Farzeen Arshad Ghuman

004-resume-selection 1.0.1 STEP 1: IMPORTING LIBRARIES [1]: # INSTALLING NLTK, GENSIM AND WORDCLOUD !pip install pandas !pip install numpy !pip install matplotlib !pip install seaborn !pip install -upgrade pip !pip install nltk !pip install gensim !pip install sklearn !pip install wordcloud import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from wordcloud import import nltk from nltk.stem import WordCloud, STOPWORDS PorterStemmer, WordNetLemmatizer from nltk.corpus import stopwords from nltk.tokenize import word_tokenize, sent_tokenize import gensim from gensim.utils import fromsimple_preprocess gensim.utils import simple_preprocess from gensim.parsing.preprocessing import STOPWORDS from sklearn.metrics import classification_report, confusion_matrix Collecting pandas Downloading pandas-1.3.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.3 MB) |��������������������������������| 11.3 MB 4.7 MB/s eta 0:00:01 Collecting numpy>=1.17.3 1 Downloading numpy-1.21.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB) |��������������������������������| 15.7 MB 63.8 MB/s eta 0:00:01 Requirement already satisfied: python-dateutil>=2.7.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2.8.2) Requirement already satisfied: pytz>=2017.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas) (2021.3) Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pythondateutil>=2.7.3->pandas) (1.16.0) Installing collected packages: numpy, pandas Successfully installed numpy-1.21.3 pandas-1.3.4 Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.7/site-packages (1.21.3) Collecting matplotlib Downloading matplotlib-3.4.3-cp37-cp37m-manylinux1_x86_64.whl (10.3 MB) |��������������������������������| 10.3 MB 4.7 MB/s eta 0:00:01 Collecting kiwisolver>=1.0.1 Downloading kiwisolver-1.3.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.1 MB) |��������������������������������| 1.1 MB 64.9 MB/s eta 0:00:01 Requirement already satisfied: numpy>=1.16 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib) (1.21.3) Requirement already satisfied: pyparsing>=2.2.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib) (2.4.7) Collecting cycler>=0.10 Downloading cycler-0.11.0-py3-none-any.whl (6.4 kB) Collecting pillow>=6.2.0 Downloading Pillow-8.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB) |��������������������������������| 3.1 MB 49.2 MB/s eta 0:00:01 Requirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib) (2.8.2) Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pythondateutil>=2.7->matplotlib) (1.16.0) Installing collected packages: pillow, kiwisolver, cycler, matplotlib Successfully installed cycler-0.11.0 kiwisolver-1.3.2 matplotlib-3.4.3 pillow-8.4.0 Collecting seaborn Downloading seaborn-0.11.2-py3-none-any.whl (292 kB) |��������������������������������| 292 kB 4.7 MB/s eta 0:00:01 Requirement already satisfied: pandas>=0.23 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from seaborn) (1.3.4) Requirement already satisfied: numpy>=1.15 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from seaborn) (1.21.3) Collecting scipy>=1.0 Downloading scipy-1.7.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl 2 (28.5 MB) |��������������������������������| 28.5 MB 50.6 MB/s eta 0:00:01 Requirement already satisfied: matplotlib>=2.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from seaborn) (3.4.3) Requirement already satisfied: kiwisolver>=1.0.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.3.2) Requirement already satisfied: pillow>=6.2.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (8.4.0) Requirement already satisfied: cycler>=0.10 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.11.0) Requirement already satisfied: pyparsing>=2.2.