_counts().reset_index()['index']
plt.title("Categorywise Distribution", fontsize=20)
plt.pie(Category, labels = Labels, autopct = '%1.2f%%', shadow = True)
df["Category"].value_counts()*100/df.shape[0]Java Developer 8.73
Testing 7.28
DevOps Engineer 5.72
Python Developer 4.99
Web Designing 4.68
HR 4.57
Hadoop 4.37
Data Science 4.16
Operations Manager 4.16
Mechanical Engineer 4.16
Sales 4.16
ETL Developer 4.16
Blockchain 4.16
Arts 3.74
Database 3.43
PMO 3.12
Electrical Engineering 3.12
Health and fitness 3.12
Business Analyst 2.91
DotNet Developer 2.91
Automation Testing 2.70
Network Security Engineer 2.60
Civil Engineer 2.49
SAP Developer 2.49
Advocate 2.08
Name: Category, dtype: float64
# Function to clean the data
def clean(data):
data = re.sub('httpS+s*', ' ', data) # Removing the links
data = re.sub('RT|cc', ' ', data) # Removing the RT and cc
data = re.sub('#S+', ' ', data) # Removing the hashtags
data = re.sub('@S+', ' ', data) # Removing the mentions
data = data.lower() # Changing the test to lowercase
data = ''.join([i if 32 < ord(i) < 128 else ' ' for i in data]) # Removing all the special characters
data = re.sub('s+', 's', data) # Removing extra whitespaces
data = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[]^_`{|}~"""), ' ', data) # Removing punctuations
return data
cleaned_df = df['Category'].to_frame()
cleaned_df['Resume'] = df['Resume'].apply(lambda x: clean(x)) # Applying the clean function
cleaned_df| Category | Resume | |
|---|---|---|
| 0 | Data Science | skills programming languages python pandas... |
| 1 | Data Science | education details may 2013 to may 2017 b e ... |
| 2 | Data Science | areas of interest deep learning control syste... |
| 3 | Data Science | skills r python sap hana table... |
| 4 | Data Science | education details mca ymcaust faridabad... |
| ... | ... | ... |
| 957 | Testing | computer skills proficient in ms office ... |
| 958 | Testing | willingnes to a ept the challenges po... |
| 959 | Testing | personal skills quick learner eagerne... |
| 960 | Testing | computer skills software knowledge ms power ... |
| 961 | Testing | skill set os windows xp 7 8 8 1 10 database my... |
962 rows × 2 columns
# Encoding the Category column using LabelEncoder
encoder = LabelEncoder()
cleaned_df['Category'] = encoder.fit_transform(cleaned_df['Category'])
cleaned_df| Category | Resume | |
|---|---|---|
| 0 | 6 | skills programming languages python pandas... |
| 1 | 6 | education details may 2013 to may 2017 b e ... |
| 2 | 6 | areas of interest deep learning control syste... |
| 3 | 6 | skills r python sap hana table... |
| 4 | 6 | education details mca ymcaust faridabad... |
| ... | ... | ... |
| 957 | 23 | computer skills proficient in ms office ... |
| 958 | 23 | willingnes to a ept the challenges po... |
| 959 | 23 | personal skills quick learner eagerne... |
| 960 | 23 | computer skills software knowledge ms power ... |
| 961 | 23 | skill set os windows xp 7 8 8 1 10 database my... |
962 rows × 2 columns
# Encoded Classes
encoder.classes_array(['Advocate', 'Arts', 'Automation Testing', 'Blockchain',
'Business Analyst', 'Civil Engineer', 'Data Science', 'Database',
'DevOps Engineer', 'DotNet Developer', 'ETL Developer',
'Electrical Engineering', 'HR', 'Hadoop', 'Health and fitness',
'Java Developer', 'Mechanical Engineer',
'Network Security Engineer', 'Operations Manager', 'PMO',
'Python Developer', 'SAP Developer', 'Sales', 'Testing',
'Web Designing'], dtype=object)
# Creating a Word Vectorizer and transforming it
Resume = cleaned_df['Resume'].values
Category = cleaned_df['Category'].values
word_vectorizer = TfidfVectorizer(sublinear_tf = True, stop_words = 'english', max_features = 1000)
word_vectorizer.fit(Resume)
WordFeatures = word_vectorizer.transform(Resume)# Splitting the data into train, test, printing the shape of each and running KNeighborsClassifier with OneVsRest method
X_train, X_test, y_train, y_test = train_test_split(WordFeatures, Category, random_state=2, test_size = 0.2)
print(f'The shape of the training data {X_train.shape}')
print(f'The shape of the test data {X_test.shape}')
clf = OneVsRestClassifier(KNeighborsClassifier())
clf.fit(X_train, y_train)The shape of the training data (769, 1000)
The shape of the test data (193, 1000)
OneVsRestClassifier(estimator=KNeighborsClassifier())
# Predicting the values using the model built with train data and checking the appropriate metrics
prediction = clf.predict(X_test)
print(f'Accuracy of KNeighbors Classifier on test set: {clf.score(X_test, y_test):.2f}\n')
print(f'The classification report \n {metrics.classification_report(y_test, prediction)}\n\n')Accuracy of KNeighbors Classifier on test set: 0.98
The classification report
precision recall f1-score support
0 1.00 1.00 1.00 4
1 1.00 1.00 1.00 3
2 1.00 0.80 0.89 5
3 1.00 1.00 1.00 9
4 1.00 1.00 1.00 3
5 1.00 1.00 1.00 4
6 1.00 1.00 1.00 5
7 0.78 1.00 0.88 7
8 1.00 1.00 1.00 11
9 1.00 1.00 1.00 7
10 1.00 1.00 1.00 10
11 0.83 1.00 0.91 5
12 1.00 1.00 1.00 6
13 1.00 1.00 1.00 7
14 1.00 1.00 1.00 10
15 1.00 1.00 1.00 16
16 1.00 1.00 1.00 8
17 1.00 1.00 1.00 9
18 1.00 1.00 1.00 7
19 1.00 1.00 1.00 7
20 1.00 1.00 1.00 8
21 1.00 0.75 0.86 8
22 1.00 1.00 1.00 8
23 1.00 1.00 1.00 14
24 1.00 1.00 1.00 12
accuracy 0.98 193
macro avg 0.98 0.98 0.98 193
weighted avg 0.99 0.98 0.98 193