final_report.ipynb

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  {
   "cell_type": "markdown",
   "id": "58adc7fc-a001-4cc5-b852-71ec9b6fe01b",
   "metadata": {},
   "source": [
    "# Programming Language Prediction Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bde2140-d0ee-4ac2-8326-0a7d51876925",
   "metadata": {},
   "source": [
    "## Goal:\n",
    "- Discover features of README.md files that indicate the repository programming language\n",
    "- Use features to develop a machine learning model to predict the repository's main programming language"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd199d9a-2435-47fd-bf9e-bf8b4591fb00",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e5addb5-7672-4766-8666-e4a7a440a482",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import acquire as a\n",
    "import wrangle as w\n",
    "import explore as e\n",
    "# import model as m\n",
    "# import evaluate as ev\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import matplotlib.pyplot as plt\n",
    "import unicodedata\n",
    "import nltk\n",
    "from wordcloud import WordCloud\n",
    "import os\n",
    "import json\n",
    "\n",
    "# import for exploring\n",
    "from sklearn.model_selection import train_test_split\n",
    "import scipy.stats as stats\n",
    "\n",
    "# import for model \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae87c0f-cef8-4380-97ec-0b20930d5395",
   "metadata": {},
   "source": [
    "# Acquire\n",
    "- Aquire a corpus of GitHub repository README files via GitHub API. \n",
    "- Our data comes from the top 100 most-starred repositories on GitHub current as of 27 Jun 2023.\n",
    "- It contained 119 rows and 3 columns before cleaning\n",
    "- Each row represents a repository.\n",
    "- Each column represents the features of repository: repo name, language, README text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70cbae68-514e-4709-a682-fbb8eddf254c",
   "metadata": {},
   "source": [
    "# Prepare\n",
    "- Clean text by making all text lowercase\n",
    "- Removed special characters\n",
    "- Tokenizing the words into discrete units\n",
    "- lemmatizing them to get word roots\n",
    "- Nulls: dropped 9 rows that had null for language\n",
    "- Checked that column data types were appropriate\n",
    "- We changed language to 'other' for languages other than the top 6 languages\n",
    "    - The top 6 languages in this dataset are Objective-C, Java, JavaScript, Python, Ruby, and Swift\n",
    "    - There were 19 different languages in our dataset, 6 of which occurred only once, and 4 only twice\n",
    "- Split data into train, validate, and test (60/20/20), stratifying on language."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d639ef5a-93ff-49ea-bee9-d91f8c2c7ee3",
   "metadata": {},
   "source": [
    "# Data Dictionary\n",
    "\n",
    "| Feature Name | Data Type | Description | Example |\n",
    "| ----- | ----- | ----- | ----- |\n",
    "| repo | object | Name of Repository | 'spolu/breach_core' |\n",
    "| language | object | Predominant coding language of Repository | 'JavaScript' |\n",
    "| readme_contents | object | Contents of Repository's README file | '### Breach: A Browser for the HTML5 Era...' |\n",
    "| lemmatized | object | Cleaned version of contents of Repository's README file | 'breach browser html5 era modular everything...' |\n",
    "| readme_length | integer | number of characters in the README file | 1069 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c840832b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## acquire and prepare data for exploration\n",
    "df = a.get_data_json()\n",
    "df = w.clean_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dbda891-a4bc-42a8-93f4-004c02e57547",
   "metadata": {},
   "outputs": [],
   "source": [
    "## split data into train, validate, and test\n",
    "train, validate, test = w.split_data(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d48fbc-168e-4fb4-b6f0-3c48309f4cbe",
   "metadata": {},
   "source": [
    "# A brief look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fe6a1b7-7ca0-4b60-96ab-33605f206802",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>repo</th>\n",
       "      <th>language</th>\n",
       "      <th>readme_contents</th>\n",
       "      <th>lemmatized</th>\n",
       "      <th>readme_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>inkling/Subliminal</td>\n",
       "      <td>Objective-C</td>\n",
       "      <td>&lt;p align=\"center\" &gt;\\n  &lt;img src=\"http://inklin...</td>\n",
       "      <td>p aligncenter img srchttpinklinggithubiosublim...</td>\n",
       "      <td>11215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>microsoftarchive/android-sliding-layer-lib</td>\n",
       "      <td>Java</td>\n",
       "      <td>6Wunderkinder SlidingLayer for Android\\n======...</td>\n",
       "      <td>6wunderkinder slidinglayer android repository ...</td>\n",