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.7) Requirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.8.2) Requirement already satisfied: pytz>=2017.3 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2021.3) Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pythondateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0) Installing collected packages: scipy, seaborn Successfully installed scipy-1.7.1 seaborn-0.11.2 Requirement already satisfied: pip in /srv/conda/envs/notebook/lib/python3.7/site-packages (21.2.4) Collecting pip Downloading pip-21.3.1-py3-none-any.whl (1.7 MB) |��������������������������������| 1.7 MB 4.8 MB/s eta 0:00:01 Installing collected packages: pip Attempting uninstall: pip Found existing installation: pip 21.2.4 Uninstalling pip-21.2.4: Successfully uninstalled pip-21.2.4 Successfully installed pip-21.3.1 Collecting nltk Downloading nltk-3.6.5-py3-none-any.whl (1.5 MB) |��������������������������������| 1.5 MB 4.7 MB/s Collecting regex>=2021.8.3 Downloading regex--cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB) |��������������������������������| 749 kB 60.1 MB/s Collecting click Downloading click-8.0.3-py3-none-any.whl (97 kB) 3 |��������������������������������| 97 kB 9.0 MB/s Collecting tqdm Downloading tqdm-4.62.3-py2.py3-none-any.whl (76 kB) |��������������������������������| 76 kB 6.2 MB/s Collecting joblib Downloading joblib-1.1.0-py2.py3-none-any.whl (306 kB) |��������������������������������| 306 kB 64.1 MB/s Requirement already satisfied: importlib-metadata in /srv/conda/envs/notebook/lib/python3.7/site-packages (from click->nltk) (4.8.1) Requirement already satisfied: zipp>=0.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from importlibmetadata->click->nltk) (3.6.0) Requirement already satisfied: typing-extensions>=3.6.4 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from importlibmetadata->click->nltk) (3.10.0.2) Installing collected packages: tqdm, regex, joblib, click, nltk Successfully installed click-8.0.3 joblib-1.1.0 nltk-3.6.5 regex- tqdm-4.62.3 Collecting gensim Downloading gensim-4.1.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (24.1 MB) |��������������������������������| 24.1 MB 4.8 MB/s Requirement already satisfied: scipy>=0.18.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from gensim) (1.7.1) Collecting smart-open>=1.8.1 Downloading smart_open-5.2.1-py3-none-any.whl (58 kB) |��������������������������������| 58 kB 6.2 MB/s Requirement already satisfied: numpy>=1.17.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from gensim) (1.21.3) Installing collected packages: smart-open, gensim Successfully installed gensim-4.1.2 smart-open-5.2.1 Collecting sklearn Downloading sklearn-0.0.tar.gz (1.1 kB) Preparing metadata (setup.py) … done Collecting scikit-learn Downloading scikit_learn-1.0.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.2 MB) |��������������������������������| 23.2 MB 4.6 MB/s Collecting threadpoolctl>=2.0.0 Downloading threadpoolctl-3.0.0-py3-none-any.whl (14 kB) Requirement already satisfied: joblib>=0.11 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikitlearn->sklearn) (1.1.0) Requirement already satisfied: scipy>=1.1.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikitlearn->sklearn) (1.7.1) Requirement already satisfied: numpy>=1.14.6 in 4 /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikitlearn->sklearn) (1.21.3) Building wheels for collected packages: sklearn Building wheel for sklearn (setup.py) … done Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1309 sha256=6738d2fbc27092a22c7113dc8aa5f1b394605c7668c3c33ec48f-e85b Stored in directory: /home/jovyan/.cache/pip/wheels/46/ef/c3/157e41f5ee1372d1b e90b09f74f82b10e391eaacca8f22d33e Successfully built sklearn Installing collected packages: threadpoolctl, scikit-learn, sklearn Successfully installed scikit-learn-1.0.1 sklearn-0.0 threadpoolctl-3.0.0 Collecting wordcloud Downloading wordcloud-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (366 kB) |��������������������������������| 366 kB 4.8 MB/s Requirement already satisfied: pillow in /srv/conda/envs/notebook/lib/python3.7/site-packages (from wordcloud) (8.4.0) Requirement already satisfied: numpy>=1.6.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from wordcloud) (1.21.3) Requirement already satisfied: matplotlib in /srv/conda/envs/notebook/lib/python3.7/site-packages (from wordcloud) (3.4.3) Requirement already satisfied: cycler>=0.10 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib->wordcloud) (0.11.0) Requirement already satisfied: pyparsing>=2.2.