kiva_classification_simple_record.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6818 entries, 0 to 6817\n",
      "Data columns (total 9 columns):\n",
      "loan_id        6818 non-null int64\n",
      "status         6818 non-null object\n",
      "sector         6818 non-null object\n",
      "en             6818 non-null object\n",
      "country        6818 non-null object\n",
      "gender         6818 non-null object\n",
      "loan_amount    6818 non-null float64\n",
      "nonpayment     6818 non-null object\n",
      "en_clean       6802 non-null object\n",
      "dtypes: float64(1), int64(1), object(7)\n",
      "memory usage: 479.5+ KB\n"
     ]
    }
   ],
   "source": [
    "kiva_df = pd.read_csv(\"data/kiva_cleaned.csv\")\n",
    "kiva_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "kiva_df = kiva_df.dropna()\n",
    "kiva_df = kiva_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6802 entries, 0 to 6801\n",
      "Data columns (total 9 columns):\n",
      "loan_id        6802 non-null int64\n",
      "status         6802 non-null object\n",
      "sector         6802 non-null object\n",
      "en             6802 non-null object\n",
      "country        6802 non-null object\n",
      "gender         6802 non-null object\n",
      "loan_amount    6802 non-null float64\n",
      "nonpayment     6802 non-null object\n",
      "en_clean       6802 non-null object\n",
      "dtypes: float64(1), int64(1), object(7)\n",
      "memory usage: 478.3+ KB\n"
     ]
    }
   ],
   "source": [
    "kiva_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>loan_id</th>\n",
       "      <th>status</th>\n",
       "      <th>sector</th>\n",
       "      <th>en</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>nonpayment</th>\n",
       "      <th>en_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>&lt;i&gt;This description is written by Rowland Amul...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Robert, 40, is married and has 6 children. In ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Food</td>\n",
       "      <td>&lt;i&gt;This description is written by Rowland Amul...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Petronilla, 30, was deserted by her husband an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Food</td>\n",
       "      <td>&lt;i&gt;This description was written by Richard Maz...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Tom Mung'ahu, 45, is married and has 6 childre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Services</td>\n",
       "      <td>&lt;i&gt;This description was written by Rowland Amu...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Benedina, 42, is married and has 4 girls. In a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Construction</td>\n",
       "      <td>&lt;i&gt;This description was written by Rowland Amu...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Vincent Ondego 40, is married and has 8 childr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_id     status        sector  \\\n",
       "0        0  defaulted   Agriculture   \n",
       "1        1  defaulted          Food   \n",
       "2        2  defaulted          Food   \n",
       "3        3  defaulted      Services   \n",
       "4        4  defaulted  Construction   \n",
       "\n",
       "                                                  en country gender  \\\n",
       "0  <i>This description is written by Rowland Amul...   Kenya      M   \n",
       "1  <i>This description is written by Rowland Amul...   Kenya      F   \n",
       "2  <i>This description was written by Richard Maz...   Kenya      M   \n",
       "3  <i>This description was written by Rowland Amu...   Kenya      F   \n",
       "4  <i>This description was written by Rowland Amu...   Kenya      M   \n",
       "\n",
       "   loan_amount nonpayment                                           en_clean  \n",
       "0        500.0     lender  Robert, 40, is married and has 6 children. In ...  \n",
       "1        500.0     lender  Petronilla, 30, was deserted by her husband an...  \n",
       "2        500.0     lender  Tom Mung'ahu, 45, is married and has 6 childre...  \n",
       "3        500.0     lender  Benedina, 42, is married and has 4 girls. In a...  \n",