       "      <td>4266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>jamesob/tinychain</td>\n",
       "      <td>Python</td>\n",
       "      <td># ⛼  tinychain\\n\\n*Putting the rough in \"[roug...</td>\n",
       "      <td>tinychain putting rough rough consensushttpsto...</td>\n",
       "      <td>8333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Cleveroad/CRNetworkButton</td>\n",
       "      <td>Swift</td>\n",
       "      <td># CRNetworkButton [![Awesome](https://cdn.rawg...</td>\n",
       "      <td>crnetworkbutton awesomehttpscdnrawgitcomsindre...</td>\n",
       "      <td>5268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>zhangxinxu/mobilebone</td>\n",
       "      <td>JavaScript</td>\n",
       "      <td>mobilebone.js\\n=============\\n\\n&lt;img src=\"http...</td>\n",
       "      <td>mobilebonejs img srchttpswwwzhangxinxucomgithu...</td>\n",
       "      <td>5908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           repo     language  \\\n",
       "70                           inkling/Subliminal  Objective-C   \n",
       "59   microsoftarchive/android-sliding-layer-lib         Java   \n",
       "92                            jamesob/tinychain       Python   \n",
       "104                   Cleveroad/CRNetworkButton        Swift   \n",
       "97                        zhangxinxu/mobilebone   JavaScript   \n",
       "\n",
       "                                       readme_contents  \\\n",
       "70   <p align=\"center\" >\\n  <img src=\"http://inklin...   \n",
       "59   6Wunderkinder SlidingLayer for Android\\n======...   \n",
       "92   # ⛼  tinychain\\n\\n*Putting the rough in \"[roug...   \n",
       "104  # CRNetworkButton [![Awesome](https://cdn.rawg...   \n",
       "97   mobilebone.js\\n=============\\n\\n<img src=\"http...   \n",
       "\n",
       "                                            lemmatized  readme_length  \n",
       "70   p aligncenter img srchttpinklinggithubiosublim...          11215  \n",
       "59   6wunderkinder slidinglayer android repository ...           4266  \n",
       "92   tinychain putting rough rough consensushttpsto...           8333  \n",
       "104  crnetworkbutton awesomehttpscdnrawgitcomsindre...           5268  \n",
       "97   mobilebonejs img srchttpswwwzhangxinxucomgithu...           5908  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5423f5c5-8e6a-453f-91c9-4e94665a470c",
   "metadata": {},
   "source": [
    "## A summary of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e70fe2b1-c77a-43ae-86ac-4a2632dc3225",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 66 entries, 70 to 75\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   repo             66 non-null     object\n",
      " 1   language         66 non-null     object\n",
      " 2   readme_contents  66 non-null     object\n",
      " 3   lemmatized       66 non-null     object\n",
      " 4   readme_length    66 non-null     int64 \n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b24999eb-a486-4a38-8264-597f3056570b",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9124.742424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25242.379029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2835.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4877.500000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>6918.250000</td>\n",
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       "      <th>max</th>\n",
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      "text/plain": [
       "       readme_length\n",
       "count      66.000000\n",
       "mean     9124.742424\n",
       "std     25242.379029\n",
       "min       251.000000\n",
       "25%      2835.000000\n",
       "50%      4877.500000\n",
       "75%      6918.250000\n",
       "max    206311.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f098fa1c-9ded-4a78-9ab9-ecfa70543e59",
   "metadata": {},
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c5a01d-31a8-40cb-aa0b-b9f14a750e31",
   "metadata": {},
   "source": [
    "## Question 1 What are the most common words in READMEs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83bf0662",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lemmatize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rust</th>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>build</th>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use</th>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>library</th>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>file</th>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>using</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>install</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>example</th>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         lemmatize\n",
       "rust           451\n",
       "build          277\n",
       "use            215\n",
       "library        204\n",
       "file           147\n",
       "user           143\n",
       "using          138\n",
