project2code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0b74c78f",
   "metadata": {},
   "source": [
    "## Predicting FinTech Bootcamp Graduate Salaries\n",
    "##### Project 2 - Group C (Andrew, Margee, Rachel, Jinhyeong)\n",
    "##### Presentation Date: January 15, 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c210b1be",
   "metadata": {},
   "source": [
    "#### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95390249",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import hvplot.pandas\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Import matplotlib and seaborn libraries to visualize the data\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "# Import regressor algorithms\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "# Import model evaluations\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# For panel/dashboard\n",
    "import panel as pn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "23ec6f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create File Path\n",
    "filepath = \"/Users/piercerachel/Desktop/git/Project2_GroupC/\"\n",
    "filename = \"monster_india_latest_jobs_free_dataset.json\"\n",
    "\n",
    "# Opening JSON file\n",
    "f = open(filepath + filename)\n",
    "\n",
    "# returns JSON object as a dictionary\n",
    "data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b90b35c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create data list\n",
    "address_country = []\n",
    "address_locality = []\n",
    "address_region = []\n",
    "company = []\n",
    "description = []\n",
    "industry = []\n",
    "postal_code = []\n",
    "posted_at = []\n",
    "salary = []  # the target\n",
    "salary_type = []\n",
    "skills = []\n",
    "title = []\n",
    "url = []\n",
    "\n",
    "# iterate through each element in the data list\n",
    "for each in data:\n",
    "\n",
    "    # append values to list, given each key\n",
    "    address_country.append(each[\"address_country\"])\n",
    "    address_locality.append(each[\"address_locality\"])\n",
    "    address_region.append(each[\"address_region\"])\n",
    "    company.append(each[\"company\"])\n",
    "    description.append(each[\"description\"])\n",
    "    industry.append(each[\"industry\"])\n",
    "    postal_code.append(each[\"postal_code\"])\n",
    "    posted_at.append(each[\"posted_at\"])\n",
    "    salary.append(each[\"salary\"])\n",
    "    salary_type.append(each[\"salary_type\"])\n",
    "    skills.append(each[\"skills\"]) \n",
    "    title.append(each[\"title\"])\n",
    "    url.append(each[\"url\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cb285707",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create df, using string header and list as rows\n",
    "df = pd.DataFrame({\"address_country\": address_country\n",
    "                   ,\"address_locality\": address_locality\n",
    "                   ,\"address_region\": address_region\n",
    "                   ,\"company\": company\n",
    "                   ,\"description\": description\n",
    "                   ,\"industry\": industry\n",
    "                   ,\"postal_code\": postal_code\n",
    "                   ,\"posted_at\": posted_at\n",
    "                   ,\"salary\": salary\n",
    "                   ,\"salary_type\": salary_type\n",
    "                   ,\"skills\": skills\n",
    "                   ,\"title\": title\n",
    "                   ,\"url\": url\n",
    "                  })\n",
    "\n",
    "df[\"description\"] = df[\"description\"].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0b5fa5aa",
   "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",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>address_country</th>\n",
       "      <th>address_locality</th>\n",
       "      <th>address_region</th>\n",
       "      <th>company</th>\n",
       "      <th>description</th>\n",
       "      <th>industry</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>posted_at</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_type</th>\n",
       "      <th>skills</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IN</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>Kotak Mahindra Bank Limited</td>\n",
       "      <td>job description : -   . client engagement o...</td>\n",
       "      <td>Banking/Accounting/Financial Services</td>\n",
       "      <td>NA</td>\n",
       "      <td>2021-09-16</td>\n",
       "      <td>Not disclosed</td>\n",
       "      <td>P.A.</td>\n",
       "      <td>Service Relationship Manager-POS-BRANCH BANKIN...</td>\n",
       "      <td>Service Relationship Manager-POS-BRANCH BANKIN...</td>\n",
       "      <td>https://www.monsterindia.com/job/service-relat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IN</td>\n",
       "      <td>Noida</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>ANI Calls India Private Limited</td>\n",
       "      <td>job description : . axway b2bi or axway gatew...</td>\n",
       "      <td>IT/Computers - Software</td>\n",
       "      <td>NA</td>\n",
       "      <td>2021-09-13</td>\n",
       "      <td>1600000</td>\n",
       "      <td>P.A.</td>\n",
       "      <td>Axway B2B EDI Integration consultant</td>\n",
       "      <td>Axway B2B EDI Integration consultant</td>\n",
       "      <td>https://www.monsterindia.com/job/axway-b2b-edi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IN</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>job description : job description fueled by a...</td>\n",
       "      <td>Banking/Accounting/Financial Services</td>\n",
       "      <td>NA</td>\n",
       "      <td>2021-08-18</td>\n",
       "      <td>Not disclosed</td>\n",
       "      <td>P.A.</td>\n",
       "      <td>Data Engineer/MTS 2</td>\n",
       "      <td>MTS 2, Data Engineer</td>\n",
       "      <td>https://www.monsterindia.com/job/mts-2-data-en...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IN</td>\n",
       "      <td>Bengaluru / Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>ANI Calls India Private Limited</td>\n",
       "      <td>job description : at least three years weblog...</td>\n",
       "      <td>IT/Computers - Software</td>\n",
       "      <td>NA</td>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>1800000</td>\n",
       "      <td>P.A.</td>\n",
       "      <td>Senior Oracle Fusion Middleware developer</td>\n",
       "      <td>Senior Oracle Fusion Middleware developer</td>\n",
       "      <td>https://www.monsterindia.com/job/senior-oracle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IN</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>ANI Calls India Private Limited</td>\n",
       "      <td>job description : • linux engineer with ksh s...</td>\n",
       "      <td>IT/Computers - Software</td>\n",
       "      <td>NA</td>\n",
       "      <td>2021-09-08</td>\n",
       "      <td>1400000</td>\n",
       "      <td>P.A.</td>\n",
       "      <td>Korn Shell (KSH) - Linux System Engineer</td>\n",
       "      <td>Korn Shell (KSH) - Linux System Engineer</td>\n",
       "      <td>https://www.monsterindia.com/job/korn-shell-ks...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  address_country       address_locality address_region  \\\n",
       "0              IN                  India          India   \n",
       "1              IN                  Noida  Uttar Pradesh   \n",
       "2              IN                Chennai     Tamil Nadu   \n",
       "3              IN  Bengaluru / Bangalore      Karnataka   \n",
       "4              IN                  India          India   \n",
       "\n",
       "                           company  \\\n",
       "0      Kotak Mahindra Bank Limited   \n",
       "1  ANI Calls India Private Limited   \n",
       "2                           PayPal   \n",
       "3  ANI Calls India Private Limited   \n",
       "4  ANI Calls India Private Limited   \n",
       "\n",
       "                                         description  \\\n",
       "0     job description : -   . client engagement o...   \n",
       "1   job description : . axway b2bi or axway gatew...   \n",
       "2   job description : job description fueled by a...   \n",
       "3   job description : at least three years weblog...   \n",