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib->wordcloud) (2.4.7) Requirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib->wordcloud) (2.8.2) Requirement already satisfied: kiwisolver>=1.0.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from matplotlib->wordcloud) (1.3.2) Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from pythondateutil>=2.7->matplotlib->wordcloud) (1.16.0) Installing collected packages: wordcloud Successfully installed wordcloud-1.8.1 1.0.2 STEP 2: LOADING THE DATASET [2]: resume_df = pd.read_csv('data/resume_data.csv', encoding = 'latin-1') resume_df [2]: 0 1 resume_id class \ resume_1 not_flagged resume_2 not_flagged 5 2 3 4 .- resume not_flagged _3 not_flagged resume flagged _4 … … resume not_flagged resume_12 1 _5 not_flagged resume_12 not_flagged 2 flagged resume_12 not_flagged 3 resume_12 resume_text 4 0 1 \rCustome Service Supervisor/Tier Isabella… 2 3 rresume_12 / Scientist - IBM Microelectronics… 4 .. 5 \rEngineer Software Engineer ComputationalLithogra… 120 \rLTS TUTOR\rWillistonVT - Email meon Indeed:ind… 121 \rIndependentConsultant - Self-employed\rBurl… 122 … 123 \rBrattleboro VT - Email me on Indeed: indeed… 124 \rResearch and Teaching Assistant - University… \rMedical Coder - Highly Skilled - Entry Level… \rWaterbury VT - Email me on Indeed: indeed.co… \rResearch and Development Scientist - Burling… [125 rows x 3 columns] [3]: resume_df = resume_df[['resume_text', 'class']] resume_df [3]: 0 1 2 3 4 .- resume_text class \rCustomer ServiceSupervisor/Tier- Isabella… not_flagged \rEngineer / Scientist - IBMMicroelectronics… not_flagged \rLTSSoftware Engineer ComputationalLithogra… not_flagged not_flagged TUTOR\rWilliston VT - Email meonIndeed:ind… flagged \rIndependentConsultant -Self-employed\rBurl… … … \rBrattleboro VT - Email me on Indeed: indeed… not_flagged \rResearch and Teaching Assistant - University… not_flagged \rMedical Coder - Highly Skilled - Entry Level… not_flagged \rWaterbury VT - Email me on Indeed: indeed.co… flagged \rResearch and Development Scientist - Burling… not_flagged [125 rows x 2 columns] 6 1.0.3 STEP 3: PERFORMING EXPLORATORY DATA ANALYSIS: [4]: resume_df.info() RangeIndex: 125 entries, 0 to 124 Data columns (total 2 columns): # Column Count Non-Null Dtype -------------------- -----object 0 resume_text 125 non-null class 125non-null object 1 dtypes: object(2) memory usage: 2.1+ KB [5]: resume_df['class'].value_counts() [5]: not_flagged 92 flagged 33 Name: class, dtype: int64 [6]: # HERE WE OBSERVE, WE HAVE NO NULL POINTS IN OUR DATASET resume_df['class'] = resume_df['class'].apply(lambda x:1 if x == 'flagged' 0) ↪ resume_df /srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandasdocs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy [6]: 0 1 2 3 4 .- resume_text class \rCustomer ServiceSupervisor/TierIsabella… 0 \rEngineer / Scientist - IBM Microelectronics… 0 \rLTSSoftware Engineer ComputationalLithogra… 0 TUTOR\rWilliston VT - Email me onIndeed:ind… 0 \rIndependentConsultant -Self-employed\rBurl… 1 … … \rBrattleboro VT - Email me on Indeed: indeed… 0 \rResearch and Teaching Assistant - University… 0 \rMedical Coder - Highly Skilled - Entry Level… 0 \rWaterbury VT - Email me on Indeed: indeed.co… 1 \rResearch and Development Scientist - Burling… 0 [125 rows x 2 columns] 7 else␣ 1.0.4 STEP 4: PERFORMING DATA CLEANING: [7]: # PREMOVING UNNECESSARY WORDS FROM DATASET ↪ resume_df['resume_text'] = resume_df['resume_text'].apply(lambda x: x . replace('\r', '')) nltk.download('punkt') nltk.download('stopwords') from nltk.corpus import stopwords stop_words = stopwords.words('english') stop_words.extend(['from', 'subject', 'edu', 're', 'use', 'email', 'com']) def preprocess(text): result = [] for token in gensim.utils.simple_preprocess(text): if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) >␣ 2andtokennotinstop_words: ↪ result.append(token) return ' '.join(result) /srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandasdocs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy This is separate from the ipykernel package so we can avoid doing imports until [nltk_data] Downloading package punkt to /home/jovyan/nltk_data… [nltk_data] Unzippingtokenizers/punkt.zip. [nltk_data] Downloading package stopwords to /home/jovyan/nltk_data… [nltk_data] Unzippingcorpora/stopwords.zip. [8]: resume_df [8]: 0 1 2 3 4 .. 