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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Wk2_Submission.ipynb",
"provenance": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/patternproject/c8e973abeebc9238cdedae219168bfdd/wk2_submission.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ExrcEKOJqRMS",
"colab_type": "text"
},
"source": [
"Manning LP \"Using Online Job Postings to Improve Your Data Science Resume\" "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GTXpVFDmqdGi",
"colab_type": "text"
},
"source": [
"Week 2 - Ranking Job Postings by Similarity"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cA2s2jr6uJhM",
"colab_type": "text"
},
"source": [
"# 2.1 Find the Most Similar Job Postings to Our Resume"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6EWGJZG3uhs2",
"colab_type": "text"
},
"source": [
"## Objective\n",
"\n",
"Narrow down the set of job postings to those that are most similar to our resume in preparation for further analysis."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X1LYPAywxnbp",
"colab_type": "text"
},
"source": [
"## Importing Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xXHEd5UKxnFe",
"colab_type": "code",
"outputId": "dd53e64c-2e96-4d63-8ea5-24e1e9cd0440",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 70
}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"\n",
"# for count vectorizer\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"# for freq vectorizer\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"# string processing, reg expressions\n",
"import re, string\n",
"\n",
"# Compute Cosine Similarity\n",
"from sklearn.metrics.pairwise import cosine_similarity\n",
"\n",
"#import the nltk package\n",
"import nltk\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.corpus import stopwords\n",
"nltk.download('stopwords')"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GBWZzknYOPLJ",
"colab_type": "text"
},
"source": [
"## Loading the Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "asdB9SEnOOs-",
"colab_type": "code",
"colab": {}
},
"source": [
"df_pkl = pd.read_pickle('step1_df.pk')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KQ3RKcloxzw9",
"colab_type": "code",
"outputId": "5a81f973-30c8-4519-a182-cb93080916f6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197
}
},
"source": [
"df_pkl.head()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>body</th>\n",
" <th>bullets</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Research Post Doctoral Fellow - Oakland, CA</td>\n",
" <td>Research Post Doctoral Fellow - Oakland, CA\\nA...</td>\n",
" <td>(Supports well-defined research with minimal s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Personal Care Assistant - Yuba City, CA</td>\n",
" <td>Personal Care Assistant - Yuba City, CA\\nKNOWL...</td>\n",
" <td>(Actively participates in the resident's plan ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Data Scientist - Cambridge, MA</td>\n",
" <td>Data Scientist - Cambridge, MA\\nLocation: Camb...</td>\n",
" <td>(Analyzing patient EEG data from both internal...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Data Engineer - San Francisco, CA</td>\n",
" <td>Data Engineer - San Francisco, CA\\nGSN Games i...</td>\n",
" <td>(3-5 years experience as a Data Engineer in a ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Senior Data Scientist - New York, NY 10038</td>\n",
" <td>Senior Data Scientist - New York, NY 10038\\nWe...</td>\n",
" <td>(Supports the company's product, sales, leader...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title ... bullets\n",
"0 Research Post Doctoral Fellow - Oakland, CA ... (Supports well-defined research with minimal s...\n",
"1 Personal Care Assistant - Yuba City, CA ... (Actively participates in the resident's plan ...\n",
"2 Data Scientist - Cambridge, MA ... (Analyzing patient EEG data from both internal...\n",
"3 Data Engineer - San Francisco, CA ... (3-5 years experience as a Data Engineer in a ...\n",
"4 Senior Data Scientist - New York, NY 10038 ... (Supports the company's product, sales, leader...\n",
"\n",
"[5 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xgZXH3gfx-up",
"colab_type": "code",
"outputId": "bfd01146-1370-4c41-d014-437f55041d27",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
}
},
"source": [
"df_pkl.describe"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<bound method NDFrame.describe of title ... bullets\n",
"0 Research Post Doctoral Fellow - Oakland, CA ... (Supports well-defined research with minimal s...\n",
"1 Personal Care Assistant - Yuba City, CA ... (Actively participates in the resident's plan ...\n",
"2 Data Scientist - Cambridge, MA ... (Analyzing patient EEG data from both internal...\n",
"3 Data Engineer - San Francisco, CA ... (3-5 years experience as a Data Engineer in a ...\n",
"4 Senior Data Scientist - New York, NY 10038 ... (Supports the company's product, sales, leader...\n",
"... ... ... ...\n",
"1332 Process & Automation Engineer - Menlo Park, CA ... (Work closely with other automation team membe...\n",
"1333 Software Engineer, Data - San Francisco, CA ... (You find large challenges exciting and enjoy ...\n",
"1334 Patient Care Assistant, Acute Rehab Unit - New... ... (Under direction of the RN, performs bedside p...\n",
"1335 Quality Data Scientist, Analytics - San Franci... ... (Link separate data sources into usable format...\n",
"1336 Data Scientist - Grid Analytics - Burlingame, ... ... (Using historical distribution grid event and ...\n",
"\n",
"[1328 rows x 3 columns]>"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IpfKfXv7yFis",
"colab_type": "code",
"outputId": "a05a3c0b-4c19-4096-f33d-115f61de1f07",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"df_pkl.dtypes"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"title object\n",
"body object\n",
"bullets object\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_0N5_-yfyMn2",
"colab_type": "code",
"outputId": "466608e0-5468-4487-b951-fb4198d00b93",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 158
}
},
"source": [
"df_pkl.info()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 1328 entries, 0 to 1336\n",
"Data columns (total 3 columns):\n",
"title 1328 non-null object\n",
"body 1328 non-null object\n",
"bullets 1328 non-null object\n",
"dtypes: object(3)\n",
"memory usage: 41.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CxCcmszPyXq4",
"colab_type": "code",
"outputId": "28cfe7eb-f3f6-4275-ac72-ce358f230f9b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
}
},
"source": [
"df_pkl.infer_objects().dtypes"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"title object\n",
"body object\n",
"bullets object\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xdln35hF0d2y",
"colab_type": "code",
"outputId": "47dc3da3-547d-45b9-a4dd-6a2328db12f4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 374
}
},
"source": [
"df_pkl['bullets'][1]"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(\"Actively participates in the resident's plan of care and assists in implementing care intervention at residents level of care.\",\n",
" 'Upon resident admission, assists licensed personnel with data collection, through basic physical appearance, vital signs, weights, and documents accordingly. Continues basic assessments during routine resident care on a daily basis, reporting change of condition to Facility Director/Physician/and family members in a timely manner.',\n",
" 'Charts clearly and concisely in a timely manner, according to policy and procedure.',\n",
" 'Provides a clean and safe environment for all residents. Assists residents to their individual level of capability, their cleanliness, grooming, nourishments, rest, activity and elimination. Ensure teeth brushing daily.',\n",
" 'Demonstrates ability to assess emergency situations and take appropriate action.',\n",