       "4   job description : • linux engineer with ksh s...   \n",
       "\n",
       "                                industry postal_code   posted_at  \\\n",
       "0  Banking/Accounting/Financial Services          NA  2021-09-16   \n",
       "1                IT/Computers - Software          NA  2021-09-13   \n",
       "2  Banking/Accounting/Financial Services          NA  2021-08-18   \n",
       "3                IT/Computers - Software          NA  2021-08-26   \n",
       "4                IT/Computers - Software          NA  2021-09-08   \n",
       "\n",
       "          salary salary_type  \\\n",
       "0  Not disclosed        P.A.   \n",
       "1        1600000        P.A.   \n",
       "2  Not disclosed        P.A.   \n",
       "3        1800000        P.A.   \n",
       "4        1400000        P.A.   \n",
       "\n",
       "                                              skills  \\\n",
       "0  Service Relationship Manager-POS-BRANCH BANKIN...   \n",
       "1               Axway B2B EDI Integration consultant   \n",
       "2                                Data Engineer/MTS 2   \n",
       "3          Senior Oracle Fusion Middleware developer   \n",
       "4           Korn Shell (KSH) - Linux System Engineer   \n",
       "\n",
       "                                               title  \\\n",
       "0  Service Relationship Manager-POS-BRANCH BANKIN...   \n",
       "1               Axway B2B EDI Integration consultant   \n",
       "2                               MTS 2, Data Engineer   \n",
       "3          Senior Oracle Fusion Middleware developer   \n",
       "4           Korn Shell (KSH) - Linux System Engineer   \n",
       "\n",
       "                                                 url  \n",
       "0  https://www.monsterindia.com/job/service-relat...  \n",
       "1  https://www.monsterindia.com/job/axway-b2b-edi...  \n",
       "2  https://www.monsterindia.com/job/mts-2-data-en...  \n",
       "3  https://www.monsterindia.com/job/senior-oracle...  \n",
       "4  https://www.monsterindia.com/job/korn-shell-ks...  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d2acc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data, drop unnecessary columns\n",
    "df.drop(['address_country', 'address_locality', 'address_region', 'postal_code', 'salary_type', 'skills', 'url', 'company', 'industry', 'posted_at', 'title'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ca0b3682",
   "metadata": {},
   "outputs": [
    {
     "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>description</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>job description : -   . client engagement o...</td>\n",
       "      <td>Not disclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>job description : . axway b2bi or axway gatew...</td>\n",
       "      <td>1600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>job description : job description fueled by a...</td>\n",
       "      <td>Not disclosed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>job description : at least three years weblog...</td>\n",
       "      <td>1800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>job description : • linux engineer with ksh s...</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description         salary\n",
       "0     job description : -   . client engagement o...  Not disclosed\n",
       "1   job description : . axway b2bi or axway gatew...        1600000\n",
       "2   job description : job description fueled by a...  Not disclosed\n",
       "3   job description : at least three years weblog...        1800000\n",
       "4   job description : • linux engineer with ksh s...        1400000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview updated columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "28ae2877",
   "metadata": {},
   "outputs": [
    {
     "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>description</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>job description : . axway b2bi or axway gatew...</td>\n",
       "      <td>1600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>job description : at least three years weblog...</td>\n",
       "      <td>1800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>job description : • linux engineer with ksh s...</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>job description : . oracle ebs apps dba imple...</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>requirement  mulesoft developer</td>\n",
       "      <td>2500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         description   salary\n",
       "1   job description : . axway b2bi or axway gatew...  1600000\n",
       "3   job description : at least three years weblog...  1800000\n",
       "4   job description : • linux engineer with ksh s...  1400000\n",
       "5   job description : . oracle ebs apps dba imple...  1400000\n",
       "7                   requirement  mulesoft developer   2500000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop any salaries that are Not disclosed\n",
    "df = df[df[\"salary\"] != \"Not disclosed\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "989dc553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description    0\n",
       "salary         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any NAs/nulls\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "af05ccda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create CSV file from new DataFrame if desired\n",
    "#df.to_csv(r'/Users/andrewcrawford/Desktop/Project2_GroupC/keyword_output.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2ad68fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Keywords for Searching\n",
    "keywords_database = \"sql|oracle|database|db|datawarehouse|datawarehousing|mysql|mssql|postgresql|db2|mongodb|rbs| etl |sqlite|redis\"\n",
    "keywords_python = \"python|scripting|pep8|programmer|developer|pandas|numpy|django| pip |conda|venv|github|gitlab|spyder|pycharm|jupyter\"\n",
    "keywords_api = \" api |restful\"\n",
    "keywords_algorithm = \"algorithm|algorithmic| ai |artificial intelligence|knn|logistic regression|classification|regression|k-means|tensorflow|keras|scikit-learn\"\n",
    "keywords_cloud = \" aws |amazon web services|ec2|s3|cloud|lambda|cloudwatch|cloudfront|azure\"\n",
    "keywords_forecast = \"forecast|predict| arma |arima|garch|straight line\"\n",
    "keywords_bigdata = \"bigdata|big data|nosql|hadoop\"\n",
    "keywords_dashboard = \"dashboard|tableau|power bi|qlik| domo |visualization|tufte|bi tools|business intelligence\"\n",
    "keywords_project_management = \"agile|scrum|jira|kanban|stand up|black belt|waterfall|gantt\"\n",
    "keywords_finance = \"fintech|financial technology|payments|investment|diversification|finance|cryptocurrency|crypto|defi|decentralized finance\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f0a22336",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Searching for keyword skills and updating columns from 0s to 1s as applicable\n",
    "df[\"database\"] = np.where(df[\"description\"].str.contains(keywords_database), 1, 0)\n",
    "df[\"python\"] = np.where(df[\"description\"].str.contains(keywords_python), 1, 0)\n",
    "df[\"api\"] = np.where(df[\"description\"].str.contains(keywords_api), 1, 0)\n",
    "df[\"algorithm\"] = np.where(df[\"description\"].str.contains(keywords_algorithm), 1, 0)\n",
    "df[\"cloud\"] = np.where(df[\"description\"].str.contains(keywords_cloud), 1, 0)\n",
    "df[\"forecast\"] = np.where(df[\"description\"].str.contains(keywords_forecast), 1, 0)\n",
    "df[\"bigdata\"] = np.where(df[\"description\"].str.contains(keywords_bigdata), 1, 0)\n",
    "df[\"dashboard\"] = np.where(df[\"description\"].str.contains(keywords_dashboard), 1, 0)\n",
    "df[\"project_management\"] = np.where(df[\"description\"].str.contains(keywords_project_management), 1, 0)\n",
    "df[\"finance\"] = np.where(df[\"description\"].str.contains(keywords_finance), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f67038b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description            job description : . axway b2bi or axway gatew...\n",
       "salary                1600000180000014000001400000250000014400001400...\n",
       "database                                                            108\n",
