Resume Screening using Deep Learning¶
In this project, we need to determine the category of domain from the resume that is provided. The dataset consists of two columns - Resume and Category, where Resume is the input and Category the output.¶
In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
resume = pd.read_csv("https://cainvas-static.s3.amazonaws.com/media/user_data/AmrutaKoshe/UpdatedResumeDataSet.csv")
In [3]:
resume
Out[3]:
| 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... |
| ... | ... | ... |
| 957 | Testing | Computer Skills: ⢠Proficient in MS office (... |
| 958 | Testing | â Willingness to accept the challenges. â ... |
| 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
In [4]:
#view an example of a resume from our data
resume['Resume'][0]
Out[4]:
'Skills * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), Sql, Java, JavaScript/JQuery. * Machine learning: Regression, SVM, Naïve Bayes, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis, Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), PCA & Neural Nets. * Database Visualizations: Mysql, SqlServer, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau. * Others: Regular Expression, HTML, CSS, Angular 6, Logstash, Kafka, Python Flask, Git, Docker, computer vision - Open CV and understanding of Deep learning.Education Details \r\n\r\nData Science Assurance Associate \r\n\r\nData Science Assurance Associate - Ernst & Young LLP\r\nSkill Details \r\nJAVASCRIPT- Exprience - 24 months\r\njQuery- Exprience - 24 months\r\nPython- Exprience - 24 monthsCompany Details \r\ncompany - Ernst & Young LLP\r\ndescription - Fraud Investigations and Dispute Services Assurance\r\nTECHNOLOGY ASSISTED REVIEW\r\nTAR (Technology Assisted Review) assists in accelerating the review process and run analytics and generate reports.\r\n* Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain, this tool implements predictive coding and topic modelling by automating reviews, resulting in reduced labor costs and time spent during the lawyers review.\r\n* Understand the end to end flow of the solution, doing research and development for classification models, predictive analysis and mining of the information present in text data. Worked on analyzing the outputs and precision monitoring for the entire tool.\r\n* TAR assists in predictive coding, topic modelling from the evidence by following EY standards. Developed the classifier models in order to identify "red flags" and fraud-related issues.\r\n\r\nTools & Technologies: Python, scikit-learn, tfidf, word2vec, doc2vec, cosine similarity, Naïve Bayes, LDA, NMF for topic modelling, Vader and text blob for sentiment analysis. Matplot lib, Tableau dashboard for reporting.\r\n\r\nMULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS (USA CLIENTS)\r\nTEXT ANALYTICS - MOTOR VEHICLE CUSTOMER REVIEW DATA * Received customer feedback survey data for past one year. Performed sentiment (Positive, Negative & Neutral) and time series analysis on customer comments across all 4 categories.\r\n* Created heat map of terms by survey category based on frequency of words * Extracted Positive and Negative words across all the Survey categories and plotted Word cloud.\r\n* Created customized tableau dashboards for effective reporting and visualizations.\r\nCHATBOT * Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation, reservation options and so on.\r\n* This chat bot serves entire product related questions. Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer.\r\n* This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant /recommended questions.\r\n\r\nTools & Technologies: Python, Natural language processing, NLTK, spacy, topic modelling, Sentiment analysis, Word Embedding, scikit-learn, JavaScript/JQuery, SqlServer\r\n\r\nINFORMATION GOVERNANCE\r\nOrganizations to make informed decisions about all of the information they store. The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk.\r\n* Scan data from multiple sources of formats and parse different file formats, extract Meta data information, push results for indexing elastic search and created customized, interactive dashboards using kibana.\r\n* Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant, Outdated, or Trivial.\r\n* Preforming full-text search analysis on elastic search with predefined methods which can tag as (PII) personally identifiable information (social security numbers, addresses, names, etc.) which frequently targeted during cyber-attacks.\r\nTools & Technologies: Python, Flask, Elastic Search, Kibana\r\n\r\nFRAUD ANALYTIC PLATFORM\r\nFraud Analytics and investigative platform to review all red flag cases.\r\nâ\x80¢ FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems.\r\n* It can be used by clients to interrogate their Accounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics\r\nTools & Technologies: HTML, JavaScript, SqlServer, JQuery, CSS, Bootstrap, Node.js, D3.js, DC.js'
In [5]:
resume['Category'].value_counts()
Out[5]:
Java Developer 84 Testing 70 DevOps Engineer 55 Python Developer 48 Web Designing 45 HR 44 Hadoop 42 Operations Manager 40 Blockchain 40 Mechanical Engineer 40 Sales 40 Data Science 40 ETL Developer 40 Arts 36 Database 33 Electrical Engineering 30 Health and fitness 30 PMO 30 Business Analyst 28 DotNet Developer 28 Automation Testing 26 Network Security Engineer 25 SAP Developer 24 Civil Engineer 24 Advocate 20 Name: Category, dtype: int64
In [6]:
sns.countplot(y="Category", data=resume)
Out[6]:
<AxesSubplot:xlabel='count', ylabel='Category'>
In [7]:
#pre-processing of data to remove special characters, hashtags, urls etc
import re
def cleanResume(resumeText):
resumeText = re.sub('http\S+\s*', ' ', resumeText) # remove URLs
resumeText = re.sub('RT|cc', ' ', resumeText) # remove RT and cc
resumeText = re.sub('#\S+', '', resumeText) # remove hashtags
resumeText = re.sub('@\S+', ' ', resumeText) # remove mentions
resumeText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', resumeText) # remove punctuations
resumeText = re.sub(r'[^\x00-\x7f]',r' ', resumeText)
resumeText = re.sub('\s+', ' ', resumeText) # remove extra whitespace
return resumeText
resume['cleaned_resume'] = resume.Resume.apply(lambda x: cleanResume(x))
In [8]:
#data-set after pre-processing
resume
Out[8]:
| Category | Resume | cleaned_resume | |
|---|---|---|---|
| 0 | Data Science | Skills * Programming Languages: Python (pandas... | Skills Programming Languages Python pandas num... |
| 1 | Data Science | Education Details \r\nMay 2013 to May 2017 B.E... | Education Details May 2013 to May 2017 B E UIT... |
| 2 | Data Science | Areas of Interest Deep Learning, Control Syste... | Areas of Interest Deep Learning Control System... |
| 3 | Data Science | Skills ⢠R ⢠Python ⢠SAP HANA ⢠Table... | Skills R Python SAP HANA Tableau SAP HANA SQL ... |
| 4 | Data Science | Education Details \r\n MCA YMCAUST, Faridab... | Education Details MCA YMCAUST Faridabad Haryan... |
| ... | ... | ... | ... |
| 957 | Testing | Computer Skills: ⢠Proficient in MS office (... | Computer Skills Proficient in MS office Word B... |
| 958 | Testing | â Willingness to accept the challenges. â ... | Willingness to a ept the challenges Positive ... |
| 959 | Testing | PERSONAL SKILLS ⢠Quick learner, ⢠Eagerne... | PERSONAL SKILLS Quick learner Eagerness to lea... |
| 960 | Testing | COMPUTER SKILLS & SOFTWARE KNOWLEDGE MS-Power ... | COMPUTER SKILLS SOFTWARE KNOWLEDGE MS Power Po... |
| 961 | Testing | Skill Set OS Windows XP/7/8/8.1/10 Database MY... | Skill Set OS Windows XP 7 8 8 1 10 Database MY... |
962 rows × 3 columns
In [9]:
# Printing an original resume
print('--- Original resume ---')
print(resume['Resume'][0])
--- Original resume --- Skills * Programming Languages: Python (pandas, numpy, scipy, scikit-learn, matplotlib), Sql, Java, JavaScript/JQuery. * Machine learning: Regression, SVM, Naïve Bayes, KNN, Random Forest, Decision Trees, Boosting techniques, Cluster Analysis, Word Embedding, Sentiment Analysis, Natural Language processing, Dimensionality reduction, Topic Modelling (LDA, NMF), PCA & Neural Nets. * Database Visualizations: Mysql, SqlServer, Cassandra, Hbase, ElasticSearch D3.js, DC.js, Plotly, kibana, matplotlib, ggplot, Tableau. * Others: Regular Expression, HTML, CSS, Angular 6, Logstash, Kafka, Python Flask, Git, Docker, computer vision - Open CV and understanding of Deep learning.Education Details Data Science Assurance Associate Data Science Assurance Associate - Ernst & Young LLP Skill Details JAVASCRIPT- Exprience - 24 months jQuery- Exprience - 24 months Python- Exprience - 24 monthsCompany Details company - Ernst & Young LLP description - Fraud Investigations and Dispute Services Assurance TECHNOLOGY ASSISTED REVIEW TAR (Technology Assisted Review) assists in accelerating the review process and run analytics and generate reports. * Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain, this tool implements predictive coding and topic modelling by automating reviews, resulting in reduced labor costs and time spent during the lawyers review. * Understand the end to end flow of the solution, doing research and development for classification models, predictive analysis and mining of the information present in text data. Worked on analyzing the outputs and precision monitoring for the entire tool. * TAR assists in predictive coding, topic modelling from the evidence by following EY standards. Developed the classifier models in order to identify "red flags" and fraud-related issues. Tools & Technologies: Python, scikit-learn, tfidf, word2vec, doc2vec, cosine similarity, Naïve Bayes, LDA, NMF for topic modelling, Vader and text blob for sentiment analysis. Matplot lib, Tableau dashboard for reporting. MULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS (USA CLIENTS) TEXT ANALYTICS - MOTOR VEHICLE CUSTOMER REVIEW DATA * Received customer feedback survey data for past one year. Performed sentiment (Positive, Negative & Neutral) and time series analysis on customer comments across all 4 categories. * Created heat map of terms by survey category based on frequency of words * Extracted Positive and Negative words across all the Survey categories and plotted Word cloud. * Created customized tableau dashboards for effective reporting and visualizations. CHATBOT * Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation, reservation options and so on. * This chat bot serves entire product related questions. Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer. * This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant /recommended questions. Tools & Technologies: Python, Natural language processing, NLTK, spacy, topic modelling, Sentiment analysis, Word Embedding, scikit-learn, JavaScript/JQuery, SqlServer INFORMATION GOVERNANCE Organizations to make informed decisions about all of the information they store. The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk. * Scan data from multiple sources of formats and parse different file formats, extract Meta data information, push results for indexing elastic search and created customized, interactive dashboards using kibana. * Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant, Outdated, or Trivial. * Preforming full-text search analysis on elastic search with predefined methods which can tag as (PII) personally identifiable information (social security numbers, addresses, names, etc.) which frequently targeted during cyber-attacks. Tools & Technologies: Python, Flask, Elastic Search, Kibana FRAUD ANALYTIC PLATFORM Fraud Analytics and investigative platform to review all red flag cases. ⢠FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems. * It can be used by clients to interrogate their Accounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics Tools & Technologies: HTML, JavaScript, SqlServer, JQuery, CSS, Bootstrap, Node.js, D3.js, DC.js
In [10]:
# Printing the same resume after text cleaning
print('--- Cleaned resume ---')
print(resume['cleaned_resume'][0])
--- Cleaned resume --- Skills Programming Languages Python pandas numpy scipy scikit learn matplotlib Sql Java JavaScript JQuery Machine learning Regression SVM Na ve Bayes KNN Random Forest Decision Trees Boosting techniques Cluster Analysis Word Embedding Sentiment Analysis Natural Language processing Dimensionality reduction Topic Modelling LDA NMF PCA Neural Nets Database Visualizations Mysql SqlServer Cassandra Hbase ElasticSearch D3 js DC js Plotly kibana matplotlib ggplot Tableau Others Regular Expression HTML CSS Angular 6 Logstash Kafka Python Flask Git Docker computer vision Open CV and understanding of Deep learning Education Details Data Science Assurance Associate Data Science Assurance Associate Ernst Young LLP Skill Details JAVASCRIPT Exprience 24 months jQuery Exprience 24 months Python Exprience 24 monthsCompany Details company Ernst Young LLP description Fraud Investigations and Dispute Services Assurance TECHNOLOGY ASSISTED REVIEW TAR Technology Assisted Review assists in a elerating the review process and run analytics and generate reports Core member of a team helped in developing automated review platform tool from scratch for assisting E discovery domain this tool implements predictive coding and topic modelling by automating reviews resulting in reduced labor costs and time spent during the lawyers review Understand the end to end flow of the solution doing research and development for classification models predictive analysis and mining of the information present in text data Worked on analyzing the outputs and precision monitoring for the entire tool TAR assists in predictive coding topic modelling from the evidence by following EY standards Developed the classifier models in order to identify red flags and fraud related issues Tools Technologies Python scikit learn tfidf word2vec doc2vec cosine similarity Na ve Bayes LDA NMF for topic modelling Vader and text blob for sentiment analysis Matplot lib Tableau dashboard for reporting MULTIPLE DATA SCIENCE AND ANALYTIC PROJECTS USA CLIENTS TEXT ANALYTICS MOTOR VEHICLE CUSTOMER REVIEW DATA Received customer feedback survey data for past one year Performed sentiment Positive Negative Neutral and time series analysis on customer comments across all 4 categories Created heat map of terms by survey category based on frequency of words Extracted Positive and Negative words across all the Survey categories and plotted Word cloud Created customized tableau dashboards for effective reporting and visualizations CHATBOT Developed a user friendly chatbot for one of our Products which handle simple questions about hours of operation reservation options and so on This chat bot serves entire product related questions Giving overview of tool via QA platform and also give recommendation responses so that user question to build chain of relevant answer This too has intelligence to build the pipeline of questions as per user requirement and asks the relevant recommended questions Tools Technologies Python Natural language processing NLTK spacy topic modelling Sentiment analysis Word Embedding scikit learn JavaScript JQuery SqlServer INFORMATION GOVERNANCE Organizations to make informed decisions about all of the information they store The integrated Information Governance portfolio synthesizes intelligence across unstructured data sources and facilitates action to ensure organizations are best positioned to counter information risk Scan data from multiple sources of formats and parse different file formats extract Meta data information push results for indexing elastic search and created customized interactive dashboards using kibana Preforming ROT Analysis on the data which give information of data which helps identify content that is either Redundant Outdated or Trivial Preforming full text search analysis on elastic search with predefined methods which can tag as PII personally identifiable information social security numbers addresses names etc which frequently targeted during cyber attacks Tools Technologies Python Flask Elastic Search Kibana FRAUD ANALYTIC PLATFORM Fraud Analytics and investigative platform to review all red flag cases FAP is a Fraud Analytics and investigative platform with inbuilt case manager and suite of Analytics for various ERP systems It can be used by clients to interrogate their A ounting systems for identifying the anomalies which can be indicators of fraud by running advanced analytics Tools Technologies HTML JavaScript SqlServer JQuery CSS Bootstrap Node js D3 js DC js
In [11]:
#Obtaining the most common words
import nltk
nltk.download('stopwords')
nltk.download('punkt')
from nltk.corpus import stopwords
import string
from wordcloud import WordCloud
oneSetOfStopWords = set(stopwords.words('english')+['``',"''"])
totalWords =[]
Sentences = resume['cleaned_resume'].values
cleanedSentences = ""
for i in range(len(resume)):
cleanedText = cleanResume(Sentences[i])
cleanedSentences += cleanedText
requiredWords = nltk.word_tokenize(cleanedText)
for word in requiredWords:
if word not in oneSetOfStopWords and word not in string.punctuation:
totalWords.append(word)
wordfreqdist = nltk.FreqDist(totalWords)
mostcommon = wordfreqdist.most_common(50)
print(mostcommon)
[nltk_data] Downloading package stopwords to /home/jupyter- [nltk_data] AmrutaKoshe/nltk_data... [nltk_data] Package stopwords is already up-to-date! [nltk_data] Downloading package punkt to /home/jupyter- [nltk_data] AmrutaKoshe/nltk_data... [nltk_data] Package punkt is already up-to-date!