" \"Reports to physician and resident's responsible party change in condition in a timely manner and performs all necessary documentation. This includes calling families when a resident falls.\",\n",
" 'As assigned, completes any resident care responsibilities or assignments left from previous shift and reports any incomplete assignments to the next shift.',\n",
" 'Makes routine rounds of assigned area and report pertinent information to Facility Director or Assisted Living Manager throughout the shift, also charts accordingly.',\n",
" 'Makes resident rounds at beginning and end of shift relaying pertinent information and fulfilling resident needs. At the end of shift, also gives verbal report to on-coming staff while making rounds.',\n",
" \"Strip and make residents' beds as often as needed. Launder resident's linen and towels, fold and return to residents.\",\n",
" 'Assist in serving meals and bussing tables during mealtimes.',\n",
" 'Assist with the activity program; conduct activities as directed by the Activity Coordinator or assigned by supervisor when needed.',\n",
" \"Document information pertaining to changes in residents' functional status, personal needs, appointments, etc.\",\n",
" 'Provide one-on-one support/visitation to assigned residents on a weekly basis or as requested by supervisor or as specified in the assistance plan or daily communication log.',\n",
" 'Identify supplies needed to perform tasks and report needs to supervisor.',\n",
" 'Practice safety in working around and with equipment, chemicals, tools and utensils. Using the buddy system with a challenging resident.',\n",
" 'Carry out other duties as assigned by supervisor.',\n",
" 'Acknowledges and assists others promptly and courteously, refers to other departments as appropriate, and demonstrates compassion allowing time for residents and significant others to verbalize.',\n",
" 'Performs other duties as assigned.')"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M07VuUUE_UDt",
"colab_type": "text"
},
"source": [
"# 2.2 Action Starts Here ... "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f94mzdUg_emN",
"colab_type": "text"
},
"source": [
"## 1.Importing Libraries\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0JTjCS5h_WQN",
"colab_type": "code",
"colab": {}
},
"source": [
"# merged above"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "oZmSDyy0_tSm",
"colab_type": "text"
},
"source": [
"## 2.Helper Functions"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MSDwxhTw_t0h",
"colab_type": "code",
"outputId": "236353d5-64f6-466e-c167-f8a24b78e6c1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Python3 code to convert tuple \n",
"# into string \n",
"def convertTuple(tup): \n",
" str = ''.join(tup) \n",
" return str\n",
" \n",
"# Driver code \n",
"tuple = ('g', 'e', 'e', 'k', 's') \n",
"str = convertTuple(tuple) \n",
"print(str) "
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"geeks\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6sG4jedW_yUe",
"colab_type": "code",
"colab": {}
},
"source": [
"def pre_process(text):\n",
" \n",
" # lowercase\n",
" text=text.lower()\n",
" \n",
" #remove tags\n",
" #text=re.sub(\"<!--?.*?-->\",\"\",text)\n",
" \n",
" # remove special characters and digits\n",
" text=re.sub(\"(\\\\d|\\\\W)+\",\" \",text)\n",
"\n",
" # stemming\n",
" ## create an object of class PorterStemmer\n",
" porter = PorterStemmer()\n",
" text = porter.stem(text)\n",
"\n",
" # removing stop words\n",
" #text = ''.join(w for w in text if not w in stopwords.words('english'))\n",
" temp = ''.join(w for w in text if not w in stopwords.words('english'))\n",
" #print(text)\n",
" #print(temp)\n",
" \n",
" return text"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTqu-vbS_3Qp",
"colab_type": "text"
},
"source": [
"## 3.Reading Input Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IcY10499_zvt",
"colab_type": "code",
"colab": {}
},
"source": [
"# already done above"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "mtrYnoKM_-Ha",
"colab_type": "text"
},
"source": [
"## 4.Some Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gBsIFMkvDY5O",
"colab_type": "text"
},
"source": [
"convert tuples to strings in bullets column"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zGQoiJYpDbjQ",
"colab_type": "code",
"colab": {}
},
"source": [
"df_pkl['bull_strings'] = df_pkl['bullets'].map(convertTuple)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0vLu3StbDYCg",
"colab_type": "code",
"outputId": "56eb8669-170a-43e3-da6c-fdec99c68d57",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 176
}
},
"source": [
"df_pkl.info()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 1328 entries, 0 to 1336\n",
"Data columns (total 4 columns):\n",
"title 1328 non-null object\n",
"body 1328 non-null object\n",
"bullets 1328 non-null object\n",
"bull_strings 1328 non-null object\n",
"dtypes: object(4)\n",
"memory usage: 91.9+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "edKzm15U_6lG",
"colab_type": "code",
"outputId": "55e9de1d-9d59-454b-f7eb-806c4c3c9d62",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"df_idf = df_pkl \n",
"df_idf['text'] = df_idf['body'] + df_idf['bull_strings']\n",
"df_idf['text'] = df_idf['text'].apply(lambda x:pre_process(x))\n",
" \n",
"#show the second 'text' \n",
"df_idf['text'][2]\n",
" "
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'data scientist cambridge ma location cambridge ma company description cognito is a venture backed morningside ventures startup company translating scientific findings from mit into therapeutic approaches for alzheimer s disease and other neurodegenerative conditions we are a fast moving highly motivated team of people with the ambitious goal of helping the millions of alzheimer s disease patients and caregivers around the world with our patented gamma stimulation technology we are committed to developing non invasive device based therapies developing this unique platform requires expertise across a range of fields engineering software engineering regulatory clinical product design data science qa business development marketing just to name a few we are looking to grow our world class team of scientists and medical device innovators who want to make a difference and improve people s lives on an everyday basis summary of role we are looking for a highly motivated data scientist to join cognito s cambridge team where you will work closely with our clinical translational and r d teams to glean insights about the use and efficacy of our device as a member of our r d team you will be working with large data sets related to a variety of aspects of our business eeg responses eeg changes over time patients adherence data behavioral changes in patients device usage data user experiences etc the overarching goal will be to analyze these data sets in meaningful ways in order to glean biological scientific and patient insights our culture is very open and innovative and due to the multidisciplinary nature of building a complex device you will be collaborating with teams from a variety of fields including neuroscientists engineers and medical device professionals deliverables you will be responsible for analyzing patient eeg data from both internal and external clinical trialsanalyzing changes in eeg data over time and gleaning temporal insights on efficacy data mining using state of the art methodsstudying patient adherence data and identifying trends or useful correlations causation testing out hypotheses both prospective and retrospective communicating problems concerns and insights stemming from data collectiondoing ad hoc analysis and presenting results in a clear manner who you are you re someone who likes the pace and energy of a startup company and wants to influence your own development you re looking for an