       "install        138\n",
       "code           137\n",
       "example        131"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "# split the words individually up and lemmatize them                                                                                                                                                                                                                                                                                                            \n",
    "git_lem_words =\" \".join(train.lemmatized).split()\n",
    "\n",
    "lem_freq = pd.Series(git_lem_words).value_counts()\n",
    "\n",
    "# create df of freq words\n",
    "word_counts = pd.concat([lem_freq], axis=1).fillna(0).astype(int)\n",
    "\n",
    "# rename the col names\n",
    "word_counts.columns = [\"lemmatize\"]\n",
    "\n",
    "# create df of most common words\n",
    "word_counts.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060aa8e7",
   "metadata": {},
   "source": [
    "* The top five most common words are: rust, build, use, library, and file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c27983-8642-4d93-897b-a54dec0cd25f",
   "metadata": {},
   "source": [
    "## Question 2:  Is readme_length associated with language?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296bc67b-52ef-4834-8a97-ab995e4cdd1e",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79e963fc-f423-42b4-9f64-6ff637525700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first plot distribution of readme_length\n",
    "train.readme_length.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea74cb15-818a-4cbf-9617-58c787be3c68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One entry has a readme_length far above normal\n",
    "# In order to zoom in on the distribution of the majority, let's view distribution without that outlier\n",
    "train[train.readme_length<50000].readme_length.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b80654f6-d635-4d44-bcf9-fc3f024df151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 630.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a histplot hued by language to visualize if readme_length is related to language at all\n",
    "sns.displot(data=train[train.readme_length<50000],x='readme_length', hue='language', kind='hist') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e2d9a6ee-0063-4493-8096-6190cd986a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 579.5x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 619.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 587x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 584.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 630.625x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 585x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the plot with everything on top of each other wasn't very useful, so\n",
    "# plot histplots of readme_length separately for each language, still leaving off the outlier for now\n",
    "for l in df.language.unique():\n",
    "    sns.displot(data=train[(train.readme_length<50000) & (train.language == l)],x='readme_length', hue='language', kind='hist') \n",
    "    plt.xticks(ticks=range(0,28000, 4000))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f1dbac-a503-47aa-89a5-65d5c55f1159",
   "metadata": {},
   "source": [
    "### Analyze readme_length vs language with statistics\n",
    "- $H_0$ There is NO relationship between readme_length and language\n",
    "- $H_a$ There IS a relationship\n",
    "- $\\alpha$ = .05\n",
    "continuous vs categorical, not normally distributed, comparing > 2 subgroup means to each other\n",
    "=> kruskal wallis test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e703888-7621-4956-a40f-10db74b8f6d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KruskalResult(statistic=9.8560431371574, pvalue=0.13084673173089884)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kruskal wallis requires array-like inputs: sending in all readme_lengths for each language\n",
    "stats.kruskal(train[train.language=='Java'].readme_length\n",
    "            , train[train.language=='Python'].readme_length\n",
    "            , train[train.language=='Swift'].readme_length\n",
    "            , train[train.language=='JavaScript'].readme_length\n",
    "            , train[train.language=='Ruby'].readme_length\n",
    "            , train[train.language=='Objective-C'].readme_length\n",
    "            , train[train.language=='other'].readme_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099f499c-0aba-4c73-a0b4-a7c2c7866470",
   "metadata": {},
   "source": [
    "### Summarize readme_length vs language\n",
    "\n",
    "- p is > .05, so we CANNOT reject the null hypothesis \n",
    "- In summary, there is no association between readme_length and language"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e292d8f2-6733-411a-b830-87b5ab223e43",
   "metadata": {},
   "source": [