[('Exprience', 3829), ('months', 3233), ('company', 3130), ('Details', 2967), ('description', 2634), ('1', 2134), ('Project', 1808), ('project', 1579), ('6', 1499), ('data', 1438), ('team', 1424), ('Maharashtra', 1385), ('year', 1244), ('Less', 1137), ('January', 1086), ('using', 1041), ('Skill', 1018), ('Pune', 1016), ('Management', 1010), ('SQL', 990), ('Ltd', 934), ('management', 927), ('C', 896), ('Engineering', 855), ('Education', 833), ('Developer', 806), ('Java', 773), ('2', 754), ('development', 752), ('monthsCompany', 746), ('Pvt', 730), ('application', 727), ('System', 715), ('reports', 697), ('business', 696), ('India', 693), ('requirements', 693), ('I', 690), ('various', 688), ('A', 688), ('Data', 674), ('The', 672), ('University', 656), ('process', 648), ('Testing', 646), ('test', 638), ('Responsibilities', 637), ('system', 636), ('testing', 634), ('Software', 632)]
In [12]:
#Visualising most common words with Wordcloud
wordcloud = WordCloud( background_color='black',
width=1600,
height=800,
).generate(cleanedSentences)
fig = plt.figure(figsize=(30,20))
plt.imshow(wordcloud)
plt.axis('off')
plt.tight_layout(pad=0)
fig.savefig("tag.png")
plt.show()
In [13]:
from sklearn.utils import shuffle
# Get features and labels from data and shuffle
features = resume['cleaned_resume'].values
original_labels = resume['Category'].values
labels = original_labels[:]
for i in range(len(resume)):
labels[i] = str(labels[i].lower()) # convert to lowercase
labels[i] = labels[i].replace(" ", "") # use hyphens to convert multi-token labels into single tokens
features, labels = shuffle(features, labels)
# Print example feature and label
print(features[0])
print(labels[0])
Technical Skills Web Technologies Angular JS HTML5 CSS3 SASS Bootstrap Jquery Javascript Software Brackets Visual Studio Photoshop Visual Studio Code Education Details January 2015 B E CSE Nagpur Maharashtra G H Raisoni College of Engineering October 2009 Photography Competition Click Nagpur Maharashtra Maharashtra State Board College Magazine OCEAN Web Designer Web Designer Trust Systems and Software Skill Details PHOTOSHOP Exprience 28 months BOOTSTRAP Exprience 6 months HTML5 Exprience 6 months JAVASCRIPT Exprience 6 months CSS3 Exprience Less than 1 year months Angular 4 Exprience Less than 1 year monthsCompany Details company Trust Systems and Software description Projects worked on 1 TrustBank CBS Project Description TrustBank CBS is a core banking solution by Trust Systems Roles and Responsibility Renovated complete UI to make it more modern user friendly maintainable and optimised for bank use Shared the UI structure and guidelines to be incorporated with development team of around 50 members Achieved the target of project completion in given time frame Made required graphics for the project in photoshop 2 Loan Bazar Loan Appraisal Project Description Loan Bazar is a MVC based application dedicated to creating and managing loan applications The goal of this application is to streamline the process of loan application and integrate with existing CBS Roles and Responsibility Designed and developed modern and responsive UI of entire application and achieved the target in given time frame Made required graphics for the project in photoshop 3 Capital Security Bond Application Project Description Capital Security Bond Application is a MVC based application which provided an online platform to purchase gold bond Roles and Responsibility Designed and developed modern and responsive UI of entire application and achieved the target in given time frame Made required graphics for the project in photoshop 4 SoftGST Project Description SoftGST Web Based Application is an ASP application to every tax payers and its vendors for generating the GSTR returns on the basis of sales purchase data additionally the application can do the reconciliation of GSTR 2 A with purchase register Roles and Responsibility Designed and developed the UI of Dashboard 5 Trust Analytica Project Description Trust Analytika is the mobile web app that shows bank asset liability income expenses Roles and Responsibility Designed and developed the landing page of the application Supported the developers in UI implementation 6 Website s Project Name 1 TSR Technology Services 2 Vidarbha Merchants Urban Co Op Bank 3 GISSS 4 Softtrust USA Roles and Responsibility Communicated with clients to understand their requirement Made mocks for the website Designed and developed complete website and hosted them in stipulated time company www jalloshband com description Project Name 1 Jallosh Band www jalloshband com 2 An Endeavor Foundation Roles and Responsibility Communicated with clients to understand their requirement Made mocks for the website Designed and developed complete website and hosted them in stipulated time company 10MagicalFingers description National and international client interaction Management of digital data webdesigning
In [14]:
# Split into train and test
train_split = 0.8
train_size = int(train_split * len(resume))
train_features = features[:train_size]
train_labels = labels[:train_size]
test_features = features[train_size:]
test_labels = labels[train_size:]
# Print size of each split
print(len(train_labels))
print(len(test_labels))
769 193
In [15]:
#tokenize features and labels
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
# Tokenize feature data
vocab_size = 6000
oov_tok = '<>'
feature_tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
feature_tokenizer.fit_on_texts(features)
feature_index = feature_tokenizer.word_index
print(dict(list(feature_index.items())))
# Print example sequences from train and test datasets
train_feature_sequences = feature_tokenizer.texts_to_sequences(train_features)
test_feature_sequences = feature_tokenizer.texts_to_sequences(test_features)
{'<>': 1, 'and': 2, 'the': 3, 'of': 4, 'to': 5, 'in': 6, 'for': 7, 'exprience': 8, 'with': 9, 'company': 10, 'a': 11, 'project': 12, 'months': 13, 'description': 14, 'details': 15, 'on': 16, 'as': 17, 'data': 18, '1': 19, 'management': 20, 'team': 21, 's': 22, 'is': 23, '6': 24, 'maharashtra': 25, 'system': 26, 'testing': 27, 'year': 28, 'database': 29, 'from': 30, 'all': 31, 'development': 32, 'business': 33, 'than': 34, 'ltd': 35, 'test': 36, 'by': 37, 'less': 38, 'using': 39, 'sql': 40, 'skill': 41, 'january': 42, 'client': 43, 'java': 44, 'developer': 45, 'engineering': 46, 'application': 47, 'pune': 48, 'work': 49, 'services': 50, 'skills': 51, 'c': 52, 'software': 53, 'pvt': 54, 'education': 55, 'responsibilities': 56, 'sales': 57, 'reports': 58, 'process': 59, 'it': 60, 'operations': 61, 'requirements': 62, 'customer': 63, 'server': 64, 'technical': 65, 'technologies': 66, 'that': 67, 'university': 68, 'india': 69, '2': 70, 'i': 71, 'monthscompany': 72, 'working': 73, 'design': 74, 'various': 75, 'environment': 76, 'web': 77, 'python': 78, 'college': 79, 'at': 80, 'are': 81, 'engineer': 82, 'automation': 83, 'like': 84, 'which': 85, 'role': 86, 'time': 87, 'support': 88, 'an': 89, 'windows': 90, 'mysql': 91, 'based': 92, 'e': 93, 'technology': 94, 'worked': 95, 'per': 96, 'knowledge': 97, 'quality': 98, 'this': 99, 'activities': 100, 'manager': 101, '3': 102, 'issues': 103, 'projects': 104, 'used': 105, 'training': 106, '4': 107, 'mumbai': 108, 'information': 109, 'new': 110, 'computer': 111, 'be': 112, 'systems': 113, 'b': 114, 'different': 115, 'experience': 116, 'etc': 117, 'managing': 118, 'handling': 119, 'network': 120, 'their': 121, 'or': 122, 'requirement': 123, 'oracle': 124, 'end': 125, 'ms': 126, 'o': 127, 'performance': 128, 'job': 129, 'monitoring': 130, 'user': 131, 'maintenance': 132, 'science': 133, 'ensure': 134, 'involved': 135, 'tools': 136, 'analysis': 137, 'hadoop': 138, 'users': 139, 'control': 140, 'm': 141, '5': 142, 'maintaining': 143, 'completed': 144, 'developing': 145, 'report': 146, 'service': 147, 'developed': 148, 'servers': 149, 'html': 150, 'security': 151, 'related': 152, 'office': 153, 'responsible': 154, 'sap': 155, '2016': 156, 'customers': 157, 'ensuring': 158, 'javascript': 159, 'required': 160, 'electrical': 161, 'jquery': 162, 'good': 163, 'creating': 164, 'clients': 165, 'linux': 166, 'power': 167, '2017': 168, 'provide': 169, 'shell': 170, 'school': 171, 'production': 172, 'etl': 173, '7': 174, 'up': 175, 'solutions': 176, 'reporting': 177, 'documentation': 178, 'internal': 179, 'also': 180, 'product': 181, 'key': 182, 'processes': 183, 'maintain': 184, 'my': 185, 'have': 186, 'applications': 187, 'net': 188, 'through': 189, 'build': 190, 'can': 191, 'hive': 192, 'roles': 193, 'other': 194, '24': 195, '10': 196, 'level': 197, 'operating': 198, 'learning': 199, 'queries': 200, 'into': 201, 'configuration': 202, 'scripts': 203, 'out': 204, 'name': 205, 'high': 206, 'machine': 207, 'daily': 208, '2012': 209, 'preparing': 210, 'delivery': 211, 'state': 212, 'su': 213, 'review': 214, '2015': 215, 'well': 216, 'plan': 217, 'communication': 218, 'functional': 219, 'meetings': 220, 'platform': 