early stage company with an exciting technology where you have the opportunity to make a difference for patients around the world minimal qualifications master s or phd in statistics mathematics computer science or another quantitative field years of experience manipulating data sets and building statistical modelsstrong problem solving skills with an emphasis on product development experience using statistical computer languages r python slq etc to manipulate data and draw insights from large data sets experience working with and creating data architectures knowledge of a variety of machine learning techniques clustering decision tree learning artificial neural networks etc and their real world advantages drawbacks knowledge of advanced statistical techniques and concepts regression properties of distributions statistical tests and proper usage etc and experience with applications excellent written and verbal communication skills for coordinating across teams a drive to learn and master new technologies and techniques desired qualifications years of experience manipulating data sets and building statistical models prior experience with eeg data experience with medical devices and working with patient data coding knowledge and experience with several languages c c java java etc knowledge and experience in statistical and data mining techniques glm regression random forest boosting trees text mining social network analysis etc experience using web services redshift s spark digitalocean etc contact interested send a short introduction and resume to jobs cognitotx comanalyzing patient eeg data from both internal and external clinical trialsanalyzing changes in eeg data over time and gleaning temporal insights on efficacydata mining using state of the art methodsstudying patient adherence data and identifying trends or useful correlations causationtesting out hypotheses both prospective and retrospective communicating problems concerns and insights stemming from data collectiondoing ad hoc analysis and presenting results in a clear mannermaster s or phd in statistics mathematics computer science or another quantitative years of experience manipulating data sets and building statistical modelsstrong problem solving skills with an emphasis on product development experience using statistical computer languages r python slq etc to manipulateexperience working with and creating data architectures knowledge of a variety of machine learning techniques clustering decision treeknowledge of advanced statistical techniques and concepts regression properties ofexcellent written and verbal communication skills for coordinating across teams a drive to learn and master new technologies and techniques years of experience manipulating data sets and building statistical model'"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DWVKEqt2AgAl",
"colab_type": "text"
},
"source": [
"Drop unnecessary columns"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uBK801iYAGal",
"colab_type": "code",
"colab": {}
},
"source": [
"df_idf.drop(columns=['bull_strings'],inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lkDw1StzArp5",
"colab_type": "text"
},
"source": [
"## 5.Word Frequencies with TfidfVectorizer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n18Mqib3AwGp",
"colab_type": "text"
},
"source": [
"Transform job posting text and our resume into TFIDF vectors using sklearn’s TFIDF vectorizer class.\n",
"\n",
"---\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TkFRmmMJAz9C",
"colab_type": "text"
},
"source": [
"### 1.Downloaded HTML Resume "
]
},
{
"cell_type": "code",
"metadata": {
"id": "cRYOhhFxAiJE",
"colab_type": "code",
"colab": {}
},
"source": [
"# create the transform\n",
"tfidf_vectorizer = TfidfVectorizer(stop_words='english') \n",
"\n",
"# tokenize and build vocab\n",
"tfidf_matrix = tfidf_vectorizer.fit_transform(df_idf['text'])\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_QclJEYrAqI-",
"colab_type": "code",
"outputId": "05216859-898d-49cd-8c56-d6a7f02569ef",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"tfidf_matrix.shape"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1328, 24928)"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "16UmaPSdA6Lz",
"colab_type": "code",
"outputId": "0939dbbf-9697-476a-c483-050486c92790",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"# summarize\n",
"print(tfidf_vectorizer.vocabulary_)\n"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"{'research': 18614, 'post': 16337, 'doctoral': 6443, 'fellow': 8343, 'oakland': 14501, 'ca': 2849, 'year': 24578, 'position': 16303, 'appointment': 1329, 'involves': 11199, 'time': 22645, 'supervision': 21620, 'scientists': 19505, 'biostatistician': 2394, 'ii': 10293, 'iii': 10294, 'expected': 7871, 'publish': 17434, 'assist': 1650, 'development': 5964, 'serve': 19824, 'multiple': 13878, 'going': 9366, 'efforts': 6880, 'hiring': 9906, 'senior': 19766, 'scientist': 19499, 'essential': 7574, 'functions': 8998, 'supports': 21660, 'defined': 5585, 'minimal': 13417, 'dor': 6535, 'prepares': 16613, 'draft': 6569, 'grant': 9454, 'proposals': 17288, 'internal': 11051, 'reports': 18419, 'publications': 17423, 'independently': 10517, 'collaboratively': 3848, 'review': 18878, 'provides': 17369, 'input': 10770, 'study': 21429, 'design': 5824, 'develops': 6014, 'analysis': 896, 'plans': 15994, 'interpretation': 11076, 'results': 18783, 'include': 10467, 'presentation': 16636, 'tpmg': 22892, 'kfh': 11622, 'managers': 12692, 'committees': 3972, 'work': 24397, 'consult': 4447, 'local': 12364, 'state': 21120, 'national': 13982, 'voluntary': 24037, 'governmental': 9404, 'agencies': 543, 'institutions': 10884, 'provide': 17359, 'consultation': 4454, 'direction': 6156, 'programmer': 17115, 'analysts': 1009, 'regard': 18166, 'data': 5201, 'management': 12643, 'strategies': 21295, 'presents': 16654, 'abstracts': 62, 'papers': 15333, 'scientific': 19497, 'meetings': 13045, 'publication': 17422, 'journals': 11471, 'seeks': 19703, 'specific': 20777, 'administrative': 384, 'issues': 11274, 'kaiser': 11565, 'permanente': 15679, 'conducts': 4299, 'compensation': 4090, 'reviews': 18885, 'positions': 16318, 'routine': 19134, 'basis': 2100, 'reserves': 18658, 'right': 18964, 'reevaluate': 18119, 'change': 3328, 'job': 11423, 'descriptions': 5816, 'salaried': 19244, 'hourly': 10039, 'pay': 15494, 'status': 21180, 'changes': 3338, 'generally': 9179, 'implemented': 10388, 'notice': 14379, 'given': 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'training': 22950, 'addiction': 321, 'mental': 13103, 'services': 19845, 'integrated': 10949, 'behavioral': 2186, 'dissemination': 6351, 'implementation': 10379, 'clinical': 3665, 'addictions': 322, 'working': 24427, 'broad': 2680, 'range': 17799, 'healthcare': 9801, 'patient': 15474, 'populations': 16278, 'developing': 5961, 'interventions': 11105, 'strong': 21378, 'analytical': 1022, 'writing': 24505, 'verbal': 23804, 'communications': 3999, 'skills': 20211, 'primarylocation': 16706, 'california': 2903, 'broadway': 2687, 'hoursperweek': 10047, 'shift': 20005, 'day': 5416, 'workdays': 24406, 'mon': 13690, 'fri': 8904, 'workinghoursstart': 24430, 'workinghoursend': 24429, 'pm': 16188, 'schedule': 19415, 'type': 23232, 'standard': 21070, 'employee': 7046, 'regular': 18200, 'group': 9541, 'union': 23425, 'affiliation': 506, 'non': 14303, 'exempt': 7826, 'level': 12127, 'individual': 10548, 'contributor': 4556, 'category': 3135, 'department': 5733, 'division': 6403, 'travel': 23050, 'equal': 7479, 'opportunity': 14859, 'employer': 7055, 'committed': 3970, 'diverse': 6392, 'inclusive': 10472, 'workforce': 24423, 'applicants': 1272, 'receive': 17946, 'consideration': 4395, 'employment': 7066, 'race': 17752, 'color': 3894, 'religion': 18305, 'sex': 19937, 'including': 10470, 'pregnancy': 16592, 'age': 539, 