       "python                                                               81\n",
       "api                                                                  26\n",
       "algorithm                                                            18\n",
       "cloud                                                                73\n",
       "forecast                                                              9\n",
       "bigdata                                                              14\n",
       "dashboard                                                            21\n",
       "project_management                                                   49\n",
       "finance                                                              55\n",
       "dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing counts of each\n",
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "06a96d19",
   "metadata": {},
   "outputs": [
    {
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       "      <td>job description : • linux engineer with ksh s...</td>\n",
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       "      <th>5</th>\n",
       "      <td>job description : . oracle ebs apps dba imple...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>requirement  mulesoft developer</td>\n",
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      "text/plain": [
       "                                         description   salary  database  \\\n",
       "1   job description : . axway b2bi or axway gatew...  1600000         0   \n",
       "3   job description : at least three years weblog...  1800000         1   \n",
       "4   job description : • linux engineer with ksh s...  1400000         0   \n",
       "5   job description : . oracle ebs apps dba imple...  1400000         1   \n",
       "7                   requirement  mulesoft developer   2500000         0   \n",
       "\n",
       "   python  api  algorithm  cloud  forecast  bigdata  dashboard  \\\n",
       "1       0    0          0      0         0        0          0   \n",
       "3       0    0          0      0         0        0          0   \n",
       "4       1    0          0      0         0        0          0   \n",
       "5       0    0          0      1         0        0          0   \n",
       "7       1    0          0      0         0        0          0   \n",
       "\n",
       "   project_management  finance  \n",
       "1                   0        0  \n",
       "3                   0        0  \n",
       "4                   0        0  \n",
       "5                   0        0  \n",
       "7                   0        0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show updated dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "08b3b1cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                         description   salary  database  \\\n",
       "0   job description : at least three years weblog...  1800000         1   \n",
       "1   job description : • linux engineer with ksh s...  1400000         0   \n",
       "2   job description : . oracle ebs apps dba imple...  1400000         1   \n",
       "3                   requirement  mulesoft developer   2500000         0   \n",
       "4   job description : . principal engineer cloud ...  1400000         1   \n",
       "\n",
       "   python  api  algorithm  cloud  forecast  bigdata  dashboard  \\\n",
       "0       0    0          0      0         0        0          0   \n",
       "1       1    0          0      0         0        0          0   \n",
       "2       0    0          0      1         0        0          0   \n",
       "3       1    0          0      0         0        0          0   \n",
       "4       0    0          0      1         0        0          0   \n",
       "\n",
       "   project_management  finance  \n",
       "0                   0        0  \n",
       "1                   0        0  \n",
       "2                   0        0  \n",
       "3                   0        0  \n",
       "4                   0        0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop all rows where all keywords equal 0\n",
    "all_skills = ['database','python','api','algorithm','cloud','forecast','bigdata','dashboard','project_management','finance']\n",
    "df_updated = df[~(df[all_skills] == 0).all(axis=1)]\n",
    "df_updated.reset_index(drop=True, inplace=True)\n",
    "df_updated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "09939e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "description           object\n",
       "salary                 int32\n",
       "database               int32\n",
       "python                 int32\n",
       "api                    int32\n",
       "algorithm              int32\n",
       "cloud                  int32\n",
       "forecast               int32\n",
       "bigdata                int32\n",
       "dashboard              int32\n",
       "project_management     int32\n",
       "finance                int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert salary data type before currency conversion\n",
    "df_updated['salary'] = df_updated['salary'].astype('int')\n",
    "df_updated.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c569c8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>33656.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18847.60</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "     salary  database  python  api  algorithm  cloud  forecast  bigdata  \\\n",
       "0  24232.63         1       0    0          0      0         0        0   \n",
       "1  18847.60         0       1    0          0      0         0        0   \n",
       "2  18847.60         1       0    0          0      1         0        0   \n",
       "3  33656.44         0       1    0          0      0         0        0   \n",
       "4  18847.60         1       0    0          0      1         0        0   \n",
       "\n",
       "   dashboard  project_management  finance  \n",
       "0          0                   0        0  \n",
       "1          0                   0        0  \n",
       "2          0                   0        0  \n",
       "3          0                   0        0  \n",
       "4          0                   0        0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert Indian rupees to US dollars (conversion rate is as of 1/8/2022)\n",
    "df_updated['salary'] = round(df_updated['salary']/74.28, 2)\n",
    "df_updated.drop(columns=['description'], inplace=True)\n",
    "df_updated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e70958f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235, 11)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine data count\n",
    "df_updated.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8893ae99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>235.000000</td>\n",
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       "      <td>235.000000</td>\n",
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       "      <td>235.000000</td>\n",
       "      <td>235.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20196.380340</td>\n",
       "      <td>0.459574</td>\n",
       "      <td>0.344681</td>\n",
       "      <td>0.110638</td>\n",
       "      <td>0.076596</td>\n",
       "      <td>0.310638</td>\n",
       "      <td>0.038298</td>\n",
       "      <td>0.059574</td>\n",
       "      <td>0.089362</td>\n",
       "      <td>0.208511</td>\n",
       "      <td>0.234043</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9380.401814</td>\n",
       "      <td>0.499427</td>\n",
       "      <td>0.476279</td>\n",
       "      <td>0.314353</td>\n",
       "      <td>0.266517</td>\n",
       "      <td>0.463743</td>\n",
       "      <td>0.192324</td>\n",
       "      <td>0.237202</td>\n",
       "      <td>0.285874</td>\n",
       "      <td>0.407111</td>\n",
       "      <td>0.424303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18847.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18847.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21540.120000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72697.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             salary    database      python         api   algorithm  \\\n",
