Project on Decision Tree and Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Random Forest Project\n",
    "For this project we will be exploring publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this.\n",
    "\n",
    "Lending club had a very interesting year in 2016, so let's check out some of their data and keep the context in mind. This data is from before they even went public.\n",
    "\n",
    "We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from here or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values.\n",
    "\n",
    "Here are what the columns represent:\n",
    "\n",
    "credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "purpose: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "installment: The monthly installments owed by the borrower if the loan is funded.\n",
    "log.annual.inc: The natural log of the self-reported annual income of the borrower.\n",
    "dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "fico: The FICO credit score of the borrower.\n",
    "days.with.cr.line: The number of days the borrower has had a credit line.\n",
    "revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries\n",
    "\n",
    "**Import the usual libraries for pandas and plotting. You can import sklearn later on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Data\n",
    "\n",
    "** Use pandas to read loan_data.csv as a dataframe called loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check out the info(), head(), and describe() methods on loans.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  not.fully.paid     9578 non-null   int64  \n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
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       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot.\n",
    "\n",
    "** Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome.**\n",
    "\n",
    "*Note: This is pretty tricky, feel free to reference the solutions. You'll probably need one line of code for each histogram, I also recommend just using pandas built in .hist()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FICO ')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df[df['credit.policy']==1]['fico'].hist(bins=35,label='Credit Policy=1',alpha=0.6)\n",
    "df[df['credit.policy']==0]['fico'].hist(bins=35,label='Credit Policy=0',alpha=0.6)\n",
    "plt.legend()\n",
    "plt.xlabel('FICO ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a similar figure, except this time select by the not.fully.paid column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'FICO')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df[df['not.fully.paid']==1]['fico'].hist(bins=35,label='Not Fully Paid=1',color='blue',alpha=0.6)\n",
    "df[df['not.fully.paid']==0]['fico'].hist(bins=35,label='Not Fully Paid=0',color='red',alpha=0.6)\n",
    "plt.legend()\n",
    "plt.xlabel('FICO')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='purpose', ylabel='count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(data=df,x='purpose',hue='not.fully.paid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Let's see the trend between FICO score and interest rate. Recreate the following jointplot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0xd18eeab970>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df,x='fico',y='int.rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create the following lmplots to see if the trend differed between not.fully.paid and credit.policy. Check the documentation for lmplot() if you can't figure out how to separate it into columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xd18fcefe20>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 789.25x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=df,x='fico',y='int.rate',hue='credit.policy',col='not.fully.paid',palette='Set1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Data\n",
    "\n",
    "Let's get ready to set up our data for our Random Forest Classification Model!\n",
    "\n",
    "**Check loans.info() again.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   credit.policy      9578 non-null   int64  \n",
      " 1   purpose            9578 non-null   object \n",
      " 2   int.rate           9578 non-null   float64\n",
      " 3   installment        9578 non-null   float64\n",
      " 4   log.annual.inc     9578 non-null   float64\n",
      " 5   dti                9578 non-null   float64\n",
      " 6   fico               9578 non-null   int64  \n",
      " 7   days.with.cr.line  9578 non-null   float64\n",
      " 8   revol.bal          9578 non-null   int64  \n",
      " 9   revol.util         9578 non-null   float64\n",
      " 10  inq.last.6mths     9578 non-null   int64  \n",
      " 11  delinq.2yrs        9578 non-null   int64  \n",
      " 12  pub.rec            9578 non-null   int64  \n",
      " 13  not.fully.paid     9578 non-null   int64  \n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features\n",
    "\n",
    "Notice that the **purpose** column as categorical\n",
    "\n",
    "That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies.\n",
    "\n",
    "Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary.\n",
    "\n",
    "**Create a list of 1 element containing the string 'purpose'. Call this list cat_feats.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feats=['purpose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data=pd.get_dummies(df,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9573</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1461</td>\n",
       "      <td>344.76</td>\n",
       "      <td>12.180755</td>\n",
       "      <td>10.39</td>\n",
       "      <td>672</td>\n",
       "      <td>10474.000000</td>\n",
       "      <td>215372</td>\n",
       "      <td>82.1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9574</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1253</td>\n",
       "      <td>257.70</td>\n",
       "      <td>11.141862</td>\n",
       "      <td>0.21</td>\n",
       "      <td>722</td>\n",
       "      <td>4380.000000</td>\n",
       "      <td>184</td>\n",
       "      <td>1.1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9575</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>97.81</td>\n",
       "      <td>10.596635</td>\n",
       "      <td>13.09</td>\n",
       "      <td>687</td>\n",
       "      <td>3450.041667</td>\n",
       "      <td>10036</td>\n",
       "      <td>82.9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9576</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>351.58</td>\n",
       "      <td>10.819778</td>\n",
       "      <td>19.18</td>\n",
       "      <td>692</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9577</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1392</td>\n",
       "      <td>853.43</td>\n",
       "      <td>11.264464</td>\n",
       "      <td>16.28</td>\n",
       "      <td>732</td>\n",
       "      <td>4740.000000</td>\n",
       "      <td>37879</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9578 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0                 1    0.1189       829.10       11.350407  19.48   737   \n",
       "1                 1    0.1071       228.22       11.082143  14.29   707   \n",
       "2                 1    0.1357       366.86       10.373491  11.63   682   \n",