'sexual': 19940, 'orientation': 15037, 'origin': 15054, 'marital': 12790, 'parental': 15360, 'ancestry': 1081, 'disability': 6179, 'gender': 9166, 'identity': 10265, 'veteran': 23859, 'genetic': 9202, 'information': 10634, 'distinguishing': 6366, 'characteristics': 3360, 'diversity': 6396, 'inclusion': 10471, 'protected': 17317, 'external': 8121, 'hires': 9904, 'pass': 15434, 'background': 1996, 'check': 3398, 'drug': 6626, 'screen': 19576, 'qualified': 17639, 'arrest': 1509, 'conviction': 4607, 'records': 18043, 'considered': 4399, 'manner': 12725, 'consistent': 4407, 'federal': 8322, 'laws': 11901, 'applicable': 1266, 'ordinances': 14962, 'limited': 12252, 'san': 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'rounds': 19127, 'area': 1453, 'report': 18396, 'pertinent': 15741, 'manager': 12687, 'beginning': 2180, 'end': 7117, 'relaying': 18281, 'fulfilling': 8962, 'needs': 14089, 'gives': 9289, 'coming': 3938, 'staff': 20997, 'making': 12607, 'strip': 21372, 'make': 12597, 'beds': 2171, 'needed': 14057, 'launder': 11888, 'linen': 12266, 'towels': 22882, 'fold': 8670, 'return': 18860, 'serving': 19869, 'meals': 12979, 'bussing': 2832, 'tables': 21905, 'mealtimes': 12982, 'program': 17098, 'conduct': 4294, 'activities': 265, 'directed': 6153, 'coordinator': 4636, 'supervisor': 21629, 'document': 6448, 'pertaining': 15739, 'functional': 8978, 'appointments': 1330, 'support': 21647, 'visitation': 23958, 'weekly': 24222, 'requested': 18490, 'specified': 20789, 'assistance': 1651, 'communication': 3991, 'log': 12394, 'identify': 10262, 'supplies': 21643, 'perform': 15613, 'safety': 19226, 'equipment': 7487, 'chemicals': 3413, 'tools': 22786, 'utensils': 23649, 'using': 23638, 'buddy': 2728, 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'virtues': 23929, 'wellness': 24251, 'equally': 7483, 'empowering': 7075, 'professionally': 17058, 'competitive': 4109, 'benefits': 2238, 'ready': 17891, 'rewarding': 18906, 'achieve': 188, 'potential': 16365, 'leader': 11925, 'su': 21449, 'mmary': 13513, 'enterprise': 7277, 'discovering': 6271, 'visualization': 23972, 'integrates': 10951, 'varied': 23741, 'architects': 1423, 'communicates': 3989, 'validation': 23684, 'major': 12587, 'accountabilities': 142, 'leads': 11969, 'discovery': 6273, 'strategic': 21293, 'recommendations': 17986, 'integration': 10954, 'retention': 18835, 'practices': 16432, 'effective': 6838, 'solve': 20600, 'collaborates': 3840, 'subject': 21461, 'matter': 12922, 'experts': 8053, 'regarding': 18168, 'relevant': 18292, 'recommends': 18019, 'improved': 10418, 'search': 19635, 'quality': 17645, 'prescriptive': 16632, 'capabilities': 2965, 'specialized': 20768, 'database': 5219, 'creates': 4861, 'chain': 3285, 'partners': 15405, 'operationalize': 14790, 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'signings': 20091, 'objections': 14511, 'mobilizes': 13527, 'recovers': 18053, 'persistently': 15706, 'tenaciously': 22341, 'mmcg': 13514, 'pmdrive': 16192, 'marketsachieve': 12812, 'targetsexecute': 21982, 'cyclesell': 5134, 'solutionsspend': 20587, 'volumesell': 24033, 'requirementsproactively': 18595, 'propositiondocuments': 17296, 'benefitsgains': 2250, 'objectionsovercomes': 14512, 'actiondemonstrates': 241, 'setbacksproactively': 19879, 'ariseuses': 1487, 'servicesutilizes': 19864, 'outcomesactively': 15132, 'probabilitytenaciously': 16779, 'contactseffectively': 4477, 'valuepositions': 23714, 'solutionsunderstands': 20592, 'regul': 18199, 'uhm': 23283, 'manoa': 12745, 'hamilton': 9669, 'hawai': 9758, 'broadens': 2683, 'speakers': 20750, 'ala': 655, 'hawaiiedu': 9760, 'interviewees': 11108, 'permissible': 15684, 'verb': 23803, 'kinematic': 11640, 'surgeons': 21677, 'unimagined': 23423, 'ethicon': 7643, 'plate': 16023, 'throwing': 22611, 'went': 24253, 'apeel': 1226, 'trailblazer': 22939, 'tirelessly': 22711, 'selflessly': 19741, 'plethora': 16094, 'pymc': 17522, 'realms': 17908, 'logisticsdevelop': 12406, 'consumerdevelop': 4466, 'modelslearn': 13626, 'optimizationms': 14902, 'pymcproblem': 17523, 'operationalizes': 14792, 'sustains': 21714, 'subsequently': 21487, 'bilateral': 2328, 'restraining': 18769, 'councils': 4729, 'assured': 1688, 'npwvc': 14416, 'tangibly': 21962, 'intensity': 10994, 'pairing': 15301, 'alarm': 659, 'mitigated': 13477, 'isma': 11254, 'affiliations': 507, 'iahss': 10186, 'assaultive': 1586, 'avade': 1908, 'mcafee': 12952, 'stark': 21095, 'kickback': 11627, 'statute': 21187, 'false': 8225, 'aks': 652, 'stpi': 21281, 'ostp': 15093, 'multiyear': 13896, 'ffrdcs': 8368, 'prose': 17303, 'employera': 7056, 'procedurestroubleshoots': 16866, 'expertiseextensive': 8048, 'modelsbs': 13600, 'shp': 20053, 'equivalents': 7519, 'economists': 6765, 'calculates': 2882, 'complicate': 4155, 'dbas': 5432, 'applicationresearchdevelop': 1283, 'testingdocumentationu': 22427, 'residencygraduating': 18673, 'angularsqljavaresearchwriting': 1146, 'pyramid': 17525, 'sybase': 21752, 'distributer': 6373, 'hortonworks': 10016, 'mapr': 12770, 'distributers': 6374, 'aretove': 1476, 'meritocracy': 13139, 'amplero': 863, 'smartly': 20394, 'cuxtcporz': 5106, 'engagementssmartly': 7159, 'anomaliesdevelop': 1177, 'graphswork': 9479, 'lifecycleyou': 12212, 'learningable': 11998, 'plusyou': 16183, 'evaluationyou': 7686, 'capabilitiesyou': 2976, 'apton': 1400, 'liblinear': 12174, 'biosystems': 2399, 'submittal': 21472, 'aptonbio': 1401, 'commasters': 3946, 'matlabknowledge': 12905, 'plusactively': 16110, 'environmentsolid': 7414, 'simultaneouslydemonstrated': 20158, 'mak': 12594, 'alleviate': 745, 'suffering': 21547, 'finite': 8533, 'similarfamiliarity': 20111, 'kerasexperience': 11602, 'neuroscientist': 14193, 'toolboxes': 22771, 'grus': 9577, 'fieldsdemonstrated': 8432, 'customersbe': 5067, 'privacygreat': 16756, 'churchill': 3474, 'esy': 7606, 'orderly': 14957, 'nonconfrontational': 14307, 'resort': 18705, 'harming': 9720, 'dismissal': 6306, 'officially': 14664, 'neiman': 14130, 'discerning': 6208, 'bergdorf': 2266, 'goodman': 9381, 'mytheresa': 13943, 'jewelry': 11397, 'landmark': 11800, 'upscale': 23553, 'neimanmarcus': 14131, 'bergdorfgoodman': 2267, 'horchow': 10007, 'lastcall': 11860, 'cusp': 5054, 'nmg': 14284, 'gitflow': 9277, 'colwell': 3902, 'pmdesigning': 16191, 'systemsrunning': 21871, 'experimentsimplementing': 8035, 'prototypesdesign': 17344, 'storesexpanding': 21257, 'applicationaws': 1274, 'skillsoutstanding': 20296, 'mia': 13304, 'gardens': 9095, 'lendingclub': 12099, 'lc': 11918, 'erm': 7531, 'benchmarked': 2224, 'businessensure': 2808, 'modelshave': 13619, 'foundationsability': 8804, 'topicsattention': 22846, 'effortsms': 6891, 'stakeholdersworking': 21058, 'plusinterest': 16144, 'campbell': 2934, 'frost': 8924, 'sullivan': 21579, 'herring': 9855, 'similara': 20107, 'ohsu': 14695, 'specifying': 20792, 'biostatisticial': 2393, 'cheminformatician': 3414, 'aaeo': 8, 'experiencedemonstrated': 7916, 'timeexcellent': 22655, 'diseasemaster': 6296, 'researchertwo': 18626, 'environmentone': 7380, 'dataat': 5213, 'innov': 10749, 'undersecretary': 23355, 'pythonsoftware': 17571, 'gansexperience': 9086, 'pipelinesexcellent': 15920, 'evade': 7668, 'deliberately': 5638, 'inadvertently': 10450, 'unnecessarily': 23476, 'propagating': 17273, 'dataapplying': 5210, 'behavioranalyzing': 2188, 'experiencehaving': 7937, 'tensorflowscripting': 22362, 'correlationdedicated': 4694, 'challengescommunication': 3301, 'experienceexperimentation': 7922, 'ecom': 6748, 'experienceadobe': 7896, 'leadershipin': 11947, 'experienceinfluence': 7940, 'insightsdefine': 10803, 'organizationsevangelize': 15014, 'capacitystrong': 2984, 'testingstrong': 22445, 'analyticsinternet': 1050, 'bare': 2074, 'installs': 10860, 'departure': 5747, 'disaggregated': 6197, 'rethinking': 18837, 'saltstack': 19277, 'jinja': 11408, 'napalm': 13964, 'netbox': 14151, 'peering': 15563, 'ztp': 24679, 'juniper': 