120 resume_text class CustomerService Supervisor/Tier -IsabellaCa… 0 Engineer / Scientist IBMMicroelectronicsDi… 0 LTSSoftware Engineer ComputationalLithograph… 0 TUTORWillistonVT- Emailme on Indeed: indee… 0 IndependentConsultant - Self-employedBurlingt… 1 0 … … Brattleboro VT - Email me on Indeed: indeed.co… 8 - Research and Teaching Assistant - University o… Medical Coder - Highly Skilled - Entry LevelSu… Waterbury VT - Email me on Indeed: indeed.com/… Research and Development Scientist - Burlingto… 0 0 1 0 [125 rows x 2 columns] [9]: resume_df['cleaned'] = resume_df['resume_text'].apply(preprocess) resume_df [9]: 0 1 2 3 4 .- resume_text class CustomerService Supervisor/Tier -IsabellaCa… 0 Engineer / Scientist IBMMicroelectronicsDi… 0 LTSSoftware Engineer ComputationalLithograph… 0 TUTORWillistonVT- Emailme on Indeed: indee… 0 IndependentConsultant - Self-employedBurlingt… 1 0 … …0 Brattleboro VT - Email me on Indeed: indeed.co… 0 Research and Teaching Assistant - University o… 1 Medical Coder - Highly Skilled - Entry LevelSu… 0 Waterbury VT - Email me on Indeed: indeed.com/… Research and Development Scientist - Burlingto… 0 1 2 3 4 .- cleaned customerservice supervisor tier isabellacata… engineer scientistibm albert gregoritschecaw… ltssoftware engineer computational lithograph… tutorwilliston alec schwartz aworkcollegebio… independentconsultant self alex reutter fefwo… … brattleboro bcc skilled presenter trainer micr… research teaching assistant university cdd gra… medical coder highly skilled entry levelsudbur… waterbury bec fcwilling relocate work employer… research development scientist burlington cda … \ [125 rows x 3 columns] [10]: resume_df['cleaned'][0] [10]: 'customer service supervisor tier isabella catalog companysouth burlington aecf work service supervisor tierisabella catalog company shelburne august present customer service visual set display website maintenance supervise customer service team popular catalog company manage day day issues resolution customer upset ensure customer satisfaction troubleshoot order shipping issues lost transit order errors damages manage resolve escalated customer calls ensure 9 customer satisfaction assist customers order placing cross selling upselling catalog merchandise set display sample merchandise catalog library customer pick area facility website clean adding images type product information assistant events coordinator office services assistanteileen fisher irvington february july support director architecture architecture coordinator daily activities including preparing monthly expense reports scheduling calendar maintenance arranging aspects travel logistics catering interior design research projects manage event set ups entire process eileen fisher corporate locations catering overseeing set walk space client review event forms facilities team daily management professional calendars require heavy scheduling office services include companywide room reservations office supply orders filtered calls chief creative officer owner companytemp white plains december february office services assistant receptionist managed heavy volume orthopedic specialty benefit management company directed heavy daily incoming mail flow processed daily checks entered data excel generate totals accounting personal january december home office assistant personal assistant provided professional office support established psychologists new york area carefully handled personal confidential patient information organized uncluttered simplified office space create user friendly atmosphere coordinated researched travel related details flights hotels