    "## Question 3 Are there any words that uniquely identify a programming language?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4d89248-6fdb-476c-9aec-7056d432effc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 word visualizes\n",
    "e.get_word_viz(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ab5e9fe-7620-4a38-a94a-a3f4ddd08f68",
   "metadata": {},
   "source": [
    "### Summarize\n",
    "* `Rust` was the most common for the other languages,\n",
    "* `Use` was more profound in the JavaScript Langauge\n",
    "* `User` was used more in Ruby "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcb94d7-9b6f-499c-a02c-5007c91cef09",
   "metadata": {},
   "source": [
    "## Question 4 Are there bigrams or n-grams that can uniquely identify a programming language?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a12986b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual of most common bigram by language\n",
    "e.plot_bigrams_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83242087",
   "metadata": {},
   "source": [
    "### Summarize\n",
    "* All of the languages had a unique bigrams except for JavaScript and Objective-C. \n",
    "* `npm install` npm is a package manager for the JavaScript programming language"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61405f05-c95d-4259-8199-749c1c5687c0",
   "metadata": {},
   "source": [
    "## Exploration Summary\n",
    "* The top five most common words are: rust, build, use, library, and file\n",
    "* Length of the readme was NOT associated with the programming language\n",
    "* Features that have a relationship with target are:\n",
    "    * The words themselves. Many words show up in a particular programming language README much more often than in the other language README's.\n",
    "        * Example: The language category other is the only category that had the word 'rust'\n",
    "    * Bigrams. Several bigrams were unique to a particular language\n",
    "        * Example: (plugged, app) was only found in Java"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edf3028-b066-4bc7-b36f-973e93d1cf00",
   "metadata": {},
   "source": [
    "### Features we are moving to modeling with: words, bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a5db40",
   "metadata": {},
   "source": [
    "### Features we are not moving to modeling with: readme_length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5644a9-dd6d-4cca-8967-551b6b7204d6",
   "metadata": {},
   "source": [
    "# Modeling\n",
    "* Evaluation Metrics: We will use accuracy as our evaluation metrics\n",
    "    * Baseline is 0.27 (guessing 'JavaScript' for every entry would have been correct 27% of the time)\n",
    "* We will evaluate 3 different model types and various hyperparameter configurations\n",
    "    * The three model types are DecisionTree, Random Forest, KNN\n",
    "    * We will evaluate on words alone, bigrams alone, and words and bigrams*\n",
    "* Models will be evaluated on train and validate data\n",
    "* The model that performs the best will then be evaluated on test\n",
    "\n",
    "\\*words alone produced the best models, and that is all that is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0c1e70d0-b1eb-431b-ae70-73ae4275f7be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train -> (66, 5)\n",
      "validate -> (22, 5)\n",
      "test -> (22, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.27"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## prep data for modeling\n",
    "# get X y splits for modeling\n",
    "train, validate, test, x_train, y_train, x_validate, y_validate, x_test, y_test = e.split_data_xy(train, validate, test)\n",
    "\n",
    "# create baseline \n",
    "baseline_accuracy = (train.language == \"JavaScript\").mean()\n",
    "round(baseline_accuracy, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0576d38-a36d-4c24-a707-c932337b9578",
   "metadata": {},
   "source": [
    "### Note: CountVectorizer is used below. Additionally, TfidfVectorizer was tried, but it did not yield better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72a76a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create bag of words for train, validate, test (fitting on train only)\n",
    "cv = CountVectorizer()\n",
    "x_bow = cv.fit_transform(x_train)\n",
    "x_validate_bow = cv.transform(x_validate)\n",
    "x_test_bow = cv.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe366a63-f8f6-4a08-931b-b8211c26393f",
   "metadata": {},
   "source": [
    "## Model Name: DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0253d4ed-9907-441a-9f56-826b916f87f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Train Score: 0.8939393939393939, Validation Score: 0.5\n"
     ]
    }
   ],
   "source": [
    "#build the model\n",
    "tree = DecisionTreeClassifier(max_depth=7, random_state=123)\n",
    "tree.fit(x_bow, y_train)\n",
    "\n",
    "# get model results\n",