221, 'them': 222, 'providing': 223, '2008': 224, 'june': 225, 'css': 226, 'scripting': 227, 'solution': 228, 'website': 229, 'document': 230, 'bank': 231, 'members': 232, 'manage': 233, 'timely': 234, 'board': 235, 'documents': 236, 'check': 237, 'created': 238, 'site': 239, 'develop': 240, 'planning': 241, 'responsibility': 242, 'core': 243, 'global': 244, 'understanding': 245, 'diploma': 246, 'designed': 247, 'about': 248, 'co': 249, 'products': 250, 'order': 251, 'multiple': 252, 'administration': 253, 'nagpur': 254, 'international': 255, 'any': 256, 'cases': 257, '8': 258, 'limited': 259, 'billing': 260, 'informatica': 261, 'programming': 262, 'will': 263, 'languages': 264, 'databases': 265, 'preparation': 266, 'lead': 267, '2014': 268, 'health': 269, 'unit': 270, 'implementation': 271, '12': 272, 'ajax': 273, 'activity': 274, 'was': 275, 'tool': 276, 'institute': 277, 'senior': 278, 'create': 279, 'framework': 280, 'integration': 281, 'within': 282, 'vendor': 283, 'bachelor': 284, 'monitor': 285, 'jobs': 286, 'performed': 287, 'bootstrap': 288, 'type': 289, 'marketing': 290, 'fitness': 291, 'weekly': 292, 'complete': 293, '2018': 294, 'cisco': 295, 'one': 296, 'unix': 297, 'implemented': 298, 'program': 299, '2010': 300, 'inventory': 301, 'may': 302, 'online': 303, 'mechanical': 304, 'writing': 305, 'microsoft': 306, 'deployment': 307, 'risk': 308, 'over': 309, 'backup': 310, 'plans': 311, 'blockchain': 312, 'excel': 313, 'cloud': 314, 'devops': 315, 'analyzing': 316, 'including': 317, 'sla': 318, 'identify': 319, 'commerce': 320, 'ability': 321, 'has': 322, 'creation': 323, 'script': 324, 'across': 325, 'teams': 326, 'manual': 327, 'no': 328, 'analytics': 329, 'monthly': 330, 'tech': 331, 'payment': 332, 'vendors': 333, 'essfully': 334, 'understand': 335, 'ess': 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1522, 'deliverable': 1523, 'cabinets': 1524, 'lob': 1525, 'liasing': 1526, 'imports': 1527, 'forwarders': 1528, 'gea': 1529, 'east': 1530, 'dhl': 1531, 'dicom': 1532, 'cad': 1533, 'staging': 1534, 'mocks': 1535, 'show': 1536, 'message': 1537, 'utilities': 1538, 'moving': 1539, 'easily': 1540, 'sports': 1541, 'timelines': 1542, 'hoc': 1543, 'dec': 1544, 'hyderabad': 1545, 'telangana': 1546, 'rational': 1547, 'dba': 1548, 'representative': 1549, 'gold': 1550, 'asset': 1551, 'digital': 1552, 'wallet': 1553, 'color': 1554, 'quickly': 1555, 'putty': 1556, 'proactive': 1557, 'coe': 1558, 'important': 1559, 'svn': 1560, 'closing': 1561, 'maxgen': 1562, 'insights': 1563, 'allow': 1564, 'forecast': 1565, 'improvements': 1566, 'visualization': 1567, 'prototype': 1568, 'delivering': 1569, 'sbi': 1570, 'sourcing': 1571, 'formalities': 1572, 'filing': 1573, 'hydraulic': 1574, 'component': 1575, 'kerala': 1576, 'box': 1577, 'nanded': 1578, 'multinational': 1579, 'discussing': 1580, 'travel': 1581, 'before': 1582, 'sea': 1583, 'screen': 1584, 'safe': 1585, 'express': 1586, 'tcs': 1587, 'karnataka': 1588, 'playing': 1589, 'desk': 1590, 'github': 1591, 'itil': 1592, 'suggesting': 1593, 'mar': 1594, 'associated': 1595, '26': 1596, 'space': 1597, 'sublime': 1598, 'specially': 1599, 'going': 1600, 'classes': 1601, 'attend': 1602, '3d': 1603, 'lines': 1604, 'trustbank': 1605, 'bazar': 1606, 'responsive': 1607, 'softgst': 1608, 'gstr': 1609, 'returns': 1610, 'jalloshband': 1611, 'pivotal': 1612, 'hardworking': 1613, 'detail': 1614, 'sentiment': 1615, 'impala': 1616, 'compare': 1617, 'declaration': 1618, 'due': 1619, 'intellimatch': 1620, 'added': 1621, 'sound': 1622, 'ideas': 1623, 'causes': 1624, 'remote': 1625, 'routine': 1626, 'kpi': 1627, 'zone': 1628, 'delhi': 1629, 'units': 1630, 'cold': 1631, 'strength': 1632, 'allahabad': 1633, 'coaching': 1634, 'incidents': 1635, 'hvac': 1636, 'mcc': 1637, 'clearance': 1638, 'hours': 1639, 'bajaj': 1640, 'abap': 1641, 'black': 1642, 'books': 1643, 'al': 1644, 'guidance': 1645, 'lng': 1646, 'sense': 1647, 'consultants': 1648, 'loop': 1649, 'stations': 1650, 'programmes': 1651, 'lanka': 1652, 'ionic': 1653, 'institutional': 1654, 'gap': 1655, 'rm': 1656, 'valves': 1657, 'midc': 1658, 'cse': 1659, 'record': 1660, '08': 1661, 'presenting': 1662, 'command': 1663, 'extra': 1664, 'cultural': 1665, '80': 1666, 'library': 1667, 'marathi': 1668, 'carrying': 1669, 'president': 1670, 'your': 1671, 'search': 1672, 'mitigation': 1673, 'proposals': 1674, 'home': 1675, 'dns': 1676, 'october': 1677, 'restful': 1678, 'exchange': 1679, 'telecom': 1680, 'district': 1681, 'javaee': 1682, 'aes': 1683, 'rfid': 1684, 'parkar': 1685, 'labs': 1686, 'six': 1687, 'lambda': 1688, 'tibco': 1689, 'spotfire': 1690, 'xen': 1691, 'cpi': 1692, 'genworth': 1693, 'cyber': 1694, 'taken': 1695, 'diagram': 1696, 'survey': 1697, 'promotion': 1698, 'modifications': 1699, 'ap': 1700, 'sme': 1701, 'electronic': 1702, 'video': 1703, 'organizational': 1704, '23': 1705, 'kyc': 1706, 'medical': 1707, 'micro': 1708, 'workforce': 1709, 'llp': 1710, 'needed': 1711, 'class': 1712, 'ansible': 1713, 'merging': 1714, 'standby': 1715, 'advertising': 1716, 'vehicle': 1717, 'extraction': 1718, '2019': 1719, 'million': 1720, 'advocate': 1721, 'claims': 1722, 'costing': 1723, 'drafting': 1724, 'total': 1725, 'productivity': 1726, 'long': 1727, 'uft': 1728, 'modeling': 1729, 'fraud': 1730, 'ip': 1731, 'small': 1732, 'parent': 1733, 'better': 1734, 'telecommunications': 1735, 'simultaneously': 1736, 'collection': 1737, 'selected': 1738, 'particular': 1739, 'sept': 1740, 'means': 1741, 'error': 1742, 'now': 1743, 'norms': 1744, 'idea': 1745, 'collaboration': 1746, 'always': 1747, '12c': 1748, 'middle': 1749, 'tablespaces': 1750, 'multimedia': 1751, 'organize': 1752, 'human': 1753, '2001': 1754, 'mirroring': 1755, 'turn': 1756, 'stream': 1757, 'serve': 1758, 'inside': 1759, 'classification': 1760, 'pandas': 1761, 'joined': 1762, 'auditing': 1763, 'object': 1764, 'waste': 1765, 'extracting': 1766, 'computing': 1767, 'reliance': 1768, 'flexibility': 1769, 'iec': 1770, 'topic': 1771, 'study': 1772, 'residential': 1773, 'quotation': 1774, 'mw': 1775, 'carried': 1776, 'would': 1777, 'supervise': 1778, 'calculate': 1779, 'calling': 1780, 'receive': 1781, 'giving': 1782, 'correspondence': 1783, 'organized': 1784, 'syntel': 1785, 'drive': 1786, 'goals': 1787, 'coded': 1788, 'authority': 1789, 'modified': 1790, 'stories': 1791, 'disease': 1792, 'asm': 1793, 'weeks': 1794, 'alternate': 1795, 'traceability': 1796, 'catia': 1797, 'table': 1798, 'editing': 1799, 'xinfin': 1800, 'gateway': 1801, 'yes': 1802, 'establish': 1803, 'discrepancies': 1804, 'alert': 1805, 'bandra': 1806, 'interacted': 1807, 'dbms': 1808, 'responses': 1809, 'finalizing': 1810, 'term': 1811, 'late': 1812, 'generates': 1813, 'appraisals': 1814, 'hotels': 1815, 'statutory': 1816, 'disk': 1817, 'hp': 1818, 'pacs': 1819, 'bed': 1820, 'money': 1821, 'srs': 1822, 'ccu': 1823, 'cmu': 1824, 'protocols': 1825, 'encrypted': 1826, 'oct': 1827, 'workshops': 1828, 'configure': 1829, 'linear': 1830, 'faster': 1831, 'initiative': 1832, 'promoted': 1833, 'context': 1834, 'corrective': 1835, 'mentor': 1836, 'printing': 1837, 'pack': 1838, 'soft': 1839, 'solar': 1840, 'failure': 1841, 'innovative': 1842, 'registration': 1843, 'low': 1844, 'globally': 1845, '2008r2': 1846, 'appraisal': 1847, 'bhopal': 1848, 'hereby': 1849, 'proxy': 1850, 'speaking': 1851, 'party': 1852, 'bill': 1853, 'negotiation': 1854, 'train': 1855, 'associate': 1856, 'distributed': 1857, 'joining': 1858, 'loaded': 1859, 'brd': 1860, 'f5': 1861, 'issuance': 1862, 'complaints': 1863, 'insight': 1864, 'atos': 1865, 'zaggle': 1866, 'inr': 1867, 'improving': 1868, 'prgx': 1869, 'subject': 1870, 'february': 1871, 'ca': 1872, 'clarity': 1873, 'establishing': 1874, 'recording': 1875, 'sop': 1876, 'headcount': 1877, 'ratio': 1878, 'tagging': 1879, 'gm': 1880, 'rookie': 1881, 'privileges': 1882, 'mssql': 1883, 'medicines': 1884, 'indian': 1885, 'pcb': 1886, 'dc': 1887, 'winding': 1888, 'body': 1889, 'calibration': 1890, 'unique': 1891, 'columns': 1892, 'holding': 1893, 'compensation': 1894, 'sectors': 1895, 'draw': 1896, 'capita': 1897, 'assessing': 1898, 'carry': 1899, 'kom': 1900, 'cabinet': 1901, 'loops': 1902, 'focal': 1903, 'points': 1904, 'experion': 1905, 'jbk': 1906, 'schematic': 1907, 'presence': 1908, 'instrumentation': 1909, 'constraints': 1910, 'fee': 1911, 'reducing': 1912, 'rcsa': 1913, 'concurrence': 1914, 'utilized': 1915, 'receivables': 1916, 'delinquent': 1917, 'transit': 1918, 'quantity': 1919, 'transshipment': 1920, 'serck': 1921, 'quotes': 1922, 'elements': 1923, 'vlan': 1924, '49': 1925, 'plane': 1926, 'promoting': 1927, 'estate': 1928, 'automating': 1929, 'graduate': 1930, 'elastic': 1931, 'synechron': 1932, 'redhat': 1933, 'others': 1934, 'alerts': 1935, 'rack': 1936, 'earthing': 1937, 'motivate': 1938, 'recognized': 1939, 'some': 1940, 'ticketing': 1941, 'qualities': 1942, 'websites': 1943, 'american': 1944, '18': 1945, 'options': 1946, 'professionals': 1947, 'finding': 1948, 'globe': 1949, 'concept': 1950, 'collect': 1951, 'therapies': 