       "count    235.000000  235.000000  235.000000  235.000000  235.000000   \n",
       "mean   20196.380340    0.459574    0.344681    0.110638    0.076596   \n",
       "std     9380.401814    0.499427    0.476279    0.314353    0.266517   \n",
       "min        0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%    18847.600000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%    18847.600000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%    21540.120000    1.000000    1.000000    0.000000    0.000000   \n",
       "max    72697.900000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "            cloud    forecast     bigdata   dashboard  project_management  \\\n",
       "count  235.000000  235.000000  235.000000  235.000000          235.000000   \n",
       "mean     0.310638    0.038298    0.059574    0.089362            0.208511   \n",
       "std      0.463743    0.192324    0.237202    0.285874            0.407111   \n",
       "min      0.000000    0.000000    0.000000    0.000000            0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000            0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000            0.000000   \n",
       "75%      1.000000    0.000000    0.000000    0.000000            0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000            1.000000   \n",
       "\n",
       "          finance  \n",
       "count  235.000000  \n",
       "mean     0.234043  \n",
       "std      0.424303  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      0.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review new data set\n",
    "df_updated.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b4d696dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a total_skills column that sums all 1's across the rows\n",
    "df_updated['total_skills'] = df_updated[all_skills].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "306298d0",
   "metadata": {},
   "outputs": [
    {
     "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>salary</th>\n",
       "      <th>database</th>\n",
       "      <th>python</th>\n",
       "      <th>api</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>cloud</th>\n",
       "      <th>forecast</th>\n",
       "      <th>bigdata</th>\n",
       "      <th>dashboard</th>\n",
       "      <th>project_management</th>\n",
       "      <th>finance</th>\n",
       "      <th>total_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24232.63</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18847.60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18847.60</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33656.44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18847.60</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     salary  database  python  api  algorithm  cloud  forecast  bigdata  \\\n",
       "0  24232.63         1       0    0          0      0         0        0   \n",
       "1  18847.60         0       1    0          0      0         0        0   \n",
       "2  18847.60         1       0    0          0      1         0        0   \n",
       "3  33656.44         0       1    0          0      0         0        0   \n",
       "4  18847.60         1       0    0          0      1         0        0   \n",
       "\n",
       "   dashboard  project_management  finance  total_skills  \n",
       "0          0                   0        0             1  \n",
       "1          0                   0        0             1  \n",
       "2          0                   0        0             2  \n",
       "3          0                   0        0             1  \n",
       "4          0                   0        0             2  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preview updated columns\n",
    "df_updated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e6ae3431",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S6TUeMzMGAHCo2fA42v7bqjojs8uJn03y00nS3XdU1bVJ7kzyaJLzu3vX1OZVSa5IcnSS66dHklye5Kqq2p7ZjNjWxzEuAIB1o9brBNTCwkIvLi6OHgYAwIqq6rbuXlhun2/gBwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGGjVYayqjqiqP62qD0zPj6+qD1bVp6efx80de2FVba+qT1XVi+bqz6uq26d9b62qmuobq+qaqX5LVZ10EM8RAGDN2p+ZsVcnuWvu+S8k+VB3n5LkQ9PzVNVzkmxNclqSs5O8vaqOmNpcmuS8JKdMj7On+rYkD3b3yUkuSfKmAzobAIB1ZlVhrKq2JPnHSX5zrnxOkiun7SuTvGSu/t7ufqS7P5Nke5Izq+qEJMd0983d3UnevaTN7r7el+SFu2fNAAAOZaudGXtLkp9P8vW52jO7+94kmX4+Y6pvTnL33HE7ptrmaXtpfY823f1okoeSPG3pIKrqvKparKrFnTt3rnLoAABr14phrKr+SZL7u/u2Vfa53IxW76O+rzZ7Frrf0d0L3b2wadOmVQ4HAGDt2rCKY743yQ9X1YuTHJXkmKp6T5L7quqE7r53ugR5/3T8jiQnzrXfkuSeqb5lmfp8mx1VtSHJsUkeOMBzAgBYN1acGevuC7t7S3eflNnC/Bu6+8eTXJfk3Omwc5O8f9q+LsnW6Q7JZ2W2UP/W6VLmw1V11rQe7BVL2uzu66XTazxmZgwA4FCzmpmxvfnVJNdW1bYkf5nkR5Oku++oqmuT3Jnk0STnd/euqc2rklyR5Ogk10+PJLk8yVVVtT2zGbGtj2NcAADrRq3XCaiFhYVeXFwcPQwAgBVV1W3dvbDcPt/ADwAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMNCKYayqjqqqW6vq41V1R1W9caq/oao+V1Ufmx4vnmtzYVVtr6pPVdWL5urPq6rbp31vraqa6hur6pqpfktVnfQEnCsAwJqzmpmxR5L8QHc/N8kZSc6uqrOmfZd09xnT4w+SpKqek2RrktOSnJ3k7VV1xHT8pUnOS3LK9Dh7qm9L8mB3n5zkkiRvetxnBgCwDqwYxnrmy9PTI6dH76PJOUne292PdPdnkmxPcmZVnZDkmO6+ubs7ybuTvGSuzZXT9vuSvHD3rBkAwKFsVWvGquqIqvpYkvuTfLC7b5l2/UxVfaKq3lVVx021zUnunmu+Y6ptnraX1vdo092PJnkoydOWGcd5VbVYVYs7d+5czdABANa0VYWx7t7V3Wck2ZLZLNfpmV1yfHZmly7vTfJr0+HLzWj1Pur7arN0HO/o7oXuXti0adNqhg4AsKbt192U3f3FJB9OcnZ33zeFtK8neWeSM6fDdiQ5ca7ZliT3TPUty9T3aFNVG5Icm+SB/RkbAMB6tJq7KTdV1bdO20cn+cEkfzatAdvtR5J8ctq+LsnW6Q7JZ2W2UP/W7r43ycNVdda0HuwVSd4/1+bcafulSW6Y1pUBABzSNqzimBOSXDndEfmUJNd29weq6qqqOiOzy4mfTfLTSdLdd1TVtUnuTPJokvO7e9fU16uSXJHk6CTXT48kuTzJVVW1PbMZsa2P/9QAANa+Wq8TUAsLC724uDh6GAAAK6qq27p7Ybl9voEfAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGAgYQwAYCBhDABgIGEMAGCgFcNYVR1VVbdW1cer6o6qeuNUP76qPlhVn55+HjfX5sKq2l5Vn6qqF83Vn1dVt0/73lpVNdU3VtU1U/2WqjrpCThXAIA1ZzUzY48k+YHufm6SM5KcXVVnJfmFJB/q7lOSfGh6nqp6TpKtSU5LcnaSt1fVEVNflyY5L8kp0+Psqb4tyYPdfXKSS5K86fGfGgDA2rdiGOuZL09Pj5weneScJFdO9SuTvGTaPifJe7v7ke7+TJLtSc6sqhOSHNPdN3d3J3n3kja7+3pfkhfunjUDADiUrWrNWFUdUVUfS3J/kg929y1Jntnd9ybJ9PMZ0+Gbk9w913zHVNs8bS+t79Gmux9N8lCSpy0zjvOqarGqFnfu3LmqEwQAWMtWFca6e1d3n5FkS2azXKfv4/DlZrR6H/V9tVk6jnd090J3L2zatGmFUQMArH37dTdld38xyYczW+t133TpMdPP+6fDdiQ5ca7ZliT3TPUty9T3aFNVG5Icm+SB/RkbAMB6tJq7KTdV1bdO20cn+cEkf5bkuiTnToedm+T90/Z1SbZOd0g+K7OF+rdOlzIfrqqzpvVgr1jSZndfL01yw7SuDADgkLZhFceckOTK6Y7IpyS5trs/UFU3J7m2qrYl+cskP5ok3X1HVV2b5M4kjyY5v7t3TX29KskVSY5Ocv30SJLLk1xVVdszmxHbejBODgBgrav1OgG1sLDQi4uLo4cBALCiqrqtuxeW2+cb+AEABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAZaMYxV1YlVdWNV3VVVd1TVq6f6G6rqc1X1senx4rk2F1bV9qr6VFW9aK7+vKq6fdr31qqqqb6xqq6Z6rdU1UlPwLkCAKw5q5kZezTJa7r71CRnJTm/qp4z7buku8+YHn+QJNO+rUlOS3J2krdX1RHT8ZcmOS/JKdPj7Km+LcmD3X1ykkuSvOnxnxoAwNq3Yhjr7nu7+0+m7YeT3JVk8z6anJPkvd39SHd/Jsn2JGdW1QlJjunum7u7k7w7yUvm2lw5bb8vyQt3z5oBABzK9mvN2HT58LuT3DKVfqaqPlFV76qq46ba5iR3zzXbMdU2T9tL63u06e5HkzyU5GnLvP55VbVYVYs7d+7cn6EDAKxJqw5jVfXUJL+T5Ge7+0uZXXJ8dpIzktyb5Nd2H7pM895HfV9t9ix0v6O7F7p7YdOmTasdOgDAmrWqMFZVR2YWxH6ru383Sbr7vu7e1d1fT/LOJGdOh+9IcuJc8y1J7pnqW5ap79GmqjYkOTbJAwdyQgAA68lq7qasJJcnuau73zxXP2HusB9J8slp+7okW6c7JJ+V2UL9W7v73iQPV9VZU5+vSPL+uTbnTtsvTXLDtK4MAOCQtmEVx3xvkp9IcntVfWyqvS7Jy6rqjMwuJ342yU8nSXffUVXXJrkzszsxz+/uXVO7VyW5IsnRSa6fHsks7F1VVdszmxHb+nhOCgBgvaj1OgG1sLDQi4uLo4cBALCiqrqtuxeW2+cb+AEABhLGAAAGEsYAAAYSxgAABlrN3ZQAa9J6+lfT1uvNUsATTxgD1q0nIuBUleAEPKlcpgQAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABjIV1sAT4rjjz8+Dz744OhhrMp6+P6y4447Lg888MDoYQAHgTAGPCkefPBB3991EK2HwAisjsuUAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAAwljAAADCWMAAAMJYwAAA20YPQDg8NCvPyZ5w7Gjh3HI6NcfM3oIwEEijAFPinrjl9Ldo4dxyKiq9BtGjwI4GFymBAAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhoxTBWVSdW1Y1VdVdV3VFVr57qx1fVB6vq09PP4+baXFhV26vqU1X1orn686rq9mnfW6uqpvrGqrpmqt9SVSc9AecKALDmrGZm7NEkr+nuU5OcleT8qnpOkl9I8qHuPiXJh6bnmfZtTXJakrOTvL2qjpj6ujTJeUlOmR5nT/VtSR7s7pOTXJLkTQfh3AAA1rwVw1h339vdfzJtP5zkriSbk5yT5MrpsCuTvGTaPifJe7v7ke7+TJLtSc6sqhOSHNPdN3d3J3n3kja7+3pfkhfunjUDADiU7deaseny4XcnuSXJM7v73mQW2JI8Yzpsc5K755rtmGqbp+2l9T3adPejSR5K8rRlXv+8qlqsqsWdO3fuz9ABANakVYexqnpqkt9J8rPd/aV9HbpMrfdR31ebPQvd7+juhe5e2LRp00pDBgBY81YVxqrqyMyC2G919+9O5fumS4+Zft4/1XckOXGu+ZYk90z1LcvU92hTVRuSHJvkgf09GQCA9WY1d1NWksuT3NXdb57bdV2Sc6ftc5O8f66+dbpD8lmZLdS/dbqU+XBVnTX1+YolbXb39dIkN0zrygAADmkbVnHM9yb5iSS3V9XHptrrkvxqkmuraluSv0zyo0nS3XdU1bVJ7szsTszzu3vX1O5VSa5IcnSS66dHMgt7V1XV9sxmxLY+vtMCAFgfar1OQC0sLPTi4uLoYQCrVFVZr/+/WYu8n7C+VNVt3b2w3D7fwA8AMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADDQav45JICDYvbP0nIwHHfccaOHABwkwhjwpFgv/3SPf2YIeLK5TAkAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMJAwBgAwkDAGADCQMAYAMNCKYayq3lVV91fVJ+dqb6iqz1XVx6bHi+f2XVhV26vqU1X1orn686rq9mnfW6uqpvrGqrpmqt9SVScd5HMEAFizVjMzdkWSs5epX9LdZ0yPP0iSqnpOkq1JTpvavL2qjpiOvzTJeUlOmR67+9yW5MHuPjnJJUnedIDnAgCw7qwYxrr7o0keWGV/5yR5b3c/0t2fSbI9yZlVdUKSY7r75u7uJO9O8pK5NldO2+9L8sLds2YAAIe6x7Nm7Geq6hPTZczjptrmJHfPHbNjqm2etpfW92jT3Y8meSjJ05Z7wao6r6oWq2px586dj2PoAABrw4GGsUuTPDvJGUnuTfJrU325Ga3eR31fbR5b7H5Hdy9098KmTZv2a8AAAGvRAYWx7r6vu3d199eTvDPJmdOuHUlOnDt0S5J7pvqWZep7tKmqDUmOzeoviwIArGsHFMamNWC7/UiS3XdaXpdk63SH5LMyW6h/a3ffm+ThqjprWg/2iiTvn2tz7rT90iQ3TOvKAAAOeRtWOqCqrk7ygiRPr6odSV6f5AVVdUZmlxM/m+Snk6S776iqa5PcmeTRJOd3966pq1dldmfm0Umunx5JcnmSq6pqe2YzYlsPwnkBAKwLtV4noRYWFnpxcXH0MIBDTFVlvf5/EVi7quq27l5Ybp9v4AcAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYSBgDABhIGAMAGEgYAwAYaMPoAQAcqKpaN/1290HvEzg0CGPAuiXgAIcClykBAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGWjGMVdW7qur+qvrkXO34qvpgVX16+nnc3L4Lq2p7VX2qql40V39eVd0+7XtrVdVU31hV10z1W6rqpIN8jgAAa9ZqZsauSHL2ktovJPlQd5+S5EPT81TVc5JsTXLa1ObtVXXE1ObSJOclOWV67O5zW5IHu/vkJJckedOBngwAwHqzYhjr7o8meWBJ+ZwkV07bVyZ5yVz9vd39SHd/Jsn2JGdW1QlJjunum7u7k7x7SZvdfb0vyQt3z5oBABzqDnTN2DO7+94kmX4+Y6pvTnL33HE7ptrmaXtpfY823f1okoeSPG25F62q86pqsaoWd+7ceYBDBwBYOw72Av7lZrR6H/V9tXlssfsd3b3Q3QubNm06wCECAKwdBxrG7psuPWb6ef9U35HkxLnjtiS5Z6pvWaa+R5uq2pDk2Dz2sigAwCHpQMPYdUnOnbbPTfL+ufrW6Q7JZ2W2UP/W6VLmw1V11rQe7BVL2uzu66VJbpjWlQEAHPI2rHRAVV2d5AVJnl5VO5K8PsmvJrm2qrYl+cskP5ok3X1HVV2b5M4kjyY5v7t3TV29KrM7M49Ocv30SJLLk1xVVdszmxHbelDODABgHaj1Ogm1sLDQi4uLo4cBALCiqrqtuxeW2+cb+AEABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgAABhLGAAAGEsYAAAYSxgCSXHDBBTnqqKNSVTnqqKNywQUXjB4ScJgQxoDD3gUXXJDLLrssF198cb7yla/k4osvzmWXXSaQAU+K6u7RYzggCwsLvbi4OHoYwCHgqKOOysUXX5yf+7mf+0btzW9+c173utflq1/96sCRAYeKqrqtuxeW3SeMAYe7qspXvvKVfNM3fdM3an/913+db/7mb856/X8ksLbsK4y5TAkc9jZu3JjLLrtsj9pll12WjRs3DhoRcDjZMHoAAKP91E/9VF772tcmSV75ylfmsssuy2tf+9q88pWvHDwy4HAgjAGHvbe97W1Jkte97nV5zWtek40bN+aVr3zlN+oATyRrxgAAnmDWjAEArFHCGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQNXdo8dwQKpqZ5K/GD0O4JDz9CRfGD0I4JDzHd29abkd6zaMATwRqmqxuxdGjwM4fLhMCQAwkDAGADCQMAawp3eMHgBweLFmDABgIDNjAAADCWMAAAMJYwBJqupdVXV/VX1y9FiAw4swBjBzRZKzRw8COPwIYwBJuvujSR4YPQ7g8COMAQAMJIwBAAwkjAEADCSMAQAMJIwBJKmqq5PcnOQ7q2pHVW0bPSbg8OCfQwIAGMjMGADAQMIYAMBAwhgAwEDCGADAQMIYAMBAwhgAwEDCGADAQP8/B6jrsEcv2MMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0krOTXLyMX5zkGcvts5O8tbu/3t2fTHJ9krOq6qFJTujuq3pVgG/esM7ati5L8tQlAgEAjmjbumasqo6pqg8luSXJu7v7g0ke0t03Jcny54OXxU9O8pl1q9+wjJ283N44frd1uvvOJF9K8sBN5nFeVe2uqt179uzZ1g4CABzKthVj3X1Xd5+Z5JSsjnKdscXimx3R6i3Gt1pn4zze2N07u3vnjh079jFrAIBD3369m7K7v5jkfUmenuTm5dRjlj9vWRa7Icmp61Y7JcmNy/gpm4zfbZ2qOjbJA5Lcuj9zAwA4HO0zxqpqR1V953L7vkl+OMmfJXlHknOXxc5N8vbl9juSnFNVx1fVw5M8KsnVy6nM26vqicv1YM/fsM7atp6V5L19uP42WgCA/bCd/5vyoUkurqpjsoq3S7v7D6vqqiSXVtWuJJ9O8uwk6e5rq+rSJB9LcmeS87t77VdYvyDf/NUW71o+kuTCJG+pquuzOiJ2zsHYOQCAQ53/DgkA4F7mv0MCADhEiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYtM8Yq6pTq+ryqrquqq6tqhct4y+vqs9W1YeWjx9Zt85Lq+r6qvp4VT1t3fjjq+ojy32vq6paxo+vqrct4x+sqtPuhX0FADjkbOfI2J1Jfra7T0/yxCTnV9Vjlvte091nLh/vTJLlvnOSPDbJ05O8vqqOWZZ/Q5Lzkjxq+Xj6Mr4ryW3d/cgkr0nyqnu+awAAh759xlh339Tdf7Lcvj3JdUlO3mKVs5O8tbu/3t2fTHJ9krOq6qFJTujuq7q7k7w5yTPWrXPxcvuyJE9dO2oGAHAk269rxpbTh9+f5IPL0E9X1Yer6k1VdeIydnKSz6xb7YZl7OTl9sbxu63T3Xcm+VKSB27y+OdV1e6q2r1nz579mToAwCFp2zFWVfdL8ntJXtzdX87qlOMjkpyZ5KYkv7q26Car9xbjW61z94HuN3b3zu7euWPHju1OHQDgkLWtGKuq47IKsd/p7t9Pku6+ubvv6u6/SvKbSc5aFr8hyanrVj8lyY3L+CmbjN9tnao6NskDktx6IDsEAHA42c67KSvJhUmu6+5Xrxt/6LrFfjTJR5fb70hyzvIOyYdndaH+1d19U5Lbq+qJyzafn+Tt69Y5d7n9rCTvXa4rAwA4oh27jWV+IMlPJPlIVX1oGfv5JM+tqjOzOp34qSQ/lSTdfW1VXZrkY1m9E/P87r5rWe8FSS5Kct8k71o+klXsvaWqrs/qiNg592SnAAAOF3W4HoDauXNn7969e3oaAAD7VFXXdPfOze7zG/gBAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYNB2/qNwgENSVU1PYdsO1/8HGLj3iTHgsHVvBE5VCSfg28ppSgCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYdOz0BICjw0knnZTbbrttehrbUlXTU9inE088Mbfeeuv0NICDQIwB3xa33XZbunt6GkeMwyEYge1xmhIAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGLTPGKuqU6vq8qq6rqquraoXLeMnVdW7q+oTy58nrlvnpVV1fVV9vKqetm788VX1keW+11VVLePHV9XblvEPVtVp98K+AgAccrZzZOzOJD/b3acneWKS86vqMUl+Lsl7uvtRSd6zfJ7lvnOSPDbJ05O8vqqOWbb1hiTnJXnU8vH0ZXxXktu6+5FJXpPkVQdh3wAADnn7jLHuvqm7/2S5fXuS65KcnOTsJBcvi12c5BnL7bOTvLW7v97dn0xyfZKzquqhSU7o7qu6u5O8ecM6a9u6LMlT146aAQAcyfbrmrHl9OH3J/lgkod0903JKtiSPHhZ7OQkn1m32g3L2MnL7Y3jd1unu+9M8qUkD9zk8c+rqt1VtXvPnj37M3UAgEPStmOsqu6X5PeSvLi7v7zVopuM9RbjW61z94HuN3b3zu7euWPHjn1NGQDgkLetGKuq47IKsd/p7t9fhm9eTj1m+fOWZfyGJKeuW/2UJDcu46dsMn63darq2CQPSHLr/u4MAMDhZjvvpqwkFya5rrtfve6udyQ5d7l9bpK3rxs/Z3mH5MOzulD/6uVU5u1V9cRlm8/fsM7atp6V5L3LdWUAAEe0Y7exzA8k+YkkH6mqDy1jP5/klUkurapdST6d5NlJ0t3XVtWlST6W1Tsxz+/uu5b1XpDkoiT3TfKu5SNZxd5bqur6rI6InXPPdgsA4PBQh+sBqJ07d/bu3bunpwFsU1XlcP375lDk+YTDS1Vd0907N7vPb+AHABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGDQsdMTAI4O/bITkpc/YHoaR4x+2QnTUwAOEjEGfFvUK76c7p6exhGjqtIvn54FcDA4TQkAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAw6dnoCwNGjqqancMQ48cQTp6cAHCRiDPi26O7pKWxLVR02cwWODE5TAgAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAzaZ4xV1Zuq6paq+ui6sZdX1Wer6kPLx4+su++lVXV9VX28qp62bvzxVfWR5b7XVVUt48dX1duW8Q9W1WkHeR8BAA5Z2zkydlGSp28y/pruPnP5eGeSVNVjkpyT5LHLOq+vqmOW5d+Q5Lwkj1o+1ra5K8lt3f3IJK9J8qoD3BcAgMPOPmOsu69Icus2t3d2krd299e7+5NJrk9yVlU9NMkJ3X1Vd3eSNyd5xrp1Ll5uX5bkqWtHzQAAjnT35Jqxn66qDy+nMU9cxk5O8pl1y9ywjJ283N44frd1uvvOJF9K8sDNHrCqzquq3VW1e8+ePfdg6gAAh4YDjbE3JHlEkjOT3JTkV5fxzY5o9RbjW63zrYPdb+zund29c8eOHfs1YQCAQ9EBxVh339zdd3X3XyX5zSRnLXfdkOTUdYuekuTGZfyUTcbvtk5VHZvkAdn+aVEAgMPaAcXYcg3Ymh9NsvZOy3ckOWd5h+TDs7pQ/+ruvinJ7VX1xOV6sOcnefu6dc5dbj8ryXuX68oAAI54x+5rgaq6JMmTkzyoqm5I8rIkT66qM7M6nfipJD+VJN19bVVdmuRjSe5Mcn5337Vs6gVZvTPzvknetXwkyYVJ3lJV12d1ROycg7BfAACHhTpcD0Lt3Lmzd+/ePT0N4AhTVTlc/14EDl1VdU1379zsPr+BHwBgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQfuMsap6U1XdUlUfXTd2UlW9u6o+sfx54rr7XlpV11fVx6vqaevGH19VH1nue11V1TJ+fFW9bRn/YFWddpD3EQDgkLWdI2MXJXn6hrGfS/Ke7n5Ukvcsn6eqHpPknCSPXdZ5fVUds6zzhiTnJXnU8rG2zV1JbuvuRyZ5TZJXHejOAAAcbvYZY919RZJbNwyfneTi5fbFSZ6xbvyt3f317v5kkuuTnFVVD01yQndf1d2d5M0b1lnb1mVJnrp21AwA4Eh3oNeMPaS7b0qS5c8HL+MnJ/nMuuVuWMZOXm5vHL/bOt19Z5IvJXngZg9aVedV1e6q2r1nz54DnDoAwKHjYF/Av9kRrd5ifKt1vnWw+43dvbO7d+7YseMApwgAcOg40Bi7eTn1mOXPW5bxG5Kcum65U5LcuIyfssn43dapqmOTPCDfeloUAOCIdKAx9o4k5y63z03y9nXj5yzvkHx4VhfqX72cyry9qp64XA/2/A3rrG3rWUneu1xXBgBwxDt2XwtU1SVJnpzkQVV1Q5KXJXllkkuraleSTyd5dpJ097VVdWmSjyW5M8n53X3XsqkXZPXOzPsmedfykSQXJnlLVV2f1RGxcw7KngEAHAbqcD0ItXPnzt69e/f0NIAjTFXlcP17ETh0VdU13b1zs/v8Bn4AgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYJMYAAAaJMQCAQWIMAGCQGAMAGCTGAAAGiTEAgEFiDABgkBgDABgkxgAABokxAIBBYgwAYJAYAwAYdOz0BAAOVFUdNtvt7oO+TeDIIMaAw5bAAY4ETlMCAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAMEmMAAIPEGADAIDEGADBIjAEADBJjAACDxBgAwKDq7uk5HJCq2pPkL6bnARxxHpTk89OTAI44393dOza747CNMYB7Q1Xt7u6d0/MAjh5OUwIADBJjAACDxBjA3b1xegLA0cU1YwAAgxwZAwAYJMYAAAaJMYAkVfWmqrqlqj46PRfg6CLGAFYuSvL06UkARx8xBpCku69Icuv0PICjjxgDABgkxgAABokxAIBBYgwAYJAYA0hSVZckuSrJo6vqhqraNT0n4Ojgv0MCABjkyBgAwCAxBgAwSIwBAAwSYwAAg8QYAMAgMQYAMEiMAQAM+v8BfqdNOeUOHdoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplots for each skill\n",
    "# Loop through all the dataframe columns, exluding the 'salary' column\n",
    "for skill in df_updated.columns[1:10]:\n",
    "    \n",
    "    # Create a matplotlib new figure\n",
    "    plt.figure(figsize=(10,10))\n",
    "    \n",
    "    # Add a title to the boxplot\n",
    "    plt.title(f\"Salary Distribution for Jobs with {skill} skill\")\n",
    "    \n",
    "    # PLot boxplot\n",
    "    plt.boxplot(df_updated[df_updated[skill]==1]['salary'], vert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fd347245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplot for total skills\n",
    "df_updated[df_updated['total_skills']!=0][['salary','total_skills']]\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Salary Distribution by Count of Skills\")\n",
    "sns.boxplot(x='total_skills', y='salary', data=df_updated[df_updated['total_skills']!=0][['salary','total_skills']]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0675a760",
   "metadata": {},
   "source": [
