final-report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93d8e3f9",
   "metadata": {},
   "source": [
    "# NLP Group Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1ea3f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import prepare as pr\n",
    "import explore as ex\n",
    "import modeling as mod\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0aae825",
   "metadata": {},
   "source": [
    "# Project Description:\n",
    "Github is an online version control system that stores your code history. Repsositories are made to store different files that are written in various programming languages and named; usually based on what is inside the repository. The format for the repository titles are user_name/title. A github search query was made for COVID-19 and repository file titles were scraped and stored in a .json file. Natural Language Processing was applied to the README files in each repository and words were analyzed. A machine learning algoritm was applied to the information obtained from the natural language processing statifying on programming language in order to predict primary programming language of repository."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc57e307",
   "metadata": {},
   "source": [
    "# Project Goals:\n",
    "* Explore words and frequencies of covid-19 README's.\n",
    "* Develop a machine learning model to predict `language`\n",
    "* Deliver a final report to stakeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3879d55a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#acquiring and preparing data\n",
    "df=pd.read_json('data.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "807f7b13",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>repo</th>\n",
       "      <th>language</th>\n",
       "      <th>readme_contents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Priesemann-Group/covid19_inference_forecast</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td># Bayesian inference and forecast of COVID-19\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CodeExplainedRepo/COVID-19_TRACKER_HTML_CSS_JA...</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td># COVID-19_TRACKER_HTML_CSS_JAVASCRIPT\\nCOVID-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>joanby/covid19</td>\n",
       "      <td>HTML</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                repo          language  \\\n",
       "0        Priesemann-Group/covid19_inference_forecast  Jupyter Notebook   \n",
       "1  CodeExplainedRepo/COVID-19_TRACKER_HTML_CSS_JA...        JavaScript   \n",
       "2                                     joanby/covid19              HTML   \n",
       "\n",
       "                                     readme_contents  \n",
       "0  # Bayesian inference and forecast of COVID-19\\...  \n",
       "1  # COVID-19_TRACKER_HTML_CSS_JAVASCRIPT\\nCOVID-...  \n",
       "2                                                     "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a look at the data\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a500d7",
   "metadata": {},
   "source": [
    "# Data Dictionary:\n",
    "\n",
    "| Feature | Definition |\n",
    "|:--------|:-----------|\n",
    "|repo| The file path of the repository on github.|\n",
    "|language| The programming language that the readme of the repository is written in.|\n",
    "|readme_contents| The raw contents of the readme for the repository.|\n",
    "|stemmed| The contents of the readme after they have been stemmed.|\n",
    "|lemmatized| The contents of the readme after they have been lemmatized.|"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a14a44b",
   "metadata": {},
   "source": [
    "#  Explore Preperation:\n",
    "* Data was prepared for explore:\n",
    "    * Removed blank README files:\n",
    "        * 4 repos had README files but contained no contents (\"\") so they were removed\n",
    "    * Data was:\n",
    "        * stop words were removed\n",
    "        * stemmed\n",
    "        * lemmatized\n",
    "        * tokenized\n",
    "    * `language` was binned:\n",
    "        * Took the top 5 languages and kept them unique and all other languages used less than them were catagorized as 'other'\n",
    "    * Data was split; stratifying on `language`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7e64baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing the data\n",
    "df=pr.prep_data(df, column='readme_contents', extra_words=['covid19', 'coronavirus'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da1475e",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Exploration Question 1:</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dt>What are the top languages of READMEs from repos that focus on COVID-19?</dt>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60dc2c5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python              38\n",
       "JavaScript          37\n",
       "Jupyter Notebook    30\n",
       "HTML                20\n",
       "R                    9\n",
       "TypeScript           7\n",
       "Kotlin               7\n",
       "Vue                  5\n",
       "Dart                 3\n",
       "Ruby                 3\n",
       "PHP                  2\n",
       "TeX                  2\n",
       "Go                   2\n",
       "Stan                 1\n",
       "C#                   1\n",
       "Svelte               1\n",
       "Java                 1\n",
       "SCSS                 1\n",
       "PowerShell           1\n",
       "TSQL                 1\n",
       "Objective-C          1\n",
       "CSS                  1\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#question 1 function\n",
    "ex.ex_q1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f6d895e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "others              69\n",
       "Python              38\n",
       "JavaScript          37\n",
       "Jupyter Notebook    30\n",
       "HTML                20\n",
       "R                    9\n",
       "Name: language, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#separating out the languages into its own column\n",
    "df=pr.seperate_language(df)\n",
    "df['language'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac2fe7ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>repo</th>\n",
       "      <th>language</th>\n",
       "      <th>clean</th>\n",
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       "      <th>lemmatized</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Priesemann-Group/covid19_inference_forecast</td>\n",
       "      <td>Jupyter Notebook</td>\n",
       "      <td>bayesian inference forecast documentation stat...</td>\n",
       "      <td>bayesian infer forecast document statushttpsre...</td>\n",
       "      <td>bayesian inference forecast documentation stat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CodeExplainedRepo/COVID-19_TRACKER_HTML_CSS_JA...</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>covid19trackerhtmlcssjavascript html5 css3 jav...</td>\n",
       "      <td>covid19trackerhtmlcssjavascript html5 css3 jav...</td>\n",
       "      <td>covid19trackerhtmlcssjavascript html5 css3 jav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>joanby/covid19</td>\n",