1952, 'searching': 1953, 'food': 1954, 'represent': 1955, 'lists': 1956, 'face': 1957, 'measures': 1958, 'qualification': 1959, 'procedure': 1960, '50': 1961, 'op': 1962, 'sow': 1963, 'foundry': 1964, 'called': 1965, 'driver': 1966, 'lake': 1967, 'banks': 1968, 'sheet': 1969, 'presented': 1970, 'phase': 1971, 'tree': 1972, 'interpret': 1973, 'teradata': 1974, 'connection': 1975, 'downtime': 1976, 'traffic': 1977, 'plsql': 1978, 'york': 1979, 'scd': 1980, 'round': 1981, 'brand': 1982, 'highlighting': 1983, 'severity': 1984, 'add': 1985, 'mitigate': 1986, 'scientist': 1987, 'response': 1988, 'concern': 1989, 'dynamics': 1990, 'deliveries': 1991, 'makes': 1992, 'fetch': 1993, 'directory': 1994, 'interact': 1995, 'smoke': 1996, 'resume': 1997, 'entry': 1998, 'london': 1999, 'career': 2000, 'rfq': 2001, '29': 2002, 'interviewing': 2003, 'partnerships': 2004, 'port': 2005, 'increased': 2006, 'assurance': 2007, 'johnson': 2008, 'court': 2009, 'clarifications': 2010, 'classifier': 2011, 'uh': 2012, 'steel': 2013, 'as400': 2014, 'switch': 2015, 'multisim': 2016, 'algorithm': 2017, 'aspects': 2018, 'visits': 2019, 'few': 2020, 'audience': 2021, 'way': 2022, 'verbal': 2023, 'functionality': 2024, 'patil': 2025, 'infosys': 2026, 'stack': 2027, 'controlled': 2028, 'objectives': 2029, 'vice': 2030, 'gaps': 2031, 'shopping': 2032, 'mangers': 2033, 'instruments': 2034, 'authentication': 2035, 'utilizing': 2036, 'middleware': 2037, 'sangli': 2038, '2004': 2039, 'tier': 2040, 'upgrading': 2041, 'efforts': 2042, 'io': 2043, 'exposys': 2044, 'notepad': 2045, 'fruits': 2046, 'practice': 2047, 'failures': 2048, 'visiting': 2049, 'doctor': 2050, 'locations': 2051, 'individuals': 2052, 'questions': 2053, 'income': 2054, 'landing': 2055, 'urban': 2056, 'trainings': 2057, 'db2': 2058, 'struts': 2059, 'loyalty': 2060, 'updated': 2061, 'indore': 2062, 'count': 2063, 'agents': 2064, 'watch': 2065, 'fill': 2066, 'gas': 2067, 'coming': 2068, 'optimize': 2069, 'qualifications': 2070, 'holds': 2071, 'brought': 2072, 'relational': 2073, 'launch': 2074, 'deliver': 2075, 'expectations': 2076, 'closure': 2077, 'atm': 2078, 'effectiveness': 2079, 'expe': 2080, 'boarding': 2081, 'charts': 2082, 'timesheet': 2083, 'communities': 2084, 'comes': 2085, 'j': 2086, 'input': 2087, 'proof': 2088, 'switchgear': 2089, 'pcc': 2090, 'ct': 2091, 'motivated': 2092, 'gate': 2093, 'damage': 2094, 'dossier': 2095, 'ledger': 2096, 'dispatch': 2097, 'protocol': 2098, 'involving': 2099, 'think': 2100, 'parties': 2101, 'epted': 2102, 'held': 2103, 'europe': 2104, '1998': 2105, '1995': 2106, 'systematic': 2107, 'tours': 2108, 'experiences': 2109, 'examination': 2110, 'verification': 2111, 'star': 2112, 'warehouses': 2113, 'standardized': 2114, 'angularjs': 2115, 'motors': 2116, 'sep': 2117, 'sorting': 2118, 'sweden': 2119, 'cvs': 2120, 'patni': 2121, 'insert': 2122, 'delete': 2123, 'already': 2124, 'several': 2125, 'heterogeneous': 2126, 'slas': 2127, 'noc': 2128, 'coach': 2129, 'allows': 2130, 'sign': 2131, 'usability': 2132, 'require': 2133, 'b2c': 2134, 'managements': 2135, 'clustering': 2136, 'logical': 2137, 'club': 2138, 'innovations': 2139, 'controlling': 2140, '120': 2141, 'bw': 2142, 'ii': 2143, 'booking': 2144, 'you': 2145, 'rajasthan': 2146, 'willing': 2147, 'fast': 2148, 'spa': 2149, 'suggestions': 2150, '25': 2151, 'sungard': 2152, 'upto': 2153, 'abstract': 2154, 'crystal': 2155, 'instructions': 2156, '43': 2157, 'goqii': 2158, 'crawling': 2159, 'rpg': 2160, 'kpit': 2161, 'ranchi': 2162, 'logger': 2163, 'hyperledger': 2164, 'block': 2165, 'declare': 2166, 'california': 2167, 'postman': 2168, 'password': 2169, 'police': 2170, 'belief': 2171, 'tasking': 2172, 'vidya': 2173, 'courses': 2174, 'meaningful': 2175, 'capture': 2176, 'dedication': 2177, 'oltp': 2178, 'vb': 2179, 'demo': 2180, 'salesforce': 2181, 'detailing': 2182, 'informed': 2183, 'display': 2184, 'see': 2185, 'embedded': 2186, 'legacy': 2187, 'nasscom': 2188, 'ieee': 2189, 'vs': 2190, 'optimizing': 2191, 'marital': 2192, 'corpcloud': 2193, 'proposed': 2194, 'listener': 2195, 'frames': 2196, 'shivaji': 2197, 'actuators': 2198, 'mesa': 2199, 'rise': 2200, 'bex': 2201, 'oss': 2202, 'bar': 2203, 'suitable': 2204, 'cement': 2205, 'inouvelle': 2206, 'ventures': 2207, 'hybrid': 2208, 'diverse': 2209, 'extractor': 2210, 'honda': 2211, 'dealership': 2212, 'last': 2213, 'marshall': 2214, 'extensively': 2215, 'places': 2216, 'comparison': 2217, 'contractor': 2218, 'editor': 2219, 'curriculum': 2220, 'delta': 2221, 'heat': 2222, 'pair': 2223, 'answering': 2224, 'karate': 2225, 'foreign': 2226, 'actuator': 2227, 'electric': 2228, 'rewards': 2229, 'determined': 2230, 'purchasing': 2231, 'tdd': 2232, 'taf': 2233, 'hl7': 2234, 'reconstruction': 2235, 'logix': 2236, 'talk': 2237, 'rslinx': 2238, 'classic': 2239, 'increment': 2240, 'sprint': 2241, 'vbscripts': 2242, 'aegis': 2243, 'extracted': 2244, 'tensorflow': 2245, 'asst': 2246, 'manuals': 2247, 'subcontractors': 2248, 'svm': 2249, 'sqlserver': 2250, 'arsys': 2251, 'mas': 2252, 'anti': 2253, 'laundering': 2254, 'tester': 2255, 'beginner': 2256, 'r3': 2257, 'cbse': 2258, 'rpa': 2259, 'mvc5': 2260, 'disabled': 2261, 'greyed': 2262, 'facets': 2263, 'tac': 2264, 'tmap': 2265, 'po': 2266, 'painting': 2267, 'dedicated': 2268, 'streamline': 2269, 'band': 2270, 'institution': 2271, 'mechanism': 2272, 'willingness': 2273, 'newly': 2274, 'shut': 2275, 'yrs': 2276, 'foods': 2277, 'funds': 2278, 'uml': 2279, 'install': 2280, 'highest': 2281, 'outs': 2282, 'slave': 2283, 'offline': 2284, 'art': 2285, 'appreciation': 2286, 'ongoing': 2287, 'planned': 2288, 'transformation': 2289, 'engagement': 2290, 'permanent': 2291, 'natural': 2292, 'facilitating': 2293, 'letters': 2294, 'significant': 2295, 'emerging': 2296, '1999': 2297, 'settings': 2298, 'experts': 2299, 'spaces': 2300, 'german': 2301, 'a1': 2302, 'flows': 2303, 'her': 2304, 'speed': 2305, 'jaunpur': 2306, 'offer': 2307, 'measure': 2308, 'commissioner': 2309, 'regional': 2310, 'substation': 2311, '1st': 2312, 'mining': 2313, 'timeframe': 2314, 'findings': 2315, 'machines': 2316, 'write': 2317, 'lookup': 2318, 'joiner': 2319, 'attended': 2320, 'royal': 2321, 'society': 2322, 'solapur': 2323, 'director': 2324, 'chinchwad': 2325, 'gui': 2326, 'linked': 2327, 'statistical': 2328, 'book': 2329, '22': 2330, 'break': 2331, 'shipment': 2332, 'interior': 2333, 'bharat': 2334, 'feasibility': 2335, 'equipments': 2336, 'organisation': 2337, 'statements': 2338, 'personally': 2339, 'either': 2340, 'tamil': 2341, 'nadu': 2342, 'statement': 2343, 'blue': 2344, 'man': 2345, 'resolved': 2346, 'estimates': 2347, 'ranjangaon': 2348, 'oversight': 2349, 'oversee': 2350, 'buildings': 2351, 'together': 2352, 'brain': 2353, 'publication': 2354, 'consistently': 2355, 'peer': 2356, 'depending': 2357, 'names': 2358, 'consistent': 2359, 'how': 2360, 'aviation': 2361, 'items': 2362, 'ksa': 2363, 'cargo': 2364, 'dimensions': 2365, 'feasible': 2366, 'consumed': 2367, 'semi': 2368, 'sensor': 2369, 'commands': 2370, 'ec2': 2371, 'scaling': 2372, 'recruit': 2373, 'downloaded': 2374, 'brainstorming': 2375, 'oral': 2376, 'arrange': 2377, 'talwalkars': 2378, 'applied': 2379, '03': 2380, 'force': 2381, 'obtain': 2382, 'batches': 2383, 'financials': 2384, 'tracked': 2385, 'jntu': 2386, 'compile': 2387, 'rac': 2388, 'nodes': 2389, 'asia': 2390, 'tieto': 2391, 'fundamentals': 2392, 'miami': 2393, 'llc': 2394, 'consultation': 2395, 'discussions': 2396, 'allocation': 2397, 'additional': 2398, 'frameworks': 2399, 'introducing': 2400, 'presentations': 2401, 'guide': 2402, 'window': 2403, 'console': 2404, 'generations': 2405, 'remediation': 2406, 'flask': 2407, 'explorer': 2408, 'birthday': 2409, 'kind': 2410, 'programmer': 2411, 'scikit': 2412, 'leave': 2413, 'functioning': 2414, 'amount': 2415, 'rds': 2416, 'dump': 2417, 'wan': 2418, 'ipsec': 2419, 'webdriver': 2420, 'testng': 2421, 'enhanced': 2422, 'cpp': 2423, 'applocker': 2424, 'crimes': 2425, 'aims': 2426, 'sld': 2427, 'layouts': 2428, 'treatment': 2429, 'useful': 2430, 'modification': 2431, 'technological': 2432, 'reduction': 2433, 'computers': 2434, 'eliminate': 2435, 'remedy': 2436, 'ssl': 2437, 'juniper': 2438, 'merchant': 2439, 'alarm': 2440, 'abilities': 2441, 'respond': 2442, 'inquiries': 2443, 'maximizing': 2444, 'minimizing': 2445, 'losses': 2446, 'measuring': 2447, 'axis': 2448, 'venture': 2449, 'infotek': 2450, 'yalamanchili': 2451, 'ftc': 2452, 'sale': 2453, 'perfect': 2454, 'inbound': 2455, 'terminals': 2456, 'family': 2457, 'because': 2458, 'session': 2459, 'fact': 2460, 'attacks': 2461, '125': 2462, 'responsiblites': 2463, 'deliery': 2464, 'mom': 2465, 'chasing': 2466, 'rr': 2467, 'deputation': 2468, 'invoicings': 2469, 'activites': 2470, 'invocing': 2471, 'cem': 2472, 'sdm': 2473, 'save': 2474, 'towers': 2475, 'tofa': 2476, 'tailgating': 2477, 'asall': 2478, 'drivekey': 2479, 'roadblocks': 2480, 'compliancereport': 2481, 'quarter': 2482, 'allowances': 2483, 'milestone': 2484, 'addressed': 2485, 'dob': 2486, 'wagh': 