    "#### Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a7b360b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the random seed\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "from tensorflow import random\n",
    "random.set_seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "84db49d8",
   "metadata": {},
   "outputs": [
    {
     "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>database</th>\n",
       "      <th>python</th>\n",
       "      <th>api</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>cloud</th>\n",
       "      <th>forecast</th>\n",
       "      <th>bigdata</th>\n",
       "      <th>dashboard</th>\n",
       "      <th>project_management</th>\n",
       "      <th>finance</th>\n",
       "      <th>total_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   database  python  api  algorithm  cloud  forecast  bigdata  dashboard  \\\n",
       "0         1       0    0          0      0         0        0          0   \n",
       "1         0       1    0          0      0         0        0          0   \n",
       "2         1       0    0          0      1         0        0          0   \n",
       "3         0       1    0          0      0         0        0          0   \n",
       "4         1       0    0          0      1         0        0          0   \n",
       "5         0       0    0          0      0         0        0          0   \n",
       "6         0       0    0          0      1         0        0          0   \n",
       "7         0       0    1          0      0         0        0          0   \n",
       "8         0       0    0          1      0         0        0          0   \n",
       "9         0       1    0          0      0         0        0          0   \n",
       "\n",
       "   project_management  finance  total_skills  \n",
       "0                   0        0             1  \n",
       "1                   0        0             1  \n",
       "2                   0        0             2  \n",
       "3                   0        0             1  \n",
       "4                   0        0             2  \n",
       "5                   1        0             1  \n",
       "6                   0        0             1  \n",
       "7                   1        0             2  \n",
       "8                   0        0             1  \n",
       "9                   0        1             2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    24232.63\n",
       "1    18847.60\n",
       "2    18847.60\n",
       "3    33656.44\n",
       "4    18847.60\n",
       "5    18847.60\n",
       "6    18847.60\n",
       "7    24232.63\n",
       "8    26925.15\n",
       "9    20193.86\n",
       "Name: salary, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assign X and y\n",
    "#X = df_updated[['database','python','api','algorithm','cloud','forecast','bigdata','dashboard','project_management','total_skills']]\n",
    "X = df_updated.drop(columns=\"salary\")\n",
    "y = df_updated['salary']\n",
    "display(X.head(10))\n",
    "display(y.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fd3d40dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>database</th>\n",
       "      <th>python</th>\n",
       "      <th>api</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>cloud</th>\n",
       "      <th>forecast</th>\n",
       "      <th>bigdata</th>\n",
       "      <th>dashboard</th>\n",
       "      <th>project_management</th>\n",
       "      <th>finance</th>\n",
       "      <th>total_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
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       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     database  python  api  algorithm  cloud  forecast  bigdata  dashboard  \\\n",
       "224         1       0    0          0      0         0        0          1   \n",
       "186         1       1    0          0      0         0        0          0   \n",
       "190         0       0    0          0      0         1        0          0   \n",
       "7           0       0    1          0      0         0        0          0   \n",
       "28          0       0    0          0      0         0        0          1   \n",
       "..        ...     ...  ...        ...    ...       ...      ...        ...   \n",
       "87          0       0    0          0      1         0        0          0   \n",
       "103         0       1    0          0      0         0        0          0   \n",
       "67          1       0    0          1      0         0        0          0   \n",
       "24          0       1    0          0      0         0        0          0   \n",
       "8           0       0    0          1      0         0        0          0   \n",
       "\n",
       "     project_management  finance  total_skills  \n",
       "224                   0        0             2  \n",
       "186                   1        0             3  \n",
       "190                   0        0             1  \n",
       "7                     1        0             2  \n",
       "28                    0        0             1  \n",
       "..                  ...      ...           ...  \n",
       "87                    1        0             2  \n",
       "103                   0        0             1  \n",
       "67                    0        0             2  \n",
       "24                    0        1             2  \n",
       "8                     0        0             1  \n",
       "\n",
       "[164 rows x 11 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use 70% of the data for training and the remainder for testing\n",
    "x_train, x_test,y_train,y_test = train_test_split(X, y, train_size=0.7, test_size =0.3, random_state = 100)\n",
    "\n",
    "# print the data\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f354257d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run linear regression model\n",
    "clf = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5f134180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19836.776889204477\n",
      "[-2058.88993757  1031.51866865   209.4808471   4600.21267524\n",
      "   323.80645339 -1461.85858215 -1040.90021355 -1698.03940629\n",
      "  -822.20101965  1150.15477575   233.28426092]\n"
     ]
    }
   ],
   "source": [
    "clf.fit(x_train,y_train)\n",
    "print(clf.intercept_)\n",
    "print(clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa33a883",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c9b6463f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.014218461549869277"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7999a1fa",
   "metadata": {},
   "source": [
    "#### Model Building, Comparison, and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bd162d",
   "metadata": {},
   "source": [
    "We ran five machine learning algorithms to compare to the linear regression model to determine the best model to use.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cb1eb4",
   "metadata": {},
   "source": [
    "##### Extra Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "32ecd276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Model\n",
    "et_model = ExtraTreesRegressor(n_estimators=1000, max_depth=4, random_state=25)\n",
    "\n",
    "# Fit the Model\n",
    "et_model.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions using the testing data\n",
    "y_pred_et = et_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199728e3",
   "metadata": {},
   "source": [
    "##### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3c93486b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Model\n",
    "r_model = Ridge(alpha=1.0, random_state=25)\n",
    "\n",
    "# Fit the Model\n",
    "r_model.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions using the testing data\n",
    "y_pred_r = r_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741e1694",
   "metadata": {},
   "source": [
    "##### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "92e44ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate machine learning model\n",
    "lasso = Lasso()\n",
    "\n",
    "# Fit the machine learning model with the training data\n",
    "lasso.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions using the testing data\n",
    "y_pred_lasso = lasso.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf8cb566",