11531, 'arista': 1489, 'kentik': 11594, 'organizationworking': 15025, 'standardsimplementing': 21083, 'networkautomated': 14164, 'systemsautomated': 21827, 'saltstackdeep': 19278, 'scalestrong': 19389, 'goknowledge': 9367, 'grafanahigh': 9442, 'engineeringhands': 7188, 'vendorexperience': 23782, 'alternativestravel': 798, 'intriguing': 11119, 'varmax': 23747, 'ets': 7660, 'saslife': 19317, 'unfairly': 23399, 'linkedinprocess': 12284, '액셀러레이터인': 24805, 'addventures': 339, '으로부터': 24830, '누적': 24722, '억원': 24808, '가량': 24688, '투자를': 24883, '유치하여': 24829, '성장목표를': 24788, '함께': 24906, '달성할': 24723, '미래의': 24746, '미소': 24747, '인재를': 24839, '채용합니다': 24866, '미소는': 24748, '홈클리닝': 24914, '펫시팅': 24895, '이사와': 24831, '같은': 24692, '홈서비스가': 24913, '필요한': 24903, '고객과': 24705, '서비스를': 24784, '제공할': 24854, '있는': 24843, '파트너를': 24892, '이어주는': 24834, '플랫폼': 24901, '회사입니다': 24920, '그렇다면': 24715, '미소의': 24749, 'product팀은': 17049, '어떤': 24807, '역할을': 24812, '맡을까요': 24740, '고객은': 24707, '만족스러운': 24736, '받고': 24752, '파트너는': 24891, '적합한': 24848, '일자리를': 24842, '신청할': 24800, '있도록': 24844, 'product를': 17047, '만듭니다': 24735, '예로': 24816, '파트너': 24890, '앱에선': 24806, '사용자': 24778, '행동': 24909, '패턴을': 24894, '분석해': 24770, '수많은': 24790, '일자리': 24841, '특정': 24886, '노출하고': 24721, '서비스': 24783, '완료까지의': 24818, '유저': 24824, '경험을': 24701, '확인합니다': 24915, '또한': 24732, '최상의': 24867, '품질을': 24897, '유지하기': 24827, '위해': 24822, '파트너의': 24893, '평점을': 24896, '관리합니다': 24713, 'engineer는': 7212, '머신러닝과': 24742, '예측': 24817, '분석을': 24769, '통해': 24881, '고객의': 24708, '선호와': 24785, '트렌드': 24885, '데이터': 24728, '인사이트를': 24838, '찾아내': 24865, '고객에게': 24706, '행복한': 24910, '순간을': 24793, '더욱': 24727, '많이': 24737, '제공하려': 24853, '합니다': 24907, '프로젝트별': 24900, '여러': 24811, '이해관계가': 24836, '팀들의': 24887, '흐름과': 24924, '수집을': 24791, '최적화할뿐만': 24872, '아니라': 24802, 'architectures를': 1439, '구축하고': 24714, '유지': 24825, '최적화를': 24870, '수행합니다': 24792, '프로젝트를': 24899, '진행하면서': 24864, '일관된': 24840, 'architecture를': 1443, '상태로': 24782, '유지하며': 24828, '팀의': 24888, 'scientists를': 19520, '지원합니다': 24862, '담당업무': 24724, 'businees에': 2792, '맞춰': 24739, '대량의': 24725, '복잡한': 24761, 'set을': 19931, '가공합니다': 24685, '가공과': 24684, '정보': 24850, '검색을': 24697, '위한': 24821, '툴을': 24884, '개발합니다': 24696, 'pipeline을': 15934, '개발하고': 24695, 'analyst와': 1017, 'scientist가': 19521, '최적화하기': 24871, '유지와': 24826, '확장이': 24917, '가능한': 24687, '효율적인': 24922, '솔루션을': 24789, '아키텍쳐를': 24803, '발전시킵니다': 24753, '지속적으로': 24860, 'model을': 13651, 'product팀': 17048, '개발자분들과': 24694, 'scientist와': 19522, '협업하여': 24911, '시스템': 24797, '기능을': 24718, '최적화': 24869, '자격요건': 24846, '통계학': 24880, '컴퓨터': 24876, '관련': 24711, '학사': 24905, '이상': 24832, '박사': 24751, '우대': 24819, '영어': 24815, '필수': 24902, '업무가': 24809, '가능하신': 24686, '데이터를': 24729, '활용해': 24919, '제품혁신을': 24857, '경험해보신': 24704, '하나': 24904, '이상을': 24833, '사용해': 24781, '프로그래밍': 24898, '경험하신': 24703, '또는': 24731, '관련된': 24712, '경험': 24700, '있으신': 24845, '전문적인': 24849, '통계지식과': 24879, '직관을': 24863, '보유하고': 24759, '가설': 24689, '테스트': 24878, '실험': 24801, '인과': 24837, '추론에': 24873, '열정을': 24813, '보유한': 24760, '우대사항': 24820, '기타': 24720, '분산': 24768, '활용가능': 24918, '혹은': 24912, '지식을': 24861, '확장성': 24916, '아키텍쳐와': 24804, '스키마': 24794, '설계': 24786, '개발': 24693, '배포': 24754, 'microservice를': 13340, '효율적으로': 24921, '사용하기': 24779, 'swarm을': 21734, 'cycle동안': 5136, '기능통합': 24719, '스킬과': 24795, '민첩하고': 24750, '빠른': 24777, '스킬이': 24796, '빅데이터': 24776, '사용한': 24780, '경험이': 24702, '제출서류': 24856, '영문이력서': 24814, '경력기술서': 24698, '보상': 24757, '제도': 24855, '제공': 24851, '대보험': 24726, '가입': 24690, '퇴직금': 24882, '별도': 24755, '업무환경': 24810, '비즈니스': 24775, '조직과': 24859, '맞아': 24738, '마찰이': 24734, '적은': 24847, '최신기종': 24868, '맥북': 24741, '듀얼': 24730, '모니터': 24743, '유연근무제': 24823, '시행': 24798, '추후': 24874, '고려가능': 24709, '휴게공간': 24923, '라운지': 24733, '이용': 24835, '조식': 24858, '간식': 24691, '커피': 24875, '크래프트': 24877, '비어': 24774, '무제한': 24744, '핫플레이스': 24908, '성수': 24787, '근무': 24716, 'swarm': 21733, '분java': 24763, '분nosql': 24765, '분전문적인': 24772, '분확장성': 24773, '분docker': 24762, '분microservice를': 24764, '분개발': 24766, '분빅데이터': 24767, '분영문이력서': 24771, '경력기술서stock': 24699, '보상홈클리닝': 24758, '별도비즈니스': 24756, '팀최신기종': 24889, '제공유연근무제': 24852, '시행remote': 24799, '고려가능휴게공간': 24710, '무제한핫플레이스': 24745, '근무assembling': 24717, 'requirementsdeveloping': 18582, 'retrievaldesigning': 18849, 'pipelinescreating': 15915, 'productsbuilding': 17023, 'solutionsevolving': 20561, 'performanceconstantly': 15623, 'performancecollaborating': 15620, 'innovationexperience': 10757, 'workflowsexperience': 24420, 'microserviceagile': 13336, 'foresite': 8728, 'derives': 5808, 'incubation': 10504, 'risked': 18985, 'incubated': 10501, 'hardened': 9708, 'insightstranslate': 10825, 'systemsdevelop': 21838, 'problemsevaluate': 16814, 'diligencedistribute': 6130, 'publicationsphd': 17430, 'adminstrator': 389, 'fairfield': 8210, 'veritas': 23828, 'netbackup': 14149, 'certifi': 3250, 'affective': 500, 'quintessential': 17734, 'cadre': 2866, 'cyberscience': 5128, 'huck': 10109, 'devens': 6015, 'holman': 9967, 'laurel': 11892, 'dealership': 5474, 'ari': 1482, 'leasing': 12060, 'companyholman': 4059, 'andari': 1090, 'worldmaintain': 24466, 'operationassure': 14795, 'purchasedmaintain': 17466, 'titlesobtain': 22720, 'scoresprocess': 19554, 'customersrun': 5086, 'applicationmaintain': 1280, 'policiestrain': 16234, 'productsmaintain': 17036, 'contactsassist': 4476, 'necessarymust': 14050, 'recordstrong': 18046, 'serviceexcellent': 19841, 'instilling': 10870, 'eclectic': 6741, 'viewjoin': 23900, 'teamcommunicate': 22069, 'plusprofessional': 16162, 'destined': 5896, 'usher': 23636, 'existed': 7843, 'transferred': 22991, 'objectively': 14514, 'emrs': 7082, 'ihe': 10291, 'xds': 24541, 'pacs': 15283, 'ris': 18977, 'learningrapid': 12041, 'architecturedevelopment': 1429, 'solutionssupport': 20590, 'evaluationintegration': 7676, 'cloudplanning': 3705, 'supervisionconduct': 21624, 'frameworksstrong': 8863, 'journalsdemonstrated': 11473, 'writtenstrong': 24516, 'emrsinterest': 7083, 'oneida': 14731, 'esc': 7545, 'oesc': 14609, 'otie': 15111, 'oes': 14608, 'munitions': 13904, 'concluding': 4258, 'theses': 22545, 'dkl': 6409, 'examining': 7744, 'rubrics': 19185, 'argumentation': 1480, 'citation': 3512, 'srsllc': 20953, 'comsrs': 4217, 'vermont': 23832, 'takeaways': 21934, 'templatesimprove': 22331, 'visualizationsanalyze': 23985, 'strategiesgather': 21308, 'intuitionsome': 11135, 'startersuperb': 21103, 'settingsexperience': 19923, 'fundamentalsstatistics': 9016, 'languagesdigital': 11828, 'kpisagile': 11723, 'mastered': 12848, 'classics': 3567, 'convnet': 4611, 'expertiseblending': 8044, 'modelsworking': 13649, 'efficientlyworking': 6873, 'metconducting': 13178, 'visu': 23968, 'eyeview': 8153, 'videoiq': 23886, 'elemental': 6950, 'holders': 9949, 'pmml': 16194, 'mahalanobis': 12558, 'modelingwork': 13582, 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'levelsactively': 12139, 'managersanalyzes': 12694, 'securitydesigns': 19686, 'schemaschecking': 19434, 'usedmonitors': 23605, 'proceduresverifying': 16867, 'verifychecking': 23822, 'perspectiveperforming': 15730, 'monthsreviewing': 13727, 'serverscheduling': 19833, 'servershands': 19834, 'databasesgood': 5235, 'scenariosgood': 19411, 'administrationmust': 383, 'querymust': 17698, 'pythonmust': 17563, 'replicationable': 18393, 'makingexcellent': 12615, 'skillsorganized': 20293, 'mongodbbachelor': 13702, 'proceduresexperience': 16861, 'insuranceaccidental': 10911, 'coveragewellness': 4798, 'coachinggroup': 3760, 'coverageaccident': 4785, 'plansgroup': 16002, 'legalstudent': 12084, 'programidentity': 17107, 'contributionchoice': 4550, 'dollarsreferral': 6504, 'bonusesperformance': 2517, 'systemtuition': 21886, 'accountscommuter': 153, 'benefitsadoption': 2239, 'assistancebuckle': 1653, 'programpet': 17141, 'discountcharitable': 6256, 'matchinggive': 12863, 'programpaid': 17139, 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],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L4epsRPnA7Kd",