2487, 'servereducation': 2488, 'ab': 2489, 'steps': 2490, 'charges': 2491, 'takes': 2492, 'trouble': 2493, 'shooting': 2494, 'tripping': 2495, 'magnetic': 2496, 'cables': 2497, 'competitive': 2498, 'rajapur': 2499, 'rakesh': 2500, '2026': 2501, '1180': 2502, 'inverter': 2503, 'converter': 2504, '3rd': 2505, 'why': 2506, 'idt': 2507, 'cmc': 2508, 'universes': 2509, 'reference': 2510, 'tower': 2511, 'ba': 2512, 'finish': 2513, 'housing': 2514, 'bring': 2515, 'discussion': 2516, 'promote': 2517, 'enthusiastic': 2518, 'servlets': 2519, 'mass': 2520, 'covered': 2521, 'maruti': 2522, 'specialty': 2523, 'feedbacks': 2524, 'quantitative': 2525, 'priority': 2526, 'funding': 2527, 'learned': 2528, 'ppt': 2529, 'lifestyle': 2530, 'criminal': 2531, 'fzco': 2532, 'kick': 2533, 'profits': 2534, 'clarification': 2535, 'em': 2536, 'revised': 2537, 'proto': 2538, 'sis': 2539, 'fgs': 2540, 'amc': 2541, 'tps': 2542, 'pu': 2543, 'downloading': 2544, 'egypt': 2545, 'r410': 2546, 'fdm': 2547, 'truck': 2548, 'fox': 2549, '9001': 2550, 'matters': 2551, 'country': 2552, 'personnel': 2553, 'priorities': 2554, 'push': 2555, 'treasury': 2556, 'corporations': 2557, 'broker': 2558, 'dealer': 2559, 'escalated': 2560, 'assessments': 2561, 'initiate': 2562, 'competency': 2563, 'transitions': 2564, 'volumes': 2565, 'hires': 2566, 'siebel': 2567, '95': 2568, 'hour': 2569, 'flotilla': 2570, 'exceed': 2571, '4000': 2572, 'landmark': 2573, 'brokers': 2574, 'customs': 2575, 'ecoflex': 2576, 'forwarding': 2577, 'pricing': 2578, 'destinations': 2579, 'gcc': 2580, 'reasonable': 2581, 'shore': 2582, 'parts': 2583, 'chat': 2584, 'bot': 2585, 'child': 2586, 'ept': 2587, 'kisan': 2588, 'abacus': 2589, 'villa': 2590, 'matplotlib': 2591, 'ospf': 2592, 'eigrp': 2593, 'tftp': 2594, 'violation': 2595, 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6354, 'ecc': 6355, 'logon': 6356, 'acn': 6357, 'mgt': 6358, 's4hana': 6359, 'edi': 6360, 'extensibility': 6361, 'varies': 6362, 'prince2': 6363, 'practitioner': 6364, 'implementations': 6365, 'america': 6366, 'fricew': 6367, 'ps': 6368, 'qm': 6369, 'solman': 6370, 'gts': 6371, 'vistex': 6372, 'proship': 6373, 'managenow': 6374, 'loftware': 6375, 'pocs': 6376, 'roll': 6377, 'ecc6': 6378, 'unicode': 6379, 'complicated': 6380, 'estimations': 6381, 'sepsoft': 6382, '24th': 6383, 'anna': 6384, 'jacobs': 6385, '150': 6386, 'medicine': 6387, 'etp': 6388, 'drain': 6389, 'shall': 6390, 'vise': 6391, 'itr': 6392, 'checklist': 6393, 'issued': 6394, 'punch': 6395, 'dossiers': 6396, 'progression': 6397, 'chawla': 6398, 'architectural': 6399, '2b': 6400, 'roof': 6401, 'upcoming': 6402, 'entered': 6403, 'guinness': 6404, 'gac': 6405, 'arm': 6406, 'dollar': 6407, 'rp': 6408, 'pour': 6409, '793': 6410, 'cu': 6411, 'dh600': 6412, '163': 6413, 'barsha': 6414, 'western': 6415, 'amendments': 6416, 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'continual': 6534, 'practicality': 6535, 'adjust': 6536, 'budgetary': 6537, 'cohesion': 6538, 'fluidity': 6539, 'inspect': 6540, 'sonia': 6541, 'renovation': 6542, 'malls': 6543, 'compiling': 6544, 'tendering': 6545, 'complies': 6546, 'sustainability': 6547, 'grandeurs': 6548, 'realetors': 6549, 'materails': 6550, 'labours': 6551, 'regulated': 6552, 'subcontractor': 6553, 'equipement': 6554, 'cheack': 6555, 'progrees': 6556, 'devolopment': 6557, 'adviser': 6558, 'craftspeople': 6559, 'operatives': 6560, 'planners': 6561, 'surveyors': 6562, 'trees': 6563, 'boosting': 6564, 'pca': 6565, 'nets': 6566, 'elasticsearch': 6567, 'plotly': 6568, 'ggplot': 6569, 'logstash': 6570, 'kafka': 6571, 'dispute': 6572, 'elerating': 6573, 'discovery': 6574, 'implements': 6575, 'resulting': 6576, 'lawyers': 6577, 'outputs': 6578, 'precision': 6579, 'ey': 6580, 'flags': 6581, 'tfidf': 6582, 'word2vec': 6583, 'doc2vec': 6584, 'vader': 6585, 'blob': 6586, 'matplot': 6587, 'lib': 6588, 'plotted': 6589, 'reservation': 6590, 'serves': 6591, 'recommendation': 6592, 'too': 6593, 'asks': 6594, 'spacy': 6595, 'synthesizes': 6596, 'facilitates': 6597, 'positioned': 6598, 'counter': 6599, 'parse': 6600, 'rot': 6601, 'outdated': 6602, 'trivial': 6603, 'pii': 6604, 'identifiable': 6605, 'addresses': 6606, 'frequently': 6607, 'flag': 6608, 'fap': 6609, 'interrogate': 6610, 'anomalies': 6611, 'dibrugarh': 6612, 'dairy': 6613, 'jagiroad': 6614, 'tribunals': 6615, 'bodies': 6616, 'forum': 6617, 'judge': 6618, 'nagaon': 6619, '2d': 6620, '3deducation': 6621, 'talreja': 6622, 'hoping': 6623, 'thanks': 6624, 'apeksha': 6625, 'naharkar': 6626, 'ado': 6627, 'mozilla': 6628, 'firefox10': 6629, 'biography': 6630, 'gallery': 6631, 'contactus': 6632, 'hms': 6633, 'conceivable': 6634, 'importantly': 6635, 'backed': 6636, 'dependable': 6637, 'multispecialty': 6638, 'cover': 6639, 'bess': 6640, 'sms': 6641, 'exams': 6642, 'calendar': 6643, 'parents': 6644, 'paperless': 6645, 'today': 6646, 'uhs': 6647, 'cmus': 6648, 'overdrafts': 6649, 'sblc': 6650, 'fg': 6651, 'cheques': 6652, 'nbf': 6653, 'cb': 6654, 'again': 6655, 'davv': 6656, 'cognizant': 6657, 'stadio': 6658, 'thergaon': 6659, 'entityframewok': 6660, 'corecode': 6661, 'inetsoft': 6662, 'pivot': 6663, 'applicationseducation': 6664, 'queen': 6665, 'margaret': 6666, 'edinburg': 6667, 'atri': 6668, 'checklists': 6669, 'leaves': 6670, 'salaries': 6671, 'esic': 6672, 'deduction': 6673, '24q': 6674, 'topics': 6675, 'shops': 6676, 'establishments': 6677, '1972': 6678, 'naukri': 6679, 'preliminary': 6680, 'orientation': 6681, 'facilitation': 6682, 'pms': 6683, 'disbursement': 6684, 'kra': 6685, 'welfare': 6686, 'celebration': 6687, 'diwali': 6688, 'dhamaka': 6689, 'offsite': 6690, 'md': 6691, 'offering': 6692, 'reimbursement': 6693, 'resigned': 6694, 'langages': 6695, 'posse': 6696, 'adichunchanagiri': 6697, 'chikmagalur': 6698, 'truly': 6699, 'jayashree': 6700, 'workload': 6701, 'allocate': 6702, 'q': 6703, 'aeronautics': 6704, 'satish': 6705, 'hangar': 6706, 'mirage': 6707, 'fighter': 6708, 'aircraft': 6709, 'labview': 6710, 'webframework': 6711, 'ltspice': 6712, 'mipower': 6713, 'gitbash': 6714, 'notebook': 6715, 'interpreters': 6716, 'python2': 6717, 'pycharm': 6718, 'debian': 6719, 'kali': 6720, 'deeksha': 6721, 'little': 6722, 'flower': 6723, 'mathematics': 6724, 'themathcompany': 6725, 'casino': 6726, 'operator': 6727, 'disclosed': 6728, 'macau': 6729, 'segment': 6730, 'patrons': 6731, 'prove': 6732, 'henceforth': 6733, 'imcost': 6734, 'menon': 6735, 'springer': 6736, 'smartforms': 6737, 'lenm': 6738, 'netherland': 6739, 'dsp': 6740, 'dsm': 6741, 'sinochem': 6742, 'paints': 6743, 'coatings': 6744, 'producer': 6745, 'intra': 6746, 'barc': 6747, 'motion': 6748, 'daigram': 6749, 'graduation': 6750, 'ifix': 6751, 'specialization': 6752, 'behaviour': 6753, 'calicut': 6754, 'thrissur': 6755, 'prajyoti': 6756, 'foster': 6757, 'breads': 6758, 'kinfra': 6759, 'santhwana': 6760, 'psychotherapy': 6761, 'cochin': 6762, 'counsellor': 6763, 'monorail': 6764, 'ambedkar': 6765, 'llm': 6766, 'operative': 6767, 'deed': 6768, 'representation': 6769, 'disputes': 6770, 'panvel': 6771, 'ryan': 6772, 'bp': 6773, 'bottlenecks': 6774, 'iabac': 6775, 'versatile': 6776, 'advocator': 6777, 'augmented': 6778, 'fahed': 6779, 'mohali': 6780, 'indo': 6781, 'querying': 6782, 'cleaning': 6783, 'heretic': 6784, 'cleansing': 6785, 'conclusions': 6786, 'formulating': 6787, 'injury': 6788, 'death': 6789, 'former': 6790, 'ireport': 6791, 'baramati': 6792, 'highschool': 6793, 'singhad': 6794, 'vasundhara': 6795, 'nirmiti': 6796, 'balaji': 6797, 'ganesh': 6798, 'valid': 6799, '2006education': 6800, 'oxford': 6801, 'banglore': 6802, 'nabinagar': 6803, 'bihar': 6804, 'ntpc': 6805, 'brbcl': 6806, 'stc': 6807, 'qatif': 6808, '2065864': 6809, 'arif': 6810, 'kinfolk': 6811, 'king': 6812, 'fahad': 6813, 'airport': 6814, 'dar': 6815, 'hadassah': 6816, 'dpf': 6817, 'eleven': 6818, 'arabian': 6819, 'army': 6820, 'ommodation': 6821, 'arabia': 6822, 'foremen': 6823, 'subsequently': 6824, 'rfi': 6825, 'realizing': 6826, 'ducting': 6827, 'chilled': 6828, 'fittings': 6829, 'padke': 6830, 'pba': 6831, 'tipu': 6832, 'santnamdev': 6833, 'jammu': 6834, 'takeoff': 6835, 'plumbing': 6836, 'paintha': 6837, 'chock': 6838, 'paripora': 6839, 'nagar': 6840, 'spearheading': 6841, 'ghatkesar': 6842, 'aurora': 6843, 'scientific': 6844, 'warangal': 6845, '017': 6846, 'pdf': 6847, 'utilise': 6848, 'convert': 6849, 'contacts': 6850, 'later': 6851, 'angular1': 6852, 'typescript': 6853, 'kakinada': 6854, 'knockout': 6855, 'consuming': 6856, 'alternative': 6857, 'cooperation': 6858, 'tiebar': 6859, 'destination': 6860, 'luxury': 6861, 'menswear': 6862, 'premium': 6863, 'dress': 6864, 'shirts': 6865, 'bow': 6866, 'second': 6867, 'spots': 6868, 'tourists': 6869, 'tourist': 6870, 'hamsa': 6871, 'hitech': 6872, 'switzerland': 6873, 'norway': 6874, 'relation': 6875, 'appointment': 6876, 'inscripts': 6877, 'contains': 6878, 'chd': 6879, '09876971076': 6880, 'vversatile': 6881, 'targeting': 6882, 'netbeans': 6883, 'notebooks': 6884, 'ug': 6885, 'schooling': 6886, 'xii': 6887, 'moti': 6888, 