   "metadata": {},
   "source": [
    "##### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ac438aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate machine learning model\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Fit the machine learning model with the training data\n",
    "rf.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions using the testing data\n",
    "y_pred_rf = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b00fb1b",
   "metadata": {},
   "source": [
    "##### SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b0737d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate machine learning model\n",
    "sgd = SGDRegressor()\n",
    "\n",
    "# Fit the machine learning model with the training data\n",
    "sgd.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions using the testing data\n",
    "y_pred_sgd = sgd.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab03b97b",
   "metadata": {},
   "source": [
    "##### Compare each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "933a669a",
   "metadata": {},
   "outputs": [
    {
     "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>Model</th>\n",
       "      <th>r2 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>-0.014218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>-0.091259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>-0.010731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>-0.014036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>-0.141564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SGD</td>\n",
       "      <td>-0.015898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  r2 Score\n",
       "0  Linear Regression -0.014218\n",
       "1        Extra Trees -0.091259\n",
       "2              Ridge -0.010731\n",
       "3              Lasso -0.014036\n",
       "4      Random Forest -0.141564\n",
       "5                SGD -0.015898"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the coefficient of determination R2\n",
    "# R2 Variables\n",
    "r2_Linear_Regression = r2_score(y_test, y_pred)\n",
    "r2_et = r2_score(y_test, y_pred_et)\n",
    "r2_r = r2_score(y_test, y_pred_r)\n",
    "r2_lasso = r2_score(y_test, y_pred_lasso)\n",
    "r2_rf = r2_score(y_test, y_pred_rf)\n",
    "r2_sgd = r2_score(y_test, y_pred_sgd)\n",
    "\n",
    "# Create Dataframe for R2 Scores\n",
    "r2 = [['Linear Regression', r2_Linear_Regression], ['Extra Trees', r2_et], ['Ridge', r2_r], [\"Lasso\", r2_lasso], ['Random Forest', r2_rf], ['SGD', r2_sgd]]\n",
    "df_r2 = pd.DataFrame(r2, columns=['Model', 'r2 Score'])\n",
    "df_r2.head(6)"
   ]
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      "text/plain": [
       ":Bars   [Model]   (r2 Score)"
      ]
     },
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   "source": [
    "# Plot the values\n",
    "#plt.plot(r2)\n",
    "df_r2.hvplot.bar(\n",
    "    x=\"Model\", y=\"r2 Score\", xlabel=\"Model\", ylabel=\"r2 Score\", rot=90\n",
    ")"
   ]
  },
  {
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   "execution_count": 80,
   "id": "283c1100",
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Mean Squared Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>9.890427e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>1.064171e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>9.856417e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>9.888647e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.113227e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SGD</td>\n",
       "      <td>9.906804e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               Model  Mean Squared Error\n",
       "0  Linear Regression        9.890427e+07\n",
       "1        Extra Trees        1.064171e+08\n",
       "2              Ridge        9.856417e+07\n",
       "3              Lasso        9.888647e+07\n",
       "4      Random Forest        1.113227e+08\n",
       "5                SGD        9.906804e+07"
      ]
     },
     "execution_count": 80,
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   "source": [
    "# Get the Mean Squared Error\n",
    "# MSE Variables\n",
    "mse_Linear_Regression = mean_squared_error(y_test, y_pred)\n",
    "mse_et = mean_squared_error(y_test, y_pred_et)\n",
    "mse_r = mean_squared_error(y_test, y_pred_r)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred_lasso)\n",
    "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
    "mse_sgd = mean_squared_error(y_test, y_pred_sgd)\n",
    "\n",
    "# Create Dataframe for R2 Scores\n",
    "mse = [['Linear Regression', mse_Linear_Regression], ['Extra Trees', mse_et], ['Ridge', mse_r], [\"Lasso\", mse_lasso], ['Random Forest', mse_rf], ['SGD', mse_sgd]]\n",
    "df_mse = pd.DataFrame(mse, columns=['Model', 'Mean Squared Error'])\n",
    "df_mse.head(6)"
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       ":Bars   [Model]   (Mean Squared Error)"
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    "# Plot the values\n",
    "#plt.plot(r2)\n",
    "df_mse.hvplot.bar(\n",
    "    x=\"Model\", y=\"Mean Squared Error\", xlabel=\"Model\", ylabel=\"Mean Squared Error\", rot=90\n",
    ")"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Root Mean Squared Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>9.890427e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Extra Trees</td>\n",
       "      <td>1.064171e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>9.856417e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>9.888647e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>1.113227e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SGD</td>\n",
       "      <td>9.906804e+07</td>\n",
       "    </tr>\n",
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       "               Model  Root Mean Squared Error\n",
       "0  Linear Regression             9.890427e+07\n",
       "1        Extra Trees             1.064171e+08\n",
       "2              Ridge             9.856417e+07\n",
       "3              Lasso             9.888647e+07\n",
       "4      Random Forest             1.113227e+08\n",
       "5                SGD             9.906804e+07"
      ]
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   "source": [
    "# Get the Root Mean Squared Error\n",
    "# RMSE Variables\n",
    "rmse_Linear_Regression = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "rmse_et = np.sqrt(mean_squared_error(y_test, y_pred_et))\n",
    "rmse_r = np.sqrt(mean_squared_error(y_test, y_pred_r))\n",
    "rmse_lasso = np.sqrt(mean_squared_error(y_test, y_pred_lasso))\n",
    "rmse_rf = np.sqrt(mean_squared_error(y_test, y_pred_rf))\n",
    "rmse_sgd = np.sqrt(mean_squared_error(y_test, y_pred_sgd))\n",
    "\n",
    "# Create Dataframe for R2 Scores\n",
    "rmse = [['Linear Regression', rmse_Linear_Regression], ['Extra Trees', rmse_et], ['Ridge', rmse_r], [\"Lasso\", rmse_lasso], ['Random Forest', rmse_rf], ['SGD', rmse_sgd]]\n",
    "df_rmse = pd.DataFrame(mse, columns=['Model', 'Root Mean Squared Error'])\n",
    "df_rmse.head(6)"
   ]
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       ":Bars   [Model]   (Root Mean Squared Error)"
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   "source": [
    "# Plot the values\n",
    "#plt.plot(r2)\n",
    "df_rmse.hvplot.bar(\n",
    "    x=\"Model\", y=\"Root Mean Squared Error\", xlabel=\"Model\", ylabel=\"Root Mean Squared Error\", rot=90\n",
    ")"
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    "R2 - Linear Regression is closest to 1  \n",
    "Mean Squared Error - Linear Regression is the lowest  \n",
    "Root Mean Squared Error - Linear Regression is the lowest  "
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   "cell_type": "markdown",
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   "source": [
    "Overall, the Linear Regression model was the most favorable according to a comparison of results."
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   "source": [
    "#### Model Performance"
   ]
  },
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   "cell_type": "markdown",
   "id": "fcb21c23",
   "metadata": {},
   "source": [
    "We evaluated the performance of the most accurate model chosen above (Linear Regression)."