       "      <td>HTML</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sambreen27/covid19</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>simple reactjs app displays latest stats insta...</td>\n",
       "      <td>simpl reactj app display latest stat instal gi...</td>\n",
       "      <td>simple reactjs app display latest stats instal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SJang1/2021-korea-covid-19-remaining-vaccine-m...</td>\n",
       "      <td>Python</td>\n",
       "      <td>code archived 2021 9 22 koreacovid19remainingv...</td>\n",
       "      <td>code archiv 2021 9 22 koreacovid19remainingvac...</td>\n",
       "      <td>code archived 2021 9 22 koreacovid19remainingv...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                repo          language  \\\n",
       "0        Priesemann-Group/covid19_inference_forecast  Jupyter Notebook   \n",
       "1  CodeExplainedRepo/COVID-19_TRACKER_HTML_CSS_JA...        JavaScript   \n",
       "2                                     joanby/covid19              HTML   \n",
       "3                                 sambreen27/covid19        JavaScript   \n",
       "4  SJang1/2021-korea-covid-19-remaining-vaccine-m...            Python   \n",
       "\n",
       "                                               clean  \\\n",
       "0  bayesian inference forecast documentation stat...   \n",
       "1  covid19trackerhtmlcssjavascript html5 css3 jav...   \n",
       "2                                                      \n",
       "3  simple reactjs app displays latest stats insta...   \n",
       "4  code archived 2021 9 22 koreacovid19remainingv...   \n",
       "\n",
       "                                             stemmed  \\\n",
       "0  bayesian infer forecast document statushttpsre...   \n",
       "1  covid19trackerhtmlcssjavascript html5 css3 jav...   \n",
       "2                                                      \n",
       "3  simpl reactj app display latest stat instal gi...   \n",
       "4  code archiv 2021 9 22 koreacovid19remainingvac...   \n",
       "\n",
       "                                          lemmatized  \n",
       "0  bayesian inference forecast documentation stat...  \n",
       "1  covid19trackerhtmlcssjavascript html5 css3 jav...  \n",
       "2                                                     \n",
       "3  simple reactjs app display latest stats instal...  \n",
       "4  code archived 2021 9 22 koreacovid19remainingv...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30c8f41",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Exploration Question 2:</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dt>What are the top 20 words in all of the READMEs?</dt>\n",
    "    </dl>\n",
    "    <header>\n",
    "    <h2>Exploration Question 3:</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dt>How often do common words occur in each programming language?</dt>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b907cceb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#splitting the data\n",
    "train, val, test=ex.tts(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c352800",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all</th>\n",
       "      <th>other</th>\n",
       "      <th>js</th>\n",
       "      <th>py</th>\n",
       "      <th>jn</th>\n",
       "      <th>html</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>td</th>\n",
       "      <td>1512</td>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1428</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data</th>\n",
       "      <td>1100</td>\n",
       "      <td>343</td>\n",
       "      <td>46</td>\n",
       "      <td>399</td>\n",
       "      <td>167</td>\n",
       "      <td>83</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>styletextalignleft</th>\n",
       "      <td>672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de</th>\n",
       "      <td>547</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>370</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526</td>\n",
       "      <td>451</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>br</th>\n",
       "      <td>475</td>\n",
       "      <td>388</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>435</td>\n",
       "      <td>376</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arxiv</th>\n",
       "      <td>386</td>\n",
       "      <td>385</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tr</th>\n",
       "      <td>346</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>312</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case</th>\n",
       "      <td>249</td>\n",
       "      <td>104</td>\n",
       "      <td>11</td>\n",
       "      <td>53</td>\n",
       "      <td>35</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>using</th>\n",
       "      <td>243</td>\n",
       "      <td>161</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <td>232</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "      <td>116</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>image</th>\n",
       "      <td>207</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <td>198</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doi</th>\n",
       "      <td>197</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>191</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <td>188</td>\n",
       "      <td>96</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chest</th>\n",
       "      <td>170</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <td>170</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>82</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file</th>\n",
       "      <td>167</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     all  other  js   py   jn  html   r\n",
       "td                  1512     56  20    8    0  1428   0\n",
       "data                1100    343  46  399  167    83  62\n",
       "styletextalignleft   672      0   0    0    0   672   0\n",
       "de                   547    102   1   19  370     4  51\n",
       "1                    526    451   5   21   39     4   6\n",
       "br                   475    388   0   83    3     1   0\n",
       "2020                 435    376   0   10   17    19  13\n",
       "arxiv                386    385   0    1    0     0   0\n",
       "tr                   346     20   6    8    0   312   0\n",
       "case                 249    104  11   53   35    21  25\n",
       "using                243    161   6   32   18    20   6\n",
       "source               232     61  11  116   22    12  10\n",
       "image                207    158   0   23   21     5   0\n",
       "dataset              198     66   0   50   32    21  29\n",
       "doi                  197    172   0    8    8     3   6\n",
       "0                    191     23   0   11  157     0   0\n",
       "code                 188     96   6   42   29    11   4\n",
       "chest                170    161   0    7    1     1   0\n",
       "number               170     35   5   82   18     6  24\n",
       "file                 167     67  10   43   27     9  11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#question 2 and 3 function\n",
    "ex.explore_q2(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e188e2d",
   "metadata": {},
   "source": [
    "**Takeaway**  \n",
    "* Some words favor a specific language\n",