visas cars managed personal errands phone calls emails responsible mail processing bank deposits psychologists service representative account managercm almy sons greenwich january january greenwich january january customer service representative account manager provided high level customer service clergy church members denominations answered heavy volume assisted customers highly efficient manor assisted customers overall design garments final decision making church item purchases managed maintained large account database daily phone calls customer accounts responsible tracking large shipments replacement lost damaged items dorset street south burlington aimeerblair gmail assistant chief financial salute americas heroes ossining january january ossining january january administrative assistant chief financial officer interviewed military veterans families considered financial aid reviewed highly confidential database candidate mediated discussions military veterans collectors arrange final payouts debt incurred time injury finalized paperwork award payouts coordinated travel logistics large sponsored events assisted disabled veterans events provided basic administrative assistant sales team trade coordinatorleo electron microscopy thornwood august thornwood august administrative assistant sales team trade coordinator communicated general information provided quotes high end buyers worked closely team sales associates arranging meetings potential buyers prepared final proposals closing sale information purchased electron microscopes arranged aspects travel logistics trade shows united states canada attended trade shows sales associates scientists insure electron microscopes arrived safely set assisted demonstrations close sales trade floorartist charles fazzino pop artistcharles fazzino new rochelle freelance artist assembled dimensional piece art weekly basis home office responsible detailed finishing work making pieces presentable purchase galleries world visual artswestchester community college new york school years combined office services focus administrative assistance customer 10 service event coordination trade coordination facilitating' 1.0.5 STEP 5: VISUALIZING CLEANED DATASETS [11]: # PLOTTING COUNTS OF SAMPLE LABELLED AS 1 AND 0 sns.countplot(resume_df['class'], label = 'Count Plot') plt.show() /srv/conda/envs/notebook/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. FutureWarning [12]: # PLOTTING THE WORDCLOUD: # 1) FOR CLASS 1: %matplotlib inline plt.figure(figsize = (20, 20)) wc = WordCloud(max_words = 2000, width = 1600, height = 800, stopwords =␣ ↪stop_words).generate(str(resume_df[resume_df['class']==1].cleaned)) 11 plt.imshow(wc) [12]: [13]: #1) FOR CLASS 0: %matplotlib inline plt.figure(figsize = (20, 20)) ↪ wc = WordCloud(max_words = 2000, width = 1600, height = 800, stopwords =␣ stop_words).generate(str(resume_df[resume_df['class']==0].cleaned)) plt.imshow(wc) [13]: 12 1.0.6 STEP6: PREPARINGTHEDATABYAPPLYINGCOUNTVECTORIZATION ↪ [14]: # CONVERTING FORMS AND THEN CONVERTING TO NUMERICAL␣ VALUES INSENTENCES ORDER FORINTO THE TOKENIZED MODEL TO TRAIN: from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer() countvectorizer = vectorizer.fit_transform(resume_df['cleaned']) print(vectorizer.get_feature_names()) ['aaalac', 'aabb', 'aac', 'aacn', 'aacr', 'aacrjournals', 'aakeroõ_y', 'aanpcp', 'aaron', 'abbott', 'abdomen', 'abdominal', 'abdul', 'aberdeen', 'abi', 'abilities', 'ability', 'abiotic', 'able', 'abnormal', 'aboard', 'abosalem', 'abraham', 'abreast', 'abs', 'absence', 'absorbance', 'abstract', 'abstracta', 'abstractdisease', 'abstracted', 'abstractin', 'abstracts', 'abualrub', 'abundance', 'abureehan', 'abuse', 'abusiness', 'academia', 'academic', 'academics', 'academy', 'acaeefbc', 'accelerated', 'accept', 'acceptability', 'acceptable', 'acceptance', 'accepted', 'accepting', 'access', 'accessibility', 'accessible', 'accession', 'accident', 'accidents', 'accolateî', 'accommodations', 'accomplished', 'accomplishment', 'accomplishments', 'accord', 'accordance', 'according', 'accordingly', 'accords', 