slides_kiva.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "import re\n",
    "import string\n",
    "import unicodedata\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize\n",
    "from nltk import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import LancasterStemmer, WordNetLemmatizer\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "import os\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 237 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['status',\n",
       " 'sector',\n",
       " 'en',\n",
       " 'country',\n",
       " 'gender',\n",
       " 'loan_amount',\n",
       " 'nonpayment',\n",
       " 'en_length']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7882 entries, 0 to 7987\n",
      "Data columns (total 8 columns):\n",
      "status         7882 non-null object\n",
      "sector         7882 non-null object\n",
      "en             7882 non-null object\n",
      "country        7882 non-null object\n",
      "gender         7882 non-null object\n",
      "loan_amount    7882 non-null float64\n",
      "nonpayment     7882 non-null object\n",
      "en_length      7882 non-null int64\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 554.2+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7882, 8)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      status        sector                                                 en  \\\n",
       "0  defaulted   Agriculture  <i>This description is written by Rowland Amul...   \n",
       "1  defaulted          Food  <i>This description is written by Rowland Amul...   \n",
       "2  defaulted          Food  <i>This description was written by Richard Maz...   \n",
       "3  defaulted      Services  <i>This description was written by Rowland Amu...   \n",
       "4  defaulted  Construction  <i>This description was written by Rowland Amu...   \n",
       "\n",
       "  country gender  loan_amount nonpayment  en_length  \n",
       "0   Kenya      M        500.0     lender        897  \n",
       "1   Kenya      F        500.0     lender       1279  \n",
       "2   Kenya      M        500.0     lender        897  \n",
       "3   Kenya      F        500.0     lender        737  \n",
       "4   Kenya      M        500.0     lender        798  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>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_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7983</th>\n",
       "      <td>paid</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>Rosa is a member of the 3 de Mayo Communal Ban...</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>1401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7984</th>\n",
       "      <td>paid</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>The communal bank, \"Cumbe,\" is located on the ...</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>5315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7985</th>\n",
       "      <td>paid</td>\n",
       "      <td>Food</td>\n",
       "      <td>Gladys Burgos Macas is a member of the 3 de Ma...</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>775.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7986</th>\n",
       "      <td>paid</td>\n",
       "      <td>Retail</td>\n",
       "      <td>The Communal Bank &lt;em&gt;Las Caleras&lt;/em&gt; is loca...</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>275.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7987</th>\n",
       "      <td>paid</td>\n",
       "      <td>Services</td>\n",
       "      <td>Eliud Ouma owns the Purple Hut salon located o...</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>275.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     status       sector                                                 en  \\\n",
       "7983   paid     Clothing  Rosa is a member of the 3 de Mayo Communal Ban...   \n",
       "7984   paid  Agriculture  The communal bank, \"Cumbe,\" is located on the ...   \n",
       "7985   paid         Food  Gladys Burgos Macas is a member of the 3 de Ma...   \n",
       "7986   paid       Retail  The Communal Bank <em>Las Caleras</em> is loca...   \n",
       "7987   paid     Services  Eliud Ouma owns the Purple Hut salon located o...   \n",
       "\n",
       "      country gender  loan_amount nonpayment  en_length  \n",
       "7983  Ecuador      F       1050.0     lender       1401  \n",
       "7984  Ecuador      F       2650.0    partner       5315  \n",
       "7985  Ecuador      F        775.0    partner        809  \n",
       "7986  Ecuador      F        275.0    partner       1800  \n",
       "7987    Kenya      M        275.0    partner        642  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_dir = \"C:/Users/st50/Documents/sandbox/data\"\n",
    "out_dir = \"C:/Users/st50/Documents/sandbox/out\"\n",
    "\n",
    "%time kiva_df=pd.read_csv(os.path.join(in_dir, \"kiva.csv\"))\n",
    "#kiva_df = kiva_df.sample(frac=0.1)\n",
    "\n",
    "kiva_df.dropna(inplace=True)\n",
    "kiva_df['en_length'] = kiva_df['en'].apply(len)\n",
    "\n",
    "list(kiva_df)\n",