    "* 'styletextalignleft' only occurs in HTML READMEs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432a20bf",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Exploration Question 4:</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dt>What words are more common in READMEs across multiple programming langauges?</dt>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab8c4695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#question 4 visualizations\n",
    "ex.explore_plots(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "148afe09",
   "metadata": {},
   "source": [
    "**Takeaway**  \n",
    "* The top ten words for most of these languages are the same. This could make it harder to predict the correct language from a README"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25fd5f58",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Exploration Question 5:</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dt>What words have the highest Inverse Document Frequency (IDF)?</dt>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f28584e3",
   "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>word</th>\n",
       "      <th>idf_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>001030</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>002</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0025httpspypiorgprojectsentimentanalysisspanish</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>004</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00406376</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00bncbitaxonomynotebooksdataprep00bncbitaxonom...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                word  idf_score\n",
       "0                                                 00        1.0\n",
       "1                                                000        1.0\n",
       "2                                               0000        1.0\n",
       "3                                                001        1.0\n",
       "4                                             001030        1.0\n",
       "5                                                002        1.0\n",
       "6    0025httpspypiorgprojectsentimentanalysisspanish        1.0\n",
       "7                                                004        1.0\n",
       "8                                           00406376        1.0\n",
       "9  00bncbitaxonomynotebooksdataprep00bncbitaxonom...        1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#question 4 visualizations\n",
    "ex.get_top_idf_words(train, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e706f1f",
   "metadata": {},
   "source": [
    "**Takeaway**\n",
    "* These \"words\" are all unique\n",
    "* Because of the high idf, these values would not contribute much when making predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2169be",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Exploration Summary</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dd>- Languages share similar syntax.</dd>\n",
    "        <dd>- Even though there are some distinct wording, they don't account for all data in the dataset.</dd>\n",
    "        <dd>- Top words for several languages are the same.</dd>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8018e3d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data and giving baseline\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = mod.split_cv_models(df, 'lemmatized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4dbcc4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy is 33.80%\n"
     ]
    }
   ],
   "source": [
    "# baseline model\n",
    "baseline = mod.baseline_model(df, 'lemmatized')\n",
    "baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71ccc701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy is 33.80%\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>train_accuracy</th>\n",
       "      <th>validate_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.612676</td>\n",
       "      <td>0.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.471831</td>\n",
       "      <td>0.387097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.802817</td>\n",
       "      <td>0.354839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baseline</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.338028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.322581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  train_accuracy  validate_accuracy\n",
       "1                  KNN        0.612676           0.419355\n",
       "0        Decision Tree        0.471831           0.387097\n",
       "2  Logistic Regression        0.802817           0.354839\n",
       "4             Baseline        0.338028           0.338028\n",
       "3        Random Forest        0.338028           0.322581"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#best models\n",
    "mod.models(df, 'lemmatized')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c185ae7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline accuracy is 33.80%\n"
     ]
    },
    {
     "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",
       "        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>train_accuracy</th>\n",
       "      <th>validate_accuracy</th>\n",
       "      <th>test_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.612676</td>\n",
       "      <td>0.419355</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>baseline</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.338028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      model  train_accuracy  validate_accuracy  test_accuracy\n",
       "0       KNN        0.612676           0.419355       0.433333\n",
       "1  baseline        0.338028           0.338028       0.338028"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#running the test data through the model\n",
    "mod.best_model(df, 'lemmatized')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d9761f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <header>\n",
    "    <h2>Modeling Summary</h2>\n",
    "    </header>\n",
    "    <dl>\n",
    "        <dd>- The knn was the top performing model.</dd>\n",
    "        <dd>- All models seemed to overfit to varying degree. Train and validate performed well in some cases, but the test did not perform well on the final model.</dd>\n",
    "    </dl>\n",
    "    <header>\n",
    "    <h2>Conclusion</h2>\n",
    "    </header>\n",
    "        <dl>\n",
    "        <dd>- With 199 repos, it was difficult to predict language.</dd>\n",
    "        <dd>- Additional data manipulation should be done to attempt to create a better model.</dd>\n",
    "        <dd>- There are distinct words for each programing language, but still may not account for all README files.</dd>\n",
    "    <header>\n",
    "    <h2>Next Steps</h2>\n",
    "    </header>\n",
    "        <dd>- Additional steps would include adding more repos and README files to the dataset. We also could try to filter out some of the \"noise\" from common words among all README files to prioritize more unique words. We would explore different ways to group the data by language that may influence the outcome of modeling. Try modeling with the highest and lowest idf words removed.</dd>\n",
    "    </dl>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c837f57d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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