   ]
  },
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>20512.663060</td>\n",
       "      <td>13462.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>26492.167469</td>\n",
       "      <td>18847.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>25935.076755</td>\n",
       "      <td>40387.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>17979.345168</td>\n",
       "      <td>18847.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>21101.579819</td>\n",
       "      <td>21540.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Prediction    Actual\n",
       "165  20393.867604  18847.60\n",
       "179  20627.587134  34868.07\n",
       "133  26492.167469  18847.60\n",
       "148  17783.104985  13462.57\n",
       "59   24670.273825  18847.60\n",
       "..            ...       ...\n",
       "75   20512.663060  13462.57\n",
       "182  26492.167469  18847.60\n",
       "214  25935.076755  40387.72\n",
       "156  17979.345168  18847.60\n",
       "126  21101.579819  21540.12\n",
       "\n",
       "[71 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict outcomes for test data set\n",
    "predictions = clf.predict(x_test)\n",
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b262a3dd",
   "metadata": {},
   "outputs": [
    {
     "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>18847.60</td>\n",
       "      <td>20393.867604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>34868.07</td>\n",
       "      <td>20627.587134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>18847.60</td>\n",
       "      <td>26492.167469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13462.57</td>\n",
       "      <td>17783.104985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>18847.60</td>\n",
       "      <td>24670.273825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual     Predicted\n",
       "164  18847.60  20393.867604\n",
       "165  34868.07  20627.587134\n",
       "166  18847.60  26492.167469\n",
       "167  13462.57  17783.104985\n",
       "168  18847.60  24670.273825"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame of Real and Predicted values\n",
    "salaries = pd.DataFrame({\n",
    "    \"Actual\": y_test.ravel(),\n",
    "    \"Predicted\": predictions.ravel()\n",
    "}, index = df_updated.index[-len(y_test): ]) \n",
    "\n",
    "# Show the DataFrame's head\n",
    "salaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "91cacd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Actual Vs. Predicted Salaries'}>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the real vs predicted values as a line chart\n",
    "salaries.plot(title=\"Actual Vs. Predicted Salaries\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea1d39f7",
   "metadata": {},
   "source": [
    "In conclusion, our model was not able to accurately predict salaries due to the limited data set.  Implications are included below in the dashboard."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70141ed4",
   "metadata": {},
   "source": [
    "#### DASHBOARD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44238e0c",
   "metadata": {},
   "source": [
    "We created a dashboard for the presentation. The dashboard was saved as an html."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b594e9",
   "metadata": {},
   "source": [
    "First Step: Create Markdowns for Overview Tabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e14f8221",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_titlepage = \"\"\"\n",
    "# Predicting FinTech Bootcamp Graduate Salaries  \n",
    "  \n",
    "Presentation Date: January 15, 2022 \n",
    "    \n",
    "Prepared by Andrew, Rachel, Margee, and Jinhyeong  \n",
    "  \n",
    "![image](./images/programmingsalaries.png)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fbf3501a",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_intro = \"\"\"\n",
    "# Project Objective and Hypothesis  \n",
    "  \n",
    "Our objective was to research and learn what salaries are possible with skills obtained in this FinTech Bootcamp.  \n",
    "We used the Monster India API to retrieve data regarding job descriptions, skills, and salaries  \n",
    "to determine the following:  \n",
    "- What jobs require skills from this bootcamp?  \n",
    "- What are the potential jobs and salaries for graduates with these skills?  \n",
    "- Can we predict an accurate salary range based on these skills?  \n",
    "\n",
    "Our hypothesis is that the more key skills you possess, the higher the salary.\n",
    "  \n",
    "![image](./images/salaryafterbootcamp.png)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "cd03f116",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_background1 = \"\"\"\n",
    "# What Skills Have We Gained as FinTech Bootcamp Students?\n",
    "  \n",
    "As part of the U of MN FinTech Bootcamp, we have obtained the necessary skills to automate  \n",
    "and improve financial services using cutting-edge technology. Upon graduation, skills gained will include the following:\n",
    "  \n",
    "![image](./images/Skills.png)\n",
    "\n",
    "We selected key skills from this course to use in our analysis.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "24570f7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_background2 = \"\"\"\n",
    "# Datasets Used  \n",
    "\n",
    "We used data obtained from the Monster India API.  \n",
    "  \n",
    "We selected the following skills obtained during this FinTech Bootcamp to use in our model:  \n",
    "1. Database  \n",
    "2. Python  \n",
    "3. API  \n",
    "4. Algorithm  \n",
    "5. Cloud  \n",
    "6. Forecast  \n",
    "7. Big data    \n",
    "8. Dashboard  \n",
    "9. Project Management  \n",
    "10. Finance  \n",
    "  \n",
    "We also used like-terms in our model to capture these skills using various terminology.  \n",
    "  \n",
    "![image](./images/indemandskills.png)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5eca4b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_dataphases = \"\"\"\n",
    "# Data Phases  \n",
    "  \n",
    "Our process consisted of data exploration,  data preparation, and data cleanup.  \n",
    "  \n",
    "- Data Exploration: Google seaches, API searches, Monster India API, FinTech Bootcamp Curriculum    \n",
    "- Data Preparation: Searching through variables in Spyder, selecting key data, narrowing down jobs, determining key skills  \n",
    "- Data Cleanup: Dropping unnecessary information, searching the data to find jobs that include at least one key skill  \n",
    "    \n",
    "Data Issues:   \n",
    "  \n",
    "- Most salaries were in Indian rupees, not USD, so conversion was necessary.  \n",
    "- Some salaries may be skewed because there are some outliers from different countries/currencies in the data obtained from the API.  \n",
    "- There were likely job postings/salaries posted multiple times.\n",
    "- The job posting \"skills\" section did not include the information we needed, so we had to rely on the job description to find job skills.  \n",
    "- Determining like-words for key skills was subjective, which may not capture all.  \n",
    "- As shown in the graph below, the most broad salary range includes at least one skill.    \n",
    "  \n",
    "![image](./images/salarydistributiontotalskills.png)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d5ae863c",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_model = \"\"\"\n",
    "# Training/Testing and Predictive Model Evaluation  \n",
    "  \n",
    "We ran a variety of machine learning algorithms to determine the best model to use:  \n",
    "1. Linear Regression  \n",
    "2. Extra Trees  \n",
    "3. Lasso  \n",
    "4. Random Forest  \n",
    "5. Ridge  \n",
    "6. Stochastic Gradient Descent    \n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3d7866c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_modelperf = \"\"\"\n",
    "# Model Performance  \n",
    "  \n",
    "Techniques used to evaluate the model performance included running various machine learning algorithms \n",
    "to determine the best model.  Overall, we determined the Linear Regression model was the best fit for \n",
    "our project because it resulted in an R2 value closest to 1 and the lowest Mean Squared Error and Root \n",
    "Mean Squared Error scores the most times when re-running the model.  \n",
    "  \n",
    "![image](./images/data.png)  \n",
    "  \n",
    "![image](./images/r2.png)  \n",
    "  \n",
    "![image](./images/mse.png) \n",
    "  \n",
    "![image](./images/rmse.png)  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1a435cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_summaryend = \"\"\"\n",
    "# Summary of Conclusions/Predictions  \n",
    "  \n",
    "Our model was not able to accurately predict salaries due to the limited data set.  \n",
    "Predicted salaries appear to be more of an average across the population.  \n",
    "Data implications, as discussed earlier, resulted in less reliable results,\n",
    "but could be improved with more data and a more relevant API.  \n",
    "  \n",
    "![image](./images/predictedvaluechart.png)  \n",
    "  \n",
    "![image](./images/predictedsalaries.png)  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "96514d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "markdown_end = \"\"\"\n",
    "# Conclusion  \n",
    "  \n",
    "Based on our analysis, we were not able to determine salary ranges for FinTech Bootcamp graduates.  \n",
    "However, the job salaries that included skills we have learned in this bootcamp were up to $80,000 \n",
    "(converted from Indian rupees, of course!)  \n",
    "   \n",
    "*Difficulties/Implications:*  \n",
    " Overall, the API used was not the best and resulted in various data implications as discussed previously.  \n",
    " We could have spent $200 for access to the US Monster API, but we were cheap and perhaps will consider \n",
    " it for another future project based on the frustration of our data outcome!  \n",
    "  \n",
    "*Potential Next Steps:*  \n",
    "Continue searching for an API with US-based jobs in USD, or pay the fee for the Monster US API.  \n",
    "     \n",
    "![image](./images/fintechjobs.png)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e48fdb",
   "metadata": {},
   "source": [
    "Create the Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "bbe62839",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       "Tabs\n",
       "    [0] Markdown(str, name='XX')\n",
       "    [1] Markdown(str, name='XX')\n",
       "    [2] Markdown(str, name='XX')\n",
       "    [3] Markdown(str, name='XX')\n",
       "    [4] Markdown(str, name='XX')\n",
       "    [5] Markdown(str, name='XX')\n",
       "    [6] Markdown(str, name='XX')\n",
       "    [7] Markdown(str, name='XX')\n",
       "    [8] Markdown(str, name='XX')"
      ]
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     "execution_count": 345,
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       "id": "1105"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create Dashboard\n",
    "pn.Tabs(\n",
    "    (\"Project 2 Group C\",markdown_titlepage),\n",
    "    (\"Project Objective\",markdown_intro),\n",
    "    (\"Skills Gained in Bootcamp\",markdown_background1),\n",
    "    (\"Datasets Used\",markdown_background2),\n",
    "    (\"Data Phases and Issues\",markdown_dataphases),\n",
    "    (\"Training/Testing and Evaluation\",markdown_model),\n",
    "    (\"Model Performance\",markdown_modelperf),\n",
    "    (\"Summary\",markdown_summaryend),\n",
    "    (\"Conclusion\",markdown_end)\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c256a60b",
   "metadata": {},
   "source": [
    "Final Step: Save to html for presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bb194837",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create an html of the dashboard for presentation purposes once finalized\n",
    "pn.Tabs(\n",
    "    (\"Project 2 Group C\",markdown_titlepage),\n",
    "    (\"Project Objective\",markdown_intro),\n",
    "    (\"Skills Gained in Bootcamp\",markdown_background1),\n",
    "    (\"Datasets Used\",markdown_background2),\n",
    "    (\"Data Phases and Issues\",markdown_dataphases),\n",
    "    (\"Training/Testing and Evaluation\",markdown_model),\n",
    "    (\"Model Performance\",markdown_modelperf),\n",
    "    (\"Summary\",markdown_summaryend),\n",
    "    (\"Conclusion\",markdown_end)\n",
    "       ).save(\"Project2_Presentation.html\", embed=True)"
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