    "kiva_df.info()\n",
    "kiva_df.shape\n",
    "kiva_df.head()\n",
    "kiva_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1708f1168d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708ef57a20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1708ef57b70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,0,'2391')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,1,'1862')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,2,'1097')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,3,'1070')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,4,'813')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,5,'156')"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,6,'143')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,7,'141')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,8,'87')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,9,'48')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "Text(0.8,10,'35')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,11,'18')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,12,'14')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,13,'4')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.8,14,'3')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([   0.,  500., 1000., 1500., 2000., 2500., 3000.]),\n",
       " <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Number of loans were given')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sector Name')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Top sectors in which more loans were given')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708f0a3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708ef57a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708e9d4da0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1708e9d4f60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708e9d4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708e9e5c18>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1708e46b7b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'index')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'loan_amount')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Loan Amount Distribution')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1708e9e5c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "color = sns.color_palette()\n",
    "\n",
    "kiva_df['en_length'].plot(bins=50, kind='hist') \n",
    "\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "sector_name = kiva_df['sector'].value_counts()\n",
    "sns.barplot(sector_name.values, sector_name.index)\n",
    "for i, v in enumerate(sector_name.values):\n",
    "    plt.text(0.8,i,v,color='k',fontsize=19)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Number of loans were given')\n",
    "plt.ylabel('Sector Name')\n",
    "plt.title(\"Top sectors in which more loans were given\")\n",
    "plt.show()\n",
    "#Food is in the top sectors\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "\n",
    "sns.distplot(kiva_df['loan_amount'])\n",
    "plt.show() \n",
    "\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "plt.scatter(range(kiva_df.shape[0]), np.sort(kiva_df.loan_amount.values))\n",
    "plt.xlabel('index', fontsize=12)\n",
    "plt.ylabel('loan_amount', fontsize=12)\n",
    "plt.title(\"Loan Amount Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 15.8 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_length</th>\n",
       "      <th>en_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>897</td>\n",
       "      <td>description written rowland amulyoto staff mem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1279</td>\n",
       "      <td>description written rowland amulyoto staff mem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>897</td>\n",
       "      <td>description written richard mazengo staff memb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>737</td>\n",
       "      <td>description written rowland amulyoto staff mem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>798</td>\n",
       "      <td>description written rowland amulyoto staff mem...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      status        sector                                                 en  \\\n",
       "0  defaulted   Agriculture  <i>This description is written by Rowland Amul...   \n",
       "1  defaulted          Food  <i>This description is written by Rowland Amul...   \n",
       "2  defaulted          Food  <i>This description was written by Richard Maz...   \n",
       "3  defaulted      Services  <i>This description was written by Rowland Amu...   \n",
       "4  defaulted  Construction  <i>This description was written by Rowland Amu...   \n",
       "\n",
       "  country gender  loan_amount nonpayment  en_length  \\\n",
       "0   Kenya      M        500.0     lender        897   \n",
       "1   Kenya      F        500.0     lender       1279   \n",
       "2   Kenya      M        500.0     lender        897   \n",
       "3   Kenya      F        500.0     lender        737   \n",
       "4   Kenya      M        500.0     lender        798   \n",