'account', 'accountability', 'accountable', 'accounted', 'accounting', 'accounts', 'accreditation', 'accredited', 'accrual', 'accumulation', 'accuracy', 'accurate', 'accurately', 'accustomed', 'acetylsalicylic', 'acf', 'acg', 'acheived', 'achieve', 'achieved', 'achievement', 'achievements', 'achieving', 13 'acid', 'acknowledgment', 'acknowledgments', 'acls', 'aco', 'aconsistent', 'acoustic', 'acoustics', 'acousticschief', 'acousticscto', 'acquire', 'acquired', 'acquiring', 'acquisition', 'acquisitions', 'acr', 'acre', 'acrylamide', 'acs', 'act', 'acted', 'acting', 'action', 'actions', 'activate', 'activation', 'active', 'actively', 'activities', 'activitieso', 'activity', 'acton', 'actor', 'actors', 'acts', 'actuated', 'actuators', 'acumen', 'acustomer', 'acute', 'acwestford', 'acwork', 'ada', 'adaboost', 'adapt', 'adaptability', 'adaptable', 'adaptation', 'adaptations', 'adapted', 'adapting', 'adaption', 'adaptive', 'adb', 'adcc', 'add', 'added', 'adding', 'addison', 'addition', 'additional', 'additionally', 'additions', 'additives', 'address', 'addressed', 'addresses', 'addressing', 'adenovirus', 'adept', 'adequate', 'adfe', 'adhere', 'adherence', 'adhering', 'adifferent', 'adjunct', 'adjust', 'adjusted', 'adjuster', 'adjustereberl', 'adjusters', 'adjusting', 'adjustments', 'adjuvanted', 'adjuvants', 'admin', 'administer', 'administered', 'administering', 'administration', 'administratione', 'administrative', 'administrator', 'administrators', 'admission', 'admissions', 'adn', 'adobe', 'adobeîâ', 'adolescent', 'adolescents', 'adopt', 'adopted', 'adopters', 'adoption', 'adoptions', 'adp', 'adt', 'adult', 'adults', 'advance', 'advanced', 'advancement', 'advancements', 'advancing', 'advantage', 'advantages', 'advantest', 'adventist', 'adventures', 'adverse', 'advertised', 'advertising', 'advia', 'advice', 'advised', 'advisement', 'adviser', 'adviserag', 'adviseressex', 'advisermcginn', 'adviserstifel', 'advising', 'advisor', 'advisorbp', 'advisors', 'advisory', 'advocate', 'advocating', 'aecf', 'aed', 'aeration', 'aerial', 'aero', 'aerobic', 'aerodynamic', 'aerodynamics', 'aerojet', 'aeromechanics', 'aeropropulsion', 'aerospace', 'aeroxchange', 'afa', 'affairs', 'affairswhite', 'affarsamerican', 'affect', 'affecting', 'affiliation', 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'uiq', 'ukraine', 'ulala', 'ultimate', 'ultra', 'ultrasonic', 'ultrasound', 'umass', 'uml', 'unacceptable', 'unauthorized', 'unavailable', 'unbiased', 'unburned', 'uncertainty', 'unclipping', 'uncluttered', 'unctad', 'underestimated', 'underflow', 'undergoing', 'undergrad', 'undergraduate', 'undergraduates', 'undergraduation', 'underground', 'underhill', 'underlying', 'understand', 'understandable', 'understanding', 'understood', 'undertake', 'undertaking', 'underwater', 'undocumented', 'unemployment', 'unexpected', 'unexpectedly', 'unexplored', 'unh', 'unifier', 'uniform', 'unilever', 'unimportant', 'union', 'unique', 'unit', 'unite', 'united', 'units', 'univ', 'univariate', 'univeristy', 'universal', 'universidad', 'universitaire', 'universitario', 'universities', 'university', 'universitydeans', 'universitymay', 'unix', 'unixsoftware', 'unknown', 'unknowns', 'unloading', 'unmanned', 'unmapped', 'unparalleled', 'unplug', 'unprecedented', 'unsaturated', 'unscented', 'unsolved', 'unsteadygas', 'unsupervised', 'unsure', 'untangling', 'untimely', 'unum', 'upbeat', 'upcoming', 'update', 'updated', 'updates', 'updating', 'upgrade', 'upgraded', 'upgradeddesign', 'upgrades', 'upgrading', 'upholt', 'upinformational', 'upkeep', 'upper', 'ups', 'upselling', 'upset', 'upstream', 'uptake', 'urban', 'urbano', 'ureca', 'urinalysis', 'urine', 'urop', 'usa', 'usability', 'usable', 'usaf', 'usage', 'usauniversity', 'usda', 'usedaseptic', 'usedcomputer', 'usedcritical', 'usedeveloped', 'usedleadership', 'usedteamwork', 'usedwhile', 'useeducationm', 'useful', 'usepa', 'user', 'users', 'uses', 'usfs', 'usfws', 'usgcb', 'usgs', 'usp', 'ust', 'ustainable', 'usually', 'uterine', 'utilities', 'utility', 'utilization', 'utilize', 'utilized', 'utilizes', 'utilizing', 