    "print(f\" Train Score: {tree.score(x_bow, y_train)}, Validation Score: {tree.score(x_validate_bow, y_validate)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fa8e44-a95e-40bf-a33e-1121643eeb5c",
   "metadata": {},
   "source": [
    "* DecisionTreeClassifier beats baseline\n",
    "* There is a noticeable drop between train and validate even on the best Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23223c6d-3e73-4023-860a-f9096996a35d",
   "metadata": {},
   "source": [
    "## Model: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6982afc1-f59d-4542-8f9f-9e5f933c6270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Train Score: 0.9545454545454546, Validation Score: 0.3181818181818182\n"
     ]
    }
   ],
   "source": [
    "# get model results\n",
    "#build my model\n",
    "rf = RandomForestClassifier(max_depth=5, min_samples_leaf=1, random_state=123)\n",
    "rf.fit(x_bow, y_train)\n",
    "\n",
    "# get model results\n",
    "print(f\" Train Score: {rf.score(x_bow, y_train)}, Validation Score: {rf.score(x_validate_bow, y_validate)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3375facb-94b0-4c75-8fff-19d51d1cdaea",
   "metadata": {},
   "source": [
    "* Random Forest beats baseline \n",
    "* Again, there is an even steeper drop between train and validate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132b48fe-5ccb-4843-acf1-0128a962c03f",
   "metadata": {},
   "source": [
    "## Model: KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "54393a40-ef22-4238-a8ed-2e9955e4716e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Train Score: 0.5909090909090909, Validation Score: 0.36363636363636365\n"
     ]
    }
   ],
   "source": [
    "# get model results\n",
    "#build my model\n",
    "knn = KNeighborsClassifier(n_neighbors=8)\n",
    "knn.fit(x_bow, y_train)\n",
    "\n",
    "# get model results\n",
    "print(f\" Train Score: {knn.score(x_bow, y_train)}, Validation Score: {knn.score(x_validate_bow, y_validate)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f23c07-e8c1-4110-b282-c5249351be47",
   "metadata": {},
   "source": [
    "* KNN beats baseline \n",
    "* Drop off between train and validate is not as great as the other two, but overall it is lower than Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38af26d7-2fc2-47c3-8bf2-cc435eca964d",
   "metadata": {},
   "source": [
    "## Comparing Models\n",
    "* Baseline was 27%\n",
    "* DecisionTreeClassifier beat baseline with 89%/50% on train/validate\n",
    "* Random Forest beat baseline with 95%/32% on train/validate\n",
    "* KNN beat baseline with 59%/36% on train/validate\n",
    "* We have chosen to move forward with the DecisionTree model on test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f757c82b-fe9e-4cff-966a-5ca738701bf6",
   "metadata": {},
   "source": [
    "## Best Model Decision Tree on Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b4e673af-5c0e-46d0-9ad2-e38c0074cd35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6363636363636364"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get test results for final model\n",
    "tree.score(x_test_bow, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b5e7d9-7b20-47a0-8d4e-5fd718457ba9",
   "metadata": {},
   "source": [
    "## Modeling Summary\n",
    "* The model outperformed the baseline by 37%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a4de71-1dfa-4fb4-9db6-16ad478ecb21",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525f5d7c-6d57-46fc-a73b-214d747fb463",
   "metadata": {},
   "source": [
    "## Exploration\n",
    "* The most popular languages in our dataset were JavaScript, Objective-C, and Java\n",
    "* There were 19 different languages in our dataset, 6 of which occurred only once, and 4 only twice\n",
    "* The length of the readme did not correlate to the type of language\n",
    "* There were some words and word combinations that correlated with the programming language. However, including only words and leaving out bigrams yielded the best model scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f540ad-bda0-4fc3-b86d-dcc1a54e0be6",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "* Our best model was a Decision Tree model. The baseline was 27% (guessing 'JavaScript' for every entry would have been correct 27% of the time), and our best model achieved a 64% accuracy on the test data set. This is the expected accuracy on unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2908b674-c768-40c2-8f99-0bc4cd59e719",
   "metadata": {},
   "source": [
    "## Recommendations\n",
    "* The  model can be used to to automate the classification of large sums of unlabeled documents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26aeb83-c95f-44d2-8d0d-d5a45a7851ef",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "* With more time, we would acquire more README files to have a larger data set. We would also explore other models such as the various Naive Bayes models, to see if we could improve the accuracy."
   ]
  }
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