       "\n",
       "                                            en_clean  \n",
       "0  description written rowland amulyoto staff mem...  \n",
       "1  description written rowland amulyoto staff mem...  \n",
       "2  description written richard mazengo staff memb...  \n",
       "3  description written rowland amulyoto staff mem...  \n",
       "4  description written rowland amulyoto staff mem...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import stopwords \n",
    "from bs4 import BeautifulSoup\n",
    "import unidecode\n",
    "\n",
    "stop_words = set(stopwords.words('english') + stopwords.words('spanish'))\n",
    "\n",
    "lemmer = WordNetLemmatizer()\n",
    "\n",
    "def preprocess(x):\n",
    "    # Remove HTML tags\n",
    "    x = BeautifulSoup(x, \"lxml\").get_text()\n",
    "\n",
    "    # Lower case\n",
    "    x = x.lower()\n",
    "    \n",
    "    # Remove punctuation\n",
    "    x = re.sub(r'[^\\w\\s]', '', x)\n",
    "    \n",
    "    # Remove non-unicode\n",
    "    x = unidecode.unidecode(x)\n",
    "    \n",
    "    # Remove numbers\n",
    "    x = re.sub(r'\\d+', '', x)\n",
    "    \n",
    "    # Remove stopwords and lemmatize\n",
    "    x = [lemmer.lemmatize(w) for w in x.split() if w not in stop_words]\n",
    "    return ' '.join(x) \n",
    "\n",
    "%time kiva_df['en_clean'] = kiva_df['en'].apply(preprocess)\n",
    "kiva_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BOW - TFIDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8.23 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7882, 342)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(7882, 349)"
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     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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       "      <th>status</th>\n",
       "      <th>sector</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>defaulted</td>\n",
       "      <td>Services</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
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       "      <th>4</th>\n",
       "      <td>defaulted</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
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       "      status        sector country gender  loan_amount nonpayment  en_length  \\\n",
       "0  defaulted   Agriculture   Kenya      M        500.0     lender        897   \n",
       "1  defaulted          Food   Kenya      F        500.0     lender       1279   \n",
       "2  defaulted          Food   Kenya      M        500.0     lender        897   \n",
       "3  defaulted      Services   Kenya      F        500.0     lender        737   \n",
       "4  defaulted  Construction   Kenya      M        500.0     lender        798   \n",
       "\n",
       "       able  access  access business  ...    working     would  would like  \\\n",
       "0  0.121480     0.0              0.0  ...        0.0  0.000000         0.0   \n",
       "1  0.000000     0.0              0.0  ...        0.0  0.106737         0.0   \n",
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       "4  0.105798     0.0              0.0  ...        0.0  0.000000         0.0   \n",
       "\n",
       "   wwwmifexorg  year ago  year experience  year old  year old married  \\\n",
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       "4          0.0       0.0              0.0       0.0               0.0   \n",
       "\n",
       "   yearold  young  \n",
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       "1      0.0    0.0  \n",
       "2      0.0    0.0  \n",
       "3      0.0    0.0  \n",
       "4      0.0    0.0  \n",
       "\n",
       "[5 rows x 349 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(min_df=.05, max_df=.5, ngram_range=[1,3])\n",
    "%time dtm_tfidf = tfidf_vectorizer.fit_transform(kiva_df['en_clean'])\n",
    "\n",
    "bow_df_tfidf = pd.DataFrame(dtm_tfidf.toarray(), columns=tfidf_vectorizer.get_feature_names(), index=kiva_df.index)\n",
    "bow_df_tfidf.shape\n",
    "\n",
    "kiva_df_bow_tfidf = pd.concat([kiva_df, bow_df_tfidf], axis=1)\n",
    "kiva_df_bow_tfidf.drop(columns=['en', 'en_clean'], inplace=True)\n",
    "kiva_df_bow_tfidf.shape\n",
    "kiva_df_bow_tfidf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOW - TF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 6.7 s\n"
     ]
    },
    {
     "data": {
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       "(7882, 342)"
      ]
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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       "      status        sector country gender  loan_amount nonpayment  en_length  \\\n",
       "0  defaulted   Agriculture   Kenya      M        500.0     lender        897   \n",
       "1  defaulted          Food   Kenya      F        500.0     lender       1279   \n",
       "2  defaulted          Food   Kenya      M        500.0     lender        897   \n",
       "3  defaulted      Services   Kenya      F        500.0     lender        737   \n",
       "4  defaulted  Construction   Kenya      M        500.0     lender        798   \n",
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       "\n",
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       "\n",
       "   yearold  young  \n",
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       "\n",
       "[5 rows x 349 columns]"
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7984</th>\n",
       "      <td>paid</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>5315</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7985</th>\n",
       "      <td>paid</td>\n",
       "      <td>Food</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>775.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7986</th>\n",
       "      <td>paid</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>F</td>\n",
       "      <td>275.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>1800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7987</th>\n",
       "      <td>paid</td>\n",
       "      <td>Services</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>275.0</td>\n",
       "      <td>partner</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 349 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     status       sector  country gender  loan_amount nonpayment  en_length  \\\n",
       "7983   paid     Clothing  Ecuador      F       1050.0     lender       1401   \n",
       "7984   paid  Agriculture  Ecuador      F       2650.0    partner       5315   \n",
       "7985   paid         Food  Ecuador      F        775.0    partner        809   \n",
       "7986   paid       Retail  Ecuador      F        275.0    partner       1800   \n",
       "7987   paid     Services    Kenya      M        275.0    partner        642   \n",
       "\n",
       "      able  access  access business  ...    working  would  would like  \\\n",
       "7983     0       0                0  ...          0      0           0   \n",
       "7984     2       0                0  ...          0      0           0   \n",
       "7985     0       0                0  ...          0      0           0   \n",
       "7986     1       0                0  ...          1      0           0   \n",
       "7987     0       0                0  ...          0      0           0   \n",
       "\n",
       "      wwwmifexorg  year ago  year experience  year old  year old married  \\\n",
       "7983            0         0                0         0                 0   \n",
       "7984            0         0                0         0                 0   \n",
       "7985            0         0                0         0                 0   \n",
       "7986            0         0                0         1                 0   \n",
       "7987            0         0                0         0                 0   \n",
       "\n",
       "      yearold  young  \n",
       "7983        0      0  \n",
       "7984        4      0  \n",
       "7985        0      0  \n",
       "7986        0      0  \n",
       "7987        1      0  \n",
       "\n",
       "[5 rows x 349 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "tf_vectorizer = CountVectorizer(min_df=.05, max_df=.5, ngram_range=[1,3])\n",
    "%time dtm_tf = tf_vectorizer.fit_transform(kiva_df['en_clean'])\n",
    "\n",
    "bow_df = pd.DataFrame(dtm_tf.toarray(), columns=tf_vectorizer.get_feature_names(), index=kiva_df.index)\n",
    "bow_df.shape\n",
    "\n",
    "kiva_df_bow = pd.concat([kiva_df, bow_df], axis=1)\n",
    "kiva_df_bow.drop(columns=['en', 'en_clean'], inplace=True)\n",
    "kiva_df_bow.shape\n",
    "kiva_df_bow.head()\n",
    "kiva_df_bow.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOW - Feature Hashing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.05 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7882, 512)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(7882, 519)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>sector</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>nonpayment</th>\n",
       "      <th>en_length</th>\n",
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       "      <td>-0.048853</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>defaulted</td>\n",
       "      <td>Services</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>F</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>737</td>\n",
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       "      <td>defaulted</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>M</td>\n",
       "      <td>500.0</td>\n",
       "      <td>lender</td>\n",
       "      <td>798</td>\n",
       "      <td>-0.067116</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.067116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 519 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      status        sector country gender  loan_amount nonpayment  en_length  \\\n",
       "0  defaulted   Agriculture   Kenya      M        500.0     lender        897   \n",
       "1  defaulted          Food   Kenya      F        500.0     lender       1279   \n",
       "2  defaulted          Food   Kenya      M        500.0     lender        897   \n",
       "3  defaulted      Services   Kenya      F        500.0     lender        737   \n",
       "4  defaulted  Construction   Kenya      M        500.0     lender        798   \n",
       "\n",
       "          0         1    2    ...     502  503  504       505       506  \\\n",
       "0  0.000000  0.000000  0.0    ...     0.0  0.0  0.0 -0.063758  0.000000   \n",
       "1  0.048853  0.048853  0.0    ...     0.0  0.0  0.0  0.000000 -0.048853   \n",
       "2  0.000000 -0.058926  0.0    ...     0.0  0.0  0.0  0.000000  0.000000   \n",
       "3  0.000000  0.000000  0.0    ...     0.0  0.0  0.0  0.000000 -0.075593   \n",
       "4 -0.067116  0.000000  0.0    ...     0.0  0.0  0.0  0.067116  0.000000   \n",
       "\n",
       "        507       508       509       510       511  \n",
       "0 -0.063758  0.127515  0.000000 -0.063758  0.000000  \n",
       "1  0.000000  0.000000 -0.048853 -0.048853  0.000000  \n",
       "2  0.058926  0.000000  0.000000  0.000000  0.000000  \n",
       "3  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "4  0.067116  0.067116  0.000000  0.000000  0.067116  \n",
       "\n",
       "[5 rows x 519 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "\n",
    "h_vectorizer = HashingVectorizer(n_features=2**9, ngram_range=[1,3])\n",
    "%time dtm_h = h_vectorizer.fit_transform(kiva_df['en_clean'])\n",
    "bow_df_h = pd.DataFrame(dtm_h.toarray(), index=kiva_df.index)\n",
    "bow_df_h.shape\n",
    "\n",
    "kiva_df_h = pd.concat([kiva_df, bow_df_h], axis=1)\n",
    "kiva_df_h.drop(columns=['en', 'en_clean'], inplace=True)\n",
    "kiva_df_h.shape\n",
    "kiva_df_h.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO Glove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the pre-trained word-embedding vectors \n",
    "embeddings_index = {}\n",
    "for i, line in enumerate(open('data/wiki-news-300d-1M.vec')):\n",
    "    values = line.split()\n",
    "    embeddings_index[values[0]] = numpy.asarray(values[1:], dtype='float32')\n",
    "\n",
    "# create a tokenizer \n",
    "token = text.Tokenizer()\n",
    "token.fit_on_texts(trainDF['text'])\n",
    "word_index = token.word_index\n",
    "\n",
    "# convert text to sequence of tokens and pad them to ensure equal length vectors \n",
    "train_seq_x = sequence.pad_sequences(token.texts_to_sequences(train_x), maxlen=70)\n",
    "valid_seq_x = sequence.pad_sequences(token.texts_to_sequences(valid_x), maxlen=70)\n",
    "\n",
    "# create token-embedding mapping\n",
    "embedding_matrix = numpy.zeros((len(word_index) + 1, 300))\n",
    "for word, i in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        embedding_matrix[i] = embedding_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO NLP/Text Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDF['char_count'] = trainDF['text'].apply(len)\n",
    "trainDF['word_count'] = trainDF['text'].apply(lambda x: len(x.split()))\n",
    "trainDF['word_density'] = trainDF['char_count'] / (trainDF['word_count']+1)\n",
    "trainDF['punctuation_count'] = trainDF['text'].apply(lambda x: len(\"\".join(_ for _ in x if _ in string.punctuation))) \n",
    "trainDF['title_word_count'] = trainDF['text'].apply(lambda x: len([wrd for wrd in x.split() if wrd.istitle()]))\n",
    "trainDF['upper_case_word_count'] = trainDF['text'].apply(lambda x: len([wrd for wrd in x.split() if wrd.isupper()]))\n",
    "pos_family = {\n",
    "    'noun' : ['NN','NNS','NNP','NNPS'],\n",
    "    'pron' : ['PRP','PRP$','WP','WP$'],\n",
    "    'verb' : ['VB','VBD','VBG','VBN','VBP','VBZ'],\n",
    "    'adj' :  ['JJ','JJR','JJS'],\n",
    "    'adv' : ['RB','RBR','RBS','WRB']\n",
    "}\n",
    "\n",
    "# function to check and get the part of speech tag count of a words in a given sentence\n",
    "def check_pos_tag(x, flag):\n",
    "    cnt = 0\n",
    "    try:\n",
    "        wiki = textblob.TextBlob(x)\n",
    "        for tup in wiki.tags:\n",
    "            ppo = list(tup)[1]\n",
    "            if ppo in pos_family[flag]:\n",
    "                cnt += 1\n",
    "    except:\n",
    "        pass\n",
    "    return cnt\n",
    "\n",
    "trainDF['noun_count'] = trainDF['text'].apply(lambda x: check_pos_tag(x, 'noun'))\n",
    "trainDF['verb_count'] = trainDF['text'].apply(lambda x: check_pos_tag(x, 'verb'))\n",
    "trainDF['adj_count'] = trainDF['text'].apply(lambda x: check_pos_tag(x, 'adj'))\n",
    "trainDF['adv_count'] = trainDF['text'].apply(lambda x: check_pos_tag(x, 'adv'))\n",
    "trainDF['pron_count'] = trainDF['text'].apply(lambda x: check_pos_tag(x, 'pron'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No Text\n",
      "(5911, 20)\n",
      "(1971, 20)\n",
      "RF\n",
      "Wall time: 3.04 s\n",
      "                      importance\n",
      "nonpayment_partner      0.586106\n",
      "country_Kenya           0.133010\n",
      "loan_amount             0.102853\n",
      "en_length               0.090525\n",
      "country_Ecuador         0.049245\n",
      "gender_M                0.007926\n",
      "sector_Health           0.006340\n",
      "sector_Food             0.005232\n",
      "sector_Retail           0.005207\n",
      "sector_Clothing         0.005135\n",
      "sector_Services         0.002821\n",
      "sector_Arts             0.001603\n",
      "sector_Manufacturing    0.001279\n",
      "sector_Construction     0.000980\n",
      "sector_Education        0.000465\n",
      "0.9157787924911213\n",
      "[[ 719  134]\n",
      " [  32 1086]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.96      0.84      0.90       853\n",
      "        paid       0.89      0.97      0.93      1118\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      1971\n",
      "   macro avg       0.92      0.91      0.91      1971\n",
      "weighted avg       0.92      0.92      0.91      1971\n",
      "\n",
      "Adaboost\n",
      "Wall time: 3.08 s\n",
      "0.8919330289193302\n",
      "[[ 714  139]\n",
      " [  74 1044]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.91      0.84      0.87       853\n",
      "        paid       0.88      0.93      0.91      1118\n",
      "\n",
      "   micro avg       0.89      0.89      0.89      1971\n",
      "   macro avg       0.89      0.89      0.89      1971\n",
      "weighted avg       0.89      0.89      0.89      1971\n",
      "\n",
      "GTB\n",
      "Wall time: 298 ms\n",
      "0.8929477422628107\n",
      "[[ 711  142]\n",
      " [  69 1049]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.91      0.83      0.87       853\n",
      "        paid       0.88      0.94      0.91      1118\n",
      "\n",
      "   micro avg       0.89      0.89      0.89      1971\n",
      "   macro avg       0.90      0.89      0.89      1971\n",
      "weighted avg       0.89      0.89      0.89      1971\n",
      "\n",
      "\n",
      "BOW - TF\n",
      "(5911, 362)\n",
      "(1971, 362)\n",
      "RF\n",
      "Wall time: 7.35 s\n",
      "                    importance\n",
      "nonpayment_partner    0.244766\n",
      "country_Kenya         0.048711\n",
      "esperanza             0.029895\n",
      "repay loan            0.026935\n",
      "repay                 0.023277\n",
      "requesting            0.021408\n",
      "loan_amount           0.017026\n",
      "thank                 0.015703\n",
      "requesting loan       0.013819\n",
      "en_length             0.011282\n",
      "microfinance          0.009967\n",
      "community             0.009808\n",
      "bank                  0.009178\n",
      "fee                   0.009121\n",
      "international         0.008968\n",
      "0.9167935058346017\n",
      "[[ 733  120]\n",
      " [  44 1074]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.94      0.86      0.90       853\n",
      "        paid       0.90      0.96      0.93      1118\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      1971\n",
      "   macro avg       0.92      0.91      0.91      1971\n",
      "weighted avg       0.92      0.92      0.92      1971\n",
      "\n",
      "Adaboost\n",
      "Wall time: 10.2 s\n",
      "0.9274479959411466\n",
      "[[ 777   76]\n",
      " [  67 1051]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.92      0.91      0.92       853\n",
      "        paid       0.93      0.94      0.94      1118\n",
      "\n",
      "   micro avg       0.93      0.93      0.93      1971\n",
      "   macro avg       0.93      0.93      0.93      1971\n",
      "weighted avg       0.93      0.93      0.93      1971\n",
      "\n",
      "GTB\n",
      "Wall time: 2.01 s\n",
      "0.9279553526128869\n",
      "[[ 784   69]\n",
      " [  73 1045]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.91      0.92      0.92       853\n",
      "        paid       0.94      0.93      0.94      1118\n",
      "\n",
      "   micro avg       0.93      0.93      0.93      1971\n",
      "   macro avg       0.93      0.93      0.93      1971\n",
      "weighted avg       0.93      0.93      0.93      1971\n",
      "\n",
      "\n",
      "BOW - TFIDF\n",
      "(5911, 362)\n",
      "(1971, 362)\n",
      "RF\n",
      "Wall time: 12.3 s\n",
      "                      importance\n",
      "nonpayment_partner      0.236468\n",
      "country_Kenya           0.044962\n",
      "repay loan              0.037327\n",
      "esperanza               0.028684\n",
      "repay                   0.027332\n",
      "requesting              0.023100\n",
      "requesting loan         0.013853\n",
      "thank                   0.013507\n",
      "community               0.013156\n",
      "loan_amount             0.012853\n",
      "microfinance            0.010774\n",
      "personal information    0.009457\n",
      "fee                     0.008814\n",
      "information             0.008743\n",
      "bank                    0.008679\n",
      "0.915271435819381\n",
      "[[ 732  121]\n",
      " [  46 1072]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.94      0.86      0.90       853\n",
      "        paid       0.90      0.96      0.93      1118\n",
      "\n",
      "   micro avg       0.92      0.92      0.92      1971\n",
      "   macro avg       0.92      0.91      0.91      1971\n",
      "weighted avg       0.92      0.92      0.91      1971\n",
      "\n",
      "Adaboost\n",
      "Wall time: 18.2 s\n",
      "0.9309994926433283\n",
      "[[ 781   72]\n",
      " [  64 1054]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.92      0.92      0.92       853\n",
      "        paid       0.94      0.94      0.94      1118\n",
      "\n",
      "   micro avg       0.93      0.93      0.93      1971\n",
      "   macro avg       0.93      0.93      0.93      1971\n",
      "weighted avg       0.93      0.93      0.93      1971\n",
      "\n",
      "GTB\n",
      "Wall time: 2.82 s\n",
      "0.9315068493150684\n",
      "[[ 788   65]\n",
      " [  70 1048]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.92      0.92      0.92       853\n",
      "        paid       0.94      0.94      0.94      1118\n",
      "\n",
      "   micro avg       0.93      0.93      0.93      1971\n",
      "   macro avg       0.93      0.93      0.93      1971\n",
      "weighted avg       0.93      0.93      0.93      1971\n",
      "\n",
      "\n",
      "Feature Hashing\n",
      "(5911, 532)\n",
      "(1971, 532)\n",
      "RF\n",
      "Wall time: 33 s\n",
      "                    importance\n",
      "nonpayment_partner    0.208508\n",
      "country_Kenya         0.057893\n",
      "loan_amount           0.015025\n",
      "358                   0.012497\n",
      "356                   0.012024\n",
      "414                   0.011710\n",
      "219                   0.011412\n",
      "country_Ecuador       0.011090\n",
      "427                   0.010147\n",
      "294                   0.008995\n",
      "131                   0.008794\n",
      "484                   0.007886\n",
      "446                   0.007725\n",
      "416                   0.006450\n",
      "450                   0.006091\n",
      "0.9066463723997971\n",
      "[[ 707  146]\n",
      " [  38 1080]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.95      0.83      0.88       853\n",
      "        paid       0.88      0.97      0.92      1118\n",
      "\n",
      "   micro avg       0.91      0.91      0.91      1971\n",
      "   macro avg       0.91      0.90      0.90      1971\n",
      "weighted avg       0.91      0.91      0.91      1971\n",
      "\n",
      "Adaboost\n",
      "Wall time: 49 s\n",
      "0.900050735667174\n",
      "[[ 748  105]\n",
      " [  92 1026]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.89      0.88      0.88       853\n",
      "        paid       0.91      0.92      0.91      1118\n",
      "\n",
      "   micro avg       0.90      0.90      0.90      1971\n",
      "   macro avg       0.90      0.90      0.90      1971\n",
      "weighted avg       0.90      0.90      0.90      1971\n",
      "\n",
      "GTB\n",
      "Wall time: 5.97 s\n",
      "0.893455098934551\n",
      "[[ 738  115]\n",
      " [  95 1023]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   defaulted       0.89      0.87      0.88       853\n",
      "        paid       0.90      0.92      0.91      1118\n",
      "\n",
      "   micro avg       0.89      0.89      0.89      1971\n",
      "   macro avg       0.89      0.89      0.89      1971\n",
      "weighted avg       0.89      0.89      0.89      1971\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "def build_model(df):\n",
    "    y = df['status']\n",
    "    X = df.drop('status', axis=1)\n",
    "\n",
    "    X = pd.get_dummies(X, columns=['sector', 'country', 'gender', 'nonpayment'], drop_first=True)\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33)\n",
    "    print(X_train.shape)\n",
    "    print(X_test.shape)\n",
    "\n",
    "    print(\"RF\")\n",
    "    clf = RandomForestClassifier(n_estimators=500, max_depth=10, random_state=55)\n",
    "    %time clf.fit(X_train, y_train)\n",
    "\n",
    "    imp = pd.DataFrame(clf.feature_importances_, index = X.columns, columns=['importance']).sort_values('importance', ascending=False).iloc[0:15,:]\n",
    "    print(imp)\n",
    "\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(accuracy_score(y_test, y_pred))\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    print(\"Adaboost\")\n",
    "    clf = AdaBoostClassifier(n_estimators=200)\n",
    "    %time clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(accuracy_score(y_test, y_pred))\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    print(\"GTB\")\n",
    "    clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=45)\n",
    "    %time clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print(accuracy_score(y_test, y_pred))\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(\"\\nNo Text\")\n",
    "build_model(kiva_df.drop(columns=['en', 'en_clean']))\n",
    "\n",
    "print(\"\\nBOW - TF\")\n",
    "build_model(kiva_df_bow)\n",
    "\n",
    "print(\"\\nBOW - TFIDF\")\n",
    "build_model(kiva_df_bow_tfidf)\n",
    "\n",
    "print(\"\\nFeature Hashing\")\n",
    "build_model(kiva_df_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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