'ram': 6889, 'valley': 6890, 'experince': 6891, 'suffering': 6892, 'prize': 6893, 'ifications': 6894, 'coursera': 6895, 'tirlok': 6896, 'yyyy': 6897, 'dd': 6898, 'detailexperiences': 6899, 'alia': 6900, 'yashwant': 6901, 'orichids': 6902, '400097': 6903, 'indiaeducation': 6904, 'marol': 6905, 'depot': 6906, '400093': 6907, 'nova': 6908, '301': 6909, 'maha': 6910, 'muweilah': 6911, 'alfalah': 6912, 'excavation': 6913, 'waterproofing': 6914, 'rcc': 6915, 'shuttering': 6916, 'masonry': 6917, 'brick': 6918, 'tile': 6919, 'slabs': 6920, 'requisition': 6921, 'ground': 6922, 'verify': 6923, 'sincerity': 6924, 'capability': 6925, '5m': 6926, 'minutes': 6927, 'thank': 6928, 'viewing': 6929, 'kherwadi': 6930, '400051': 6931, 'radha': 6932, 'dadar': 6933, 'hindu': 6934, 'colony': 6935, '400014': 6936, 'lodha': 6937, 'eternis': 6938, 'estado': 6939, 'redolog': 6940, 'duplicate': 6941, 'flashback': 6942, 'oracle11gr2': 6943, 'crsctl': 6944, 'srvctl': 6945, 'upgraded': 6946, 'capable': 6947, 'dy': 6948, '61': 6949, 'eboss': 6950, 'datafeed': 6951, 'mfdb': 6952, 'indiainx': 6953, 'fastest': 6954, 'seconds': 6955, 'transparent': 6956, 'equity': 6957, 'currencies': 6958, 'derivatives': 6959, '250': 6960, 'continues': 6961, 'pace': 6962, 'pacemaker': 6963, 'blockings': 6964, 'gssapi': 6965, 'fdw': 6966, 'extensions': 6967, 'pitr': 6968, 'rollouts': 6969, 'aag': 6970, 'raid': 6971, 'devopseducation': 6972, '108': 6973, 'quite': 6974, 'extended': 6975, 'maximized': 6976, 'p1': 6977, 'p2': 6978, 'produced': 6979, 'fw': 6980, 'openings': 6981, 'loadbalancer': 6982, 'smtp': 6983, 'ocm': 6984, 'protected': 6985, 'sod': 6986, 'rcu': 6987, 'expanded': 6988, 'france': 6989, 'compressions': 6990, 'imp': 6991, 'duplication': 6992, 'commission': 6993, 'edvancer': 6994, 'eduventures': 6995, 'sensitive': 6996, 'improved': 6997, 'courage': 6998, 'sadvidya': 6999, 'massachusetts': 7000, 'elicitation': 7001, 'diligently': 7002, 'checkouts': 7003, '101': 7004, 'optimazation': 7005, 'forward': 7006, 'rh': 7007, 'quotas': 7008, 'rollback': 7009, 'extents': 7010, 'constructive': 7011, 'practically': 7012, 'elya': 7013, 'kale': 7014, 'patches': 7015, 'demands': 7016, 'cronjobs': 7017, 'raising': 7018, 'links': 7019, 'directories': 7020, 'scp': 7021, 'rebuilding': 7022, 'stats': 7023, 'deleting': 7024, 'diskgroups': 7025, 'insolutions': 7026, 'switchover': 7027, 'allocating': 7028, 'enrolling': 7029, 'snap': 7030, 'validity': 7031, 'resize': 7032, 'temp': 7033, '8i': 7034, 'ase': 7035, 'winds': 7036, 'dpa': 7037, 'dbartisan': 7038, '3t': 7039, 'pdacoe': 7040, 'autonomous': 7041, 'uploading': 7042, 'rdls': 7043, 'orphaned': 7044, 'purge': 7045, 'shipped': 7046, 'intervention': 7047, 'escalate': 7048, 'next': 7049, 'clones': 7050, 'hewlett': 7051, 'packard': 7052, 'softenger': 7053, 'unused': 7054, 'orphan': 7055, 'rebuild': 7056, 'reorganize': 7057, 'deadlocks': 7058, 'costly': 7059, 'deadlock': 7060, 'alwayson': 7061, 'restart': 7062, 'tns': 7063, 'ada': 7064, 'annet': 7065, 'birla': 7066, 'northbound': 7067, 'eclerx': 7068, 'wwf': 7069, 'info': 7070, 'solid': 7071, 'technologists': 7072, 'knoxed': 7073, '98': 7074, 'loader': 7075, 'authorized': 7076, 'hydrocarbons': 7077, 'exceeding': 7078, 'majors': 7079, '18c': 7080, 'link': 7081, 'reclaim': 7082, 'statically': 7083, 'elb': 7084, 'vpc': 7085, 'route53': 7086, 'iam': 7087, 'alm': 7088, 'occ': 7089, 'sqlplus': 7090, 'vsphere': 7091, 'windowseducation': 7092, '130': 7093, 'eagle': 7094, 'path': 7095, 'persists': 7096, 'rfs': 7097, 'mrp': 7098, 'centralize': 7099, 'refreshing': 7100, 'outage': 7101, 'rotational': 7102, 'shifts': 7103, 'refreshes': 7104, 'migrate': 7105, 'yogesh': 7106, 'tikhat': 7107, 'raj': 7108, 'ahmad': 7109, 'g2': 7110, '702': 7111, 'dreams': 7112, 'aakruti': 7113, 'kalepadal': 7114, 'hadapsar': 7115, '411028': 7116, 'aofpt5052c': 7117, 'extending': 7118, 'iseries': 7119, 'comprise': 7120, 'litespeed': 7121, 'idera': 7122, 'infopa': 7123, 'tivoli': 7124, 'tdp': 7125, 'databasesms': 7126, '110': 7127, 'availabiity': 7128, 'sip': 7129, 'dbas': 7130, 'trainees': 7131, 'pci': 7132, 'vulnerability': 7133, 'holmes': 7134, 'ls': 7135, 'failback': 7136, 'deeply': 7137, 'pursued': 7138, 'pe8': 7139, 'ee11': 7140, 'itskills': 7141, 'periodically': 7142, 'criticality': 7143, 'impo': 7144, 'expo': 7145, 'degine': 7146, 'configuraing': 7147, 'administered': 7148, 'quota': 7149, 'default': 7150, 'cumulative': 7151, 'intervals': 7152, 'ddl': 7153, 'dml': 7154, 'appraising': 7155, 'backround': 7156, 'innovativetechnologies': 7157, 'lite': 7158, 'invest': 7159, 'employs': 7160, 'approximately': 7161, 'organised': 7162, 'barclaycard': 7163, 'troubleshoooting': 7164, 'locking': 7165, 'kakatiya': 7166, 'maintenances': 7167, 'upgradation': 7168, 'ord': 7169, 'fintech': 7170, 'sanpada': 7171, 'attach': 7172, 'detach': 7173, 'dbcc': 7174, 'dmv': 7175, 'analyzer': 7176, 'dts': 7177, 'rebuilds': 7178, 'reorganizes': 7179, 'automationtesting': 7180, 'phoenix': 7181, 'microsystems': 7182, 'swb': 7183, 'profitable': 7184, 'satisfying': 7185, 'splits': 7186, 'inserts': 7187, 'ibatis': 7188, 'seam': 7189, 'intelligen': 7190, 'mobileapp': 7191, 'kauapc': 7192, 'ims': 7193, 'essible': 7194, 'empower': 7195, 'rich': 7196, 'jersey': 7197, 'lelo': 7198, 'lo': 7199, 'divider': 7200, 'pom': 7201, 'concerned': 7202, 'artifacts': 7203, 'fsd': 7204, 'cug': 7205, 'configurations': 7206, 'onboard': 7207, 'bau': 7208, 'arriving': 7209, 'crs': 7210, 'paytech': 7211, 'zambia': 7212, 'parivartan': 7213, 'somaiya': 7214, 'asmita': 7215, 'girls': 7216, 'bhawan': 7217, 'journalist': 7218, 'lawyer': 7219, 'clacutta': 7220, 'burdwan': 7221, 'taxation': 7222, 'plaint': 7223, 'hear': 7224, 'skillful': 7225, 'typing': 7226, 'refined': 7227, 'conformiq': 7228, 'ipad': 7229, 'tl9k': 7230, 'xslt': 7231, 'connector': 7232, 'biztalk': 7233, '147': 7234, 'opex': 7235, 'administering': 7236, 'genentech': 7237, 'biogen': 7238, 'astellas': 7239, 'pharma': 7240, 'polaris': 7241, 'hibernet': 7242, 'prasanna': 7243, 'vc': 7244, 'morigaon': 7245, 'subordinate': 7246, 'penchant': 7247, 'situation': 7248, 'extrovert': 7249, 'prefer': 7250, 'walks': 7251, 'strata': 7252, 'rapport': 7253, 'hpc': 7254, 'counsels': 7255, 'cachar': 7256, 'nagaland': 7257, 'pulp': 7258, 'vetting': 7259, 'nibs': 7260, 'nits': 7261, 'formulation': 7262, 'disciplinary': 7263, 'liaisoning': 7264, 'bscit': 7265, 'airoli': 7266, 'smashingday': 7267, 'malad': 7268, 'tanks': 7269, 'anushaktinagar': 7270, 'trombay': 7271, 'kalyan': 7272, 'tank': 7273, 'float': 7274, 'bottle': 7275, 'crashing': 7276, 'separated': 7277, 'sensors': 7278, 'transmitters': 7279, 'pid': 7280, 'brands': 7281, 'nippon': 7282, 'selec': 7283, 'dol': 7284, 'induction': 7285, 'eletrical': 7286, 'specialized': 7287, 'engineered': 7288, 'manufacturers': 7289, 'vessels': 7290, 'igg': 7291, 'inert': 7292, 'calibrations': 7293, '8th': 7294, 'underwent': 7295, 'tuv': 7296, 'rheinland': 7297, 'omron': 7298, 'ladder': 7299, 'intouch': 7300, 'vijeo': 7301, 'citect': 7302, 'wincc': 7303, 'talkview': 7304, 'opc': 7305, 'kepware': 7306, 'altivar': 7307, 'druck': 7308, 'injector': 7309, 'dp': 7310, 'tubes': 7311, 'fitting': 7312, 'glanding': 7313, 'robotic': 7314, 'kuka': 7315, 'kr': 7316, 'rk': 7317, 'twido': 7318, 'step7': 7319, 'microwin': 7320, 'sp2': 7321, 'unity': 7322, 'xl': 7323, 'cx': 7324, 'wpl': 7325, 'codesys': 7326, 'wonderware': 7327, 'citectscada7': 7328, 'proficy': 7329, 'factotytalk': 7330, 'spoken': 7331, 'tutorial': 7332, 'phython': 7333, 'codestrike': 7334, 'extraeducation': 7335, 'literature': 7336, 'tirunelveli': 7337, 'manonmaniam': 7338, 'sundaranar': 7339, 'practiced': 7340, 'judicature': 7341, 'madras': 7342, 'tribunal': 7343, 'christian': 7344, 'ruah': 7345, 'church': 7346, 'nessa': 7347, 'researcher': 7348, 'laweducation': 7349, 'criminology': 7350, 'federal': 7351, 'finalising': 7352, 'agreements': 7353, 'intimation': 7354, 'suits': 7355, 'attorney': 7356, 'indemnity': 7357, 'marriage': 7358, 'divorce': 7359, 'ession': 7360, 'lok': 7361, 'adalat': 7362, 'judicial': 7363, 'committed': 7364, 'independent': 7365, 'bput': 7366, 'brahmapur': 7367, 'orissa': 7368, 'banner': 7369, 'handles': 7370, '115': 7371, 'spread': 7372, 'whitacre': 7373, 'downtown': 7374, 'texas': 7375, 'csi': 7376, 'cam': 7377, 'centralised': 7378, 'myatt': 7379, 'maintaing': 7380, 'autoit': 7381, 'manging': 7382, 'knows': 7383, 'ielts': 7384, 'bilingual': 7385, 'punjabi': 7386, 'clearly': 7387, 'concisely': 7388, 'peers': 7389, 'pretrial': 7390, 'appellate': 7391, 'competent': 7392, 'laws': 7393, 'bikaner': 7394, 'maharaja': 7395, 'ganga': 7396, 'bio': 7397, 'informatics': 7398, 'panjab': 7399, 'newcomer': 7400, 'licensed': 7401, 'admitted': 7402, 'aibe': 7403, 'bci': 7404, 'sole': 7405, 'counsel': 7406, 'plaintiff': 7407, 'respondents': 7408, 'prosecuted': 7409, 'conclusion': 7410, 'claim': 7411, 'opted': 7412, 'plead': 7413, 'precedents': 7414, 'bail': 7415, 'petitions': 7416, 'appeals': 7417, 'divorces': 7418, 'negotiates': 7419, 'settlements': 7420, 