       "4        500.0     lender  Vincent Ondego 40, is married and has 8 childr...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kiva_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import re\n",
    "import unidecode\n",
    "\n",
    "stop_words = set(stopwords.words('english') + stopwords.words('spanish'))\n",
    "\n",
    "lemmer = WordNetLemmatizer()\n",
    "\n",
    "def preprocess(x):\n",
    "    x = x.lower()\n",
    "    \n",
    "    x = re.sub(r'[^\\w\\s]', '', x)\n",
    "    \n",
    "    x = unidecode.unidecode(x)\n",
    "    \n",
    "    x = re.sub(r'\\d+', '', x)\n",
    "    \n",
    "    x = [lemmer.lemmatize(w) for w in x.split() if w not in stop_words]\n",
    "\n",
    "    return ' '.join(x)\n",
    "\n",
    "kiva_df['en_clean_pre'] = kiva_df['en_clean'].apply(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>loan_id</th>\n",
       "      <th>status</th>\n",
       "      <th>sector</th>\n",
       "      <th>en</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>nonpayment</th>\n",
       "      <th>en_clean</th>\n",
       "      <th>en_clean_pre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>&lt;i&gt;This description is written by Rowland Amul...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Robert, 40, is married and has 6 children. In ...</td>\n",
       "      <td>robert married child addition family take care...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Food</td>\n",
       "      <td>&lt;i&gt;This description is written by Rowland Amul...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Petronilla, 30, was deserted by her husband an...</td>\n",
       "      <td>petronilla deserted husband responsible upbrin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Food</td>\n",
       "      <td>&lt;i&gt;This description was written by Richard Maz...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Tom Mung'ahu, 45, is married and has 6 childre...</td>\n",
       "      <td>tom mungahu married child child attending elem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Services</td>\n",
       "      <td>&lt;i&gt;This description was written by Rowland Amu...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Benedina, 42, is married and has 4 girls. In a...</td>\n",
       "      <td>benedina married girl addition family also tak...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>defaulted</td>\n",
       "      <td>Construction</td>\n",
       "      <td>&lt;i&gt;This description was written by Rowland Amu...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>Vincent Ondego 40, is married and has 8 childr...</td>\n",
       "      <td>vincent ondego married child beside family vin...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_id     status        sector  \\\n",
       "0        0  defaulted   Agriculture   \n",
       "1        1  defaulted          Food   \n",
       "2        2  defaulted          Food   \n",
       "3        3  defaulted      Services   \n",
       "4        4  defaulted  Construction   \n",
       "\n",
       "                                                  en country gender  \\\n",
       "0  <i>This description is written by Rowland Amul...   Kenya      M   \n",
       "1  <i>This description is written by Rowland Amul...   Kenya      F   \n",
       "2  <i>This description was written by Richard Maz...   Kenya      M   \n",
       "3  <i>This description was written by Rowland Amu...   Kenya      F   \n",
       "4  <i>This description was written by Rowland Amu...   Kenya      M   \n",
       "\n",
       "   loan_amount nonpayment                                           en_clean  \\\n",
       "0        500.0     lender  Robert, 40, is married and has 6 children. In ...   \n",
       "1        500.0     lender  Petronilla, 30, was deserted by her husband an...   \n",
       "2        500.0     lender  Tom Mung'ahu, 45, is married and has 6 childre...   \n",
       "3        500.0     lender  Benedina, 42, is married and has 4 girls. In a...   \n",
       "4        500.0     lender  Vincent Ondego 40, is married and has 8 childr...   \n",
       "\n",
       "                                        en_clean_pre  \n",
       "0  robert married child addition family take care...  \n",
       "1  petronilla deserted husband responsible upbrin...  \n",
       "2  tom mungahu married child child attending elem...  \n",