       "3                 1    0.1008       162.34       11.350407   8.10   712   \n",
       "4                 1    0.1426       102.92       11.299732  14.97   667   \n",
       "...             ...       ...          ...             ...    ...   ...   \n",
       "9573              0    0.1461       344.76       12.180755  10.39   672   \n",
       "9574              0    0.1253       257.70       11.141862   0.21   722   \n",
       "9575              0    0.1071        97.81       10.596635  13.09   687   \n",
       "9576              0    0.1600       351.58       10.819778  19.18   692   \n",
       "9577              0    0.1392       853.43       11.264464  16.28   732   \n",
       "\n",
       "      days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0           5639.958333      28854        52.1               0            0   \n",
       "1           2760.000000      33623        76.7               0            0   \n",
       "2           4710.000000       3511        25.6               1            0   \n",
       "3           2699.958333      33667        73.2               1            0   \n",
       "4           4066.000000       4740        39.5               0            1   \n",
       "...                 ...        ...         ...             ...          ...   \n",
       "9573       10474.000000     215372        82.1               2            0   \n",
       "9574        4380.000000        184         1.1               5            0   \n",
       "9575        3450.041667      10036        82.9               8            0   \n",
       "9576        1800.000000          0         3.2               5            0   \n",
       "9577        4740.000000      37879        57.0               6            0   \n",
       "\n",
       "      pub.rec  not.fully.paid  purpose_credit_card  \\\n",
       "0           0               0                    0   \n",
       "1           0               0                    1   \n",
       "2           0               0                    0   \n",
       "3           0               0                    0   \n",
       "4           0               0                    1   \n",
       "...       ...             ...                  ...   \n",
       "9573        0               1                    0   \n",
       "9574        0               1                    0   \n",
       "9575        0               1                    0   \n",
       "9576        0               1                    0   \n",
       "9577        0               1                    0   \n",
       "\n",
       "      purpose_debt_consolidation  purpose_educational  \\\n",
       "0                              1                    0   \n",
       "1                              0                    0   \n",
       "2                              1                    0   \n",
       "3                              1                    0   \n",
       "4                              0                    0   \n",
       "...                          ...                  ...   \n",
       "9573                           0                    0   \n",
       "9574                           0                    0   \n",
       "9575                           1                    0   \n",
       "9576                           0                    0   \n",
       "9577                           1                    0   \n",
       "\n",
       "      purpose_home_improvement  purpose_major_purchase  purpose_small_business  \n",
       "0                            0                       0                       0  \n",
       "1                            0                       0                       0  \n",
       "2                            0                       0                       0  \n",
       "3                            0                       0                       0  \n",
       "4                            0                       0                       0  \n",
       "...                        ...                     ...                     ...  \n",
       "9573                         0                       0                       0  \n",
       "9574                         0                       0                       0  \n",
       "9575                         0                       0                       0  \n",
       "9576                         1                       0                       0  \n",
       "9577                         0                       0                       0  \n",
       "\n",
       "[9578 rows x 19 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split\n",
    "\n",
    "Now its time to split our data into a training set and a testing set!\n",
    "\n",
    "** Use sklearn to split your data into a training set and a testing set as we've done in the past.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=final_data.drop('not.fully.paid',axis=1)\n",
    "y=final_data['not.fully.paid']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Decision Tree Model\n",
    "\n",
    "Let's start by training a single decision tree first!\n",
    "\n",
    "** Import DecisionTreeClassifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_tree=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_tree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Create predictions from the test set and create a classification report and a confusion matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=d_tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.82      0.84      2431\n",
      "           1       0.18      0.22      0.20       443\n",
      "\n",
      "    accuracy                           0.73      2874\n",
      "   macro avg       0.52      0.52      0.52      2874\n",
      "weighted avg       0.75      0.73      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1994  437]\n",
      " [ 344   99]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Random Forest model\n",
    "\n",
    "Now its time to train our model!\n",
    "\n",
    "**Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnc=RandomForestClassifier(n_estimators=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=300)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "** Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rnc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a classification report from the results. Do you get anything strange or some sort of warning?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      1.00      0.92      2431\n",
      "           1       0.56      0.02      0.04       443\n",
      "\n",
      "    accuracy                           0.85      2874\n",
      "   macro avg       0.70      0.51      0.48      2874\n",
      "weighted avg       0.80      0.85      0.78      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2423    8]\n",
      " [ 433   10]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**What performed better the random forest or the decision tree?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Well it really depend on what matrix you are optimizing for. It is quiet visible that for recall and f1-score random forest performed poorly. But for the precison it performed very well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
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