'mediator': 7421, 'conciliator': 7422, 'arbitrator': 7423, 'grapheducation': 7424, 'goregoan': 7425, 'proteus': 7426, 'installations': 7427, 'plastics': 7428, 'wings': 7429, 'infonet': 7430, 'comprehensive': 7431, 'hiral': 7432, 'tektronix': 7433, 'relates': 7434, 'hrms': 7435, 'observation': 7436, 'learnereducation': 7437, 'vidishtra': 7438, 'vbs': 7439, 'purvanchal': 7440, 'redwood': 7441, 'triage': 7442, 'short': 7443, 'exponentially': 7444, 'drastically': 7445, 'games': 7446, 'apps': 7447, 'messenger': 7448, 'omplishment': 7449, 'reminders': 7450, 'hayaan': 7451, 'neighborhood': 7452, 'packaging': 7453, 'slip': 7454, 'inquiry': 7455, 'haayan': 7456, 'uloom': 7457, 'recently': 7458, 'savvy': 7459, 'lrc': 7460, 'ravichander': 7461, 'rfe': 7462, 'rally': 7463, 'td': 7464, 'devsecops': 7465, 'profit': 7466, 'maximization': 7467, 'tulu': 7468, 'harvard': 7469, 'thunderbird': 7470, 'catering': 7471, 'esign': 7472, 'countersign': 7473, 'ampd': 7474, 'cru': 7475, 'bizcomp': 7476, 'comp': 7477, 'discounts': 7478, 'cxmt': 7479, 'lets': 7480, 'centrex': 7481, 'shadow': 7482, 'signoff': 7483, 'smp': 7484, 'scorecard': 7485, 'vqi': 7486, 'subcon': 7487, 'offshoring': 7488, 'leakage': 7489, 'tail': 7490, 'oscar': 7491, 'expand': 7492, 'developments': 7493, 'resourcing': 7494, 'csl': 7495, 'kms': 7496, 'pmr': 7497, 'sqa': 7498, 'gates': 7499, 'csat': 7500, 'rehearsal': 7501, 'prevention': 7502, 'exercises': 7503, 'rebadged': 7504, 'ex': 7505, 'esaya': 7506, 'xaml': 7507, 'dhtml': 7508, 'vbscript': 7509, 'mvp': 7510, 'tfs': 7511, 'collabnet': 7512, 'qlikview': 7513, 'centreon': 7514, 'bam': 7515, 'thinker': 7516, 'planner': 7517, 'recent': 7518, 'managereducation': 7519, 'sikkim': 7520, '92': 7521, '76': 7522, 'apr': 7523, 'capex': 7524, 'surveillance': 7525, 'wearable': 7526, 'nextgen': 7527, 'aria': 7528, 'apple': 7529, 'iphone': 7530, 'sunnyvale': 7531, 'ohio': 7532, 'nunit': 7533, 'moss': 7534, 'biztalk2006': 7535, 'dreamweaver': 7536, 'biz': 7537, 'wachovia': 7538, 'carolina': 7539, 'cript': 7540, 'zd': 7541, 'doll': 7542, 'newcastle': 7543, 'tokyo': 7544, 'macromedia': 7545, 'sql2000': 7546, 'lotus': 7547, 'fuel': 7548, 'density': 7549, 'measurement': 7550, 'cmcs': 7551, 'himalaya': 7552, 'mapco': 7553, 'berco': 7554, 'anjular': 7555, 'mws': 7556, 'laravel': 7557, 'profiled': 7558, 'monitors': 7559, 'sponsor': 7560, 'revise': 7561, 'regulatory': 7562, 'venturus': 7563, 'adhere': 7564, 'viralsocials': 7565, 'xento': 7566, 'familylink': 7567, 'propertysolutions': 7568, 'speedyceus': 7569, 'ceus': 7570, 'nursing': 7571, 'stplafricaonline': 7572, 'stplafrica': 7573, '1stexpert': 7574, 'rimsys': 7575, 'eu': 7576, 'prayerlister': 7577, 'promark': 7578, 'justbe': 7579, 'mtpian': 7580, 'sababa': 7581, 'nl': 7582, 'physicaltherapy': 7583, 'hiu': 7584, '7cees': 7585, 'golwin': 7586, 'maza': 7587, 'lisa': 7588, 'catdm': 7589, 'versioning': 7590, 'disha': 7591, 'poland': 7592, 'specialize': 7593, 'cbil': 7594, 'oat': 7595, 'proving': 7596, 'cucumber': 7597, 'bdd': 7598, 'tosca': 7599, 'tdm': 7600, 'onboarding': 7601, 'ontrack': 7602}
In [16]:
# Tokenize label data
label_tokenizer = Tokenizer(lower=True)
label_tokenizer.fit_on_texts(labels)
label_index = label_tokenizer.word_index
print(dict(list(label_index.items())))
# Print example label encodings from train and test datasets
train_label_sequences = label_tokenizer.texts_to_sequences(train_labels)
test_label_sequences = label_tokenizer.texts_to_sequences(test_labels)
{'javadeveloper': 1, 'testing': 2, 'devopsengineer': 3, 'pythondeveloper': 4, 'webdesigning': 5, 'hr': 6, 'hadoop': 7, 'blockchain': 8, 'datascience': 9, 'sales': 10, 'operationsmanager': 11, 'mechanicalengineer': 12, 'etldeveloper': 13, 'arts': 14, 'database': 15, 'electricalengineering': 16, 'pmo': 17, 'healthandfitness': 18, 'businessanalyst': 19, 'dotnetdeveloper': 20, 'automationtesting': 21, 'networksecurityengineer': 22, 'sapdeveloper': 23, 'civilengineer': 24, 'advocate': 25}
In [17]:
# Pad sequences for feature data
max_length = 300
trunc_type = 'post'
pad_type = 'post'
train_feature_padded = pad_sequences(train_feature_sequences, maxlen=max_length, padding=pad_type, truncating=trunc_type)
test_feature_padded = pad_sequences(test_feature_sequences, maxlen=max_length, padding=pad_type, truncating=trunc_type)
# Print example padded sequences from train and test datasets
print(train_feature_padded[0])
print(test_feature_padded[0])
[ 65 51 77 66 563 471 461 537 2639 288 162 159 53 2640
543 367 501 543 367 340 55 15 42 215 114 93 1659 254
25 476 507 2641 79 4 46 1677 631 2642 1137 2643 254 25
25 212 235 79 1375 1473 77 517 77 517 357 113 2 53
41 15 501 8 930 13 288 8 24 13 461 8 24 13
159 8 24 13 537 8 38 34 19 28 13 563 107 8
38 34 19 28 72 15 10 357 113 2 53 14 104 95
16 19 1605 1162 12 14 1605 1162 23 11 243 388 228 37
357 113 193 2 242 2644 293 484 5 362 60 673 844 131
1376 2645 2 2646 7 231 397 699 3 484 824 2 2647 5
112 2648 9 32 21 4 1058 1961 232 988 3 524 4 12
866 6 577 87 1026 349 160 508 7 3 12 6 501 70
555 1606 555 1847 12 14 555 1606 23 11 410 92 47 2268
5 164 2 118 555 187 3 785 4 99 47 23 5 2269
3 59 4 555 47 2 2649 9 371 1162 193 2 242 247
2 148 844 2 1607 484 4 415 47 2 988 3 524 6
577 87 1026 349 160 508 7 3 12 6 501 102 825 151
970 47 12 14 825 151 970 47 23 11 410 92 47 85
375 89 303 221 5 401 1550 970 193 2 242 247 2 148
844 2 1607 484 4 415 47 2 988 3 524 6 577 87
1026 349 160 508 7 3 12 6 501 107 1608 12 14 1608
77 92 47 23 89 406]
[ 494 4 2080 3321 503 1058 196 1130 405 1146 116 9 518 244
1982 615 9 1230 651 584 1592 20 59 1566 12 59 965 241
730 1199 456 308 360 308 456 20 318 20 1709 1498 381 20
1011 61 20 51 12 20 136 1872 1873 1173 2 153 1592 1249
20 1208 2 1709 20 65 155 418 3776 3777 3646 306 153 598
895 560 97 758 16 116 6 1266 126 313 97 16 126 12
1304 177 1133 276 3778 1872 147 1590 71 1239 55 15 1191 418
2 884 1490 1588 3779 79 278 864 584 278 864 584 574 41
15 61 8 2462 13 1498 8 2462 13 418 8 3647 13 584
8 3322 72 15 10 2965 1710 14 193 2463 154 7 323 4
1158 58 2 568 3 500 17 5 278 2464 20 17 96 3
33 62 74 2 1499 75 58 17 96 3 33 62 154 7
323 4 2465 2466 399 2 644 3 318 720 16 87 37 771
3 566 2 667 16 87 770 3 12 590 6 164 3 2467
22 2468 2469 260 2470 143 1873 2 1304 18 7 147 211 20
363 63 2471 80 3 1112 4 3 2472 2 2473 292 266 4
318 2 1627 18 92 16 3 327 1239 1305 9 43 278 20
12 271 20 1091 2 260 20 2 488 6 1874 43 22 1500
178 116 6 75 211 597 84 902 50 1113 935 525 141 318
92 308 2 1501 23 160 233 3 318 566 2 2474 1501 797
157 1786 318 481 9 1666 1130 157 9 252 2475 1207 7 87
16 949 137 2476 146 1983]
In [18]:
#Train a sequential model
# Define the neural network
embedding_dim = 64
model = tf.keras.Sequential([
# Add an Embedding layer expecting input vocab of size 6000, and output embedding dimension of size 64 we set at the top
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=1),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),
#tf.keras.layers.Dense(embedding_dim, activation='relu'),
# use ReLU in place of tanh function since they are very good alternatives of each other.
tf.keras.layers.Dense(embedding_dim, activation='relu'),
# Add a Dense layer with 25 units and softmax activation for probability distribution
tf.keras.layers.Dense(26, activation='softmax')
])
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, 1, 64) 384000 _________________________________________________________________ bidirectional (Bidirectional (None, 128) 66048 _________________________________________________________________ dense (Dense) (None, 64) 8256 _________________________________________________________________ dense_1 (Dense) (None, 26) 1690 ================================================================= Total params: 459,994 Trainable params: 459,994 Non-trainable params: 0 _________________________________________________________________
In [19]:
# Compile the model and convert train/test data into NumPy arrays
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Features
train_feature_padded = np.array(train_feature_padded)
test_feature_padded = np.array(test_feature_padded)
# Labels
train_label_sequences = np.array(train_label_sequences)
test_label_sequences = np.array(test_label_sequences)
In [20]:
# Train the neural network
num_epochs = 12
history = model.fit(train_feature_padded, train_label_sequences, epochs=num_epochs, validation_data=(test_feature_padded, test_label_sequences), verbose=2)
Epoch 1/12
WARNING:tensorflow:Model was constructed with shape (None, 1) for input Tensor("embedding_input:0", shape=(None, 1), dtype=float32), but it was called on an input with incompatible shape (None, 300).
WARNING:tensorflow:Model was constructed with shape (None, 1) for input Tensor("embedding_input:0", shape=(None, 1), dtype=float32), but it was called on an input with incompatible shape (None, 300).
WARNING:tensorflow:Model was constructed with shape (None, 1) for input Tensor("embedding_input:0", shape=(None, 1), dtype=float32), but it was called on an input with incompatible shape (None, 300).