       "3  benedina married girl addition family also tak...  \n",
       "4  vincent ondego married child beside family vin...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kiva_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Robert, 40, is married and has 6 children. In addition to his family of 8, he takes care of his mother and 5 brothers. Robert started by planting vegetables and selling at the local market. He then diversified with a tea nursery which is more profitable. Given a loan of $500, Robert will be able to improve his activities by buying fertilizers, pesticides, a pump and seedlings. The pump will remove the uncertainties of the weather. The current capital in his venture is Ksh 10500 or $150, which does not allow Robert to maximize his potential. Robert completed high school and has never got employment. He got apprentice training from a tea extension officer. He is a go getter, and his main hobby is teaching music.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kiva_df.iloc[0, :].en_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'robert married child addition family take care mother brother robert started planting vegetable selling local market diversified tea nursery profitable given loan robert able improve activity buying fertilizer pesticide pump seedling pump remove uncertainty weather current capital venture ksh allow robert maximize potential robert completed high school never got employment got apprentice training tea extension officer go getter main hobby teaching music'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kiva_df.iloc[0, :].en_clean_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer(max_df = 0.5, min_df=0.05, max_features = 500, ngram_range=[1,3])\n",
    "dtm = vectorizer.fit_transform(kiva_df['en_clean_pre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6802, 270)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['able',\n",
       " 'active',\n",
       " 'active member',\n",
       " 'activity',\n",
       " 'add',\n",
       " 'additional',\n",
       " 'age',\n",
       " 'ago',\n",
       " 'allow',\n",
       " 'also',\n",
       " 'always',\n",
       " 'amount',\n",
       " 'another',\n",
       " 'applied',\n",
       " 'applied loan',\n",
       " 'area',\n",
       " 'attend',\n",
       " 'back',\n",
       " 'basic',\n",
       " 'bean',\n",
       " 'began',\n",
       " 'belief',\n",
       " 'better',\n",
       " 'born',\n",
       " 'bought',\n",
       " 'boy',\n",
       " 'business selling',\n",
       " 'buy',\n",
       " 'buying',\n",
       " 'capital',\n",
       " 'care',\n",
       " 'cement',\n",
       " 'child school',\n",
       " 'childrens',\n",
       " 'church',\n",
       " 'city',\n",
       " 'client',\n",
       " 'clothes',\n",
       " 'clothing',\n",
       " 'college',\n",
       " 'community',\n",
       " 'continue',\n",
       " 'cost',\n",
       " 'could',\n",
       " 'cow',\n",
       " 'currently',\n",
       " 'customer',\n",
       " 'dairy',\n",
       " 'dairy cow',\n",
       " 'daughter',\n",
       " 'day',\n",
       " 'decided',\n",
       " 'demand',\n",
       " 'different',\n",
       " 'domingo',\n",
       " 'dominican',\n",
       " 'dream',\n",
       " 'due',\n",
       " 'earn',\n",
       " 'educate',\n",
       " 'education',\n",
       " 'enable',\n",
       " 'enough',\n",
       " 'entrepreneur',\n",
       " 'every',\n",
       " 'every day',\n",
       " 'expand',\n",
       " 'expand business',\n",
       " 'expense',\n",
       " 'experience',\n",
       " 'explains',\n",
       " 'family',\n",
       " 'farm',\n",
       " 'farmer',\n",
       " 'farming',\n",
       " 'father',\n",
       " 'fee',\n",
       " 'feed',\n",
       " 'first',\n",
       " 'first loan',\n",
       " 'five',\n",
       " 'five child',\n",
       " 'food',\n",
       " 'four',\n",
       " 'four child',\n",
       " 'friend',\n",
       " 'fruit',\n",
       " 'fund',\n",
       " 'future',\n",
       " 'get',\n",
       " 'girl',\n",
       " 'give',\n",
       " 'given',\n",
       " 'go',\n",
       " 'goal',\n",
       " 'good',\n",
       " 'great',\n",
       " 'grocery',\n",
       " 'group',\n",
       " 'grow',\n",
       " 'growing',\n",
       " 'hard',\n",
       " 'hardworking',\n",
       " 'help',\n",