"colab_type": "code",
"outputId": "d6b82a85-5322-4434-f973-315186efa5ff",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(tfidf_vectorizer.idf_)"
],
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": [
"[7.49903488 7.49903488 7.49903488 ... 7.49903488 7.49903488 7.49903488]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4MGayM5cA8-t",
"colab_type": "code",
"outputId": "ecbc0fba-5e2b-4f70-86c0-03a5e5e28c03",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 493
}
},
"source": [
"# Convert Sparse Matrix to Pandas Dataframe if you want to see the word frequencies.\n",
"doc_term_matrix = tfidf_matrix.todense()\n",
"df_temp_idf = pd.DataFrame(doc_term_matrix, \n",
" columns=tfidf_vectorizer.get_feature_names()) \n",
"df_temp_idf"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>합니다</th>\n",
" <th>핫플레이스</th>\n",
" <th>행동</th>\n",
" <th>행복한</th>\n",
" <th>협업하여</th>\n",
" <th>혹은</th>\n",
" <th>홈서비스가</th>\n",
" <th>홈클리닝</th>\n",
" <th>확인합니다</th>\n",
" <th>확장성</th>\n",
" <th>확장이</th>\n",
" <th>활용가능</th>\n",
" <th>활용해</th>\n",
" <th>회사입니다</th>\n",
" <th>효율적으로</th>\n",
" <th>효율적인</th>\n",
" <th>휴게공간</th>\n",
" <th>흐름과</th>\n",
" <th>field</th>\n",
" <th>findings</th>\n",
" <th>fitness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.069543</td>\n",
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" <td>...</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.027026</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.034431</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1323</th>\n",
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"<p>1328 rows × 24928 columns</p>\n",
"</div>"
],
"text/plain": [
" ________________ _bb ... findings fitness\n",
"0 0.0 0.0 ... 0.0 0.0\n",
"1 0.0 0.0 ... 0.0 0.0\n",
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"... ... ... ... ... ...\n",
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"1327 0.0 0.0 ... 0.0 0.0\n",
"\n",
"[1328 rows x 24928 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TsjaVjXnBCdX",
"colab_type": "text"
},
"source": [
"### 2.Standard Resume\n",
"\n",
"\n",
"Doing the same for our standard resume"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IsxkWhDJBAEX",
"colab_type": "code",
"colab": {}
},
"source": [
"\n",
"f_p = '/content/Liveproject Resume.txt'\n",
"\n",
"list_of_lists = []\n",
"\n",
"with open(f_p) as f:\n",
" for line in f:\n",
" inner_list = [line.strip() for line in line.split(' ')]\n",
" list_of_lists.append(inner_list)\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "y3C5dolbBE5K",
"colab_type": "code",
"outputId": "7f5fb74f-f029-4d47-eb10-ef8c12c8280e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 923
}
},
"source": [
"#list_of_lists"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[['\\ufeffGood', 'Student'],\n",
" ['Data', 'Scientist'],\n",
" ['', '', ''],\n",
" [''],\n",
" ['Good', 'Student'],\n",
" ['123', 'Fake', 'Street'],\n",
" ['Some', 'City,', 'QT', '12345'],\n",
" ['123.456.7890'],\n",
" ['no_reply@fakesite.com'],\n",
" ['ㅡ'],\n",
" ['Skills'],\n",
" ['', '', ''],\n",
" [''],\n",
" ['Python,',\n",
" 'Pandas,',\n",
" 'machine',\n",
" 'learning,',\n",
" 'natural',\n",
" 'language',\n",
" 'processing'],\n",
" ['ㅡ'],\n",
" ['Experience'],\n",
" ['', '', ''],\n",
" [''],\n",
" ['Manning', '/', 'Data', 'Analyst'],\n",
" ['Oct', '2019', '-', 'PRESENT,', '', 'REMOTE'],\n",
" ['Analyzed',\n",
" 'and',\n",
" 'visualized',\n",
" 'vast',\n",
" 'amounts',\n",
" 'of',\n",
" 'data',\n",
" 'using',\n",
" 'Pandas,',\n",
" 'Python,',\n",
" 'and',\n",
" 'Matplotlib.'],\n",
" ['ㅡ'],\n",
" ['Education'],\n",
" ['', '', ''],\n",
" [''],\n",
" ['Berkeley', '/', 'B.S.', 'Mathematics'],\n",
" ['August', '2015', '-', 'May', '2019,', '', 'BERKELEY,', 'CA'],\n",
" ['Graduated', 'summa', 'cum', 'laude.'],\n",
" [''],\n",
" ['ㅡ'],\n",
" ['Awards'],\n",
" ['', '', ''],\n",
" [''],\n",
" ['Tau', 'Beta', 'Pi', 'Honors', 'Society']]"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UVyqsyMHBHXC",
"colab_type": "code",
"colab": {}
},
"source": [
"\n",
"l_resume = []\n",
"\n",
"# removing all non-alpha numeric\n",
"\n",
"# flattening all into a single list\n",
"\n",
"for l in list_of_lists:\n",
" for e in l:\n",
" l_resume.append(re.sub('[\\W_]+', '', e)) \n",
"\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nidY2WbFBJjK",
"colab_type": "code",
"outputId": "e3d15d1c-195d-46cc-bb3c-3f8af3ea40bc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"#l_resume"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Good',\n",
" 'Student',\n",
" 'Data',\n",
" 'Scientist',\n",
" '',\n",
" '',\n",
" '',\n",
" '',\n",
" 'Good',\n",
" 'Student',\n",
" '123',\n",
" 'Fake',\n",
" 'Street',\n",
" 'Some',\n",
" 'City',\n",
" 'QT',\n",
" '12345',\n",
" '1234567890',\n",
" 'noreplyfakesitecom',\n",
" 'ㅡ',\n",
" 'Skills',\n",
" '',\n",
" '',\n",
" '',\n",
" '',\n",
" 'Python',\n",
" 'Pandas',\n",
" 'machine',\n",
" 'learning',\n",
" 'natural',\n",
" 'language',\n",
" 'processing',\n",
" 'ㅡ',\n",
" 'Experience',\n",
" '',\n",
" '',\n",
" '',\n",
" '',\n",
" 'Manning',\n",
" '',\n",
" 'Data',\n",
" 'Analyst',\n",
" 'Oct',\n",
" '2019',\n",
" '',\n",
" 'PRESENT',\n",
" '',\n",
" 'REMOTE',\n",
" 'Analyzed',\n",
" 'and',\n",
" 'visualized',\n",
" 'vast',\n",
" 'amounts',\n",
" 'of',\n",
" 'data',\n",
" 'using',\n",
" 'Pandas',\n",
" 'Python',\n",
" 'and',\n",
" 'Matplotlib',\n",
" 'ㅡ',\n",
" 'Education',\n",
" '',\n",
" '',\n",
" '',\n",
" '',\n",
" 'Berkeley',\n",
" '',\n",
" 'BS',\n",
" 'Mathematics',\n",
" 'August',\n",
" '2015',\n",
" '',\n",
" 'May',\n",
" '2019',\n",
" '',\n",
" 'BERKELEY',\n",
" 'CA',\n",
" 'Graduated',\n",
" 'summa',\n",
" 'cum',\n",
" 'laude',\n",
" '',\n",
" 'ㅡ',\n",
" 'Awards',\n",
" '',\n",
" '',\n",
" '',\n",
" '',\n",
" 'Tau',\n",
" 'Beta',\n",
" 'Pi',\n",
" 'Honors',\n",
" 'Society']"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YLP4mKAbBKp1",
"colab_type": "code",
"outputId": "aebd403a-717e-40b3-ed40-7ff6c30c2c94",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# # removing empty strings\n",
"# in case we have a list\n",
"\n",
"l_test = [x for x in l_resume if x != '']\n",
"#l_test"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Good',\n",
" 'Student',\n",
" 'Data',\n",
" 'Scientist',\n",
" 'Good',\n",
" 'Student',\n",
" '123',\n",
" 'Fake',\n",
" 'Street',\n",
" 'Some',\n",
" 'City',\n",
" 'QT',\n",
" '12345',\n",
" '1234567890',\n",
" 'noreplyfakesitecom',\n",
" 'ㅡ',\n",
" 'Skills',\n",
" 'Python',\n",
" 'Pandas',\n",
" 'machine',\n",
" 'learning',\n",
" 'natural',\n",
" 'language',\n",
" 'processing',\n",
" 'ㅡ',\n",
" 'Experience',\n",
" 'Manning',\n",
" 'Data',\n",
" 'Analyst',\n",
" 'Oct',\n",
" '2019',\n",
" 'PRESENT',\n",
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" 'Analyzed',\n",
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" 'vast',\n",
" 'amounts',\n",
" 'of',\n",
" 'data',\n",
" 'using',\n",
" 'Pandas',\n",
" 'Python',\n",
" 'and',\n",
" 'Matplotlib',\n",
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" 'Education',\n",
" 'Berkeley',\n",
" 'BS',\n",
" 'Mathematics',\n",
" 'August',\n",
" '2015',\n",
" 'May',\n",
" '2019',\n",
" 'BERKELEY',\n",
" 'CA',\n",
" 'Graduated',\n",
" 'summa',\n",
" 'cum',\n",
" 'laude',\n",
" 'ㅡ',\n",
" 'Awards',\n",
" 'Tau',\n",
" 'Beta',\n",
" 'Pi',\n",
" 'Honors',\n",
" 'Society']"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5YwCxiT0BOFV",
"colab_type": "code",
"outputId": "9ca07424-fc86-413d-d501-de46fddb140a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"# combining all strings to form a long sentence\n",
"l_nonempty = ' '.join(l_test)\n",