'uttar', 'uva', 'uvm', 'uwc', 'vaassistant', 'vaassociate', 'vaauthorized', 'vacant', 'vacation', 'vaccinated', 'vaccination', 'vaccine', 'vaccines', 'vacuum', 'vaeducationph', 'valet', 'valetcountry', 'valhalla', 'validate', 'validated', 'validates', 'validating', 'validation', 'validations', 'validity', 'valley', 'valleys', 'valuable', 'valuation', 'value', 'valued', 'values', 'valve', 'valves', 'van', 'vanasse', 'vance', 'vancouver', 'vanderhyden', 'vanderveer', 'vapor', 'variability', 'variable', 'variables', 'variance', 'variances', 'variant', 49 'variants', 'variation', 'varied', 'varieties', 'variety', 'varsity', 'vary', 'varying', 'vascular', 'vaseva', 'vasodilation', 'vaulter', 'vba', 'vch', 'vdh', 'vector', 'vectors', 'vegasactivities', 'vegetable', 'vegetation', 'vegetative', 'vehicle', 'vehicles', 'velocity', 'vender', 'vendor', 'vendors', 'venture', 'ventures', 'venues', 'verbal', 'verde', 'vergennes', 'verification', 'verified', 'verify', 'verifying', 'verizon', 'verlag', 'vermont', 'vermontan', 'vermontmay', 'vermontnorthern', 'vermontteaching', 'vermontwildlife', 'vernal', 'verona', 'versatile', 'verse', 'versed', 'version', 'versioned', 'versioning', 'vertebrate', 'vessel', 'vessels', 'veteran', 'veterans', 'veterinarian', 'veterinary', 'vhb', 'viability', 'viable', 'viasat', 'vibration', 'vibrio', 'vibrios', 'vice', 'victoria', 'vidana', 'video', 'videos', 'view', 'viewcontent', 'viewed', 'viewer', 'viewpoints', 'views', 'vigorously', 'vijay', 'village', 'vimovo', 'vincent', 'vining', 'vintnerraven', 'violation', 'violations', 'violators', 'violence', 'violent', 'violet', 'vip', 'viral', 'vireos', 'virgin', 'virginia', 'virology', 'virtual', 'virtualization', 'virulence', 'virus', 'viruses', 'vis', 'visas', 'visible', 'visio', 'vision', 'visit', 'visited', 'visiting', 'visitors', 'visits', 'viso', 'vista', 'visual', 'visualization', 'visualizations', 'visuals', 'vital', 'vitamins', 'vitek', 'vitro', 'vivo', 'vlanso', 'vlsi', 'vmd', 'vmobile', 'vocabulary', 'vocalizations', 'voice', 'void', 'vol', 'volans', 'volatile', 'volksenior', 'voltage', 'volume', 'volumes', 'voluntary', 'volunteer', 'volunteered', 'volunteerism', 'volunteers', 'volunteeru', 'von', 'voucher', 'vouchers', 'vparticipating', 'vpn', 'vpr', 'vtaas', 'vtadditional', 'vtathens', 'vtburlington', 'vtcommunity', 'vtcustom', 'vtdatabase', 'vtgis', 'vtgraphic', 'vtk', 'vtskillstyping', 'vtunderhill', 'vtwork', 'vulnerabilities', 'vulnerability', 'vulnificus', 'wachovia', 'wading', 'wafer', 'wafers', 'waitress', 'waitsfield', 'waitstaff', 'walk', 'walked', 'walks', 'wall', 'walled', 'walls', 'walnut', 'walter', 'waltham', 'wanee', 'wang', 'want', 'wanted', 'wants', 'warblers', 'warburton', 'ware', 'warehouse', 'warehouses', 'warm', 'warped', 'warping', 'warrantee', 'warranties', 'warren', 'warrior', 'warwick', 'wash', 'washers', 'washing', 'washington', 'wasinstrumental', 'wasorganized', 'waste', 'wastes', 'wastestate', 'wastewater', 'watch', 'watchdog', 'watched', 'water', 'waterbury', 'watercolors', 'waterfall', 'waterfalls', 'waterfowl', 'waterloo', 'waterlooaps', 'waterquality', 'waters', 'watershed', 'watersheds', 'watersports', 'waterville', 'watson', 'wave', 'wavefront', 'way', 'wayne', 'ways', 'weapons', 'wearable', 'weather', 'weaver', 'web', 'webex', 'webinar', 'webinars', 'weblog', 'weblogic', 'webmastergreen', 'webpage', 'website', 'websitehelped', 'websites', 'websitevermont', 'websphere', 'webstorm', 'weebly', 'week', 'weekend', 'weekends', 'weeklaborer', 'weekly', 'weeks', 'weekthis', 'wefmsp', 'weiden', 'weighed', 'weighing', 'weight', 'weights', 'weinheim', 'weirs', 'welcoming', 'welded', 'weldment', 'welds', 'welfare', 'wellness', 'wells', 'wendy', 'went', 'west', 'westborough', 'western', 'westerns', 'westfield', 'westford', 'westminster', 'wet', 'wetland', 'wetlands', 'wetlandsweb', 'whale', 'whales', 'whatwe', 'wheat', 'wheelock', 'whileminimizing', 'whileopening', 'whistler', 'white', 'whitney', 'whitters', 'wichita', 'wide', 'wife', 'wiki', 'wild', 'wilder', 'wilderness', 'wildfire', 'wildfires', 'wildland', 'wildlife', 