       " 'high',\n",
       " 'home',\n",
       " 'hope',\n",
       " 'house',\n",
       " 'household',\n",
       " 'husband',\n",
       " 'improve',\n",
       " 'income',\n",
       " 'increase',\n",
       " 'intends',\n",
       " 'inventory',\n",
       " 'invest',\n",
       " 'item',\n",
       " 'job',\n",
       " 'keep',\n",
       " 'kenya',\n",
       " 'kiva',\n",
       " 'lack',\n",
       " 'land',\n",
       " 'large',\n",
       " 'last',\n",
       " 'left',\n",
       " 'life',\n",
       " 'like',\n",
       " 'little',\n",
       " 'live',\n",
       " 'living',\n",
       " 'loan buy',\n",
       " 'loan purchase',\n",
       " 'loan usd',\n",
       " 'local',\n",
       " 'located',\n",
       " 'lot',\n",
       " 'made',\n",
       " 'main',\n",
       " 'maize',\n",
       " 'make',\n",
       " 'making',\n",
       " 'many',\n",
       " 'market',\n",
       " 'married',\n",
       " 'married child',\n",
       " 'material',\n",
       " 'meet',\n",
       " 'member',\n",
       " 'merchandise',\n",
       " 'milk',\n",
       " 'money',\n",
       " 'month',\n",
       " 'monthly',\n",
       " 'mother',\n",
       " 'much',\n",
       " 'nairobi',\n",
       " 'need',\n",
       " 'need loan',\n",
       " 'neighborhood',\n",
       " 'new',\n",
       " 'next',\n",
       " 'offer',\n",
       " 'old',\n",
       " 'old married',\n",
       " 'one',\n",
       " 'open',\n",
       " 'operates',\n",
       " 'opportunity',\n",
       " 'order',\n",
       " 'owns',\n",
       " 'parent',\n",
       " 'part',\n",
       " 'pay',\n",
       " 'people',\n",
       " 'per',\n",
       " 'period',\n",
       " 'person',\n",
       " 'plan',\n",
       " 'pm',\n",
       " 'price',\n",
       " 'primary',\n",
       " 'primary school',\n",
       " 'produce',\n",
       " 'product',\n",
       " 'profit',\n",
       " 'provide',\n",
       " 'purchase',\n",
       " 'quality',\n",
       " 'receive',\n",
       " 'rent',\n",
       " 'repay',\n",
       " 'repay loan',\n",
       " 'requesting',\n",
       " 'requesting loan',\n",
       " 'rice',\n",
       " 'run',\n",
       " 'running',\n",
       " 'sale',\n",
       " 'santo',\n",
       " 'santo domingo',\n",
       " 'save',\n",
       " 'saving',\n",
       " 'say',\n",
       " 'school',\n",
       " 'school fee',\n",
       " 'season',\n",
       " 'second',\n",
       " 'second loan',\n",
       " 'secondary',\n",
       " 'secondary school',\n",
       " 'selling',\n",
       " 'service',\n",
       " 'shoe',\n",
       " 'shop',\n",
       " 'since',\n",
       " 'single',\n",
       " 'single mother',\n",
       " 'six',\n",
       " 'small',\n",
       " 'small business',\n",
       " 'sold',\n",
       " 'source',\n",
       " 'source income',\n",
       " 'start',\n",
       " 'started',\n",
       " 'started business',\n",
       " 'still',\n",
       " 'stock',\n",
       " 'store',\n",
       " 'study',\n",
       " 'supply',\n",
       " 'support',\n",
       " 'support family',\n",
       " 'take',\n",
       " 'take care',\n",
       " 'therefore',\n",
       " 'three',\n",
       " 'three child',\n",
       " 'time',\n",
       " 'together',\n",
       " 'town',\n",
       " 'training',\n",
       " 'two',\n",
       " 'two child',\n",
       " 'two year',\n",
       " 'usd',\n",
       " 'use',\n",
       " 'use loan',\n",
       " 'used',\n",
       " 'variety',\n",
       " 'vegetable',\n",
       " 'want',\n",
       " 'way',\n",
       " 'week',\n",
       " 'well',\n",
       " 'wholesale',\n",
       " 'wife',\n",
       " 'within',\n",
       " 'woman',\n",
       " 'woman group',\n",
       " 'work',\n",
       " 'working',\n",
       " 'would',\n",
       " 'would like',\n",
       " 'year ago',\n",
       " 'year old',\n",
       " 'year old married',\n",
       " 'yearold',\n",
       " 'young']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6802, 280)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_df = pd.DataFrame(dtm.toarray(), columns=vectorizer.get_feature_names(), index=kiva_df.index)\n",
    "\n",
    "kiva_df_bow = pd.concat([kiva_df, bow_df], axis=1)\n",
    "kiva_df_bow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "small_sklearn_kernel",
   "language": "python",
   "name": "small_sklearn_kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}