"l_nonempty"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Good Student Data Scientist Good Student 123 Fake Street Some City QT 12345 1234567890 noreplyfakesitecom ㅡ Skills Python Pandas machine learning natural language processing ㅡ Experience Manning Data Analyst Oct 2019 PRESENT REMOTE Analyzed and visualized vast amounts of data using Pandas Python and Matplotlib ㅡ Education Berkeley BS Mathematics August 2015 May 2019 BERKELEY CA Graduated summa cum laude ㅡ Awards Tau Beta Pi Honors Society'"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FUMfyX0pBQBh",
"colab_type": "code",
"colab": {}
},
"source": [
"# note the [] around the input string, as transform expects a list\n",
"\n",
"my_resume = tfidf_vectorizer.transform([l_nonempty])\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8KdTAYs-BSm_",
"colab_type": "code",
"outputId": "3d76c4ab-17b3-4184-a117-f678b193ac95",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"my_resume"
],
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<1x24928 sparse matrix of type '<class 'numpy.float64'>'\n",
"\twith 37 stored elements in Compressed Sparse Row format>"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1Ic6yaltBT-k",
"colab_type": "code",
"outputId": "178a9ded-d635-4f3a-9d45-2d3548a4cbd8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"# summarize encoded vector\n",
"print(my_resume.shape)\n",
"print(my_resume.toarray())\n"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
"(1, 24928)\n",
"[[0. 0. 0. ... 0. 0. 0.]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5Ebw3COnBVyq",
"colab_type": "code",
"outputId": "5b1e49fd-20ab-4168-9817-6acb571099ab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 193
}
},
"source": [
"# Convert Sparse Matrix to Pandas Dataframe if you want to see the word frequencies.\n",
"doc_term_matrix = my_resume.todense()\n",
"df_temp_resume = pd.DataFrame(doc_term_matrix, \n",
" columns=tfidf_vectorizer.get_feature_names()) #, \n",
" #index=['doc_trump', 'doc_election', 'doc_putin'])\n",
"df_temp_resume"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <th></th>\n",
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" <th>aamhr</th>\n",
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" <th>aap</th>\n",
" <th>ab</th>\n",
" <th>aba</th>\n",
" <th>abap</th>\n",
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" <th>abbreviated</th>\n",
" <th>aberdeen</th>\n",
" <th>abet</th>\n",
" <th>abide</th>\n",
" <th>abides</th>\n",
" <th>abilities</th>\n",
" <th>abilitiesfast</th>\n",
" <th>abilitieshands</th>\n",
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" <th>abilitiesstrong</th>\n",
" <th>abilitiesworking</th>\n",
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" <th>abilityexperience</th>\n",
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" <th>abm</th>\n",
" <th>abnormal</th>\n",
" <th>...</th>\n",
" <th>팀의</th>\n",
" <th>팀최신기종</th>\n",
" <th>파트너</th>\n",
" <th>파트너는</th>\n",
" <th>파트너를</th>\n",
" <th>파트너의</th>\n",
" <th>패턴을</th>\n",
" <th>펫시팅</th>\n",
" <th>평점을</th>\n",
" <th>품질을</th>\n",
" <th>프로그래밍</th>\n",
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" <th>프로젝트별</th>\n",
" <th>플랫폼</th>\n",
" <th>필수</th>\n",
" <th>필요한</th>\n",
" <th>하나</th>\n",
" <th>학사</th>\n",
" <th>함께</th>\n",
" <th>합니다</th>\n",
" <th>핫플레이스</th>\n",
" <th>행동</th>\n",
" <th>행복한</th>\n",
" <th>협업하여</th>\n",
" <th>혹은</th>\n",
" <th>홈서비스가</th>\n",
" <th>홈클리닝</th>\n",
" <th>확인합니다</th>\n",
" <th>확장성</th>\n",
" <th>확장이</th>\n",
" <th>활용가능</th>\n",
" <th>활용해</th>\n",
" <th>회사입니다</th>\n",
" <th>효율적으로</th>\n",
" <th>효율적인</th>\n",
" <th>휴게공간</th>\n",
" <th>흐름과</th>\n",
" <th>field</th>\n",
" <th>findings</th>\n",
" <th>fitness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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"</div>"
],
"text/plain": [
" ________________ _bb ... findings fitness\n",
"0 0.0 0.0 ... 0.0 0.0\n",
"\n",
"[1 rows x 24928 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9BwjxvOVBdcW",
"colab_type": "text"
},
"source": [
"## 6.Cosine Similarity "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KEc2nRcHBhrs",
"colab_type": "text"
},
"source": [
"Compute the cosine similarity between the vectorized resume and the job postings using sklearn’s cosine similarity function.\n",
"\n",
"---\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kUatqXJmBbZF",
"colab_type": "code",
"outputId": "4566494c-26f4-4cec-92da-27fefbd9f6cb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"v_cosine = cosine_similarity(my_resume, tfidf_matrix)\n",
"v_cosine"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.01926875, 0.00790762, 0.04506174, ..., 0.00534131, 0.06170631,\n",
" 0.06819977]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QxnDUUiEBk5y",
"colab_type": "code",
"colab": {}
},
"source": [
"# somehow the returned type from cosine_similarity is overlycomplex, simplyfying using squeeze\n",
"v_simple_cosine = v_cosine.squeeze()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zX-8FvrNB0z3",
"colab_type": "text"
},
"source": [
"Add cosine similarity to the dataframe"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PrOrsaidBtoN",
"colab_type": "code",
"colab": {}
},
"source": [
"df_idf['cosine'] = v_simple_cosine"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-5Ec_84B5QB",
"colab_type": "text"
},
"source": [
"Remove temporary text columns from the dataframe"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wc3sp88dB7mf",
"colab_type": "code",
"colab": {}
},
"source": [
"df_idf.drop(columns=['text'],inplace=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FbacxZhuB-z6",
"colab_type": "text"
},
"source": [
"## 7.Sort + CutOff\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B9EFsChNCBZt",
"colab_type": "text"
},
"source": [
"Sort the job postings based on similarity to our resume, and choose an appropriate cutoff for selecting the most similar jobs. \n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XrucCLN8B8D8",
"colab_type": "code",
"outputId": "2b5e53b2-5ec4-425b-a5e5-bcfbe9172023",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
}
},
"source": [
"df_idf"
],
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th></th>\n",
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" <th>0</th>\n",
" <td>Research Post Doctoral Fellow - Oakland, CA</td>\n",
" <td>Research Post Doctoral Fellow - Oakland, CA\\nA...</td>\n",
" <td>(Supports well-defined research with minimal s...</td>\n",
" <td>0.019269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Personal Care Assistant - Yuba City, CA</td>\n",
" <td>Personal Care Assistant - Yuba City, CA\\nKNOWL...</td>\n",
" <td>(Actively participates in the resident's plan ...</td>\n",
" <td>0.007908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Data Scientist - Cambridge, MA</td>\n",
" <td>Data Scientist - Cambridge, MA\\nLocation: Camb...</td>\n",
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" <td>0.045062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Data Engineer - San Francisco, CA</td>\n",
" <td>Data Engineer - San Francisco, CA\\nGSN Games i...</td>\n",
" <td>(3-5 years experience as a Data Engineer in a ...</td>\n",
" <td>0.036909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Senior Data Scientist - New York, NY 10038</td>\n",
" <td>Senior Data Scientist - New York, NY 10038\\nWe...</td>\n",
" <td>(Supports the company's product, sales, leader...</td>\n",
" <td>0.057854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1332</th>\n",
" <td>Process &amp; Automation Engineer - Menlo Park, CA</td>\n",
" <td>Process &amp; Automation Engineer - Menlo Park, CA...</td>\n",
" <td>(Work closely with other automation team membe...</td>\n",
" <td>0.020723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1333</th>\n",
" <td>Software Engineer, Data - San Francisco, CA</td>\n",
" <td>Software Engineer, Data - San Francisco, CA\\nN...</td>\n",