25/25 - 2s - loss: 3.2246 - accuracy: 0.1730 - val_loss: 3.1045 - val_accuracy: 0.1969
Epoch 2/12
25/25 - 1s - loss: 2.9972 - accuracy: 0.2029 - val_loss: 2.7300 - val_accuracy: 0.3316
Epoch 3/12
25/25 - 1s - loss: 2.4035 - accuracy: 0.2887 - val_loss: 2.4120 - val_accuracy: 0.2383
Epoch 4/12
25/25 - 1s - loss: 1.9072 - accuracy: 0.4993 - val_loss: 1.5640 - val_accuracy: 0.6373
Epoch 5/12
25/25 - 1s - loss: 1.3420 - accuracy: 0.6606 - val_loss: 1.1339 - val_accuracy: 0.7098
Epoch 6/12
25/25 - 1s - loss: 1.1872 - accuracy: 0.6840 - val_loss: 1.0093 - val_accuracy: 0.7358
Epoch 7/12
25/25 - 1s - loss: 0.8241 - accuracy: 0.7971 - val_loss: 0.7454 - val_accuracy: 0.8860
Epoch 8/12
25/25 - 1s - loss: 0.7037 - accuracy: 0.8531 - val_loss: 0.5364 - val_accuracy: 0.8653
Epoch 9/12
25/25 - 1s - loss: 0.4445 - accuracy: 0.9194 - val_loss: 0.3866 - val_accuracy: 0.9119
Epoch 10/12
25/25 - 2s - loss: 0.3093 - accuracy: 0.9402 - val_loss: 0.3086 - val_accuracy: 0.9378
Epoch 11/12
25/25 - 2s - loss: 0.2005 - accuracy: 0.9688 - val_loss: 0.2413 - val_accuracy: 0.9482
Epoch 12/12
25/25 - 1s - loss: 0.1412 - accuracy: 0.9909 - val_loss: 0.1720 - val_accuracy: 0.9689
In [21]:
#determining test score and accuracy
score = model.evaluate(test_feature_padded, test_label_sequences, verbose=1)
print("Test Score:", score[0])
print("Test Accuracy:", score[1])
7/7 [==============================] - 0s 7ms/step - loss: 0.1720 - accuracy: 0.9689 Test Score: 0.17196255922317505 Test Accuracy: 0.9689119458198547
In [22]:
#Visualising the model accuracy and loss
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train','test'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train','test'], loc='upper left')
plt.show()
In [23]:
# print any 3 random example feature and the correct label
print(test_features[3])
print(test_labels[3])
print("")
print(test_features[8])
print(test_labels[8])
print("")
print(test_features[17])
print(test_labels[17])
Technical Skills Programming Languages C C Java Net J2EE HTML5 CSS MapReduce Scripting Languages Javascript Python Databases Oracle PL SQL MY SQL IBM DB2 Tools IBM Rational Rose R Weka Operating Systems Windows XP Vista UNIX Windows 7 Red Hat 7Education Details January 2015 B E Pimpri Chinchwad MAHARASHTRA IN Pimpri Chinchwad College of Engineering January 2012 Diploma MSBTE Dnyanganaga Polytechnic S S C New English School Takali Hadoop Big Data Developer Hadoop Big Data Developer British Telecom Skill Details APACHE HADOOP MAPREDUCE Exprience 37 months MapReduce Exprience 37 months MAPREDUCE Exprience 37 months JAVA Exprience 32 months NET Exprience 6 monthsCompany Details company British Telecom description Project British Telecom project UK Responsibilities Working on HDFS MapReduce Hive Spark Scala Sqoop Kerberos etc technologies Implemented various data mining algorithms on Spark like K means clustering Random forest Na ve bayes etc A knowledge of installing configuring maintaining and securing Hadoop company DXC technology description HPE legacy Bangalore Worked on Hadoop Java programming Worked on Azure and AWS EMR services Worked on HDInsight Hadoop cluster Design develop document and architect Hadoop applications Develop MapReduce coding that works seamlessly on Hadoop clusters Analyzing and processing the large data sets on HDFS An analytical bent of mind and ability to learn unlearn relearn surely comes in handy hadoop CORE COMPETENCIES Maintain processes to ensure project management documentation reports and plans are relevant a urate and complete Report automation Dashboard preparation and sharing feedbacks basis on performance of Project Manager Forecasting data regarding future risks Project changes and updating the delivery team on timely basis Good understanding of project management lifecycle Proven excellence in Risk Management and control Good understanding of Software Development Lifecycle SDLC Ability to synthesize qualitative and quantitative data quickly and draw meaningful insights Knowledge of Programme Project Management methodologies with full project reporting and governance Ability to work with different cross functional stakeholders to establish and ensure a reliable and productive working relationship Strong time management and organizational skills Multitasking skills and ability to meet deadlines COMPUTER SKILLS AND CE IFICATION Advance knowledge in MS office 2013 and Macros SKILLS Strategic thinking and decision making ability Sound Analytical skills Multi tasking skills in fast paced environment Leadership and Inter Personal Skills Strong information management ability particularly MS excel extraction formulae pivots and graphs Education Details January 2005 Bachelor of Business Administration Business Administration Pune Maharashtra Modern College HSC Pune Maharashtra S S P M S College SSC Pune Maharashtra Saints High School PMO Having an exp of 6 years experience in Project Management in IT Expertise in PMO Team handling Quality Analyst Proficient in Data Analyzing tools and techniques Skill Details DOCUMENTATION Exprience 47 months GOVERNANCE Exprience 19 months EXCEL Exprience 6 months FORECASTING Exprience 6 months MS EXCEL Exprience 6 monthsCompany Details company Capita India Pvt ltd description Pune Key Result Areas Responsible for su essful transition of knowledge system and operating capabilities for Prudential Multiclient Pheonix Royal London Travelled Onsite Glasgow and being part with UK team to understand the transition PMO work process and execute su essfully at Offshore Su essfully transitioned Work order Management Governance and Reporting from UK Lead a team of 6 members and follow up on the development of new Ways of Working documentation processes Manage internal and external stakeholder engagement collaboration of teams and global PMOs network Helps achieve robust operations with all the resources and infrastructure to execute steady state operations company Saviant Technologies description for Multiple Projects Established a PMO from scratch and provided seasoned leadership to the technical operations staff Defined and implemented work priority management and resource management processes Established a supportive environment that allowed employees to grow and provide imaginative solutions to complex client need Track and monitor financial performance of the program Report financials for actual to budgeted comparison for labor hours and dollars operating costs and capital costs Secure funding approvals for changes in scope Monitor program risks through an on going process of identifying assessing tracking developing and executing risk mitigation strategies Reviewed project documentation and document lessons learned and provide recommendations to mitigate them in future projects risk identification mitigation strategy issue escalation client communication project timeline and resource management company Infosys description Pune Key Result Areas Responsible for Resource management Budgeting Billing Responsible for preparing and sharing different reports with Delivery Managers Project Managers Quality team Automation of reports for entire unit Interpret data analyze results using statistical techniques and provide ongoing reports Preparing case diagrams activity diagrams for various scenarios Collate data study patterns and Conduct brainstorming sessions to identify outliers Review and approve project documentation Assist in identification of risks in the project and setting up of mitigation plan of the risk by reviewing dashboards and reports Customer feedback information and analysis Reviews and validate the inputs from Project Mangers regarding Dashboards and PPT s Supporting TL by training people on process domain as a part of the growth plan SLA compliance company Capita India Pvt ltd description Pune Key Result Areas Audits Reviews and validate the inputs from Managers regarding Dashboards and PPT s Auditing work done by onshore agents and simultaneously auditing work done for my old team and their reporting part as well Assisting reporting manager in business transformation leadership skills with proven ability to influence and collaborate across all levels of the organization Helping line managers to solve specific audit problems either on a one to one basis or in groups Reporting Preparing weekly monthly quarterly yearly MIS Variance report Performance report Feedback analysis Task activities report publish relevant business Dashboards Projects audit report pmo Additional qualifications April 2000 Web Designing Course with above average computer skillsEducation Details January 2000 to January 2001 Bachelor of Arts Sociology Mumbai Maharashtra The Mumbai University January 1998 to January 2000 Bachelor of Arts Sociology Sophia College January 1997 to January 1998 H S C Sophia College January 1995 to January 1996 S S C St Teresa s Convent High School Head business development arts Head business development arts Skill Details Company Details company British Council description Responsibilities Strategic oversight responsibility for programmes in the performing arts music theatre and dance and other cultural sectors lead on the conception and oversight of specific large scale programmes within the overall Arts programme Represent the British Council at external events in India and act as deputy to the Director Arts when required Oversee and manage resources to deliver compelling communications for applicants that convey British Council s grants like Charles Wallace India trust Young Creative Entrepreneurs and Chevening Clore scholarship programs on time and with excellence Shortlisting and Interviewing potential applicants for existing relevant grants or fellowships Oversee a diverse range of proposals progress reports and related projects Ensuring effective and timely identification and communication of program progress Lead a team of six project managers across the country and manage the performance of the team responsible for executing arts projects with partnerships built into their work expertise within their geographic region to ensure arts insight and knowledge is available as and when required Managing relations with existing partners and developing relationships with targeted new partners and key government officials and ensuring that market insight into business development opportunities is built into the planning of new programmes Primary strategic responsibility for the marketing of the Arts program in India to ensure that the program builds a reputation that will be attractive to potential partners in partnership with the Marketing and Communications team company British Council description is a cultural relations organization creating international opportunities for the people of the UK and other countries by building trust between them worldwide They have offices in six continents and over 100 countries bringing international opportunity to life every day Each year they work with millions of people connecting them with the United Kingdom sharing their cultures and the UK s most attractive assets English the Arts and Education They have 80 years experience of doing this company British Council description Responsibilities Leading the strategic development of British Council s work in the music sector in India Sri Lanka region and building and maintaining strong international partnerships across sectors in India Sri Lanka and the UK Developing strong external partnerships that lead to significant external investment in BC activities and enabling the delivery of an ambitious programme of music sector activities and events thus strengthening cultural relationships between India Sri Lanka and the UK Leading the implementation of the music programme within India and Sri Lanka along with detailed project plans in liaison with colleagues from India Sri Lanka and the UK Proactive management of budgets and timelines for all projects Ensuring systematic evaluation of projects including developing effective systems and processes for capturing both quantitative and qualitative information about effectiveness of projects and longer term impact Management of a team across India and Sri Lanka contributing to recruitment and development mentoring of staff company British Council description Responsibilities Planning and organizing logistics related to events buildings performers artists and other personnel Marketing a performance or event through social media direct mail advertising use of a website producing posters or publicity leaflets and attracting media coverage Planning and managing budgets Programming and booking performances and events including arrangements for tours in India Development of new projects and initiatives in consultation with arts professionals and key stakeholders e g local authorities local government and communities venue directors and regional partners Taking responsibility for operational and office management issues such as venue a essibility health and safety issues Implementing and maintaining office and information systems Providing administration support to managers and the director Ensuring corporate and legal requirements are complied with and reporting to the head of the unit company British Council description Responsibilities Developing of new specific new projects and initiatives in the music film and visual art sector in consultation with the Council and key stakeholders Planning and managing budgets Supporting the marketing a performance or event through social media direct mail advertising use of a website producing posters or publicity leaflets and attracting media coverage Programming the outreach and workshops for the respective programmes company AirCheck India description The company intended to launch stations in both these metros on August 29 2001 For its Mumbai FM station WIN had the basic infrastructure that includes a studio and production facilities The transmission tower for the station is located in central Mumbai Responsibilities Generating and researching ideas for programmes and pitching for commissions Sourcing potential contributors and interviewees Selecting music appropriate to the programme the audience and the station Producing pre production briefings for presenters reporters technical staff and other contributors Managing the logistics of getting people resources and equipment together to the right place at the right time undertaking editing interviewing and reporting duties as necessary Presenting programmes or managing presenters for both pre recorded and recorded output Checking that copyrights are cleared and understanding media law Using editing and mixing software s like Sonic Foundry Vegas Sonic Foundry Sound Forge Acid and Midi company Rave Magazine description Rave Magazine was the definitive voice of music emerging from the Indian sub continent and the lifestyle that surrounds it Through exclusive reporting a unique sensibility and with an editorial team with over 20 years of experience in publishing RAVE Magazine covers every genre of music emerging from the region and provides new perspectives on International music Responsibilities Maintain production schedules and report on the progress Overview the staff manage and supervise photographers and freelance writers and generally provide administrative support for the editor Participated in production meetings and brain storming sessions to decide on the direction future trends and contents of the publication company Xs Events description Xs Events is an event management company primarily dealing with corporate clients who used different events to increase an audience s exposure with a brand Responsibilities Development production and delivery of projects from proposal right up to delivery Delivering events on time within budget Maintaining timelines and priorities on every project Managing supplier relationships Managing operational and administrative functions to ensure specific projects are delivered efficiently company Banyan Tree Communications description Responsibilities Sourcing potential contributors and interviewees Selecting music appropriate to the programme the audience and the station undertaking editing interviewing and reporting duties as necessary Checking that copyrights are cleared and understanding media law company French Embassy description on a part time basis company British Council description Mumbai Advice students on various academic opportunities in the United Kingdom and assisted with various exhibitions by the British Council arts
In [24]:
# let's create an array containing the previous three examples to predict and use our model to get predictions
to_predict = [test_feature_padded[3],test_feature_padded[8],test_feature_padded[17]]
prediction = model.predict_classes(np.array(to_predict))
WARNING:tensorflow:From <ipython-input-24-2aa2adb06057>:4: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.
Instructions for updating:
Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype("int32")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).
WARNING:tensorflow:Model was constructed with shape (None, 1) for input Tensor("embedding_input:0", shape=(None, 1), dtype=float32), but it was called on an input with incompatible shape (None, 300).
In [25]:
prediction
Out[25]:
array([ 7, 17, 14])
The predicted classes are 7, 17 and 14 , which were tokenized as hadoop, pmo and arts respectively. Hence, our predictions are correct¶
In [ ]: