starter_code.ipynb

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    "<img src=\"http://imgur.com/1ZcRyrc.png\" style=\"float: left; margin: 15px; height: 80px\">\n",
    "\n",
    "\n",
    "# Web Scraping for Indeed.com and Predicting Salaries"
   ]
  },
  {
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   "source": [
    "### Business Case Overview\n",
    "\n",
    "You're working as a data scientist for a contracting firm that's rapidly expanding. Now that they have their most valuable employee (you!), they need to leverage data to win more contracts. Your firm offers technology and scientific solutions and wants to be competitive in the hiring market. Your principal wants you to\n",
    "\n",
    "   - determine the industry factors that are most important in predicting the salary amounts for these data.\n",
    "\n",
    "To limit the scope, your principal has suggested that you *focus on data-related job postings*, e.g. data scientist, data analyst, research scientist, business intelligence, and any others you might think of. You may also want to decrease the scope by *limiting your search to a single region.*\n",
    "\n",
    "Hint: Aggregators like [Indeed.com](https://www.indeed.com) regularly pool job postings from a variety of markets and industries.\n",
    "\n",
    "**Goal:** Scrape your own data from a job aggregation tool like Indeed.com in order to collect the data to best answer this question.\n",
    "\n",
    "---\n",
    "\n",
    "### Directions\n",
    "\n",
    "In this project you will be leveraging a variety of skills. The first will be to use the web-scraping and/or API techniques you've learned to collect data on data jobs from Indeed.com or another aggregator. Once you have collected and cleaned the data, you will use it to address the question above."
   ]
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    "### Factors that impact salary\n",
    "\n",
    "To predict salary the most appropriate approach would be a regression model.\n",
    "Here instead we just want to estimate which factors (like location, job title, job level, industry sector) lead to high or low salary and work with a classification model. To do so, split the salary into two groups of high and low salary, for example by choosing the median salary as a threshold (in principle you could choose any single or multiple splitting points).\n",
    "\n",
    "Use all the skills you have learned so far to build a predictive model.\n",
    "Whatever you decide to use, the most important thing is to justify your choices and interpret your results. *Communication of your process is key.* Note that most listings **DO NOT** come with salary information. You'll need to be able to extrapolate or predict the expected salaries for these listings."
   ]
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  {
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    "id": "a948d79c-5527-4c0d-ab23-f5d43ce72056"
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   "source": [
    "### Scraping job listings from Indeed.com"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "7203e0c9-e437-4802-a6ad-7dc464f94436"
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   "source": [
    "We will be scraping job listings from Indeed.com using BeautifulSoup. Luckily, Indeed.com is a simple text page where we can easily find relevant entries.\n",
    "\n",
    "First, look at the source of an Indeed.com page: (http://www.indeed.com/jobs?q=data+scientist+%2420%2C000&l=New+York&start=10\").\n",
    "\n",
    "Notice, each job listing is underneath a `div` tag with a class name of `result`. We can use BeautifulSoup to extract those. "
   ]
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    "id": "9732c901-ae26-4160-8376-42e22dd327df"
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   "source": [
    "#### Setup a request (using `requests`) to the URL below. Use BeautifulSoup to parse the page and extract all results (HINT: Look for div tags with class name result)\n",
    "\n",
    "The URL here has many query parameters:\n",
    "\n",
    "- `q` for the job search\n",
    "- This is followed by \"+20,000\" to return results with salaries (or expected salaries >$20,000)\n",
    "- `l` for a location \n",
    "- `start` for what result number to start on"
   ]
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  {
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   "source": [
    "URL = \"http://www.indeed.com/jobs?\""
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import requests\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, KFold, StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler, Binarizer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "\n",
    "pd.set_option('display.max_colwidth',1000, 'display.max_columns',1000)"
   ]
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    "Let's look at one result more closely. A single `result` looks like\n",
    "\n",
    "```\n",
    "<div class=\" row result\" data-jk=\"2480d203f7e97210\" data-tn-component=\"organicJob\" id=\"p_2480d203f7e97210\" itemscope=\"\" itemtype=\"http://schema.org/JobPosting\">\n",
    "<h2 class=\"jobtitle\" id=\"jl_2480d203f7e97210\">\n",
    "<a class=\"turnstileLink\" data-tn-element=\"jobTitle\" onmousedown=\"return rclk(this,jobmap[0],1);\" rel=\"nofollow\" target=\"_blank\" title=\"AVP/Quantitative Analyst\">AVP/Quantitative Analyst</a>\n",
    "</h2>\n",
    "<span class=\"company\" itemprop=\"hiringOrganization\" itemtype=\"http://schema.org/Organization\">\n",
    "<span itemprop=\"name\">\n",
    "<a href=\"/cmp/Alliancebernstein?from=SERP&amp;campaignid=serp-linkcompanyname&amp;fromjk=2480d203f7e97210&amp;jcid=b374f2a780e04789\" target=\"_blank\">\n",
    "    AllianceBernstein</a></span>\n",
    "</span>\n",
    "<tr>\n",
    "<td class=\"snip\">\n",
    "<nobr>$117,500 - $127,500 a year</nobr>\n",
    "<div>\n",
    "<span class=\"summary\" itemprop=\"description\">\n",
    "C onduct quantitative and statistical research as well as portfolio management for various investment portfolios. Collaborate with Quantitative Analysts and</span>\n",
    "</div>\n",
    "</div>\n",
    "</td>\n",
    "</tr>\n",
    "</table>\n",
    "</div>\n",
    "```\n",
    "\n",
    "While this has some more verbose elements removed, we can see that there is some structure to the above:\n",
    "- The salary is in a `span` with `class='salaryText'`.\n",
    "- The title of a job is in a link with class set to `jobtitle` and a `data-tn-element='jobTitle'`.  \n",
    "- The location is set in a `span` with `class='location'`. \n",
    "- The company is set in a `span` with `class='company'`. \n",
    "- Decide which other components could be relevant, for example the region or the summary of the job advert."
   ]
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    "### Write 4 functions to extract each item: location, company, job, and salary.\n",
    "\n",
    "Example: \n",
    "```python\n",
    "def extract_location_from_result(result):\n",
    "    return result.find ...\n",
    "```\n",
    "\n",
    "\n",
    "- **Make sure these functions are robust and can handle cases where the data/field may not be available.**\n",
    "    - Remember to check if a field is empty or `None` for attempting to call methods on it.\n",
    "    - Remember to use `try/except` if you anticipate errors.\n",
    "- **Test** the functions on the results above and simple examples."
   ]
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    "## YOUR CODE HERE\n",
    "\n",
    "def extract_title_from_result(result):\n",
    "    try:\n",
    "        return result.find('a', attrs={'class':'jobtitle', 'data-tn-element':'jobTitle'}).text.strip()\n",
    "    except:\n",
    "        np.nan\n",
    "    \n",
    "def extract_company_from_result(result):\n",
    "    try:\n",
    "        return result.find('span', attrs={'class':'company'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "        \n",
    "def extract_rating_from_result(result):\n",
    "    try:\n",
    "        return result.find('span', attrs={'class':'ratingsDisplay'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "        \n",
    "def extract_location_from_result(result):\n",
    "    try:\n",
    "        return result.find('span', attrs={'class':'location'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "def extract_summary_from_result(result):\n",
    "    try:\n",
    "        return result.find('div', attrs={'class':'summary'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "def extract_date_from_result(result):\n",
    "    try:\n",
    "        return result.find('span', attrs={'class':'date'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "def extract_salary_from_result(result):\n",
    "    try:\n",
    "        return result.find('span', attrs={'class':'salaryText'}).text.strip()\n",
    "    except:\n",
    "        return np.nan\n",
    "    \n",
    "def calc_salary_USD_from_salary(result):\n",
    "    try:\n",
    "        return extract_salary_from_result(result)*fx_rate[str(city)]\n",
    "    except:\n",
    "        return extract_salary_from_result(result)"
   ]
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   "source": [
    "Now, to scale up our scraping, we need to accumulate more results. We can do this by examining the URL above.\n",
    "\n",
    "- \"http://www.indeed.com/jobs?q=data+scientist+%2420%2C000&l=New+York&start=10\"\n",
    "\n",
    "There are two query parameters here we can alter to collect more results, the `l=New+York` and the `start=10`. The first controls the location of the results (so we can try a different city). The second controls where in the results to start and gives 10 results (thus, we can keep incrementing by 10 to go further in the list)."
   ]
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   "source": [
    "### Complete the following code to collect results from multiple cities and starting points. \n",
    "- Enter your city below to add it to the search.\n",
    "- Remember to convert your salary to U.S. Dollars to match the other cities if the currency is different."
   ]
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    "id": "20339c09-5032-4e27-91be-286e9b46cd13"
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   "source": [
    "#### Use the functions you wrote above to parse out the 4 fields - location, title, company and salary. Create a dataframe from the results with those 4 columns."
   ]
  },
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   "source": [
    "## YOUR CODE HERE\n",
    "\n",
    "import re\n",
    "\n",
    "UK_CITY = ['London', 'Edinburgh', 'Cambridge', 'Oxford', 'Manchester', 'Reading', 'Bristol', 'Belfast',\n",
    "            'Leeds', 'Glasgow', 'Birmingham', 'Nottingham']\n",
    "UK_FX_rate = 1.3\n",
    "\n",
    "max_results_per_city = 5000\n",
    "job_city = {}\n",
    "run_count = 0\n",
    "\n",
    "for city in set(['New+York', 'Chicago', 'San+Francisco', 'Austin', 'Seattle', 'Los+Angeles', 'Philadelphia',\n",
    "                 'Atlanta', 'Dallas', 'Pittsburgh', 'Portland', 'Phoenix', 'Denver', 'Houston',\n",
    "                 'Miami'] + UK_CITY):\n",
    "    \n",
    "    title = []\n",
    "    company = []\n",
    "    rating = []\n",
    "    search_city = []\n",
    "    location = []\n",
    "    salary = []\n",
    "    age = []\n",
    "    summary = []\n",
    "    \n",
    "    run_count+=1\n",
    "    print('Searching City number: ', run_count)\n",
    "\n",
    "    for occupation in set(['data scientist', 'data analyst', 'data analytics', 'data engineer',\n",
    "                           'business intelligence', 'machine learning', 'artificial intelligence']): \n",
    "        old_page = 0\n",
    "        print('Running: ', str(city),' & ',str(occupation))\n",
    "\n",
    "        for page in tqdm_notebook(range(0, max_results_per_city, 10)):\n",
    "            if city in UK_CITY:\n",
    "                URL = \"http://www.indeed.co.uk/jobs?\"\n",
    "                PARAMS = dict(as_phr=str(occupation), l=city, start=page)\n",
    "            else:\n",
    "                URL = \"http://www.indeed.com/jobs?\"\n",
    "                PARAMS = dict(as_phr=str(occupation), l=city, start=page)\n",
    "\n",
    "            r = requests.get(url=URL, params=PARAMS)\n",
    "            soup = BeautifulSoup(r.text, 'html.parser')\n",
    "\n",
    "            if soup.find('div', attrs={'id':'searchCountPages'}) == None:\n",
    "                break\n",
    "            else:\n",
    "                new_page = int(re.findall(re.compile(r'\\w+'), soup.find('div', attrs={'id':'searchCountPages'}).text.strip('\\n'))[1])\n",
    "\n",
    "                if new_page > old_page:\n",
    "                    old_page = new_page\n",
    "                    for posting in soup.find_all('div', attrs={'class':'result'}):\n",
    "                        title.append(extract_title_from_result(posting))\n",
    "                        company.append(extract_company_from_result(posting))\n",
    "                        rating.append(extract_rating_from_result(posting))\n",
    "                        search_city.append(city)\n",
    "                        location.append(extract_location_from_result(posting))\n",
    "                        salary.append(extract_salary_from_result(posting))\n",
    "                        age.append(extract_date_from_result(posting))\n",
    "                        summary.append(extract_summary_from_result(posting))\n",
    "                else:\n",
    "                    break\n",
    "\n",
    "    # creating a df for that city's results and writing it to [insert city].csv...\n",
    "    job_city_df = pd.DataFrame(dict(title=title,company=company,rating=rating,search_city=search_city,\n",
    "                                    location=location,salary=salary,age=age,summary=summary))\n",
    "    job_city_df.to_csv(str(city)+'.csv', index=False)\n",
    "    \n",
    "print('FINISHED')"
   ]
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   "source": [
    "# Load in CSVs and create a dataframe from it:\n",
    "\n",
    "jobs = pd.DataFrame()\n",
    "\n",
    "for city in set(['New+York', 'Chicago', 'San+Francisco', 'Austin', 'Seattle', 'Los+Angeles', 'Philadelphia',\n",
    "                 'Atlanta', 'Dallas', 'Pittsburgh', 'Portland', 'Phoenix', 'Denver', 'Houston',\n",
    "                 'Miami'] + UK_CITY):\n",
    "\n",
    "    jobs_city = pd.read_csv(str(city)+'.csv')\n",
    "\n",
    "    if 'Unnamed: 0' in pd.read_csv(str(city)+'.csv').columns: # to check if csv was saved with index (above)\n",
    "        jobs_city.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "    else:\n",
    "        break\n",
    "    jobs = jobs.append(jobs_city, ignore_index=True)\n",
    "\n",
    "print(jobs.shape)\n",
    "jobs.head()"
   ]
  },
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   "source": [
    "Lastly, we need to clean up salary data. \n",
    "\n",
    "1. Only a small number of the scraped results have salary information - only these will be used for modeling.\n",
    "1. Some of the salaries are not yearly but hourly or weekly, these will not be useful to us for now.\n",
    "1. Some of the entries may be duplicated.\n",
    "1. The salaries are given as text and usually with ranges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "ff98ce64-78a7-441f-a675-63464e32c834"
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   "source": [
    "#### Find the entries with annual salary entries, by filtering the entries without salaries or salaries that are not yearly (filter those that refer to hour or week). Also, remove duplicate entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "## YOUR CODE HERE: adding salary frequencies, and restricting on yearly salaries, also dropping dupes.\n",
    "jobs_salaried = jobs[jobs['salary'].notnull()]\n",
    "\n",
    "# find and remove dupes first:\n",
    "print('Total shape:',jobs_salaried.shape)\n",
    "print('Duplicates:',jobs_salaried[jobs_salaried.duplicated()].shape)\n",
    "\n",
    "jobs_salaried.drop_duplicates(inplace=True)\n",
    "print('Any dupes left?:',jobs_salaried[jobs_salaried.duplicated()].shape)\n",
    "\n",
    "# create salary_freq column and filter for only yearly salaries:\n",
    "salary_freq = []\n",
    "for sal in jobs_salaried['salary']:\n",
    "    if 'year' in sal:\n",
    "        salary_freq.append('year')\n",
    "    elif 'month' in sal:\n",
    "        salary_freq.append('month')\n",
    "    elif 'week' in sal:\n",
    "        salary_freq.append('week')\n",
    "    elif 'day' in sal:\n",
    "        salary_freq.append('day')\n",
    "    elif 'hour' in sal:\n",
    "        salary_freq.append('hour')\n",
    "    else:\n",
    "        salary_freq.append(np.nan)\n",
    "        \n",
    "jobs_salaried.insert(6, 'salary_freq', salary_freq)\n",
    "print('Unique Salary Frequencies found:',jobs_salaried.salary_freq.unique())\n",
    "\n",
    "jobs_salaried_year = jobs_salaried[jobs_salaried['salary_freq']=='year']\n",
    "print('Checking only year remains:',jobs_salaried_year.salary_freq.unique())\n",
    "print('Jobs Salaried Yearly:',jobs_salaried_year.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "7d4bc860-b214-4f75-9cd0-b234830b1ec2"
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   "source": [
    "#### Write a function that takes a salary string and converts it to a number, averaging a salary range if necessary."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "## YOUR CODE HERE: creating salary_avg from salary ranges, and dollarizing UK salaries\n",
    "\n",
    "import re\n",
    "from re import sub\n",
    "from decimal import Decimal\n",
    "\n",
    "jobs_salaried_year.insert(7, 'isRange', jobs_salaried_year.apply(lambda x: 'Range' if '-' in x['salary'] else\n",
    "                                                        'Not Range', axis=1))\n",
    "\n",
    "salary_range_min = []\n",
    "salary_range_max = []\n",
    "salary_avg = []\n",
    "salary_avg_USD = []\n",
    "\n",
    "for x in jobs_salaried_year[['salary','isRange']].values:\n",
    "    if x[1] == 'Not Range':\n",
    "        salary_range_min.append(Decimal(sub(r'[^\\d.]', '', x[0])))\n",
    "        salary_range_max.append(Decimal(sub(r'[^\\d.]', '', x[0])))\n",
    "        salary_avg.append(Decimal(sub(r'[^\\d.]', '', x[0])))\n",
    "\n",
    "    if x[1] == 'Range':\n",
    "        range_min = re.findall(r'[\\d.,]+',x[0])[0]\n",
    "        range_min = Decimal(sub(r',','',range_min))\n",
    "        salary_range_min.append(range_min)\n",
    "        \n",
    "        range_max = re.findall(r'[\\d.,]+',x[0])[1]\n",
    "        range_max = Decimal(sub(r',','',range_max))\n",
    "        salary_range_max.append(range_max)\n",
    "        \n",
    "        salary_avg.append((range_min+range_max)/2)\n",
    "    \n",
    "jobs_salaried_year.insert(8, 'salary_range_min', salary_range_min)\n",
    "jobs_salaried_year.insert(9, 'salary_range_max', salary_range_max)\n",
    "jobs_salaried_year.insert(10, 'salary_avg', salary_avg)\n",
    "\n",
    "jobs_salaried_year.insert(11, 'salary_avg_USD',\n",
    "                         jobs_salaried_year.apply(lambda x: round(Decimal(UK_FX_rate)*x['salary_avg'],2) \n",
    "                                                  if x['search_city'] in UK_CITY\n",
    "                                                  else x['salary_avg'], axis=1))\n",
    "\n",
    "jobs_salaried_year.head(1)"
   ]
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   "source": [
    "### Save your results as a CSV"
   ]
  },
  {
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     0
    ],
    "focus": false,
    "id": "783fd153-28ac-47ab-bfca-27e7c1de95b4"
   },
   "outputs": [],
   "source": [
    "## YOUR CODE HERE: write to csv\n",
    "jobs_salaried_year.to_csv('indeed_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "243e949e-2742-40af-872e-fec475fd306c"
   },
   "source": [
    "### Load in the the data of scraped salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [],
    "focus": false,
    "id": "588f9845-6143-4bcc-bfd1-85d45b79303d"
   },
   "outputs": [],
   "source": [
    "## YOUR CODE HERE: load in indeed.csv\n",
    "import pandas as pd\n",
    "indeed = pd.read_csv('indeed_results.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "c7631f51-07f2-4c79-a093-3e9bc7849a48"
   },
   "source": [
    "### We want to predict a binary variable - whether the salary was low or high. Compute the median salary and create a new binary variable that is true when the salary is high (above the median).\n",
    "\n",
    "We could also perform Linear Regression (or any regression) to predict the salary value here. Instead, we are going to convert this into a _binary_ classification problem, by predicting two classes, HIGH vs LOW salary.\n",
    "\n",
    "While performing regression may be better, performing classification may help remove some of the noise of the extreme salaries. We don't have to choose the `median` as the splitting point - we could also split on the 75th percentile or any other reasonable breaking point.\n",
    "\n",
    "In fact, the ideal scenario may be to predict many levels of salaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": [
     2
    ],
    "focus": false,
    "id": "c20d2498-151c-44c3-a453-3a333c79a0ac"
   },
   "outputs": [],
   "source": [
    "## YOUR CODE HERE: Binarize salary\n",
    "indeed.insert(12,\n",
    "              'target',\n",
    "              Binarizer(threshold=indeed.salary_avg_USD.median()).fit_transform([indeed.salary_avg_USD])[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "a7afb2c0-d41e-4779-8216-91cd8dd4473f"
   },
   "source": [
    "#### Thought experiment: What is the baseline accuracy for this model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": [],
    "focus": false,
    "id": "87a17d3d-b7f4-4747-9f75-f9af1d18a174"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline Accuracy: 0.507835945315105\n",
      "High vs Low threshold: 65000.0\n",
      "Normalized Value Counts:\n",
      "0.0    0.507836\n",
      "1.0    0.492164\n",
      "Name: target, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## YOUR CODE HERE: baseline accuracy\n",
    "baseline = indeed.target.value_counts(normalize=True).max()\n",
    "print('Baseline Accuracy:',baseline)\n",
    "print('High vs Low threshold:', indeed.salary_avg_USD.median())\n",
    "print('Normalized Value Counts:')\n",
    "print(indeed.target.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "4fb29de2-5b98-474c-a4ad-5170b72b9aea"
   },
   "source": [
    "### Create a classification model to predict High/Low salary. \n",
    "\n",
    "\n",
    "- Start by ONLY using the location as a feature.\n",
    "- Use at least two different classifiers you find suitable.\n",
    "- Remember that scaling your features might be necessary.\n",
    "- Display the coefficients/feature importances and write a short summary of what they mean.\n",
    "- Create a few new variables in your dataframe to represent interesting features of a job title (e.g. whether 'Senior' or 'Manager' is in the title).\n",
    "- Incorporate other text features from the title or summary that you believe will predict the salary.\n",
    "- Then build new classification models including also those features. Do they add any value?\n",
    "- Tune your models by testing parameter ranges, regularization strengths, etc. Discuss how that affects your models.\n",
    "- Discuss model coefficients or feature importances as applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "code_folding": [],
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  15 out of  15 | elapsed:    1.8s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_features': 0.3}\n",
      "Cross Val Score: 0.6233892600841375\n",
      "Confusion Matrix:\n",
      " \n",
      "[[171 210]\n",
      " [ 72 297]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.70      0.45      0.55       381\n",
      "         1.0       0.59      0.80      0.68       369\n",
      "\n",
      "    accuracy                           0.62       750\n",
      "   macro avg       0.64      0.63      0.61       750\n",
      "weighted avg       0.65      0.62      0.61       750\n",
      "\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  60 out of  60 | elapsed:    5.0s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_depth': 17, 'min_samples_split': 0.35}\n",
      "Cross Val Score: 0.6273932195001237\n",
      "Confusion Matrix:\n",
      " \n",
      "[[168 213]\n",
      " [ 64 305]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.72      0.44      0.55       381\n",
      "         1.0       0.59      0.83      0.69       369\n",
      "\n",
      "    accuracy                           0.63       750\n",
      "   macro avg       0.66      0.63      0.62       750\n",
      "weighted avg       0.66      0.63      0.62       750\n",
      "\n",
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
      "Best params: {'learning_rate': 0.1}\n",
      "Cross Val Score: 0.625615441722346\n",
      "Confusion Matrix:\n",
      " \n",
      "[[169 212]\n",
      " [ 67 302]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.72      0.44      0.55       381\n",
      "         1.0       0.59      0.82      0.68       369\n",
      "\n",
      "    accuracy                           0.63       750\n",
      "   macro avg       0.65      0.63      0.62       750\n",
      "weighted avg       0.65      0.63      0.61       750\n",
      "\n",
      "Best estimator: GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=20, presort='deprecated',\n",
      "                           random_state=1, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:    1.4s finished\n"
     ]
    },
    {
     "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>Reading</th>\n",
       "      <th>Birmingham</th>\n",
       "      <th>Nottingham</th>\n",
       "      <th>New+York</th>\n",
       "      <th>Cambridge</th>\n",
       "      <th>San+Francisco</th>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>Edinburgh</th>\n",
       "      <th>Portland</th>\n",
       "      <th>Chicago</th>\n",
       "      <th>Austin</th>\n",
       "      <th>London</th>\n",
       "      <th>Dallas</th>\n",
       "      <th>Pittsburgh</th>\n",
       "      <th>Seattle</th>\n",
       "      <th>Miami</th>\n",
       "      <th>Houston</th>\n",
       "      <th>Los+Angeles</th>\n",
       "      <th>Phoenix</th>\n",
       "      <th>Denver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>importance</th>\n",
       "      <td>0.231076</td>\n",
       "      <td>0.178165</td>\n",
       "      <td>0.147039</td>\n",
       "      <td>0.094652</td>\n",
       "      <td>0.074229</td>\n",
       "      <td>0.047271</td>\n",
       "      <td>0.041299</td>\n",
       "      <td>0.038105</td>\n",
       "      <td>0.034849</td>\n",
       "      <td>0.030104</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.01487</td>\n",
       "      <td>0.010573</td>\n",
       "      <td>0.010069</td>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.008267</td>\n",
       "      <td>0.003414</td>\n",
       "      <td>0.00331</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Reading  Birmingham  Nottingham  New+York  Cambridge  \\\n",
       "importance  0.231076    0.178165    0.147039  0.094652   0.074229   \n",
       "\n",
       "            San+Francisco  Philadelphia  Edinburgh  Portland   Chicago  \\\n",
       "importance       0.047271      0.041299   0.038105  0.034849  0.030104   \n",
       "\n",
       "            Austin   London    Dallas  Pittsburgh   Seattle     Miami  \\\n",
       "importance  0.0199  0.01487  0.010573    0.010069  0.009851  0.008267   \n",
       "\n",
       "             Houston  Los+Angeles   Phoenix  Denver  \n",
       "importance  0.003414      0.00331  0.002959     0.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## YOUR CODE HERE: using two different classifiers on location only\n",
    "\n",
    "# set X,y\n",
    "X = indeed['search_city']\n",
    "y = indeed['target']\n",
    "\n",
    "X_dum = pd.get_dummies(X, drop_first=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_dum, y, stratify=y, random_state=1)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "models = [BaggingClassifier(n_estimators=100),\n",
    "          RandomForestClassifier(n_estimators=100),\n",
    "          GradientBoostingClassifier(n_estimators=100,\n",
    "                                     random_state=1,\n",
    "                                     validation_fraction=0.1,\n",
    "                                     max_depth=3,\n",
    "                                     n_iter_no_change=20)]\n",
    "params = [{'max_features': np.linspace(0.3,0.4,3)},\n",
    "          {'max_depth': list(range(2,20,5)),\n",
    "           'min_samples_split': np.linspace(0.3,0.4,3)},\n",
    "          {'learning_rate': np.linspace(.1,1.,4)}]\n",
    "\n",
    "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)\n",
    "\n",
    "for i in range(len(models)):\n",
    "    gs = GridSearchCV(models[i], params[i], n_jobs=2, cv=kf, verbose=1)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Best params:',gs.best_params_)\n",
    "    print('Cross Val Score:',gs.best_score_)\n",
    "    predictions = gs.predict(X_test)\n",
    "    print('Confusion Matrix:')\n",
    "    print(' ')\n",
    "    print(confusion_matrix(y_test, predictions, labels=[0,1]))\n",
    "    print('Classification Report:')\n",
    "    print(' ')\n",
    "    print(classification_report(y_test, predictions))\n",
    "    \n",
    "# Add feature importances for best gs model:\n",
    "print('Best estimator:', gs.best_estimator_)\n",
    "\n",
    "if 'BaggingClassifier' in gs.best_estimator_:\n",
    "    feature_importances = pd.DataFrame({'importance':np.mean([tree.feature_importances_ for tree in gs.estimators_], axis=0)},\n",
    "                                                 index=X_dum.columns)\n",
    "else:\n",
    "    feature_importances = pd.DataFrame({'importance':gs.best_estimator_.feature_importances_},\n",
    "                                                 index=X_dum.columns)\n",
    "    \n",
    "feature_importances.sort_values('importance',ascending=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7605865331107401\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>coef</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>3.484523</td>\n",
       "      <td>director</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>3.092856</td>\n",
       "      <td>lead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2.756825</td>\n",
       "      <td>architect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2.455418</td>\n",
       "      <td>head</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>2.129793</td>\n",
       "      <td>senior</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          coef   features\n",
       "302   3.484523   director\n",
       "624   3.092856       lead\n",
       "62    2.756825  architect\n",
       "504   2.455418       head\n",
       "1019  2.129793     senior"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature-engineering Title column:\n",
    "# we start with TfidfVectorizer to get the unique features, and fit to regression to assess importance\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tvec = TfidfVectorizer(stop_words='english', token_pattern='[A-Za-z]+', ngram_range=(1,1))\n",
    "tvec.fit(indeed['title'])\n",
    "\n",
    "X = tvec.transform(indeed['title'])\n",
    "y = indeed.target\n",
    "\n",
    "lr = LogisticRegression(solver='lbfgs', multi_class='ovr')\n",
    "print(cross_val_score(lr,X,y,cv=kf).mean())\n",
    "lr.fit(X,y)\n",
    "pd.DataFrame(dict(coef=lr.coef_[0], features=tvec.get_feature_names())).sort_values('coef',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>-3.488157</td>\n",
       "      <td>graduate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>-3.456703</td>\n",
       "      <td>junior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>-2.369321</td>\n",
       "      <td>assistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-2.140103</td>\n",
       "      <td>analyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>-2.086371</td>\n",
       "      <td>recruitment</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         coef     features\n",
       "485 -3.488157     graduate\n",
       "602 -3.456703       junior\n",
       "72  -2.369321    assistant\n",
       "43  -2.140103      analyst\n",
       "924 -2.086371  recruitment"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict(coef=lr.coef_[0], features=tvec.get_feature_names())).sort_values('coef',ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>isDS</th>\n",
       "      <th>isDA</th>\n",
       "      <th>isDE</th>\n",
       "      <th>isDArch</th>\n",
       "      <th>isML</th>\n",
       "      <th>isAI</th>\n",
       "      <th>isBI</th>\n",
       "      <th>isDirector</th>\n",
       "      <th>isHead</th>\n",
       "      <th>isLead</th>\n",
       "      <th>isManager</th>\n",
       "      <th>isVP</th>\n",
       "      <th>isSenior</th>\n",
       "      <th>isJunior</th>\n",
       "      <th>isAssistant</th>\n",
       "      <th>isGraduate</th>\n",
       "      <th>isApprentice</th>\n",
       "      <th>company</th>\n",
       "      <th>rating</th>\n",
       "      <th>search_city</th>\n",
       "      <th>location</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_freq</th>\n",
       "      <th>isRange</th>\n",
       "      <th>salary_range_min</th>\n",
       "      <th>salary_range_max</th>\n",
       "      <th>salary_avg</th>\n",
       "      <th>salary_avg_USD</th>\n",
       "      <th>target</th>\n",
       "      <th>age</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>account manager</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BusinesStaff</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>$170,000 - $220,000 a year</td>\n",
       "      <td>year</td>\n",
       "      <td>Range</td>\n",
       "      <td>170000</td>\n",
       "      <td>220000</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>195000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30+ days ago</td>\n",
       "      <td>Strategic mindset with proven ability to synthesize customer financial reports, industry information and market intelligence to develop customer growth…</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             title  isDS  isDA  isDE  isDArch  isML  isAI  isBI  isDirector  \\\n",
       "0  account manager     0     0     0        0     0     0     0           0   \n",
       "\n",
       "   isHead  isLead  isManager  isVP  isSenior  isJunior  isAssistant  \\\n",
       "0       0       0          1     0         0         0            1   \n",
       "\n",
       "   isGraduate  isApprentice       company  rating search_city    location  \\\n",
       "0           0             0  BusinesStaff     NaN      Denver  Denver, CO   \n",
       "\n",
       "                       salary salary_freq isRange  salary_range_min  \\\n",
       "0  $170,000 - $220,000 a year        year   Range            170000   \n",
       "\n",
       "   salary_range_max  salary_avg  salary_avg_USD  target           age  \\\n",
       "0            220000    195000.0        195000.0     1.0  30+ days ago   \n",
       "\n",
       "                                                                                                                                                    summary  \n",
       "0  Strategic mindset with proven ability to synthesize customer financial reports, industry information and market intelligence to develop customer growth…  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We choose the below interesting features to engineer for:\n",
    "\n",
    "indeed.insert(1, 'isDS', indeed['title'].apply(lambda x: 1 if 'data science' in x.lower() or 'data scientist' in x.lower() else 0))\n",
    "indeed.insert(2, 'isDA', indeed['title'].apply(lambda x: 1 if 'data analyst' in x.lower() or 'analytics' in x.lower() else 0))\n",
    "indeed.insert(3, 'isDE', indeed['title'].apply(lambda x: 1 if 'data engineer' in x.lower() else 0))\n",
    "indeed.insert(4, 'isDArch', indeed['title'].apply(lambda x: 1 if 'architect' in x.lower() else 0))\n",
    "indeed.insert(5, 'isML', indeed['title'].apply(lambda x: 1 if 'ml' in x.lower() or 'machine learning' in x.lower() else 0))\n",
    "indeed.insert(6, 'isAI', indeed['title'].apply(lambda x: 1 if 'ai' in x.lower() or 'artifical intelligence' in x.lower() else 0))\n",
    "indeed.insert(7, 'isBI', indeed['title'].apply(lambda x: 1 if 'bi' in x.lower() or 'business intelligence' in x.lower() else 0))\n",
    "\n",
    "indeed.insert(8, 'isDirector', indeed['title'].apply(lambda x: 1 if 'director' in x.lower() else 0))\n",
    "indeed.insert(9, 'isHead', indeed['title'].apply(lambda x: 1 if 'head' in x.lower() else 0))\n",
    "indeed.insert(10, 'isLead', indeed['title'].apply(lambda x: 1 if 'lead' in x.lower() else 0))\n",
    "indeed.insert(11, 'isManager', indeed['title'].apply(lambda x: 1 if 'mgr' in x.lower() or 'manager' in x.lower() else 0))\n",
    "indeed.insert(12, 'isVP', indeed['title'].apply(lambda x: 1 if 'vp' in x.lower() or 'vice president' in x.lower() else 0))\n",
    "indeed.insert(13, 'isSenior', indeed['title'].apply(lambda x: 1 if 'sr' in x.lower() or 'senior' in x.lower() else 0))\n",
    "indeed.insert(14, 'isJunior', indeed['title'].apply(lambda x: 1 if 'jr' in x.lower() or 'junior' in x.lower() else 0))\n",
    "indeed.insert(15, 'isAssistant', indeed['title'].apply(lambda x: 1 if 'assistant' else 0))\n",
    "indeed.insert(16, 'isGraduate', indeed['title'].apply(lambda x: 1 if 'grad' in x.lower() or 'graduate' in x.lower() else 0))\n",
    "indeed.insert(17, 'isApprentice', indeed['title'].apply(lambda x: 1 if 'apprentice' in x.lower() else 0))\n",
    "\n",
    "indeed.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Clean up Rating column: replace null with None\n",
    "\n",
    "indeed['rating'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  15 out of  15 | elapsed:   40.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_features': 0.4}\n",
      "Cross Val Score: 0.8030339024993814\n",
      "Confusion Matrix:\n",
      " \n",
      "[[308  73]\n",
      " [ 79 290]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.81      0.80       381\n",
      "         1.0       0.80      0.79      0.79       369\n",
      "\n",
      "    accuracy                           0.80       750\n",
      "   macro avg       0.80      0.80      0.80       750\n",
      "weighted avg       0.80      0.80      0.80       750\n",
      "\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:    9.8s\n",
      "[Parallel(n_jobs=2)]: Done  60 out of  60 | elapsed:   13.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_depth': 12, 'min_samples_split': 0.4}\n",
      "Cross Val Score: 0.7323276416728532\n",
      "Confusion Matrix:\n",
      " \n",
      "[[286  95]\n",
      " [102 267]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.74      0.75      0.74       381\n",
      "         1.0       0.74      0.72      0.73       369\n",
      "\n",
      "    accuracy                           0.74       750\n",
      "   macro avg       0.74      0.74      0.74       750\n",
      "weighted avg       0.74      0.74      0.74       750\n",
      "\n",
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'learning_rate': 0.4}\n",
      "Cross Val Score: 0.7807978223212076\n",
      "Confusion Matrix:\n",
      " \n",
      "[[321  60]\n",
      " [ 96 273]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.77      0.84      0.80       381\n",
      "         1.0       0.82      0.74      0.78       369\n",
      "\n",
      "    accuracy                           0.79       750\n",
      "   macro avg       0.79      0.79      0.79       750\n",
      "weighted avg       0.79      0.79      0.79       750\n",
      "\n",
      "Best estimator: GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.4, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=20, presort='deprecated',\n",
      "                           random_state=1, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isSenior</th>\n",
       "      <th>isLead</th>\n",
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       "      <th>company_Linux Recruit</th>\n",
       "      <th>isApprentice</th>\n",
       "      <th>company_Media IQ Recruitment</th>\n",
       "      <th>search_city_Philadelphia</th>\n",
       "      <th>search_city_San+Francisco</th>\n",
       "      <th>company_Inspiring Interns</th>\n",
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       "      <th>company_E.ON UK</th>\n",
       "      <th>search_city_Edinburgh</th>\n",
       "      <th>company_The Yerba Mate Co.</th>\n",
       "      <th>company_Harnham</th>\n",
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       "      <th>search_city_Portland</th>\n",
       "      <th>isML</th>\n",
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       "      <th>rating_3.7</th>\n",
       "      <th>rating_3.1</th>\n",
       "      <th>company_TalentPool</th>\n",
       "      <th>company_Digital Taxonomy</th>\n",
       "      <th>company_University of Oxford</th>\n",
       "      <th>rating_3.8</th>\n",
       "      <th>search_city_Dallas</th>\n",
       "      <th>company_Lloyds Banking Group</th>\n",
       "      <th>search_city_Austin</th>\n",
       "      <th>company_Tessella Ltd</th>\n",
       "      <th>company_Lead Foot Digital</th>\n",
       "      <th>search_city_Chicago</th>\n",
       "      <th>company_Concept Resourcing</th>\n",
       "      <th>company_Aspire Data Recruitment</th>\n",
       "      <th>company_ECM Selection</th>\n",
       "      <th>company_New York City DEPARTMENT OF INVESTIGATION</th>\n",
       "      <th>isVP</th>\n",
       "      <th>company_Barna Shields Recruitment</th>\n",
       "      <th>company_TechMate</th>\n",
       "      <th>company_ZenShin Talent</th>\n",
       "      <th>company_Robertson Sumner</th>\n",
       "      <th>company_Comcast</th>\n",
       "      <th>rating_3.3</th>\n",
       "      <th>company_Science Museum Group</th>\n",
       "      <th>company_Synaptic Resources Ltd</th>\n",
       "      <th>search_city_Pittsburgh</th>\n",
       "      <th>company_Fanbank</th>\n",
       "      <th>company_Navigator CRE</th>\n",
       "      <th>company_Moriati</th>\n",
       "      <th>company_Computer Enterprises</th>\n",
       "      <th>company_Understanding Recruitment</th>\n",
       "      <th>company_NYCM</th>\n",
       "      <th>company_Migacore Technologies</th>\n",
       "      <th>company_Catalyst forward</th>\n",
       "      <th>search_city_Seattle</th>\n",
       "      <th>search_city_London</th>\n",
       "      <th>company_RishTani Technologies Limited</th>\n",
       "      <th>company_Anya Consultancy Services Limited</th>\n",
       "      <th>company_IntelliSense Systems Inc.</th>\n",
       "      <th>company_UK Government - Driver and Vehicle Standards Agenc...</th>\n",
       "      <th>company_US Department of the Interior</th>\n",
       "      <th>company_New York City NYC HOUSING AUTHORITY</th>\n",
       "      <th>company_Houndstooth Capital Real Estate</th>\n",
       "      <th>company_Scottish Water</th>\n",
       "      <th>isBI</th>\n",
       "      <th>company_RaiseMe</th>\n",
       "      <th>company_EGIS INC</th>\n",
       "      <th>company_Acrotrend Solutions Limited</th>\n",
       "      <th>company_Astroscreen</th>\n",
       "      <th>company_Cortex IT Recruitment</th>\n",
       "      <th>search_city_Los+Angeles</th>\n",
       "      <th>company_DISYS</th>\n",
       "      <th>company_New York City HOUSING PRESERVATION &amp; DVLPMNT</th>\n",
       "      <th>company_Deerfoot</th>\n",
       "      <th>company_Searchability</th>\n",
       "      <th>company_GCS Recruitment Specialists Ltd</th>\n",
       "      <th>company_Jenrick Group</th>\n",
       "      <th>company_Consortia</th>\n",
       "      <th>company_Oho Group</th>\n",
       "      <th>company_People Source Consulting</th>\n",
       "      <th>company_Church International Ltd.</th>\n",
       "      <th>company_Synovus</th>\n",
       "      <th>company_InterQuest Group</th>\n",
       "      <th>company_Carrot Pharma Recruitment</th>\n",
       "      <th>rating_3.6</th>\n",
       "      <th>company_TSB Banking</th>\n",
       "      <th>company_Cottonwood Financial</th>\n",
       "      <th>company_Amazing Prospects Ltd</th>\n",
       "      <th>company_Blue Pelican</th>\n",
       "      <th>company_Kellington Protection Service, LLC</th>\n",
       "      <th>company_STFC</th>\n",
       "      <th>company_Media Contacts ltd</th>\n",
       "      <th>company_JFL Search &amp; Selection</th>\n",
       "      <th>company_Douglas Jackson</th>\n",
       "      <th>company_Adzuna</th>\n",
       "      <th>company_New York City DEPARTMENT OF BUSINESS SERV.</th>\n",
       "      <th>company_Source</th>\n",
       "      <th>company_Ninety Thousand Hours</th>\n",
       "      <th>company_Guy's and St Thomas' NHS Foundation Trust</th>\n",
       "      <th>company_PCS GlobalTech</th>\n",
       "      <th>company_Global Market Summits – Chancery Lane</th>\n",
       "      <th>company_Milshar, LLC</th>\n",
       "      <th>rating_2.9</th>\n",
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       "      <th>company_Austin Fraser</th>\n",
       "      <th>company_Langley James IT Recruitment</th>\n",
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       "      <th>company_Go, Inc.</th>\n",
       "      <th>company_Psixty Recruitment</th>\n",
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       "      <th>rating_2.8</th>\n",
       "      <th>company_Stanton House</th>\n",
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       "      <th>company_SPEKTRIX</th>\n",
       "      <th>company_Indigent Legal Services, Office of</th>\n",
       "      <th>company_Association for Mental Health &amp; Wellness</th>\n",
       "      <th>company_WORLDWIDE BUSINESS RESEARCH Ltd</th>\n",
       "      <th>company_Teleboom Inc D/B/A Deter24 Monitoring</th>\n",
       "      <th>company_DeVries Global</th>\n",
       "      <th>company_PR Futures</th>\n",
       "      <th>company_CatchFish</th>\n",
       "      <th>company_Spring</th>\n",
       "      <th>company_Boulder County, CO</th>\n",
       "      <th>company_Truthful Trading Inc</th>\n",
       "      <th>company_Client Server</th>\n",
       "      <th>company_Omnicell</th>\n",
       "      <th>company_AKUVO</th>\n",
       "      <th>company_Cubex LLC</th>\n",
       "      <th>company_New York City DEPT OF HEALTH/MENTAL HYGIENE</th>\n",
       "      <th>company_Informatiq Consulting</th>\n",
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       "      <th>company_L&amp;Q</th>\n",
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       "      <th>company_Advancing Analytics</th>\n",
       "      <th>company_Respect Consulting Group</th>\n",
       "      <th>company_Nonstop World (Tandemworld) Ltd</th>\n",
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       "      <th>company_Agency Within</th>\n",
       "      <th>company_System Recruitment</th>\n",
       "      <th>company_3Search</th>\n",
       "      <th>company_Snap Finance Ltd (UK)</th>\n",
       "      <th>company_TechNET IT Recruitment Ltd</th>\n",
       "      <th>company_Touch Surgery</th>\n",
       "      <th>company_Zynx Technologies Limited</th>\n",
       "      <th>company_UK Government - Office for National Statistics</th>\n",
       "      <th>company_Jonathan Lee Recruitment Ltd</th>\n",
       "      <th>company_Science Museum</th>\n",
       "      <th>company_City of Mesa, AZ</th>\n",
       "      <th>company_karros technologies</th>\n",
       "      <th>company_Neo Prism Solutions</th>\n",
       "      <th>company_US Department of Justice</th>\n",
       "      <th>isAI</th>\n",
       "      <th>company_Tier1 IT</th>\n",
       "      <th>company_Vody</th>\n",
       "      <th>company_BiSoft</th>\n",
       "      <th>company_Netleadz</th>\n",
       "      <th>company_Barran Graduate Recruitment</th>\n",
       "      <th>company_Inspire People</th>\n",
       "      <th>company_Hero Labs</th>\n",
       "      <th>company_Therapy Box</th>\n",
       "      <th>company_Venatrix</th>\n",
       "      <th>company_R2M Marketing Solutions</th>\n",
       "      <th>company_Echobox</th>\n",
       "      <th>company_University of Westminster</th>\n",
       "      <th>company_Elite Crowdfunding limted</th>\n",
       "      <th>company_Blue Legal</th>\n",
       "      <th>company_Lloyd Recruitment Services</th>\n",
       "      <th>rating_2.6</th>\n",
       "      <th>company_Regis University</th>\n",
       "      <th>company_University of Warwick</th>\n",
       "      <th>company_Excelerate Recruitment Partners</th>\n",
       "      <th>company_Vesta Home</th>\n",
       "      <th>company_City of Dallas, TX</th>\n",
       "      <th>company_Urban Empire Recruitment</th>\n",
       "      <th>company_Ultimate Asset</th>\n",
       "      <th>company_Oregon Health &amp; Science University</th>\n",
       "      <th>company_US Department of Transportation</th>\n",
       "      <th>company_Riverbright Recruitment</th>\n",
       "      <th>company_Medicine Man Technologies</th>\n",
       "      <th>company_Mechsoft Technology (USA) Co., LLC</th>\n",
       "      <th>company_Allianz</th>\n",
       "      <th>company_State of Colorado Job Opportunities</th>\n",
       "      <th>company_Avios Group</th>\n",
       "      <th>company_Diamond Light Source</th>\n",
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       "      <th>company_Carlton Recruitment</th>\n",
       "      <th>company_Clinical Professionals</th>\n",
       "      <th>company_Clockwork Recruitment</th>\n",
       "      <th>company_Cobalt Recruitment</th>\n",
       "      <th>company_Coburg Banks</th>\n",
       "      <th>company_Catch Resource Management Ltd</th>\n",
       "      <th>company_Community College of Aurora</th>\n",
       "      <th>company_Community College of Denver</th>\n",
       "      <th>company_Compass Associates</th>\n",
       "      <th>company_Compassion UK</th>\n",
       "      <th>company_ComplyAdvantage</th>\n",
       "      <th>company_Catapult Learning</th>\n",
       "      <th>company_Computer Enterprises, Inc. (CEI)</th>\n",
       "      <th>company_Conduit Data Services</th>\n",
       "      <th>company_Covered Insurance Solutions</th>\n",
       "      <th>company_Consilium Recruit</th>\n",
       "      <th>company_Care UK Healthcare</th>\n",
       "      <th>company_Context Recruitment</th>\n",
       "      <th>company_Cook County Sheriff’s Office</th>\n",
       "      <th>company_Cordius</th>\n",
       "      <th>company_Core Tech Recruitment</th>\n",
       "      <th>company_Corecom Consulting</th>\n",
       "      <th>company_Cornwaliis Elt</th>\n",
       "      <th>company_Carbon60</th>\n",
       "      <th>company_Capstone Hill Search Limited</th>\n",
       "      <th>company_County of San Mateo</th>\n",
       "      <th>company_Coventry Building Society</th>\n",
       "      <th>company_Climate Policy Initiative</th>\n",
       "      <th>company_Cathcart Associates</th>\n",
       "      <th>company_Click IT Consulting</th>\n",
       "      <th>company_Clearabee</th>\n",
       "      <th>company_Celsius Graduate Recruitment</th>\n",
       "      <th>company_Center for Employment Opportunities</th>\n",
       "      <th>company_Cervest</th>\n",
       "      <th>company_Chase &amp; Holland Recruitment</th>\n",
       "      <th>company_Chesterfield College</th>\n",
       "      <th>company_Chesterfield Royal Hospital NHS Foundation Trust</th>\n",
       "      <th>company_Chi Square Analytics</th>\n",
       "      <th>company_Childhood Cancer Data Lab</th>\n",
       "      <th>company_Chime Communications Plc</th>\n",
       "      <th>company_Chryselys</th>\n",
       "      <th>company_Catsurveys</th>\n",
       "      <th>company_Cifas</th>\n",
       "      <th>company_Cintas</th>\n",
       "      <th>company_Circle Recruitment</th>\n",
       "      <th>company_Cirrus Selection</th>\n",
       "      <th>company_Cisco</th>\n",
       "      <th>company_Cititec</th>\n",
       "      <th>company_City Pantry</th>\n",
       "      <th>company_City of Glendale, AZ</th>\n",
       "      <th>company_City of Hillsboro Oregon</th>\n",
       "      <th>company_City of Houston, TX</th>\n",
       "      <th>company_Catman Jobs</th>\n",
       "      <th>company_City of Sunny Isles Beach</th>\n",
       "      <th>company_Clarip</th>\n",
       "      <th>company_ClearBlade, Inc</th>\n",
       "      <th>company_Coventry University</th>\n",
       "      <th>company_Create Music Group</th>\n",
       "      <th>company_ERP Maestro</th>\n",
       "      <th>company_Divido</th>\n",
       "      <th>company_Detail2Recruitment</th>\n",
       "      <th>company_Digital Catapult</th>\n",
       "      <th>company_Digital Creative Institute</th>\n",
       "      <th>company_Digital Find Recruitment</th>\n",
       "      <th>company_Digital Gurus</th>\n",
       "      <th>company_Cambridge Assessment</th>\n",
       "      <th>company_Digital Uncut</th>\n",
       "      <th>company_Digitive LLC</th>\n",
       "      <th>company_Dimensions UK Ltd</th>\n",
       "      <th>company_Discourse.ai</th>\n",
       "      <th>company_Distinct Recruitment</th>\n",
       "      <th>company_Diverse Talent Solutions</th>\n",
       "      <th>company_Doctors Without Borders/Médecins Sans Frontières (...</th>\n",
       "      <th>company_Creative Personnel</th>\n",
       "      <th>company_Dome Recruitment</th>\n",
       "      <th>company_Doris IT</th>\n",
       "      <th>company_Cambridge Advisory Group</th>\n",
       "      <th>company_Drift Net Securities</th>\n",
       "      <th>company_Dudley and Walsall Mental Health Partnership NHS T...</th>\n",
       "      <th>company_E-Resourcing</th>\n",
       "      <th>company_California State University</th>\n",
       "      <th>company_Cadent Gas</th>\n",
       "      <th>company_EEG Enterprises</th>\n",
       "      <th>company_Cactus Search</th>\n",
       "      <th>company_EMR Marketing Recruitment</th>\n",
       "      <th>company_EOS Deal Advisory</th>\n",
       "      <th>company_Derotek</th>\n",
       "      <th>company_Denver Public Schools</th>\n",
       "      <th>company_Delaware Valley Regional Planning Commission</th>\n",
       "      <th>company_Degree Analytics</th>\n",
       "      <th>company_Crowd Link Consulting</th>\n",
       "      <th>company_CruiTek</th>\n",
       "      <th>company_Capital One - UK</th>\n",
       "      <th>company_Cundall</th>\n",
       "      <th>company_Curo Talent</th>\n",
       "      <th>company_Cute Resource</th>\n",
       "      <th>company_CybSafe</th>\n",
       "      <th>company_Cyber Tech Company</th>\n",
       "      <th>company_DCA Recruitment</th>\n",
       "      <th>company_DCL Search and Selection</th>\n",
       "      <th>company_Capita Plc</th>\n",
       "      <th>company_DM People Recruitment Consultants</th>\n",
       "      <th>company_DP Connect</th>\n",
       "      <th>company_DVCanvass</th>\n",
       "      <th>company_DVF Recruitment</th>\n",
       "      <th>company_Daniel Alexander Recruitment</th>\n",
       "      <th>company_Data Kraken Consultancy Ltd</th>\n",
       "      <th>company_Data Ninjas Inc</th>\n",
       "      <th>company_Datascope Recruitment</th>\n",
       "      <th>company_Datatech Analytics</th>\n",
       "      <th>company_Capita IT Resourcing</th>\n",
       "      <th>company_DearDoc</th>\n",
       "      <th>company_DecisionIQ, Inc.</th>\n",
       "      <th>company_Cancer Research UK</th>\n",
       "      <th>company_Defined Clarity</th>\n",
       "      <th>company_CCFE</th>\n",
       "      <th>company_INOV8 Consulting Ltd</th>\n",
       "      <th>company_PGS LTD</th>\n",
       "      <th>company_New Business People</th>\n",
       "      <th>company_Bobtrade</th>\n",
       "      <th>company_Nathan S. Kline Institute</th>\n",
       "      <th>company_National Entertainment Network</th>\n",
       "      <th>company_National Grid</th>\n",
       "      <th>company_National Insurance Crime Bureau</th>\n",
       "      <th>company_National Network of Public Health Institutes</th>\n",
       "      <th>company_Navagis</th>\n",
       "      <th>company_Bluetownonline Ltd</th>\n",
       "      <th>company_Nelson Global Products Inc.</th>\n",
       "      <th>company_Neudesic</th>\n",
       "      <th>company_Blue Owl</th>\n",
       "      <th>company_New Benefits</th>\n",
       "      <th>company_New York City ADMIN FOR CHILDREN'S SVCS</th>\n",
       "      <th>company_New York City FIRE DEPARTMENT</th>\n",
       "      <th>company_New York City BOARD OF CORRECTION</th>\n",
       "      <th>company_New York City DEPARTMENT OF CORRECTION</th>\n",
       "      <th>company_New York City DEPARTMENT OF FINANCE</th>\n",
       "      <th>company_Blu Digital</th>\n",
       "      <th>company_New York City DEPARTMENT OF TRANSPORTATION</th>\n",
       "      <th>company_New York City DEPT OF DESIGN &amp; CONSTRUCTION</th>\n",
       "      <th>company_Bliss Point Media</th>\n",
       "      <th>company_New York City DEPT OF INFO TECH &amp; TELECOMM</th>\n",
       "      <th>company_New York City DEPT OF PARKS &amp; RECREATION</th>\n",
       "      <th>company_New York City DEPT OF YOUTH &amp; COMM DEV SRVS</th>\n",
       "      <th>company_New York City Department of Education .</th>\n",
       "      <th>company_New York City Employees’ Retirement System (NYCERS...</th>\n",
       "      <th>company_NP Group</th>\n",
       "      <th>company_NHS Blood and Transplant</th>\n",
       "      <th>company_NFuzionIT</th>\n",
       "      <th>company_NFU Mutual</th>\n",
       "      <th>company_Brewster Partners</th>\n",
       "      <th>company_Metro Bank PLC</th>\n",
       "      <th>company_MetroPlus Health Plan</th>\n",
       "      <th>company_Michael Page UK</th>\n",
       "      <th>company_Micro Focus</th>\n",
       "      <th>company_Microsoft</th>\n",
       "      <th>company_Brewer Direct Inc.</th>\n",
       "      <th>company_Boston Consulting Group</th>\n",
       "      <th>company_Miriad Products Ltd</th>\n",
       "      <th>company_Moixa Energy Holdings</th>\n",
       "      <th>company_Monarch</th>\n",
       "      <th>company_Monzo</th>\n",
       "      <th>company_Moonshot CVE</th>\n",
       "      <th>company_Moorfields Eye Hospital NHS Foundation Trust</th>\n",
       "      <th>company_Morehouse College</th>\n",
       "      <th>company_Morgan Law</th>\n",
       "      <th>company_Morgan McKinley</th>\n",
       "      <th>company_Bond Williams</th>\n",
       "      <th>company_Morson International</th>\n",
       "      <th>company_4OC</th>\n",
       "      <th>company_Motorway</th>\n",
       "      <th>company_Mudano</th>\n",
       "      <th>company_MySense</th>\n",
       "      <th>company_Myriad</th>\n",
       "      <th>company_NFP People Limited</th>\n",
       "      <th>company_New York City FINANCIAL INFO SVCS AGENCY</th>\n",
       "      <th>company_Blayze Group</th>\n",
       "      <th>company_Memorial MRI and Diagnostic LLC</th>\n",
       "      <th>company_Optima IT Recruitment</th>\n",
       "      <th>company_BiggerPockets Inc.</th>\n",
       "      <th>company_Oliver James Associates</th>\n",
       "      <th>company_On Track Recruitment</th>\n",
       "      <th>company_On Track Recruitment &amp; Training Ltd</th>\n",
       "      <th>company_One Housing Group</th>\n",
       "      <th>company_Online Filings</th>\n",
       "      <th>company_Online Mortgage Advisor</th>\n",
       "      <th>company_Onyx Infosoft</th>\n",
       "      <th>company_OpenRent</th>\n",
       "      <th>company_Openreach</th>\n",
       "      <th>company_Openview</th>\n",
       "      <th>company_Operation Warm</th>\n",
       "      <th>company_Optimus E2E Limited</th>\n",
       "      <th>company_New York City LAW DEPARTMENT</th>\n",
       "      <th>company_Cedar Recruitment Limited</th>\n",
       "      <th>company_Opus Recruitment Solutions</th>\n",
       "      <th>company_Orbit Group</th>\n",
       "      <th>company_Ortus-iHealth</th>\n",
       "      <th>company_Oscar Technology</th>\n",
       "      <th>company_Outsource UK</th>\n",
       "      <th>company_Oviva</th>\n",
       "      <th>company_Oxford Brookes University</th>\n",
       "      <th>company_Oxford University Press</th>\n",
       "      <th>company_P N Daly Limited</th>\n",
       "      <th>company_PCS Global Tech</th>\n",
       "      <th>company_Beyond Outsourcing Inc.</th>\n",
       "      <th>company_Octagon Group</th>\n",
       "      <th>company_Ocean Media Group Ltd</th>\n",
       "      <th>company_Oakmoor Recruitment</th>\n",
       "      <th>company_OSR Recruitment</th>\n",
       "      <th>company_New York City MAYORS OFFICE OF CONTRACT SVCS</th>\n",
       "      <th>company_Blackstone &amp; Cullen</th>\n",
       "      <th>company_New York City OFFICE OF EMERGENCY MANAGEMENT</th>\n",
       "      <th>company_New York City TAXI &amp; LIMOUSINE COMMISSION</th>\n",
       "      <th>company_Newbury Building Society</th>\n",
       "      <th>company_Newmedica</th>\n",
       "      <th>company_Newtons Recruitment</th>\n",
       "      <th>company_Nexus Jobs</th>\n",
       "      <th>company_Nexus Jobs Limited</th>\n",
       "      <th>company_Niantic</th>\n",
       "      <th>company_Niantic International Technology Limited</th>\n",
       "      <th>company_Niche employment solutions</th>\n",
       "      <th>company_Nicholson Glover Consulting</th>\n",
       "      <th>company_Black Hills Energy</th>\n",
       "      <th>company_NinetyThousandHours</th>\n",
       "      <th>company_Bitcoin.com</th>\n",
       "      <th>company_Noria Water Technologies</th>\n",
       "      <th>company_Birdie</th>\n",
       "      <th>company_NuView Analytics</th>\n",
       "      <th>company_Nucleome Therapeutics Ltd</th>\n",
       "      <th>company_Nuffield Health</th>\n",
       "      <th>company_Nutrisystem</th>\n",
       "      <th>company_Nuvola Resourcing</th>\n",
       "      <th>company_Nuvola Staffing &amp; Solutions, LLC.</th>\n",
       "      <th>company_ONE WORLD EXPRESS</th>\n",
       "      <th>company_Method Resourcing</th>\n",
       "      <th>company_Melax Technologies, Inc.</th>\n",
       "      <th>company_IPROS Insurance Professionals</th>\n",
       "      <th>company_Kisaco Research</th>\n",
       "      <th>company_Jobs in Letting</th>\n",
       "      <th>company_Jumpshot</th>\n",
       "      <th>company_K3 Business Technologies</th>\n",
       "      <th>company_KIPP Texas Public Schools</th>\n",
       "      <th>company_KIZEN Technologies, Inc.</th>\n",
       "      <th>company_Kairos Recruitment Group</th>\n",
       "      <th>company_Kaplan International</th>\n",
       "      <th>company_Karagozian &amp; Case</th>\n",
       "      <th>company_Brytecore</th>\n",
       "      <th>company_Kew Gardens</th>\n",
       "      <th>company_King County</th>\n",
       "      <th>company_King's College London</th>\n",
       "      <th>company_Kite Group</th>\n",
       "      <th>company_Legal &amp; General Group Plc.</th>\n",
       "      <th>company_Klanik Corp</th>\n",
       "      <th>company_Bryant Associates</th>\n",
       "      <th>company_L'Oréal</th>\n",
       "      <th>company_LDN Apprenticeships</th>\n",
       "      <th>company_La Fosse Associates</th>\n",
       "      <th>company_Lambeth Council</th>\n",
       "      <th>company_Brunel University</th>\n",
       "      <th>company_Language Matters Recruitment Consultants Ltd.</th>\n",
       "      <th>company_Law Business Research</th>\n",
       "      <th>company_Lawrence Harvey</th>\n",
       "      <th>company_Bromford</th>\n",
       "      <th>company_Leading UK Pension Fund</th>\n",
       "      <th>company_Buckinghamshire Healthcare NHS Trust</th>\n",
       "      <th>company_Jaguar Land Rover</th>\n",
       "      <th>company_Jackson Rose Ltd</th>\n",
       "      <th>company_JPE</th>\n",
       "      <th>company_IPS Group</th>\n",
       "      <th>company_ITTStar Consulting LLC</th>\n",
       "      <th>company_Impact Proteomics</th>\n",
       "      <th>company_Imperial College Healthcare NHS Trust</th>\n",
       "      <th>company_Imperial College London</th>\n",
       "      <th>company_Incisive Media</th>\n",
       "      <th>company_Indeed</th>\n",
       "      <th>company_CBSbutler</th>\n",
       "      <th>company_Inference Solutions</th>\n",
       "      <th>company_CBRE</th>\n",
       "      <th>company_Initi8 Recruitment</th>\n",
       "      <th>company_Insights Analytics</th>\n",
       "      <th>company_CAMILLUS HEALTH CONCERN</th>\n",
       "      <th>company_BusinesStaff</th>\n",
       "      <th>company_IntelliSense.io</th>\n",
       "      <th>company_Intellidyne Business Systems</th>\n",
       "      <th>company_Burns Sheehan</th>\n",
       "      <th>company_InterSTEM Recruitment</th>\n",
       "      <th>company_Interaction Recruitment</th>\n",
       "      <th>company_Intermedia Global Ltd</th>\n",
       "      <th>company_J&amp;C Associates Ltd</th>\n",
       "      <th>company_JDX Consulting</th>\n",
       "      <th>company_Bulletproof</th>\n",
       "      <th>company_JMC Legal Recruitment</th>\n",
       "      <th>company_JP Engineering Recruitment</th>\n",
       "      <th>company_Lee College</th>\n",
       "      <th>company_Level Agency</th>\n",
       "      <th>company_Meet Recruitment</th>\n",
       "      <th>company_Marshall Wolfe</th>\n",
       "      <th>company_Mammoth Media</th>\n",
       "      <th>company_Manchester Airports Group</th>\n",
       "      <th>company_Mandeville Recruitment Group</th>\n",
       "      <th>company_Mane Contract Services Limited</th>\n",
       "      <th>company_Mango Solutions</th>\n",
       "      <th>company_Manufacturing Recruitment Ltd</th>\n",
       "      <th>company_Maricopa County, AZ</th>\n",
       "      <th>company_Marie Curie</th>\n",
       "      <th>company_Marie Stopes International</th>\n",
       "      <th>company_Brightwell</th>\n",
       "      <th>company_MarketTrust, Inc.</th>\n",
       "      <th>company_Marks Sattin Specialist Recruitment</th>\n",
       "      <th>company_Martin and Conley</th>\n",
       "      <th>company_LevelPrime Limited</th>\n",
       "      <th>company_Match Digital</th>\n",
       "      <th>company_Matthew Noah</th>\n",
       "      <th>company_Maveneer</th>\n",
       "      <th>company_Maxx Builders</th>\n",
       "      <th>company_Mayor's Office of Contract Services</th>\n",
       "      <th>company_Mazars</th>\n",
       "      <th>company_McCann Central</th>\n",
       "      <th>company_McKinsey &amp; Company</th>\n",
       "      <th>company_Mcgregor Boyall</th>\n",
       "      <th>company_Brightred</th>\n",
       "      <th>company_Brightflag</th>\n",
       "      <th>company_MediaAndLanguageJobs.co.uk</th>\n",
       "      <th>company_Magic Carpet AI</th>\n",
       "      <th>company_Madiba Inc.</th>\n",
       "      <th>company_MacGregor Black</th>\n",
       "      <th>company_British Private Equity and Venture Capital Associa...</th>\n",
       "      <th>company_Liberty Mutual Insurance</th>\n",
       "      <th>company_Lidl</th>\n",
       "      <th>company_Linnk Group Limited</th>\n",
       "      <th>company_BrokerCompare.co (eXp Realty)</th>\n",
       "      <th>company_LiveRamp</th>\n",
       "      <th>company_Livingstone Technologies</th>\n",
       "      <th>company_British Rowing</th>\n",
       "      <th>company_London School of Hygiene &amp; Tropical Medicine</th>\n",
       "      <th>company_London Tri-Borough Councils</th>\n",
       "      <th>company_Long Beach City College</th>\n",
       "      <th>company_Long Island FQHC, Inc.</th>\n",
       "      <th>company_Lorien Resourcing</th>\n",
       "      <th>company_Los Angeles County Department of Human Resources</th>\n",
       "      <th>company_Los Angeles Homeless Services Authority</th>\n",
       "      <th>company_Los Angeles Unified School District</th>\n",
       "      <th>company_Loyal Retainers</th>\n",
       "      <th>company_Lyst</th>\n",
       "      <th>company_M-Brain</th>\n",
       "      <th>company_MAPSCorps</th>\n",
       "      <th>company_MAQ Software</th>\n",
       "      <th>company_MASS</th>\n",
       "      <th>company_MBN Recruitment Solutions</th>\n",
       "      <th>company_MBN Solutions</th>\n",
       "      <th>company_MIND.AI</th>\n",
       "      <th>company_MW Appointments</th>\n",
       "      <th>company_Mortimer Bell International Ltd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>importance</th>\n",
       "      <td>0.052451</td>\n",
       "      <td>0.045105</td>\n",
       "      <td>0.044961</td>\n",
       "      <td>0.038536</td>\n",
       "      <td>0.03846</td>\n",
       "      <td>0.036507</td>\n",
       "      <td>0.033362</td>\n",
       "      <td>0.033323</td>\n",
       "      <td>0.028539</td>\n",
       "      <td>0.02839</td>\n",
       "      <td>0.025904</td>\n",
       "      <td>0.022661</td>\n",
       "      <td>0.022224</td>\n",
       "      <td>0.022006</td>\n",
       "      <td>0.020457</td>\n",
       "      <td>0.018863</td>\n",
       "      <td>0.017605</td>\n",
       "      <td>0.012372</td>\n",
       "      <td>0.01226</td>\n",
       "      <td>0.012139</td>\n",
       "      <td>0.009984</td>\n",
       "      <td>0.008801</td>\n",
       "      <td>0.008721</td>\n",
       "      <td>0.008507</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>0.008127</td>\n",
       "      <td>0.007888</td>\n",
       "      <td>0.007486</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.007056</td>\n",
       "      <td>0.007006</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.006007</td>\n",
       "      <td>0.005725</td>\n",
       "      <td>0.005633</td>\n",
       "      <td>0.005632</td>\n",
       "      <td>0.005494</td>\n",
       "      <td>0.00537</td>\n",
       "      <td>0.004894</td>\n",
       "      <td>0.004771</td>\n",
       "      <td>0.004606</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>0.004171</td>\n",
       "      <td>0.003655</td>\n",
       "      <td>0.003578</td>\n",
       "      <td>0.003519</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.003225</td>\n",
       "      <td>0.003217</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>0.002901</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>0.00278</td>\n",
       "      <td>0.00275</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.002662</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.002437</td>\n",
       "      <td>0.002431</td>\n",
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       "importance                         0.0                       0.0   \n",
       "\n",
       "            company_Doris IT  company_Cambridge Advisory Group  \\\n",
       "importance               0.0                               0.0   \n",
       "\n",
       "            company_Drift Net Securities  \\\n",
       "importance                           0.0   \n",
       "\n",
       "            company_Dudley and Walsall Mental Health Partnership NHS T...  \\\n",
       "importance                                                            0.0   \n",
       "\n",
       "            company_E-Resourcing  company_California State University  \\\n",
       "importance                   0.0                                  0.0   \n",
       "\n",
       "            company_Cadent Gas  company_EEG Enterprises  \\\n",
       "importance                 0.0                      0.0   \n",
       "\n",
       "            company_Cactus Search  company_EMR Marketing Recruitment  \\\n",
       "importance                    0.0                                0.0   \n",
       "\n",
       "            company_EOS Deal Advisory  company_Derotek  \\\n",
       "importance                        0.0              0.0   \n",
       "\n",
       "            company_Denver Public Schools  \\\n",
       "importance                            0.0   \n",
       "\n",
       "            company_Delaware Valley Regional Planning Commission  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Degree Analytics  company_Crowd Link Consulting  \\\n",
       "importance                       0.0                            0.0   \n",
       "\n",
       "            company_CruiTek  company_Capital One - UK  company_Cundall  \\\n",
       "importance              0.0                       0.0              0.0   \n",
       "\n",
       "            company_Curo Talent  company_Cute Resource  company_CybSafe  \\\n",
       "importance                  0.0                    0.0              0.0   \n",
       "\n",
       "            company_Cyber Tech Company  company_DCA Recruitment  \\\n",
       "importance                         0.0                      0.0   \n",
       "\n",
       "            company_DCL Search and Selection  company_Capita Plc  \\\n",
       "importance                               0.0                 0.0   \n",
       "\n",
       "            company_DM People Recruitment Consultants  company_DP Connect  \\\n",
       "importance                                        0.0                 0.0   \n",
       "\n",
       "            company_DVCanvass  company_DVF Recruitment  \\\n",
       "importance                0.0                      0.0   \n",
       "\n",
       "            company_Daniel Alexander Recruitment  \\\n",
       "importance                                   0.0   \n",
       "\n",
       "            company_Data Kraken Consultancy Ltd  company_Data Ninjas Inc  \\\n",
       "importance                                  0.0                      0.0   \n",
       "\n",
       "            company_Datascope Recruitment  company_Datatech Analytics  \\\n",
       "importance                            0.0                         0.0   \n",
       "\n",
       "            company_Capita IT Resourcing  company_DearDoc  \\\n",
       "importance                           0.0              0.0   \n",
       "\n",
       "            company_DecisionIQ, Inc.  company_Cancer Research UK  \\\n",
       "importance                       0.0                         0.0   \n",
       "\n",
       "            company_Defined Clarity  company_CCFE  \\\n",
       "importance                      0.0           0.0   \n",
       "\n",
       "            company_INOV8 Consulting Ltd  company_PGS LTD  \\\n",
       "importance                           0.0              0.0   \n",
       "\n",
       "            company_New Business People  company_Bobtrade  \\\n",
       "importance                          0.0               0.0   \n",
       "\n",
       "            company_Nathan S. Kline Institute  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_National Entertainment Network  company_National Grid  \\\n",
       "importance                                     0.0                    0.0   \n",
       "\n",
       "            company_National Insurance Crime Bureau  \\\n",
       "importance                                      0.0   \n",
       "\n",
       "            company_National Network of Public Health Institutes  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Navagis  company_Bluetownonline Ltd  \\\n",
       "importance              0.0                         0.0   \n",
       "\n",
       "            company_Nelson Global Products Inc.  company_Neudesic  \\\n",
       "importance                                  0.0               0.0   \n",
       "\n",
       "            company_Blue Owl  company_New Benefits  \\\n",
       "importance               0.0                   0.0   \n",
       "\n",
       "            company_New York City ADMIN FOR CHILDREN'S SVCS  \\\n",
       "importance                                              0.0   \n",
       "\n",
       "            company_New York City FIRE DEPARTMENT  \\\n",
       "importance                                    0.0   \n",
       "\n",
       "            company_New York City BOARD OF CORRECTION  \\\n",
       "importance                                        0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF CORRECTION  \\\n",
       "importance                                             0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF FINANCE  company_Blu Digital  \\\n",
       "importance                                          0.0                  0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF TRANSPORTATION  \\\n",
       "importance                                                 0.0   \n",
       "\n",
       "            company_New York City DEPT OF DESIGN & CONSTRUCTION  \\\n",
       "importance                                                  0.0   \n",
       "\n",
       "            company_Bliss Point Media  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_New York City DEPT OF INFO TECH & TELECOMM  \\\n",
       "importance                                                 0.0   \n",
       "\n",
       "            company_New York City DEPT OF PARKS & RECREATION  \\\n",
       "importance                                               0.0   \n",
       "\n",
       "            company_New York City DEPT OF YOUTH & COMM DEV SRVS  \\\n",
       "importance                                                  0.0   \n",
       "\n",
       "            company_New York City Department of Education .  \\\n",
       "importance                                              0.0   \n",
       "\n",
       "            company_New York City Employees’ Retirement System (NYCERS...  \\\n",
       "importance                                                            0.0   \n",
       "\n",
       "            company_NP Group  company_NHS Blood and Transplant  \\\n",
       "importance               0.0                               0.0   \n",
       "\n",
       "            company_NFuzionIT  company_NFU Mutual  company_Brewster Partners  \\\n",
       "importance                0.0                 0.0                        0.0   \n",
       "\n",
       "            company_Metro Bank PLC  company_MetroPlus Health Plan  \\\n",
       "importance                     0.0                            0.0   \n",
       "\n",
       "            company_Michael Page UK  company_Micro Focus  company_Microsoft  \\\n",
       "importance                      0.0                  0.0                0.0   \n",
       "\n",
       "            company_Brewer Direct Inc.  company_Boston Consulting Group  \\\n",
       "importance                         0.0                              0.0   \n",
       "\n",
       "            company_Miriad Products Ltd  company_Moixa Energy Holdings  \\\n",
       "importance                          0.0                            0.0   \n",
       "\n",
       "            company_Monarch  company_Monzo  company_Moonshot CVE  \\\n",
       "importance              0.0            0.0                   0.0   \n",
       "\n",
       "            company_Moorfields Eye Hospital NHS Foundation Trust  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Morehouse College  company_Morgan Law  \\\n",
       "importance                        0.0                 0.0   \n",
       "\n",
       "            company_Morgan McKinley  company_Bond Williams  \\\n",
       "importance                      0.0                    0.0   \n",
       "\n",
       "            company_Morson International  company_4OC  company_Motorway  \\\n",
       "importance                           0.0          0.0               0.0   \n",
       "\n",
       "            company_Mudano  company_MySense  company_Myriad  \\\n",
       "importance             0.0              0.0             0.0   \n",
       "\n",
       "            company_NFP People Limited  \\\n",
       "importance                         0.0   \n",
       "\n",
       "            company_New York City FINANCIAL INFO SVCS AGENCY  \\\n",
       "importance                                               0.0   \n",
       "\n",
       "            company_Blayze Group  company_Memorial MRI and Diagnostic LLC  \\\n",
       "importance                   0.0                                      0.0   \n",
       "\n",
       "            company_Optima IT Recruitment  company_BiggerPockets Inc.  \\\n",
       "importance                            0.0                         0.0   \n",
       "\n",
       "            company_Oliver James Associates  company_On Track Recruitment  \\\n",
       "importance                              0.0                           0.0   \n",
       "\n",
       "            company_On Track Recruitment & Training Ltd  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_One Housing Group  company_Online Filings  \\\n",
       "importance                        0.0                     0.0   \n",
       "\n",
       "            company_Online Mortgage Advisor  company_Onyx Infosoft  \\\n",
       "importance                              0.0                    0.0   \n",
       "\n",
       "            company_OpenRent  company_Openreach  company_Openview  \\\n",
       "importance               0.0                0.0               0.0   \n",
       "\n",
       "            company_Operation Warm  company_Optimus E2E Limited  \\\n",
       "importance                     0.0                          0.0   \n",
       "\n",
       "            company_New York City LAW DEPARTMENT  \\\n",
       "importance                                   0.0   \n",
       "\n",
       "            company_Cedar Recruitment Limited  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_Opus Recruitment Solutions  company_Orbit Group  \\\n",
       "importance                                 0.0                  0.0   \n",
       "\n",
       "            company_Ortus-iHealth  company_Oscar Technology  \\\n",
       "importance                    0.0                       0.0   \n",
       "\n",
       "            company_Outsource UK  company_Oviva  \\\n",
       "importance                   0.0            0.0   \n",
       "\n",
       "            company_Oxford Brookes University  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_Oxford University Press  company_P N Daly Limited  \\\n",
       "importance                              0.0                       0.0   \n",
       "\n",
       "            company_PCS Global Tech  company_Beyond Outsourcing Inc.  \\\n",
       "importance                      0.0                              0.0   \n",
       "\n",
       "            company_Octagon Group  company_Ocean Media Group Ltd  \\\n",
       "importance                    0.0                            0.0   \n",
       "\n",
       "            company_Oakmoor Recruitment  company_OSR Recruitment  \\\n",
       "importance                          0.0                      0.0   \n",
       "\n",
       "            company_New York City MAYORS OFFICE OF CONTRACT SVCS  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Blackstone & Cullen  \\\n",
       "importance                          0.0   \n",
       "\n",
       "            company_New York City OFFICE OF EMERGENCY MANAGEMENT  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_New York City TAXI & LIMOUSINE COMMISSION  \\\n",
       "importance                                                0.0   \n",
       "\n",
       "            company_Newbury Building Society  company_Newmedica  \\\n",
       "importance                               0.0                0.0   \n",
       "\n",
       "            company_Newtons Recruitment  company_Nexus Jobs  \\\n",
       "importance                          0.0                 0.0   \n",
       "\n",
       "            company_Nexus Jobs Limited  company_Niantic  \\\n",
       "importance                         0.0              0.0   \n",
       "\n",
       "            company_Niantic International Technology Limited  \\\n",
       "importance                                               0.0   \n",
       "\n",
       "            company_Niche employment solutions  \\\n",
       "importance                                 0.0   \n",
       "\n",
       "            company_Nicholson Glover Consulting  company_Black Hills Energy  \\\n",
       "importance                                  0.0                         0.0   \n",
       "\n",
       "            company_NinetyThousandHours  company_Bitcoin.com  \\\n",
       "importance                          0.0                  0.0   \n",
       "\n",
       "            company_Noria Water Technologies  company_Birdie  \\\n",
       "importance                               0.0             0.0   \n",
       "\n",
       "            company_NuView Analytics  company_Nucleome Therapeutics Ltd  \\\n",
       "importance                       0.0                                0.0   \n",
       "\n",
       "            company_Nuffield Health  company_Nutrisystem  \\\n",
       "importance                      0.0                  0.0   \n",
       "\n",
       "            company_Nuvola Resourcing  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_Nuvola Staffing & Solutions, LLC.  \\\n",
       "importance                                        0.0   \n",
       "\n",
       "            company_ONE WORLD EXPRESS  company_Method Resourcing  \\\n",
       "importance                        0.0                        0.0   \n",
       "\n",
       "            company_Melax Technologies, Inc.  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_IPROS Insurance Professionals  company_Kisaco Research  \\\n",
       "importance                                    0.0                      0.0   \n",
       "\n",
       "            company_Jobs in Letting  company_Jumpshot  \\\n",
       "importance                      0.0               0.0   \n",
       "\n",
       "            company_K3 Business Technologies  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_KIPP Texas Public Schools  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_KIZEN Technologies, Inc.  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Kairos Recruitment Group  company_Kaplan International  \\\n",
       "importance                               0.0                           0.0   \n",
       "\n",
       "            company_Karagozian & Case  company_Brytecore  company_Kew Gardens  \\\n",
       "importance                        0.0                0.0                  0.0   \n",
       "\n",
       "            company_King County  company_King's College London  \\\n",
       "importance                  0.0                            0.0   \n",
       "\n",
       "            company_Kite Group  company_Legal & General Group Plc.  \\\n",
       "importance                 0.0                                 0.0   \n",
       "\n",
       "            company_Klanik Corp  company_Bryant Associates  company_L'Oréal  \\\n",
       "importance                  0.0                        0.0              0.0   \n",
       "\n",
       "            company_LDN Apprenticeships  company_La Fosse Associates  \\\n",
       "importance                          0.0                          0.0   \n",
       "\n",
       "            company_Lambeth Council  company_Brunel University  \\\n",
       "importance                      0.0                        0.0   \n",
       "\n",
       "            company_Language Matters Recruitment Consultants Ltd.  \\\n",
       "importance                                                    0.0   \n",
       "\n",
       "            company_Law Business Research  company_Lawrence Harvey  \\\n",
       "importance                            0.0                      0.0   \n",
       "\n",
       "            company_Bromford  company_Leading UK Pension Fund  \\\n",
       "importance               0.0                              0.0   \n",
       "\n",
       "            company_Buckinghamshire Healthcare NHS Trust  \\\n",
       "importance                                           0.0   \n",
       "\n",
       "            company_Jaguar Land Rover  company_Jackson Rose Ltd  company_JPE  \\\n",
       "importance                        0.0                       0.0          0.0   \n",
       "\n",
       "            company_IPS Group  company_ITTStar Consulting LLC  \\\n",
       "importance                0.0                             0.0   \n",
       "\n",
       "            company_Impact Proteomics  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_Imperial College Healthcare NHS Trust  \\\n",
       "importance                                            0.0   \n",
       "\n",
       "            company_Imperial College London  company_Incisive Media  \\\n",
       "importance                              0.0                     0.0   \n",
       "\n",
       "            company_Indeed  company_CBSbutler  company_Inference Solutions  \\\n",
       "importance             0.0                0.0                          0.0   \n",
       "\n",
       "            company_CBRE  company_Initi8 Recruitment  \\\n",
       "importance           0.0                         0.0   \n",
       "\n",
       "            company_Insights Analytics  company_CAMILLUS HEALTH CONCERN  \\\n",
       "importance                         0.0                              0.0   \n",
       "\n",
       "            company_BusinesStaff  company_IntelliSense.io  \\\n",
       "importance                   0.0                      0.0   \n",
       "\n",
       "            company_Intellidyne Business Systems  company_Burns Sheehan  \\\n",
       "importance                                   0.0                    0.0   \n",
       "\n",
       "            company_InterSTEM Recruitment  company_Interaction Recruitment  \\\n",
       "importance                            0.0                              0.0   \n",
       "\n",
       "            company_Intermedia Global Ltd  company_J&C Associates Ltd  \\\n",
       "importance                            0.0                         0.0   \n",
       "\n",
       "            company_JDX Consulting  company_Bulletproof  \\\n",
       "importance                     0.0                  0.0   \n",
       "\n",
       "            company_JMC Legal Recruitment  company_JP Engineering Recruitment  \\\n",
       "importance                            0.0                                 0.0   \n",
       "\n",
       "            company_Lee College  company_Level Agency  \\\n",
       "importance                  0.0                   0.0   \n",
       "\n",
       "            company_Meet Recruitment  company_Marshall Wolfe  \\\n",
       "importance                       0.0                     0.0   \n",
       "\n",
       "            company_Mammoth Media  company_Manchester Airports Group  \\\n",
       "importance                    0.0                                0.0   \n",
       "\n",
       "            company_Mandeville Recruitment Group  \\\n",
       "importance                                   0.0   \n",
       "\n",
       "            company_Mane Contract Services Limited  company_Mango Solutions  \\\n",
       "importance                                     0.0                      0.0   \n",
       "\n",
       "            company_Manufacturing Recruitment Ltd  \\\n",
       "importance                                    0.0   \n",
       "\n",
       "            company_Maricopa County, AZ  company_Marie Curie  \\\n",
       "importance                          0.0                  0.0   \n",
       "\n",
       "            company_Marie Stopes International  company_Brightwell  \\\n",
       "importance                                 0.0                 0.0   \n",
       "\n",
       "            company_MarketTrust, Inc.  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_Marks Sattin Specialist Recruitment  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_Martin and Conley  company_LevelPrime Limited  \\\n",
       "importance                        0.0                         0.0   \n",
       "\n",
       "            company_Match Digital  company_Matthew Noah  company_Maveneer  \\\n",
       "importance                    0.0                   0.0               0.0   \n",
       "\n",
       "            company_Maxx Builders  \\\n",
       "importance                    0.0   \n",
       "\n",
       "            company_Mayor's Office of Contract Services  company_Mazars  \\\n",
       "importance                                          0.0             0.0   \n",
       "\n",
       "            company_McCann Central  company_McKinsey & Company  \\\n",
       "importance                     0.0                         0.0   \n",
       "\n",
       "            company_Mcgregor Boyall  company_Brightred  company_Brightflag  \\\n",
       "importance                      0.0                0.0                 0.0   \n",
       "\n",
       "            company_MediaAndLanguageJobs.co.uk  company_Magic Carpet AI  \\\n",
       "importance                                 0.0                      0.0   \n",
       "\n",
       "            company_Madiba Inc.  company_MacGregor Black  \\\n",
       "importance                  0.0                      0.0   \n",
       "\n",
       "            company_British Private Equity and Venture Capital Associa...  \\\n",
       "importance                                                            0.0   \n",
       "\n",
       "            company_Liberty Mutual Insurance  company_Lidl  \\\n",
       "importance                               0.0           0.0   \n",
       "\n",
       "            company_Linnk Group Limited  \\\n",
       "importance                          0.0   \n",
       "\n",
       "            company_BrokerCompare.co (eXp Realty)  company_LiveRamp  \\\n",
       "importance                                    0.0               0.0   \n",
       "\n",
       "            company_Livingstone Technologies  company_British Rowing  \\\n",
       "importance                               0.0                     0.0   \n",
       "\n",
       "            company_London School of Hygiene & Tropical Medicine  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_London Tri-Borough Councils  \\\n",
       "importance                                  0.0   \n",
       "\n",
       "            company_Long Beach City College  company_Long Island FQHC, Inc.  \\\n",
       "importance                              0.0                             0.0   \n",
       "\n",
       "            company_Lorien Resourcing  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_Los Angeles County Department of Human Resources  \\\n",
       "importance                                                       0.0   \n",
       "\n",
       "            company_Los Angeles Homeless Services Authority  \\\n",
       "importance                                              0.0   \n",
       "\n",
       "            company_Los Angeles Unified School District  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_Loyal Retainers  company_Lyst  company_M-Brain  \\\n",
       "importance                      0.0           0.0              0.0   \n",
       "\n",
       "            company_MAPSCorps  company_MAQ Software  company_MASS  \\\n",
       "importance                0.0                   0.0           0.0   \n",
       "\n",
       "            company_MBN Recruitment Solutions  company_MBN Solutions  \\\n",
       "importance                                0.0                    0.0   \n",
       "\n",
       "            company_MIND.AI  company_MW Appointments  \\\n",
       "importance              0.0                      0.0   \n",
       "\n",
       "            company_Mortimer Bell International Ltd  \n",
       "importance                                      0.0  \n",
       "\n",
       "[1 rows x 1151 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assess if improvement in model from adding title-features:\n",
    "\n",
    "X_dum = pd.get_dummies(indeed[['company', 'rating', 'search_city']], drop_first=True)\n",
    "X_dum_title = pd.concat([X_dum,indeed.loc[:,'isDS':'isApprentice']], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_dum_title, y, stratify=y, random_state=1)\n",
    "\n",
    "models = [BaggingClassifier(n_estimators=100),\n",
    "          RandomForestClassifier(n_estimators=100),\n",
    "          GradientBoostingClassifier(n_estimators=100,\n",
    "                                     random_state=1,\n",
    "                                     validation_fraction=0.1,\n",
    "                                     max_depth=3,\n",
    "                                     n_iter_no_change=20)]\n",
    "params = [{'max_features': np.linspace(0.3,0.4,3)},\n",
    "          {'max_depth': list(range(2,20,5)),\n",
    "           'min_samples_split': np.linspace(0.3,0.4,3)},\n",
    "          {'learning_rate': np.linspace(.1,1.,4)}]\n",
    "\n",
    "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)\n",
    "\n",
    "for i in range(len(models)):\n",
    "    gs = GridSearchCV(models[i], params[i], n_jobs=2, cv=kf, verbose=1)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Best params:',gs.best_params_)\n",
    "    print('Cross Val Score:',gs.best_score_)\n",
    "    predictions = gs.predict(X_test)\n",
    "    print('Confusion Matrix:')\n",
    "    print(' ')\n",
    "    print(confusion_matrix(y_test, predictions, labels=[0,1]))\n",
    "    print('Classification Report:')\n",
    "    print(' ')\n",
    "    print(classification_report(y_test, predictions))\n",
    "    \n",
    "# Add feature importances for best gs model:\n",
    "print('Best estimator:', gs.best_estimator_)\n",
    "\n",
    "if 'BaggingClassifier' in gs.best_estimator_:\n",
    "    feature_importances = pd.DataFrame({'importance':np.mean([tree.feature_importances_ for tree in gs.estimators_], axis=0)},\n",
    "                                                 index=X_dum_title.columns)\n",
    "else:\n",
    "    feature_importances = pd.DataFrame({'importance':gs.best_estimator_.feature_importances_},\n",
    "                                                 index=X_dum_title.columns)\n",
    "    \n",
    "feature_importances.sort_values('importance',ascending=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  15 out of  15 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_features': 0.4}\n",
      "Cross Val Score: 0.7603405097748083\n",
      "Confusion Matrix:\n",
      " \n",
      "[[306  75]\n",
      " [ 94 275]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.77      0.80      0.78       381\n",
      "         1.0       0.79      0.75      0.76       369\n",
      "\n",
      "    accuracy                           0.77       750\n",
      "   macro avg       0.78      0.77      0.77       750\n",
      "weighted avg       0.78      0.77      0.77       750\n",
      "\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:   20.4s\n",
      "[Parallel(n_jobs=2)]: Done  60 out of  60 | elapsed:   30.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_depth': 17, 'min_samples_split': 0.3}\n",
      "Cross Val Score: 0.7274466716159366\n",
      "Confusion Matrix:\n",
      " \n",
      "[[300  81]\n",
      " [105 264]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.74      0.79      0.76       381\n",
      "         1.0       0.77      0.72      0.74       369\n",
      "\n",
      "    accuracy                           0.75       750\n",
      "   macro avg       0.75      0.75      0.75       750\n",
      "weighted avg       0.75      0.75      0.75       750\n",
      "\n",
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:  2.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'learning_rate': 0.7}\n",
      "Cross Val Score: 0.7492333580796833\n",
      "Confusion Matrix:\n",
      " \n",
      "[[279 102]\n",
      " [101 268]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.73      0.73      0.73       381\n",
      "         1.0       0.72      0.73      0.73       369\n",
      "\n",
      "    accuracy                           0.73       750\n",
      "   macro avg       0.73      0.73      0.73       750\n",
      "weighted avg       0.73      0.73      0.73       750\n",
      "\n",
      "Best estimator: GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.7, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=20, presort='deprecated',\n",
      "                           random_state=1, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n"
     ]
    },
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       "      <th>company_SCRRA/Metrolink</th>\n",
       "      <th>company_Nuffield Health</th>\n",
       "      <th>company_Nucleome Therapeutics Ltd</th>\n",
       "      <th>company_NuView Analytics</th>\n",
       "      <th>company_Long Island FQHC, Inc.</th>\n",
       "      <th>company_London Tri-Borough Councils</th>\n",
       "      <th>company_London School of Hygiene &amp; Tropical Medicine</th>\n",
       "      <th>company_Lloyd Recruitment Services</th>\n",
       "      <th>company_Livingstone Technologies</th>\n",
       "      <th>company_LiveRamp</th>\n",
       "      <th>company_Linnk Group Limited</th>\n",
       "      <th>company_Lidl</th>\n",
       "      <th>company_Liberty Mutual Insurance</th>\n",
       "      <th>company_LevelPrime Limited</th>\n",
       "      <th>company_Level Agency</th>\n",
       "      <th>company_Legal &amp; General Group Plc.</th>\n",
       "      <th>company_Lee College</th>\n",
       "      <th>company_Leading UK Pension Fund</th>\n",
       "      <th>company_Lead Foot Digital</th>\n",
       "      <th>company_Lawrence Harvey</th>\n",
       "      <th>company_Law Business Research</th>\n",
       "      <th>company_Language Matters Recruitment Consultants Ltd.</th>\n",
       "      <th>company_Langley James IT Recruitment</th>\n",
       "      <th>company_Lambeth Council</th>\n",
       "      <th>company_La Fosse Associates</th>\n",
       "      <th>company_LDN Apprenticeships</th>\n",
       "      <th>company_Long Beach City College</th>\n",
       "      <th>company_Longs Peak Advisory Services, LLC</th>\n",
       "      <th>company_L&amp;Q</th>\n",
       "      <th>company_Lorien Resourcing</th>\n",
       "      <th>company_Mane Contract Services Limited</th>\n",
       "      <th>company_Mandeville Recruitment Group</th>\n",
       "      <th>company_Manchester Airports Group</th>\n",
       "      <th>company_Mammoth Media</th>\n",
       "      <th>company_Magic Carpet AI</th>\n",
       "      <th>company_Madiba Inc.</th>\n",
       "      <th>company_MacGregor Black</th>\n",
       "      <th>company_MW Recruitment</th>\n",
       "      <th>company_MW Appointments</th>\n",
       "      <th>company_MIND.AI</th>\n",
       "      <th>company_MBN Solutions</th>\n",
       "      <th>company_MBN Recruitment Solutions</th>\n",
       "      <th>company_MASS</th>\n",
       "      <th>company_MAQ Software</th>\n",
       "      <th>company_MAPSCorps</th>\n",
       "      <th>company_M-Brain</th>\n",
       "      <th>company_Lyst</th>\n",
       "      <th>company_Loyal Retainers</th>\n",
       "      <th>company_Los Angeles Unified School District</th>\n",
       "      <th>company_Los Angeles Homeless Services Authority</th>\n",
       "      <th>company_Los Angeles County Department of Human Resources</th>\n",
       "      <th>company_L'Oréal</th>\n",
       "      <th>company_Klanik Corp</th>\n",
       "      <th>company_Manufacturing Recruitment Ltd</th>\n",
       "      <th>company_Interaction Recruitment</th>\n",
       "      <th>company_InterQuest Group</th>\n",
       "      <th>company_Intellidyne Business Systems</th>\n",
       "      <th>company_IntelliSense.io</th>\n",
       "      <th>company_Insights Analytics</th>\n",
       "      <th>company_Initi8 Recruitment</th>\n",
       "      <th>company_Informatiq Consulting</th>\n",
       "      <th>company_Inference Solutions</th>\n",
       "      <th>company_Indigent Legal Services, Office of</th>\n",
       "      <th>company_Indeed</th>\n",
       "      <th>company_Incisive Media</th>\n",
       "      <th>company_Imperial College London</th>\n",
       "      <th>company_Imperial College Healthcare NHS Trust</th>\n",
       "      <th>company_Impact Proteomics</th>\n",
       "      <th>company_ITTStar Consulting LLC</th>\n",
       "      <th>company_IQPC</th>\n",
       "      <th>company_IPS Group</th>\n",
       "      <th>company_IPROS Insurance Professionals</th>\n",
       "      <th>company_INOV8 Consulting Ltd</th>\n",
       "      <th>company_IC Resources</th>\n",
       "      <th>company_IBD Registry</th>\n",
       "      <th>company_Hylink Digital Solution Limited</th>\n",
       "      <th>company_InterSTEM Recruitment</th>\n",
       "      <th>company_Intermedia Global Ltd</th>\n",
       "      <th>company_Kite Group</th>\n",
       "      <th>company_J&amp;C Associates Ltd</th>\n",
       "      <th>company_Kisaco Research</th>\n",
       "      <th>company_King's College London</th>\n",
       "      <th>company_King County</th>\n",
       "      <th>company_Kew Gardens</th>\n",
       "      <th>company_Kellington Protection Service, LLC</th>\n",
       "      <th>company_Karagozian &amp; Case</th>\n",
       "      <th>company_Kaplan International</th>\n",
       "      <th>company_Kairos Recruitment Group</th>\n",
       "      <th>company_KIZEN Technologies, Inc.</th>\n",
       "      <th>company_KIPP Texas Public Schools</th>\n",
       "      <th>company_K3 Business Technologies</th>\n",
       "      <th>company_Jumpshot</th>\n",
       "      <th>company_Jonathan Lee Recruitment Ltd</th>\n",
       "      <th>company_Jobs in Letting</th>\n",
       "      <th>company_Jenrick Group</th>\n",
       "      <th>company_Jaguar Land Rover</th>\n",
       "      <th>company_Jackson Rose Ltd</th>\n",
       "      <th>company_JPE</th>\n",
       "      <th>company_JP Engineering Recruitment</th>\n",
       "      <th>company_JMC Legal Recruitment</th>\n",
       "      <th>company_JDX Consulting</th>\n",
       "      <th>company_Mango Solutions</th>\n",
       "      <th>company_Maricopa Community Colleges</th>\n",
       "      <th>company_Novation Solutions Ltd</th>\n",
       "      <th>company_New York City DEPT OF PARKS &amp; RECREATION</th>\n",
       "      <th>company_New York City DEPT OF HEALTH/MENTAL HYGIENE</th>\n",
       "      <th>company_New York City DEPT OF DESIGN &amp; CONSTRUCTION</th>\n",
       "      <th>company_New York City DEPARTMENT OF TRANSPORTATION</th>\n",
       "      <th>company_New York City DEPARTMENT OF FINANCE</th>\n",
       "      <th>company_New York City DEPARTMENT OF CORRECTION</th>\n",
       "      <th>company_New York City BOARD OF CORRECTION</th>\n",
       "      <th>company_New York City ADMIN FOR CHILDREN'S SVCS</th>\n",
       "      <th>company_New Business People</th>\n",
       "      <th>company_New Benefits</th>\n",
       "      <th>company_Neumeister &amp; Associates, LLP</th>\n",
       "      <th>company_Neudesic</th>\n",
       "      <th>company_Netleadz</th>\n",
       "      <th>company_Neo Prism Solutions</th>\n",
       "      <th>company_Nelson Global Products Inc.</th>\n",
       "      <th>company_Navigator CRE</th>\n",
       "      <th>company_Navagis</th>\n",
       "      <th>company_National Network of Public Health Institutes</th>\n",
       "      <th>company_National Insurance Crime Bureau</th>\n",
       "      <th>company_National Grid</th>\n",
       "      <th>company_National Entertainment Network</th>\n",
       "      <th>company_Nathan S. Kline Institute</th>\n",
       "      <th>company_New York City DEPT OF INFO TECH &amp; TELECOMM</th>\n",
       "      <th>company_New York City DEPT OF YOUTH &amp; COMM DEV SRVS</th>\n",
       "      <th>company_NHS Blood and Transplant</th>\n",
       "      <th>company_New York City Department of Education .</th>\n",
       "      <th>company_Noria Water Technologies</th>\n",
       "      <th>company_Nonstop World (Tandemworld) Ltd</th>\n",
       "      <th>company_NinetyThousandHours</th>\n",
       "      <th>company_Ninety Thousand Hours</th>\n",
       "      <th>company_Nicholson Glover Consulting</th>\n",
       "      <th>company_Niche employment solutions</th>\n",
       "      <th>company_Niantic International Technology Limited</th>\n",
       "      <th>company_Niantic</th>\n",
       "      <th>company_Nexus Jobs Limited</th>\n",
       "      <th>company_Nexus Jobs</th>\n",
       "      <th>company_Newtons Recruitment</th>\n",
       "      <th>company_Newmedica</th>\n",
       "      <th>company_Newbury Building Society</th>\n",
       "      <th>company_New York City TAXI &amp; LIMOUSINE COMMISSION</th>\n",
       "      <th>company_New York City OFFICE OF EMERGENCY MANAGEMENT</th>\n",
       "      <th>company_New York City NYC HOUSING AUTHORITY</th>\n",
       "      <th>company_New York City MAYORS OFFICE OF CONTRACT SVCS</th>\n",
       "      <th>company_New York City LAW DEPARTMENT</th>\n",
       "      <th>company_New York City FIRE DEPARTMENT</th>\n",
       "      <th>company_New York City FINANCIAL INFO SVCS AGENCY</th>\n",
       "      <th>company_New York City Employees’ Retirement System (NYCERS...</th>\n",
       "      <th>company_NP Group</th>\n",
       "      <th>company_NFuzionIT</th>\n",
       "      <th>company_Maricopa County, AZ</th>\n",
       "      <th>company_Melax Technologies, Inc.</th>\n",
       "      <th>company_Medicine Man Technologies</th>\n",
       "      <th>company_MediaAndLanguageJobs.co.uk</th>\n",
       "      <th>company_Mechsoft Technology (USA) Co., LLC</th>\n",
       "      <th>company_Mcgregor Boyall</th>\n",
       "      <th>company_McKinsey &amp; Company</th>\n",
       "      <th>company_McGregor Boyall</th>\n",
       "      <th>company_McCann Central</th>\n",
       "      <th>company_Mazars</th>\n",
       "      <th>company_Mayor's Office of Contract Services</th>\n",
       "      <th>company_Maxx Builders</th>\n",
       "      <th>company_Maveneer</th>\n",
       "      <th>company_Matthew Noah</th>\n",
       "      <th>company_Match Digital</th>\n",
       "      <th>company_Mason Blake</th>\n",
       "      <th>company_Martin and Conley</th>\n",
       "      <th>company_Marshall Wolfe</th>\n",
       "      <th>company_Marks Sattin Specialist Recruitment</th>\n",
       "      <th>company_MarketTrust, Inc.</th>\n",
       "      <th>company_Market Vector</th>\n",
       "      <th>company_Marie Stopes International</th>\n",
       "      <th>company_Marie Curie</th>\n",
       "      <th>company_Meet Recruitment</th>\n",
       "      <th>company_Method Resourcing</th>\n",
       "      <th>company_NFU Mutual</th>\n",
       "      <th>company_Metro Bank PLC</th>\n",
       "      <th>company_NFP People Limited</th>\n",
       "      <th>company_Myriad</th>\n",
       "      <th>company_MySense</th>\n",
       "      <th>company_Mudano</th>\n",
       "      <th>company_Motorway</th>\n",
       "      <th>company_Mortimer Bell International Ltd</th>\n",
       "      <th>company_Morson International</th>\n",
       "      <th>company_Morgan McKinley</th>\n",
       "      <th>company_Morgan Law</th>\n",
       "      <th>company_Morehouse College</th>\n",
       "      <th>company_Moorfields Eye Hospital NHS Foundation Trust</th>\n",
       "      <th>company_Moonshot CVE</th>\n",
       "      <th>company_Monzo</th>\n",
       "      <th>company_Monarch</th>\n",
       "      <th>company_Moixa Energy Holdings</th>\n",
       "      <th>company_Miriad Products Ltd</th>\n",
       "      <th>company_Milshar, LLC</th>\n",
       "      <th>company_Microsoft</th>\n",
       "      <th>company_Micro Focus</th>\n",
       "      <th>company_Michael Page UK</th>\n",
       "      <th>company_MetroPlus Health Plan</th>\n",
       "      <th>company_Smash Entertainment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>importance</th>\n",
       "      <td>0.047372</td>\n",
       "      <td>0.035011</td>\n",
       "      <td>0.033113</td>\n",
       "      <td>0.028231</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.024717</td>\n",
       "      <td>0.02306</td>\n",
       "      <td>0.021654</td>\n",
       "      <td>0.020894</td>\n",
       "      <td>0.018033</td>\n",
       "      <td>0.017326</td>\n",
       "      <td>0.016748</td>\n",
       "      <td>0.016592</td>\n",
       "      <td>0.014716</td>\n",
       "      <td>0.014281</td>\n",
       "      <td>0.014081</td>\n",
       "      <td>0.01307</td>\n",
       "      <td>0.012896</td>\n",
       "      <td>0.011443</td>\n",
       "      <td>0.010645</td>\n",
       "      <td>0.009333</td>\n",
       "      <td>0.009201</td>\n",
       "      <td>0.008297</td>\n",
       "      <td>0.007935</td>\n",
       "      <td>0.007858</td>\n",
       "      <td>0.00783</td>\n",
       "      <td>0.007504</td>\n",
       "      <td>0.00737</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>0.007122</td>\n",
       "      <td>0.007048</td>\n",
       "      <td>0.00695</td>\n",
       "      <td>0.006923</td>\n",
       "      <td>0.006762</td>\n",
       "      <td>0.00672</td>\n",
       "      <td>0.006531</td>\n",
       "      <td>0.006471</td>\n",
       "      <td>0.006356</td>\n",
       "      <td>0.006063</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.005445</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.005286</td>\n",
       "      <td>0.005284</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.005049</td>\n",
       "      <td>0.004853</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.004593</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.00447</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.004384</td>\n",
       "      <td>0.004375</td>\n",
       "      <td>0.004335</td>\n",
       "      <td>0.004254</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.004218</td>\n",
       "      <td>0.00414</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.004105</td>\n",
       "      <td>0.004095</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.00403</td>\n",
       "      <td>0.003996</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.003635</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>0.003489</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.003381</td>\n",
       "      <td>0.003301</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0.003245</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.003164</td>\n",
       "      <td>0.003144</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.003075</td>\n",
       "      <td>0.003072</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.00297</td>\n",
       "      <td>0.002922</td>\n",
       "      <td>0.002851</td>\n",
       "      <td>0.00285</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.002811</td>\n",
       "      <td>0.002805</td>\n",
       "      <td>0.002797</td>\n",
       "      <td>0.002791</td>\n",
       "      <td>0.002764</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>0.002714</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>0.002648</td>\n",
       "      <td>0.002628</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>0.002605</td>\n",
       "      <td>0.002571</td>\n",
       "      <td>0.00255</td>\n",
       "      <td>0.002531</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>0.002462</td>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.002448</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>0.002408</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.002281</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.002271</td>\n",
       "      <td>0.002247</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.002125</td>\n",
       "      <td>0.002114</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.002068</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.002022</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>0.001983</td>\n",
       "      <td>0.001967</td>\n",
       "      <td>0.001964</td>\n",
       "      <td>0.001958</td>\n",
       "      <td>0.001933</td>\n",
       "      <td>0.00193</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.001909</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.001841</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.001793</td>\n",
       "      <td>0.00178</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.001665</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.001651</td>\n",
       "      <td>0.001644</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>0.001612</td>\n",
       "      <td>0.00161</td>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>0.001536</td>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.001486</td>\n",
       "      <td>0.001479</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.001442</td>\n",
       "      <td>0.001439</td>\n",
       "      <td>0.001436</td>\n",
       "      <td>0.001432</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.001411</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.001348</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.001344</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>0.001304</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.001296</td>\n",
       "      <td>0.001294</td>\n",
       "      <td>0.001291</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.00127</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.001262</td>\n",
       "      <td>0.001262</td>\n",
       "      <td>0.00125</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>0.001219</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.00113</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0.000936</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.000263</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000119</td>\n",
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       "importance        0.0013  0.001299                               0.001296   \n",
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       "\n",
       "            company_Nathan S. Kline Institute  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_New York City DEPT OF INFO TECH & TELECOMM  \\\n",
       "importance                                                 0.0   \n",
       "\n",
       "            company_New York City DEPT OF YOUTH & COMM DEV SRVS  \\\n",
       "importance                                                  0.0   \n",
       "\n",
       "            company_NHS Blood and Transplant  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_New York City Department of Education .  \\\n",
       "importance                                              0.0   \n",
       "\n",
       "            company_Noria Water Technologies  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Nonstop World (Tandemworld) Ltd  \\\n",
       "importance                                      0.0   \n",
       "\n",
       "            company_NinetyThousandHours  company_Ninety Thousand Hours  \\\n",
       "importance                          0.0                            0.0   \n",
       "\n",
       "            company_Nicholson Glover Consulting  \\\n",
       "importance                                  0.0   \n",
       "\n",
       "            company_Niche employment solutions  \\\n",
       "importance                                 0.0   \n",
       "\n",
       "            company_Niantic International Technology Limited  company_Niantic  \\\n",
       "importance                                               0.0              0.0   \n",
       "\n",
       "            company_Nexus Jobs Limited  company_Nexus Jobs  \\\n",
       "importance                         0.0                 0.0   \n",
       "\n",
       "            company_Newtons Recruitment  company_Newmedica  \\\n",
       "importance                          0.0                0.0   \n",
       "\n",
       "            company_Newbury Building Society  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_New York City TAXI & LIMOUSINE COMMISSION  \\\n",
       "importance                                                0.0   \n",
       "\n",
       "            company_New York City OFFICE OF EMERGENCY MANAGEMENT  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_New York City NYC HOUSING AUTHORITY  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_New York City MAYORS OFFICE OF CONTRACT SVCS  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_New York City LAW DEPARTMENT  \\\n",
       "importance                                   0.0   \n",
       "\n",
       "            company_New York City FIRE DEPARTMENT  \\\n",
       "importance                                    0.0   \n",
       "\n",
       "            company_New York City FINANCIAL INFO SVCS AGENCY  \\\n",
       "importance                                               0.0   \n",
       "\n",
       "            company_New York City Employees’ Retirement System (NYCERS...  \\\n",
       "importance                                                            0.0   \n",
       "\n",
       "            company_NP Group  company_NFuzionIT  company_Maricopa County, AZ  \\\n",
       "importance               0.0                0.0                          0.0   \n",
       "\n",
       "            company_Melax Technologies, Inc.  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Medicine Man Technologies  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_MediaAndLanguageJobs.co.uk  \\\n",
       "importance                                 0.0   \n",
       "\n",
       "            company_Mechsoft Technology (USA) Co., LLC  \\\n",
       "importance                                         0.0   \n",
       "\n",
       "            company_Mcgregor Boyall  company_McKinsey & Company  \\\n",
       "importance                      0.0                         0.0   \n",
       "\n",
       "            company_McGregor Boyall  company_McCann Central  company_Mazars  \\\n",
       "importance                      0.0                     0.0             0.0   \n",
       "\n",
       "            company_Mayor's Office of Contract Services  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_Maxx Builders  company_Maveneer  company_Matthew Noah  \\\n",
       "importance                    0.0               0.0                   0.0   \n",
       "\n",
       "            company_Match Digital  company_Mason Blake  \\\n",
       "importance                    0.0                  0.0   \n",
       "\n",
       "            company_Martin and Conley  company_Marshall Wolfe  \\\n",
       "importance                        0.0                     0.0   \n",
       "\n",
       "            company_Marks Sattin Specialist Recruitment  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_MarketTrust, Inc.  company_Market Vector  \\\n",
       "importance                        0.0                    0.0   \n",
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       "            company_Marie Stopes International  company_Marie Curie  \\\n",
       "importance                                 0.0                  0.0   \n",
       "\n",
       "            company_Meet Recruitment  company_Method Resourcing  \\\n",
       "importance                       0.0                        0.0   \n",
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       "            company_NFU Mutual  company_Metro Bank PLC  \\\n",
       "importance                 0.0                     0.0   \n",
       "\n",
       "            company_NFP People Limited  company_Myriad  company_MySense  \\\n",
       "importance                         0.0             0.0              0.0   \n",
       "\n",
       "            company_Mudano  company_Motorway  \\\n",
       "importance             0.0               0.0   \n",
       "\n",
       "            company_Mortimer Bell International Ltd  \\\n",
       "importance                                      0.0   \n",
       "\n",
       "            company_Morson International  company_Morgan McKinley  \\\n",
       "importance                           0.0                      0.0   \n",
       "\n",
       "            company_Morgan Law  company_Morehouse College  \\\n",
       "importance                 0.0                        0.0   \n",
       "\n",
       "            company_Moorfields Eye Hospital NHS Foundation Trust  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Moonshot CVE  company_Monzo  company_Monarch  \\\n",
       "importance                   0.0            0.0              0.0   \n",
       "\n",
       "            company_Moixa Energy Holdings  company_Miriad Products Ltd  \\\n",
       "importance                            0.0                          0.0   \n",
       "\n",
       "            company_Milshar, LLC  company_Microsoft  company_Micro Focus  \\\n",
       "importance                   0.0                0.0                  0.0   \n",
       "\n",
       "            company_Michael Page UK  company_MetroPlus Health Plan  \\\n",
       "importance                      0.0                            0.0   \n",
       "\n",
       "            company_Smash Entertainment  \n",
       "importance                          0.0  \n",
       "\n",
       "[1 rows x 1651 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Incorporate other text features from the title or summary that you believe will predict the salary:\n",
    "\n",
    "tvec = TfidfVectorizer(stop_words='english', token_pattern='[A-Za-z]+', ngram_range=(1,2), max_features=500)\n",
    "tvec.fit(indeed['summary'])\n",
    "\n",
    "tvec_summary = pd.DataFrame(tvec.transform(indeed['summary']).toarray(), columns=tvec.get_feature_names())\n",
    "\n",
    "X_summary = pd.concat([X_dum_title,tvec_summary], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_summary, y, stratify=y, random_state=1)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "models = [BaggingClassifier(n_estimators=100),\n",
    "          RandomForestClassifier(n_estimators=100),\n",
    "          GradientBoostingClassifier(n_estimators=100,\n",
    "                                     random_state=1,\n",
    "                                     validation_fraction=0.1,\n",
    "                                     max_depth=3,\n",
    "                                     n_iter_no_change=20)]\n",
    "params = [{'max_features': np.linspace(0.3,0.4,3)},\n",
    "          {'max_depth': list(range(2,20,5)),\n",
    "           'min_samples_split': np.linspace(0.3,0.4,3)},\n",
    "          {'learning_rate': np.linspace(.1,1.,4)}]\n",
    "\n",
    "for i in range(len(models)):\n",
    "    gs = GridSearchCV(models[i], params[i], n_jobs=2, cv=kf, verbose=1)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Best params:',gs.best_params_)\n",
    "    print('Cross Val Score:',gs.best_score_)\n",
    "    predictions = gs.predict(X_test)\n",
    "    print('Confusion Matrix:')\n",
    "    print(' ')\n",
    "    print(confusion_matrix(y_test, predictions, labels=[0,1]))\n",
    "    print('Classification Report:')\n",
    "    print(' ')\n",
    "    print(classification_report(y_test, predictions))\n",
    "    \n",
    "# Add feature importances for best gs model:\n",
    "print('Best estimator:', gs.best_estimator_)\n",
    "\n",
    "if 'BaggingClassifier' in gs.best_estimator_:\n",
    "    feature_importances = pd.DataFrame({'importance':np.mean([tree.feature_importances_ for tree in gs.estimators_], axis=0)},\n",
    "                                                 index=X_summary.columns)\n",
    "else:\n",
    "    feature_importances = pd.DataFrame({'importance':gs.best_estimator_.feature_importances_},\n",
    "                                                 index=X_summary.columns)\n",
    "    \n",
    "feature_importances.sort_values('importance',ascending=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model actually deteriorates when text from the summary is added, so it appears that location and \n",
    "# features from the job title were key in correct classification.\n",
    "\n",
    "# A ~60% accuracy was achieved only using search_city, which imporoved to 80% when included title features.\n",
    "# Vectorizer helped with feature engineering as an intermediate step.\n",
    "\n",
    "# A slight deterioration in score from adding text from summmary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "4fb29de2-5b98-474c-a4ad-5170b72b9aea"
   },
   "source": [
    "### Model evaluation:\n",
    "\n",
    "Your boss would rather tell a client incorrectly that they would get a lower salary job than tell a client incorrectly that they would get a high salary job. Adjust one of your models to ease his mind, and explain what it is doing and any tradeoffs.\n",
    "\n",
    "\n",
    "- Use cross-validation to evaluate your models.\n",
    "- Evaluate the accuracy, AUC, precision and recall of the models.\n",
    "- Plot the ROC and precision-recall curves for at least one of your models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class_0_pp</th>\n",
       "      <th>class_1_pp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.789904</td>\n",
       "      <td>0.210096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.946806</td>\n",
       "      <td>0.053194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.836273</td>\n",
       "      <td>0.163727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.395738</td>\n",
       "      <td>0.604262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.146663</td>\n",
       "      <td>0.853337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_0_pp  class_1_pp\n",
       "0    0.789904    0.210096\n",
       "1    0.946806    0.053194\n",
       "2    0.836273    0.163727\n",
       "3    0.395738    0.604262\n",
       "4    0.146663    0.853337"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best model was with city + title features (ie. excluding Summary text), so we reset X to that:\n",
    "\n",
    "X_dum = pd.get_dummies(indeed[['company', 'rating', 'search_city']], drop_first=True)\n",
    "X_dum_title = pd.concat([X_dum,indeed.loc[:,'isDS':'isApprentice']], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_dum_title, y, stratify=y, random_state=1)\n",
    "\n",
    "# And re-run the best estimator:\n",
    "\n",
    "best_model = GradientBoostingClassifier(n_estimators=100,\n",
    "                                        learning_rate=0.4,\n",
    "                                        random_state=1,\n",
    "                                        validation_fraction=0.1,\n",
    "                                        max_depth=3,\n",
    "                                        n_iter_no_change=20)\n",
    "best_model.fit(X_train, y_train)\n",
    "\n",
    "y_pp = pd.DataFrame(best_model.predict_proba(X_test), columns=['class_0_pp','class_1_pp'])\n",
    "y_pp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 842
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score, recall_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "\n",
    "# For class 0, find the area under the curve\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pp.class_0_pp, pos_label=0)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "precision, recall, threshold = precision_recall_curve(y_test, y_pp.class_0_pp, pos_label=0)\n",
    "\n",
    "# Plot ROC and precision-recall curves for class 0\n",
    "fig, ax = plt.subplots(1, 3, figsize=[14, 4])\n",
    "\n",
    "ax[0].plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "ax[0].plot([0, 1], [0, 1], 'k--')\n",
    "ax[0].set_xlim([-0.05, 1.0])\n",
    "ax[0].set_ylim([-0.05, 1.05])\n",
    "ax[0].set_xlabel('False Positive Rate', fontsize=12)\n",
    "ax[0].set_ylabel('True Positive Rate', fontsize=12)\n",
    "ax[0].set_title('ROC curve', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "ax[1].plot(recall, precision, label='PR curve')\n",
    "ax[1].set_xlim([-0.05, 1.0])\n",
    "ax[1].set_ylim([-0.05, 1.05])\n",
    "ax[1].set_xlabel('Recall', fontsize=12)\n",
    "ax[1].set_ylabel('Precision', fontsize=12)\n",
    "ax[1].set_title('PR curve', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "ax[2].plot(threshold, recall[:-1], label='Recall vs Threshold')\n",
    "ax[2].set_xlim([-0.05, 1.0])\n",
    "ax[2].set_ylim([-0.05, 1.05])\n",
    "ax[2].set_xlabel('Threshold = P(Class 0)', fontsize=12)\n",
    "ax[2].set_ylabel('Recall(Class 0)', fontsize=12)\n",
    "ax[2].set_title('Recall vs Prob(Model)', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  15 out of  15 | elapsed:   42.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_features': 0.3}\n",
      "Cross Val Score: 0.8023491055964813\n",
      "Confusion Matrix:\n",
      " \n",
      "[[308  73]\n",
      " [ 78 291]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.80      0.81      0.80       381\n",
      "         1.0       0.80      0.79      0.79       369\n",
      "\n",
      "    accuracy                           0.80       750\n",
      "   macro avg       0.80      0.80      0.80       750\n",
      "weighted avg       0.80      0.80      0.80       750\n",
      "\n",
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  46 tasks      | elapsed:   19.4s\n",
      "[Parallel(n_jobs=2)]: Done  60 out of  60 | elapsed:   22.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'max_depth': 17, 'min_samples_split': 0.3}\n",
      "Cross Val Score: 0.7345738703621576\n",
      "Confusion Matrix:\n",
      " \n",
      "[[279 102]\n",
      " [ 99 270]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.74      0.73      0.74       381\n",
      "         1.0       0.73      0.73      0.73       369\n",
      "\n",
      "    accuracy                           0.73       750\n",
      "   macro avg       0.73      0.73      0.73       750\n",
      "weighted avg       0.73      0.73      0.73       750\n",
      "\n",
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  20 out of  20 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best params: {'learning_rate': 1.0}\n",
      "Cross Val Score: 0.7704804001320931\n",
      "Confusion Matrix:\n",
      " \n",
      "[[321  60]\n",
      " [103 266]]\n",
      "Classification Report:\n",
      " \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.84      0.80       381\n",
      "         1.0       0.82      0.72      0.77       369\n",
      "\n",
      "    accuracy                           0.78       750\n",
      "   macro avg       0.79      0.78      0.78       750\n",
      "weighted avg       0.79      0.78      0.78       750\n",
      "\n",
      "Best estimator: GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
      "                           learning_rate=1.0, loss='deviance', max_depth=3,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=1, min_samples_split=2,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "                           n_iter_no_change=20, presort='deprecated',\n",
      "                           random_state=1, subsample=1.0, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0,\n",
      "                           warm_start=False)\n"
     ]
    },
    {
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       "      <th>company_MAQ Software</th>\n",
       "      <th>company_MASS</th>\n",
       "      <th>company_MBN Recruitment Solutions</th>\n",
       "      <th>company_MIND.AI</th>\n",
       "      <th>company_London School of Hygiene &amp; Tropical Medicine</th>\n",
       "      <th>company_MW Appointments</th>\n",
       "      <th>company_MW Recruitment</th>\n",
       "      <th>company_MacGregor Black</th>\n",
       "      <th>company_Madiba Inc.</th>\n",
       "      <th>company_Magic Carpet AI</th>\n",
       "      <th>company_Mammoth Media</th>\n",
       "      <th>company_Manchester Airports Group</th>\n",
       "      <th>company_Mandeville Recruitment Group</th>\n",
       "      <th>company_Mane Contract Services Limited</th>\n",
       "      <th>company_Mango Solutions</th>\n",
       "      <th>company_Manufacturing Recruitment Ltd</th>\n",
       "      <th>company_Maricopa Community Colleges</th>\n",
       "      <th>company_Maricopa County, AZ</th>\n",
       "      <th>company_Marie Curie</th>\n",
       "      <th>company_London Tri-Borough Councils</th>\n",
       "      <th>company_Lloyd Recruitment Services</th>\n",
       "      <th>company_Jumpshot</th>\n",
       "      <th>company_LDN Apprenticeships</th>\n",
       "      <th>company_KIPP Texas Public Schools</th>\n",
       "      <th>company_KIZEN Technologies, Inc.</th>\n",
       "      <th>company_Kairos Recruitment Group</th>\n",
       "      <th>company_Kaplan International</th>\n",
       "      <th>company_Karagozian &amp; Case</th>\n",
       "      <th>company_Kellington Protection Service, LLC</th>\n",
       "      <th>company_Kew Gardens</th>\n",
       "      <th>company_King County</th>\n",
       "      <th>company_King's College London</th>\n",
       "      <th>company_Kisaco Research</th>\n",
       "      <th>company_Kite Group</th>\n",
       "      <th>company_Klanik Corp</th>\n",
       "      <th>company_L&amp;Q</th>\n",
       "      <th>company_L'Oréal</th>\n",
       "      <th>company_La Fosse Associates</th>\n",
       "      <th>company_Livingstone Technologies</th>\n",
       "      <th>company_Lambeth Council</th>\n",
       "      <th>company_Langley James IT Recruitment</th>\n",
       "      <th>company_Language Matters Recruitment Consultants Ltd.</th>\n",
       "      <th>company_Law Business Research</th>\n",
       "      <th>company_Lawrence Harvey</th>\n",
       "      <th>company_Leading UK Pension Fund</th>\n",
       "      <th>company_Lee College</th>\n",
       "      <th>company_Legal &amp; General Group Plc.</th>\n",
       "      <th>company_Level Agency</th>\n",
       "      <th>company_LevelPrime Limited</th>\n",
       "      <th>company_Liberty Mutual Insurance</th>\n",
       "      <th>company_Lidl</th>\n",
       "      <th>company_Linnk Group Limited</th>\n",
       "      <th>company_LiveRamp</th>\n",
       "      <th>company_Marie Stopes International</th>\n",
       "      <th>company_MarketTrust, Inc.</th>\n",
       "      <th>company_Marks Sattin Specialist Recruitment</th>\n",
       "      <th>company_National Insurance Crime Bureau</th>\n",
       "      <th>company_Morson International</th>\n",
       "      <th>company_4OC</th>\n",
       "      <th>company_Motorway</th>\n",
       "      <th>company_Mudano</th>\n",
       "      <th>company_MySense</th>\n",
       "      <th>company_Myriad</th>\n",
       "      <th>company_NFP People Limited</th>\n",
       "      <th>company_NFU Mutual</th>\n",
       "      <th>company_NFuzionIT</th>\n",
       "      <th>company_NHS Blood and Transplant</th>\n",
       "      <th>company_NP Group</th>\n",
       "      <th>company_Nathan S. Kline Institute</th>\n",
       "      <th>company_National Entertainment Network</th>\n",
       "      <th>company_National Grid</th>\n",
       "      <th>company_National Network of Public Health Institutes</th>\n",
       "      <th>company_Marshall Wolfe</th>\n",
       "      <th>company_Navagis</th>\n",
       "      <th>company_Nelson Global Products Inc.</th>\n",
       "      <th>company_Netleadz</th>\n",
       "      <th>company_Neudesic</th>\n",
       "      <th>company_New Benefits</th>\n",
       "      <th>company_New Business People</th>\n",
       "      <th>company_New York City ADMIN FOR CHILDREN'S SVCS</th>\n",
       "      <th>company_New York City BOARD OF CORRECTION</th>\n",
       "      <th>company_New York City DEPARTMENT OF CORRECTION</th>\n",
       "      <th>company_New York City DEPARTMENT OF FINANCE</th>\n",
       "      <th>company_New York City DEPARTMENT OF TRANSPORTATION</th>\n",
       "      <th>company_New York City DEPT OF DESIGN &amp; CONSTRUCTION</th>\n",
       "      <th>company_New York City DEPT OF HEALTH/MENTAL HYGIENE</th>\n",
       "      <th>company_New York City DEPT OF INFO TECH &amp; TELECOMM</th>\n",
       "      <th>company_Morgan McKinley</th>\n",
       "      <th>company_Morgan Law</th>\n",
       "      <th>company_Morehouse College</th>\n",
       "      <th>company_Moorfields Eye Hospital NHS Foundation Trust</th>\n",
       "      <th>company_Martin and Conley</th>\n",
       "      <th>company_Mason Blake</th>\n",
       "      <th>company_Match Digital</th>\n",
       "      <th>company_Matthew Noah</th>\n",
       "      <th>company_Maveneer</th>\n",
       "      <th>company_Maxx Builders</th>\n",
       "      <th>company_Mayor's Office of Contract Services</th>\n",
       "      <th>company_Mazars</th>\n",
       "      <th>company_McCann Central</th>\n",
       "      <th>company_McGregor Boyall</th>\n",
       "      <th>company_McKinsey &amp; Company</th>\n",
       "      <th>company_Mcgregor Boyall</th>\n",
       "      <th>company_MediaAndLanguageJobs.co.uk</th>\n",
       "      <th>company_Medicine Man Technologies</th>\n",
       "      <th>company_Meet Recruitment</th>\n",
       "      <th>company_Melax Technologies, Inc.</th>\n",
       "      <th>company_Memorial MRI and Diagnostic LLC</th>\n",
       "      <th>company_Method Resourcing</th>\n",
       "      <th>company_Metro Bank PLC</th>\n",
       "      <th>company_MetroPlus Health Plan</th>\n",
       "      <th>company_Michael Page UK</th>\n",
       "      <th>company_Micro Focus</th>\n",
       "      <th>company_Microsoft</th>\n",
       "      <th>company_Milshar, LLC</th>\n",
       "      <th>company_Miriad Products Ltd</th>\n",
       "      <th>company_Moixa Energy Holdings</th>\n",
       "      <th>company_Monarch</th>\n",
       "      <th>company_Monzo</th>\n",
       "      <th>company_Moonshot CVE</th>\n",
       "      <th>company_K3 Business Technologies</th>\n",
       "      <th>company_Jonathan Lee Recruitment Ltd</th>\n",
       "      <th>company_Eligo Recruitment</th>\n",
       "      <th>company_Gemini</th>\n",
       "      <th>company_Formative Content</th>\n",
       "      <th>company_Foster Denovo</th>\n",
       "      <th>company_FourFront</th>\n",
       "      <th>company_Foxtons</th>\n",
       "      <th>company_Frazer-Nash Consultancy</th>\n",
       "      <th>company_French Selection</th>\n",
       "      <th>company_Freshtech IT</th>\n",
       "      <th>company_Futureheads Recruitment</th>\n",
       "      <th>company_G-Research</th>\n",
       "      <th>company_GDS Group</th>\n",
       "      <th>company_GMAD</th>\n",
       "      <th>company_GVC Careers</th>\n",
       "      <th>company_GVC Holdings</th>\n",
       "      <th>company_GatenbySanderson</th>\n",
       "      <th>company_Gemini People</th>\n",
       "      <th>company_Flux</th>\n",
       "      <th>company_Georgia Department of Public Health</th>\n",
       "      <th>company_Gi Group</th>\n",
       "      <th>company_Gigaclear</th>\n",
       "      <th>company_Give A Grad A Go</th>\n",
       "      <th>company_Global Accounting Network</th>\n",
       "      <th>company_Global Risk Partners Group</th>\n",
       "      <th>company_GlobalData</th>\n",
       "      <th>company_GlobalData PLC</th>\n",
       "      <th>company_Globizz Corp.</th>\n",
       "      <th>company_Go, Inc.</th>\n",
       "      <th>company_Grandview Corporation</th>\n",
       "      <th>company_Gravity Technology Solutions</th>\n",
       "      <th>company_Greene Lab</th>\n",
       "      <th>company_Greenwell Gleeson</th>\n",
       "      <th>company_Focus Multimedia Limited</th>\n",
       "      <th>company_Florida International University</th>\n",
       "      <th>company_Jobs in Letting</th>\n",
       "      <th>company_Exact Sourcing ltd</th>\n",
       "      <th>company_Eligo Recruitment Ltd</th>\n",
       "      <th>company_Elite Crowdfunding limted</th>\n",
       "      <th>company_Emma Technologies LTD</th>\n",
       "      <th>company_Emma Walsh Talent</th>\n",
       "      <th>company_Emory School of Medicine</th>\n",
       "      <th>company_Empirical Search</th>\n",
       "      <th>company_Energy Assets</th>\n",
       "      <th>company_Enterprise Recruitment Limited</th>\n",
       "      <th>company_Estio Technology</th>\n",
       "      <th>company_Estio Training</th>\n",
       "      <th>company_Eurowagens</th>\n",
       "      <th>company_Evermore Global</th>\n",
       "      <th>company_Everpress</th>\n",
       "      <th>company_Evolution Recruitment Solutions</th>\n",
       "      <th>company_Executive Recruitment Services</th>\n",
       "      <th>company_Florida Grand Opera</th>\n",
       "      <th>company_Expedia Group</th>\n",
       "      <th>company_Explore Group</th>\n",
       "      <th>company_Express Recruitment</th>\n",
       "      <th>company_Ezoic</th>\n",
       "      <th>company_FACEIT</th>\n",
       "      <th>company_FDM Group</th>\n",
       "      <th>company_FINTEC recruit</th>\n",
       "      <th>company_FL Group UK Ltd</th>\n",
       "      <th>company_FTD</th>\n",
       "      <th>company_Fair Recruitment</th>\n",
       "      <th>company_Farm-Hand Ltd</th>\n",
       "      <th>company_Five Guys</th>\n",
       "      <th>company_Flight Centre (UK) Limited</th>\n",
       "      <th>company_Flight Centre Travel Group</th>\n",
       "      <th>company_Growth Intelligence</th>\n",
       "      <th>company_Guru</th>\n",
       "      <th>company_Guru Systems</th>\n",
       "      <th>company_Informatiq Consulting</th>\n",
       "      <th>company_Hunter and Jones</th>\n",
       "      <th>company_Huntress</th>\n",
       "      <th>company_Hylink Digital Solution Limited</th>\n",
       "      <th>company_IBD Registry</th>\n",
       "      <th>company_INOV8 Consulting Ltd</th>\n",
       "      <th>company_IPROS Insurance Professionals</th>\n",
       "      <th>company_IPS Group</th>\n",
       "      <th>company_IQPC</th>\n",
       "      <th>company_ITTStar Consulting LLC</th>\n",
       "      <th>company_Imperial College Healthcare NHS Trust</th>\n",
       "      <th>company_Imperial College London</th>\n",
       "      <th>company_Incisive Media</th>\n",
       "      <th>company_Indeed</th>\n",
       "      <th>company_Inference Solutions</th>\n",
       "      <th>company_Initi8 Recruitment</th>\n",
       "      <th>company_Gwinnett County</th>\n",
       "      <th>company_Insights Analytics</th>\n",
       "      <th>company_Inspire People</th>\n",
       "      <th>company_IntelliSense.io</th>\n",
       "      <th>company_Intellidyne Business Systems</th>\n",
       "      <th>company_InterSTEM Recruitment</th>\n",
       "      <th>company_Intermedia Global Ltd</th>\n",
       "      <th>company_J&amp;C Associates Ltd</th>\n",
       "      <th>company_JDX Consulting</th>\n",
       "      <th>company_JMC Legal Recruitment</th>\n",
       "      <th>company_JP Engineering Recruitment</th>\n",
       "      <th>company_JPE</th>\n",
       "      <th>company_Jackson Rose Ltd</th>\n",
       "      <th>company_Jaguar Land Rover</th>\n",
       "      <th>company_Jenrick Group</th>\n",
       "      <th>company_Hudson Shribman</th>\n",
       "      <th>company_Howett Thorpe</th>\n",
       "      <th>company_Horniman Museum and Gardens</th>\n",
       "      <th>company_Horizon Air</th>\n",
       "      <th>company_HCL America Inc</th>\n",
       "      <th>company_HCML</th>\n",
       "      <th>company_HGS Digital</th>\n",
       "      <th>company_HM Revenue and Customs</th>\n",
       "      <th>company_HOLLA</th>\n",
       "      <th>company_HRIS Associates Ltd</th>\n",
       "      <th>company_HamlynWilliams</th>\n",
       "      <th>company_Hanami International</th>\n",
       "      <th>company_Handle Recruitment Ltd</th>\n",
       "      <th>company_Hanson Wade</th>\n",
       "      <th>company_Harcourt Matthews</th>\n",
       "      <th>company_Harnham</th>\n",
       "      <th>company_Harnham US</th>\n",
       "      <th>company_Harrington Starr</th>\n",
       "      <th>company_Harvey Thomas</th>\n",
       "      <th>company_Hasson Associates</th>\n",
       "      <th>company_Hawke Media</th>\n",
       "      <th>company_Head Resourcing Limited</th>\n",
       "      <th>company_Health Education England</th>\n",
       "      <th>company_Hearst</th>\n",
       "      <th>company_Heavens Recruitment Ltd</th>\n",
       "      <th>company_HeliosX</th>\n",
       "      <th>company_Henderson Drake</th>\n",
       "      <th>company_Hernshead Recruitment</th>\n",
       "      <th>company_Hero Labs</th>\n",
       "      <th>company_Hewett Recruitment</th>\n",
       "      <th>company_Hinduja Global Solutions</th>\n",
       "      <th>company_Holler Technologies, Inc.</th>\n",
       "      <th>company_Homes England</th>\n",
       "      <th>company_Mortimer Bell International Ltd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>importance</th>\n",
       "      <td>0.059205</td>\n",
       "      <td>0.057063</td>\n",
       "      <td>0.042217</td>\n",
       "      <td>0.037662</td>\n",
       "      <td>0.035416</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>0.034791</td>\n",
       "      <td>0.033624</td>\n",
       "      <td>0.02859</td>\n",
       "      <td>0.028545</td>\n",
       "      <td>0.024193</td>\n",
       "      <td>0.024165</td>\n",
       "      <td>0.022827</td>\n",
       "      <td>0.021697</td>\n",
       "      <td>0.021151</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.017932</td>\n",
       "      <td>0.014164</td>\n",
       "      <td>0.013354</td>\n",
       "      <td>0.011317</td>\n",
       "      <td>0.009762</td>\n",
       "      <td>0.009731</td>\n",
       "      <td>0.009633</td>\n",
       "      <td>0.009222</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.006976</td>\n",
       "      <td>0.006884</td>\n",
       "      <td>0.006718</td>\n",
       "      <td>0.005898</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.005314</td>\n",
       "      <td>0.005119</td>\n",
       "      <td>0.005072</td>\n",
       "      <td>0.004927</td>\n",
       "      <td>0.004844</td>\n",
       "      <td>0.00481</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.004516</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>0.004152</td>\n",
       "      <td>0.004065</td>\n",
       "      <td>0.003881</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>0.003623</td>\n",
       "      <td>0.003616</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.003585</td>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.00348</td>\n",
       "      <td>0.003357</td>\n",
       "      <td>0.003337</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>0.003254</td>\n",
       "      <td>0.003254</td>\n",
       "      <td>0.003224</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.002955</td>\n",
       "      <td>0.002874</td>\n",
       "      <td>0.002844</td>\n",
       "      <td>0.002819</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.002782</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>0.002674</td>\n",
       "      <td>0.00264</td>\n",
       "      <td>0.002603</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>0.002506</td>\n",
       "      <td>0.002504</td>\n",
       "      <td>0.002475</td>\n",
       "      <td>0.002449</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>0.002382</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002375</td>\n",
       "      <td>0.002373</td>\n",
       "      <td>0.002358</td>\n",
       "      <td>0.002349</td>\n",
       "      <td>0.002345</td>\n",
       "      <td>0.002329</td>\n",
       "      <td>0.002319</td>\n",
       "      <td>0.002315</td>\n",
       "      <td>0.00231</td>\n",
       "      <td>0.002307</td>\n",
       "      <td>0.002306</td>\n",
       "      <td>0.002304</td>\n",
       "      <td>0.002303</td>\n",
       "      <td>0.002301</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.002298</td>\n",
       "      <td>0.002291</td>\n",
       "      <td>0.00229</td>\n",
       "      <td>0.00229</td>\n",
       "      <td>0.002289</td>\n",
       "      <td>0.002284</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>0.002263</td>\n",
       "      <td>0.00218</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>0.002168</td>\n",
       "      <td>0.002164</td>\n",
       "      <td>0.002159</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.002156</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>0.002152</td>\n",
       "      <td>0.002139</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.002033</td>\n",
       "      <td>0.00203</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.002001</td>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.001983</td>\n",
       "      <td>0.00198</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.001925</td>\n",
       "      <td>0.001923</td>\n",
       "      <td>0.001908</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>0.001805</td>\n",
       "      <td>0.001804</td>\n",
       "      <td>0.001802</td>\n",
       "      <td>0.001799</td>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.001792</td>\n",
       "      <td>0.001789</td>\n",
       "      <td>0.00175</td>\n",
       "      <td>0.001748</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.000831</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000057</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",
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       "importance               0.0                   0.0   \n",
       "\n",
       "            company_New Business People  \\\n",
       "importance                          0.0   \n",
       "\n",
       "            company_New York City ADMIN FOR CHILDREN'S SVCS  \\\n",
       "importance                                              0.0   \n",
       "\n",
       "            company_New York City BOARD OF CORRECTION  \\\n",
       "importance                                        0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF CORRECTION  \\\n",
       "importance                                             0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF FINANCE  \\\n",
       "importance                                          0.0   \n",
       "\n",
       "            company_New York City DEPARTMENT OF TRANSPORTATION  \\\n",
       "importance                                                 0.0   \n",
       "\n",
       "            company_New York City DEPT OF DESIGN & CONSTRUCTION  \\\n",
       "importance                                                  0.0   \n",
       "\n",
       "            company_New York City DEPT OF HEALTH/MENTAL HYGIENE  \\\n",
       "importance                                                  0.0   \n",
       "\n",
       "            company_New York City DEPT OF INFO TECH & TELECOMM  \\\n",
       "importance                                                 0.0   \n",
       "\n",
       "            company_Morgan McKinley  company_Morgan Law  \\\n",
       "importance                      0.0                 0.0   \n",
       "\n",
       "            company_Morehouse College  \\\n",
       "importance                        0.0   \n",
       "\n",
       "            company_Moorfields Eye Hospital NHS Foundation Trust  \\\n",
       "importance                                                   0.0   \n",
       "\n",
       "            company_Martin and Conley  company_Mason Blake  \\\n",
       "importance                        0.0                  0.0   \n",
       "\n",
       "            company_Match Digital  company_Matthew Noah  company_Maveneer  \\\n",
       "importance                    0.0                   0.0               0.0   \n",
       "\n",
       "            company_Maxx Builders  \\\n",
       "importance                    0.0   \n",
       "\n",
       "            company_Mayor's Office of Contract Services  company_Mazars  \\\n",
       "importance                                          0.0             0.0   \n",
       "\n",
       "            company_McCann Central  company_McGregor Boyall  \\\n",
       "importance                     0.0                      0.0   \n",
       "\n",
       "            company_McKinsey & Company  company_Mcgregor Boyall  \\\n",
       "importance                         0.0                      0.0   \n",
       "\n",
       "            company_MediaAndLanguageJobs.co.uk  \\\n",
       "importance                                 0.0   \n",
       "\n",
       "            company_Medicine Man Technologies  company_Meet Recruitment  \\\n",
       "importance                                0.0                       0.0   \n",
       "\n",
       "            company_Melax Technologies, Inc.  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Memorial MRI and Diagnostic LLC  \\\n",
       "importance                                      0.0   \n",
       "\n",
       "            company_Method Resourcing  company_Metro Bank PLC  \\\n",
       "importance                        0.0                     0.0   \n",
       "\n",
       "            company_MetroPlus Health Plan  company_Michael Page UK  \\\n",
       "importance                            0.0                      0.0   \n",
       "\n",
       "            company_Micro Focus  company_Microsoft  company_Milshar, LLC  \\\n",
       "importance                  0.0                0.0                   0.0   \n",
       "\n",
       "            company_Miriad Products Ltd  company_Moixa Energy Holdings  \\\n",
       "importance                          0.0                            0.0   \n",
       "\n",
       "            company_Monarch  company_Monzo  company_Moonshot CVE  \\\n",
       "importance              0.0            0.0                   0.0   \n",
       "\n",
       "            company_K3 Business Technologies  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Jonathan Lee Recruitment Ltd  company_Eligo Recruitment  \\\n",
       "importance                                   0.0                        0.0   \n",
       "\n",
       "            company_Gemini  company_Formative Content  company_Foster Denovo  \\\n",
       "importance             0.0                        0.0                    0.0   \n",
       "\n",
       "            company_FourFront  company_Foxtons  \\\n",
       "importance                0.0              0.0   \n",
       "\n",
       "            company_Frazer-Nash Consultancy  company_French Selection  \\\n",
       "importance                              0.0                       0.0   \n",
       "\n",
       "            company_Freshtech IT  company_Futureheads Recruitment  \\\n",
       "importance                   0.0                              0.0   \n",
       "\n",
       "            company_G-Research  company_GDS Group  company_GMAD  \\\n",
       "importance                 0.0                0.0           0.0   \n",
       "\n",
       "            company_GVC Careers  company_GVC Holdings  \\\n",
       "importance                  0.0                   0.0   \n",
       "\n",
       "            company_GatenbySanderson  company_Gemini People  company_Flux  \\\n",
       "importance                       0.0                    0.0           0.0   \n",
       "\n",
       "            company_Georgia Department of Public Health  company_Gi Group  \\\n",
       "importance                                          0.0               0.0   \n",
       "\n",
       "            company_Gigaclear  company_Give A Grad A Go  \\\n",
       "importance                0.0                       0.0   \n",
       "\n",
       "            company_Global Accounting Network  \\\n",
       "importance                                0.0   \n",
       "\n",
       "            company_Global Risk Partners Group  company_GlobalData  \\\n",
       "importance                                 0.0                 0.0   \n",
       "\n",
       "            company_GlobalData PLC  company_Globizz Corp.  company_Go, Inc.  \\\n",
       "importance                     0.0                    0.0               0.0   \n",
       "\n",
       "            company_Grandview Corporation  \\\n",
       "importance                            0.0   \n",
       "\n",
       "            company_Gravity Technology Solutions  company_Greene Lab  \\\n",
       "importance                                   0.0                 0.0   \n",
       "\n",
       "            company_Greenwell Gleeson  company_Focus Multimedia Limited  \\\n",
       "importance                        0.0                               0.0   \n",
       "\n",
       "            company_Florida International University  company_Jobs in Letting  \\\n",
       "importance                                       0.0                      0.0   \n",
       "\n",
       "            company_Exact Sourcing ltd  company_Eligo Recruitment Ltd  \\\n",
       "importance                         0.0                            0.0   \n",
       "\n",
       "            company_Elite Crowdfunding limted  company_Emma Technologies LTD  \\\n",
       "importance                                0.0                            0.0   \n",
       "\n",
       "            company_Emma Walsh Talent  company_Emory School of Medicine  \\\n",
       "importance                        0.0                               0.0   \n",
       "\n",
       "            company_Empirical Search  company_Energy Assets  \\\n",
       "importance                       0.0                    0.0   \n",
       "\n",
       "            company_Enterprise Recruitment Limited  company_Estio Technology  \\\n",
       "importance                                     0.0                       0.0   \n",
       "\n",
       "            company_Estio Training  company_Eurowagens  \\\n",
       "importance                     0.0                 0.0   \n",
       "\n",
       "            company_Evermore Global  company_Everpress  \\\n",
       "importance                      0.0                0.0   \n",
       "\n",
       "            company_Evolution Recruitment Solutions  \\\n",
       "importance                                      0.0   \n",
       "\n",
       "            company_Executive Recruitment Services  \\\n",
       "importance                                     0.0   \n",
       "\n",
       "            company_Florida Grand Opera  company_Expedia Group  \\\n",
       "importance                          0.0                    0.0   \n",
       "\n",
       "            company_Explore Group  company_Express Recruitment  company_Ezoic  \\\n",
       "importance                    0.0                          0.0            0.0   \n",
       "\n",
       "            company_FACEIT  company_FDM Group  company_FINTEC recruit  \\\n",
       "importance             0.0                0.0                     0.0   \n",
       "\n",
       "            company_FL Group UK Ltd  company_FTD  company_Fair Recruitment  \\\n",
       "importance                      0.0          0.0                       0.0   \n",
       "\n",
       "            company_Farm-Hand Ltd  company_Five Guys  \\\n",
       "importance                    0.0                0.0   \n",
       "\n",
       "            company_Flight Centre (UK) Limited  \\\n",
       "importance                                 0.0   \n",
       "\n",
       "            company_Flight Centre Travel Group  company_Growth Intelligence  \\\n",
       "importance                                 0.0                          0.0   \n",
       "\n",
       "            company_Guru  company_Guru Systems  company_Informatiq Consulting  \\\n",
       "importance           0.0                   0.0                            0.0   \n",
       "\n",
       "            company_Hunter and Jones  company_Huntress  \\\n",
       "importance                       0.0               0.0   \n",
       "\n",
       "            company_Hylink Digital Solution Limited  company_IBD Registry  \\\n",
       "importance                                      0.0                   0.0   \n",
       "\n",
       "            company_INOV8 Consulting Ltd  \\\n",
       "importance                           0.0   \n",
       "\n",
       "            company_IPROS Insurance Professionals  company_IPS Group  \\\n",
       "importance                                    0.0                0.0   \n",
       "\n",
       "            company_IQPC  company_ITTStar Consulting LLC  \\\n",
       "importance           0.0                             0.0   \n",
       "\n",
       "            company_Imperial College Healthcare NHS Trust  \\\n",
       "importance                                            0.0   \n",
       "\n",
       "            company_Imperial College London  company_Incisive Media  \\\n",
       "importance                              0.0                     0.0   \n",
       "\n",
       "            company_Indeed  company_Inference Solutions  \\\n",
       "importance             0.0                          0.0   \n",
       "\n",
       "            company_Initi8 Recruitment  company_Gwinnett County  \\\n",
       "importance                         0.0                      0.0   \n",
       "\n",
       "            company_Insights Analytics  company_Inspire People  \\\n",
       "importance                         0.0                     0.0   \n",
       "\n",
       "            company_IntelliSense.io  company_Intellidyne Business Systems  \\\n",
       "importance                      0.0                                   0.0   \n",
       "\n",
       "            company_InterSTEM Recruitment  company_Intermedia Global Ltd  \\\n",
       "importance                            0.0                            0.0   \n",
       "\n",
       "            company_J&C Associates Ltd  company_JDX Consulting  \\\n",
       "importance                         0.0                     0.0   \n",
       "\n",
       "            company_JMC Legal Recruitment  company_JP Engineering Recruitment  \\\n",
       "importance                            0.0                                 0.0   \n",
       "\n",
       "            company_JPE  company_Jackson Rose Ltd  company_Jaguar Land Rover  \\\n",
       "importance          0.0                       0.0                        0.0   \n",
       "\n",
       "            company_Jenrick Group  company_Hudson Shribman  \\\n",
       "importance                    0.0                      0.0   \n",
       "\n",
       "            company_Howett Thorpe  company_Horniman Museum and Gardens  \\\n",
       "importance                    0.0                                  0.0   \n",
       "\n",
       "            company_Horizon Air  company_HCL America Inc  company_HCML  \\\n",
       "importance                  0.0                      0.0           0.0   \n",
       "\n",
       "            company_HGS Digital  company_HM Revenue and Customs  \\\n",
       "importance                  0.0                             0.0   \n",
       "\n",
       "            company_HOLLA  company_HRIS Associates Ltd  \\\n",
       "importance            0.0                          0.0   \n",
       "\n",
       "            company_HamlynWilliams  company_Hanami International  \\\n",
       "importance                     0.0                           0.0   \n",
       "\n",
       "            company_Handle Recruitment Ltd  company_Hanson Wade  \\\n",
       "importance                             0.0                  0.0   \n",
       "\n",
       "            company_Harcourt Matthews  company_Harnham  company_Harnham US  \\\n",
       "importance                        0.0              0.0                 0.0   \n",
       "\n",
       "            company_Harrington Starr  company_Harvey Thomas  \\\n",
       "importance                       0.0                    0.0   \n",
       "\n",
       "            company_Hasson Associates  company_Hawke Media  \\\n",
       "importance                        0.0                  0.0   \n",
       "\n",
       "            company_Head Resourcing Limited  company_Health Education England  \\\n",
       "importance                              0.0                               0.0   \n",
       "\n",
       "            company_Hearst  company_Heavens Recruitment Ltd  company_HeliosX  \\\n",
       "importance             0.0                              0.0              0.0   \n",
       "\n",
       "            company_Henderson Drake  company_Hernshead Recruitment  \\\n",
       "importance                      0.0                            0.0   \n",
       "\n",
       "            company_Hero Labs  company_Hewett Recruitment  \\\n",
       "importance                0.0                         0.0   \n",
       "\n",
       "            company_Hinduja Global Solutions  \\\n",
       "importance                               0.0   \n",
       "\n",
       "            company_Holler Technologies, Inc.  company_Homes England  \\\n",
       "importance                                0.0                    0.0   \n",
       "\n",
       "            company_Mortimer Bell International Ltd  \n",
       "importance                                      0.0  \n",
       "\n",
       "[1 rows x 1151 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the scoring method below to f1-score to help your boss be confident of the model?\n",
    "\n",
    "X_dum = pd.get_dummies(indeed[['company', 'rating', 'search_city']], drop_first=True)\n",
    "X_dum_title = pd.concat([X_dum,indeed.loc[:,'isDS':'isApprentice']], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_dum_title, y, stratify=y, random_state=1)\n",
    "\n",
    "models = [BaggingClassifier(n_estimators=100),\n",
    "          RandomForestClassifier(n_estimators=100),\n",
    "          GradientBoostingClassifier(n_estimators=100,\n",
    "                                     random_state=1,\n",
    "                                     validation_fraction=0.1,\n",
    "                                     max_depth=3,\n",
    "                                     n_iter_no_change=20)]\n",
    "params = [{'max_features': np.linspace(0.3,0.4,3)},\n",
    "          {'max_depth': list(range(2,20,5)),\n",
    "           'min_samples_split': np.linspace(0.3,0.4,3)},\n",
    "          {'learning_rate': np.linspace(.1,1.,4)}]\n",
    "\n",
    "kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)\n",
    "\n",
    "for i in range(len(models)):\n",
    "    gs = GridSearchCV(models[i], params[i], scoring='f1', n_jobs=2, cv=kf, verbose=1)\n",
    "    gs.fit(X_train, y_train)\n",
    "    print('Best params:',gs.best_params_)\n",
    "    print('Cross Val Score:',gs.best_score_)\n",
    "    predictions = gs.predict(X_test)\n",
    "    print('Confusion Matrix:')\n",
    "    print(' ')\n",
    "    print(confusion_matrix(y_test, predictions, labels=[0,1]))\n",
    "    print('Classification Report:')\n",
    "    print(' ')\n",
    "    print(classification_report(y_test, predictions))\n",
    "    \n",
    "# Add feature importances for best gs model:\n",
    "print('Best estimator:', gs.best_estimator_)\n",
    "\n",
    "if 'BaggingClassifier' in gs.best_estimator_:\n",
    "    feature_importances = pd.DataFrame({'importance':np.mean([tree.feature_importances_ for tree in gs.estimators_], axis=0)},\n",
    "                                                 index=X_dum_title.columns)\n",
    "else:\n",
    "    feature_importances = pd.DataFrame({'importance':gs.best_estimator_.feature_importances_},\n",
    "                                                 index=X_dum_title.columns)\n",
    "    \n",
    "feature_importances.sort_values('importance',ascending=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 842
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now to draw ROC, AUC curves for f1-scored best_model (which is now the last one, because Class 0 recall\n",
    "# improved to 84% with only 60 occurences of a Low salary being mispredicted as High, as seen from the last\n",
    "# confusion matrix:\n",
    "\n",
    "y_pp = pd.DataFrame(gs.best_estimator_.predict_proba(X_test), columns=['class_0_pp','class_1_pp'])\n",
    "\n",
    "# For class 0, find the area under the curve\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_pp.class_0_pp, pos_label=0)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "precision, recall, threshold = precision_recall_curve(y_test, y_pp.class_0_pp, pos_label=0)\n",
    "\n",
    "# Plot ROC and precision-recall curves for class 0\n",
    "fig, ax = plt.subplots(1, 3, figsize=[14, 4])\n",
    "\n",
    "ax[0].plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "ax[0].plot([0, 1], [0, 1], 'k--')\n",
    "ax[0].set_xlim([-0.05, 1.0])\n",
    "ax[0].set_ylim([-0.05, 1.05])\n",
    "ax[0].set_xlabel('False Positive Rate', fontsize=12)\n",
    "ax[0].set_ylabel('True Positive Rate', fontsize=12)\n",
    "ax[0].set_title('ROC curve', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "ax[1].plot(recall, precision, label='PR curve')\n",
    "ax[1].set_xlim([-0.05, 1.0])\n",
    "ax[1].set_ylim([-0.05, 1.05])\n",
    "ax[1].set_xlabel('Recall', fontsize=12)\n",
    "ax[1].set_ylabel('Precision', fontsize=12)\n",
    "ax[1].set_title('PR curve', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "ax[2].plot(threshold, recall[:-1], label='Recall vs Threshold')\n",
    "ax[2].set_xlim([-0.05, 1.0])\n",
    "ax[2].set_ylim([-0.05, 1.05])\n",
    "ax[2].set_xlabel('Threshold = P(Class 0)', fontsize=12)\n",
    "ax[2].set_ylabel('Recall(Class 0)', fontsize=12)\n",
    "ax[2].set_title('Recall vs Prob(Model)', fontsize=12)\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We should now use this model to tune for the right threshold level that we'd feel comfortable with.\n",
    "# e.g. if we are happy to assign salary as Class 0 even if the P(Class 0) = 40%, then Recall for Class 0 \n",
    "# would exceed 90% (from looking at the Recall vs Probability chart above)\n",
    "\n",
    "# Further possible work to improve on the above...\n",
    "# 1. Tableau visualisation map\n",
    "# 2. NLTK conversion of NLP steps\n",
    "# 3. Productionization step / piping to end it: once model / data cleaning steps identified\n",
    "# 4. Claudia (TA) told me that the Summary text was truncated, and only once you expanded it did you have it \n",
    "# all, so I could also improve the scraping to get the whole Summary box."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "focus": false,
    "id": "4fb29de2-5b98-474c-a4ad-5170b72b9aea"
   },
   "source": [
    "<img src=\"http://imgur.com/xDpSobf.png\" style=\"float: left; margin: 25px 15px 0px 0px; height: 25px\">\n",
    "\n",
    "### Bonus:\n",
    "\n",
    "- Answer the salary discussion by using your model to explain the tradeoffs between detecting high vs low salary positions. \n",
    "- Discuss the differences and explain when you want a high-recall or a high-precision model in this scenario.\n",
    "- Obtain the ROC/precision-recall curves for the different models you studied (at least the tuned model of each category) and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "focus": false,
    "id": "068dc1cf-7fd7-4f27-a1f1-7f0a5a221d29"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize your results in an executive summary written for a non-technical audience.\n",
    "   \n",
    "- Writeups should be at least 500-1000 words, defining any technical terms, explaining your approach, as well as any risks and limitations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## YOUR TEXT HERE IN MARKDOWN FORMAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <img src=\"http://imgur.com/xDpSobf.png\" style=\"float: left; margin: 25px 15px 0px 0px; height: 25px\">\n",
    "\n",
    "### BONUS\n",
    "\n",
    "Convert your executive summary into a public blog post of at least 500 words, in which you document your approach in a tutorial for other aspiring data scientists. Link to this in your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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