'wildt', 'wiley', 'wilkes', 'william', 'williamsburg', 'williamson', 'willimantic', 'willing', 'willis', 'williston', 50 'willits', 'willow', 'wiloughby', 'wilson', 'win', 'winchester', 'wind', 'window', 'windows', 'windsor', 'wine', 'winery', 'wines', 'wings', 'winner', 'winning', 'winooski', 'winooskiserved', 'wins', 'winter', 'winthrop', 'wipes', 'wireframes', 'wireless', 'wiring', 'wisconsin', 'wish', 'wistar', 'wit', 'withcanyon', 'withclinical', 'withconfidence', 'witheuropean', 'withhigh', 'witness', 'wojcik', 'wojcikit', 'women', 'wonyoung', 'wood', 'woodinville', 'woodpecker', 'woodpeckers', 'woods', 'woodstock', 'woodworking', 'woody', 'wooster', 'worcester', 'word', 'wordpress', 'wordresearch', 'words', 'work', 'workassistant', 'workbench', 'worked', 'worker', 'workers', 'workflow', 'workflows', 'workforce', 'workfunction', 'workgroups', 'working', 'workload', 'workplace', 'works', 'workshop', 'workshops', 'workspaces', 'workstation', 'world', 'worldwide', 'worth', 'wotus', 'wound', 'wounded', 'wpm', 'wrangling', 'write', 'writer', 'writers', 'writerspss', 'writes', 'writing', 'writingcontact', 'written', 'wrm', 'wrong', 'wrote', 'wrotehta', 'wroteinternal', 'www', 'wwwnc', 'wyoming', 'xactimate', 'xavier', 'xbqoctober', 'xcalibur', 'xccdf', 'xenograft', 'xestospongia', 'xiangtan', 'xiaoli', 'xls', 'xml', 'xoma', 'xpress', 'xps', 'xrd', 'xrî', 'xsd', 'xsl', 'xslt', 'yahoo', 'year', 'yearbook', 'yearfull', 'yearly', 'years', 'yearsesri', 'yearsflinders', 'yearsgraph', 'yearsinvited', 'yearso', 'yearssas', 'yeast', 'yellow', 'yield', 'yielded', 'yielding', 'yields', 'ymca', 'yolanda', 'yore', 'york', 'yorkorganized', 'yorksaint', 'young', 'youngyoung', 'yousef', 'youth', 'yrs', 'zaruk', 'zebra', 'zeldin', 'zeng', 'zengresearch', 'zero', 'zhong', 'zilembo', 'zimecki', 'zip', 'zirconocene', 'zmarch', 'zomig', 'zomigî', 'zone', 'zones', 'zoonotic', 'zosia', 'zudaõ', 'zurima', 'ãæcomputer', 'ètravel', 'ô_torrent'] /srv/conda/envs/notebook/lib/python3.7/sitepackages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead. warnings.warn(msg, category=FutureWarning) [15]: # PROCESSED DATA: print(countvectorizer.toarray()) [[00 0… [000… [000… … [000… [000… [000… 0 0 0] 00 0] 00 0] 00 0] 00 0] 00 0]] 1.0.7 STEP 7: TRAINING A NAIVE BAYES CLASSIFER [37]: X = countvectorizer y = resume_df['class'] from sklearn.model_selection import train_test_split 51 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) from sklearn.naive_bayes import MultinomialNB Bayes_clf = MultinomialNB(alpha = 3) Bayes_clf.fit(X_train, y_train) ## Training the model [37]: MultinomialNB(alpha=3) 1.0.8 STEP 8: ASSESING THE TRAINED MODEL [38]: %matplotlib inline # PLOTTING CONFUSION MATRIX: # 1) FOR TRAINING DATA y_pred_train = Bayes_clf.predict(X_train) cm = confusion_matrix(y_train, y_pred_train) sns.heatmap(cm, annot=True) plt.show() 52 [39]: %matplotlib inline ↪ # WE OURCORRECTLY MODEL PERFORMED REALLY WELL ON TRAINING DATA: IT CLASSFIED ALL␣ OFCAN THESEE POINTS # 2) FOR TEST DATA: y_pred_test = Bayes_clf.predict(X_test) cm = confusion_matrix(y_test, y_pred_test) sns.heatmap(cm, annot=True) plt.show() [40]: print(classification_report(y_test, y_pred_test)) precision 0 1 accuracy macroavg - recall f1-score support - 21 4 0.88 0.67 25 25 0.62 53 weighted avg 0.90 0.88 0.85 25 ↪ [41]: from sklearn.metrics import accuracy_score, precision_score, recall_score,␣ f1_score, roc_auc_score ↪ print("AccuracyofmodelonTestDataset={}".format(accuracy_score(y_test,␣ ↪y_pred_test))) print("Accuracy of model on Train Dataset = {}".format(accuracy_score(y_train,␣ y_pred_train))) Accuracy of model on Train Dataset = 0.93 Accuracy of model on Test Dataset = 0.88 [42]: print("Accuracy of model is = {}".format(accuracy_score(y_test, y_pred_test))) print("F1 of model is = {}".format(f1_score(y_test, y_pred_test))) print("Precision of model is = {}".format(precision_score(y_test, y_pred_test))) print("Recall of model is = {}".format(recall_score(y_test, y_pred_test))) Accuracy of model is = 0.88 F1 of model is = 0.4 Precision of model is = 1.0 Recall of model is = 0.25 []: 54