" <td>(You find large challenges exciting and enjoy ...</td>\n",
" <td>0.019064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1334</th>\n",
" <td>Patient Care Assistant, Acute Rehab Unit - New...</td>\n",
" <td>Patient Care Assistant, Acute Rehab Unit - New...</td>\n",
" <td>(Under direction of the RN, performs bedside p...</td>\n",
" <td>0.005341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1335</th>\n",
" <td>Quality Data Scientist, Analytics - San Franci...</td>\n",
" <td>Quality Data Scientist, Analytics - San Franci...</td>\n",
" <td>(Link separate data sources into usable format...</td>\n",
" <td>0.061706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1336</th>\n",
" <td>Data Scientist - Grid Analytics - Burlingame, ...</td>\n",
" <td>Data Scientist - Grid Analytics - Burlingame, ...</td>\n",
" <td>(Using historical distribution grid event and ...</td>\n",
" <td>0.068200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1328 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" title ... cosine\n",
"0 Research Post Doctoral Fellow - Oakland, CA ... 0.019269\n",
"1 Personal Care Assistant - Yuba City, CA ... 0.007908\n",
"2 Data Scientist - Cambridge, MA ... 0.045062\n",
"3 Data Engineer - San Francisco, CA ... 0.036909\n",
"4 Senior Data Scientist - New York, NY 10038 ... 0.057854\n",
"... ... ... ...\n",
"1332 Process & Automation Engineer - Menlo Park, CA ... 0.020723\n",
"1333 Software Engineer, Data - San Francisco, CA ... 0.019064\n",
"1334 Patient Care Assistant, Acute Rehab Unit - New... ... 0.005341\n",
"1335 Quality Data Scientist, Analytics - San Franci... ... 0.061706\n",
"1336 Data Scientist - Grid Analytics - Burlingame, ... ... 0.068200\n",
"\n",
"[1328 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TO9-a8m6CcqP",
"colab_type": "text"
},
"source": [
"### 1.Sort by Cosine Similarity"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aqaKa55HCDLG",
"colab_type": "code",
"outputId": "2e313b17-31f0-4654-99d4-840067346a71",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
}
},
"source": [
"df_idf.sort_values(by=['cosine'],ascending=False, inplace=True)\n",
"df_idf"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>965</th>\n",
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" <td>Institutional Data and Research Analyst (6948U...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>345</th>\n",
" <td>Data Analyst (6256U) 1737 - 1737 - Berkeley, C...</td>\n",
" <td>Data Analyst (6256U) 1737 - 1737 - Berkeley, C...</td>\n",
" <td>()</td>\n",
" <td>0.179347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>Data Science Health Innovation Fellow Job - BI...</td>\n",
" <td>Data Science Health Innovation Fellow Job - BI...</td>\n",
" <td>(Demonstrated ability to propose, initiate, an...</td>\n",
" <td>0.178867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>649</th>\n",
" <td>Senior Data Systems Analyst (0599U) - 1668 - 1...</td>\n",
" <td>Senior Data Systems Analyst (0599U) - 1668 - 1...</td>\n",
" <td>()</td>\n",
" <td>0.174220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>Research Data Analyst 2 (6256U) 1789 - 1789 - ...</td>\n",
" <td>Research Data Analyst 2 (6256U) 1789 - 1789 - ...</td>\n",
" <td>()</td>\n",
" <td>0.172853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>Senior Software Airworthiness Engineer (2019-0...</td>\n",
" <td>Senior Software Airworthiness Engineer (2019-0...</td>\n",
" <td>(Review Software requirements and associated d...</td>\n",
" <td>0.001707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>610</th>\n",
" <td>QSR Store Assistant - Pleasanton, TX</td>\n",
" <td>QSR Store Assistant - Pleasanton, TX\\nCircle K...</td>\n",
" <td>()</td>\n",
" <td>0.001699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>342</th>\n",
" <td>QSR Store Assistant - Jacksonville, FL 32226</td>\n",
" <td>QSR Store Assistant - Jacksonville, FL 32226\\n...</td>\n",
" <td>()</td>\n",
" <td>0.001693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>Page Not Found - Indeed Mobile</td>\n",
" <td>Page Not Found - Indeed Mobile\\n</td>\n",
" <td>()</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1018</th>\n",
" <td>Director of Econometric Modeling - External Ca...</td>\n",
" <td>Director of Econometric Modeling - External Ca...</td>\n",
" <td>()</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1328 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" title ... cosine\n",
"965 Institutional Data and Research Analyst (6948U... ... 0.221805\n",
"345 Data Analyst (6256U) 1737 - 1737 - Berkeley, C... ... 0.179347\n",
"95 Data Science Health Innovation Fellow Job - BI... ... 0.178867\n",
"649 Senior Data Systems Analyst (0599U) - 1668 - 1... ... 0.174220\n",
"666 Research Data Analyst 2 (6256U) 1789 - 1789 - ... ... 0.172853\n",
"... ... ... ...\n",
"81 Senior Software Airworthiness Engineer (2019-0... ... 0.001707\n",
"610 QSR Store Assistant - Pleasanton, TX ... 0.001699\n",
"342 QSR Store Assistant - Jacksonville, FL 32226 ... 0.001693\n",
"123 Page Not Found - Indeed Mobile ... 0.000000\n",
"1018 Director of Econometric Modeling - External Ca... ... 0.000000\n",
"\n",
"[1328 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y3ictAjjClkZ",
"colab_type": "text"
},
"source": [
"### 2.Cutoff to Top-5"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-gBtQP8ACjKy",
"colab_type": "code",
"colab": {}
},
"source": [
"df_top_five = df_idf.head(5)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7uOthtI5Cqs1",
"colab_type": "text"
},
"source": [
"## 8.Store for Later\n",
"\n",
"Store the most similar job postings in a new DataFrame for later use, and save the DataFrame to disk.\n",
"\n",
"\n",
"\n",
"---\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wQ5ltp9CCn6h",
"colab_type": "code",
"outputId": "f21c9aec-1ebe-4651-bc19-6d6e9f7a4d3d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 197
}
},
"source": [
"df_top_five"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>965</th>\n",
" <td>Institutional Data and Research Analyst (6948U...</td>\n",
" <td>Institutional Data and Research Analyst (6948U...</td>\n",
" <td>()</td>\n",
" <td>0.221805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>345</th>\n",
" <td>Data Analyst (6256U) 1737 - 1737 - Berkeley, C...</td>\n",
" <td>Data Analyst (6256U) 1737 - 1737 - Berkeley, C...</td>\n",
" <td>()</td>\n",
" <td>0.179347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>Data Science Health Innovation Fellow Job - BI...</td>\n",
" <td>Data Science Health Innovation Fellow Job - BI...</td>\n",
" <td>(Demonstrated ability to propose, initiate, an...</td>\n",
" <td>0.178867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>649</th>\n",
" <td>Senior Data Systems Analyst (0599U) - 1668 - 1...</td>\n",
" <td>Senior Data Systems Analyst (0599U) - 1668 - 1...</td>\n",
" <td>()</td>\n",
" <td>0.174220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>666</th>\n",
" <td>Research Data Analyst 2 (6256U) 1789 - 1789 - ...</td>\n",
" <td>Research Data Analyst 2 (6256U) 1789 - 1789 - ...</td>\n",
" <td>()</td>\n",
" <td>0.172853</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title ... cosine\n",
"965 Institutional Data and Research Analyst (6948U... ... 0.221805\n",
"345 Data Analyst (6256U) 1737 - 1737 - Berkeley, C... ... 0.179347\n",
"95 Data Science Health Innovation Fellow Job - BI... ... 0.178867\n",
"649 Senior Data Systems Analyst (0599U) - 1668 - 1... ... 0.174220\n",
"666 Research Data Analyst 2 (6256U) 1789 - 1789 - ... ... 0.172853\n",
"\n",
"[5 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kB-P-BVyCw7x",
"colab_type": "code",
"colab": {}
},
"source": [
"df_top_five.to_csv('df_top_five.csv')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kFaXcsdqCzHL",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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