DSHW3.ipynb

{
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   "source": [
    "# CS 457 - Homework Assignment 3: \n",
    "## Exploratory Data Analysis\n",
    "### Maheen Anees, ma05156@st.habib.edu.pk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "id": "rKPQaEK7xR_P"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
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     "base_uri": "https://localhost:8080/",
     "height": 409
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    "id": "GBPjzi5KxV69",
    "outputId": "5e82beba-5793-4a28-cfd8-67f2a59f2061"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>application_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-85</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr-99</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-01</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.653644</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-96</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12226.302210</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-96</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>766.9</td>\n",
       "      <td>766.9</td>\n",
       "      <td>3242.170000</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        id  member_id  ...  last_credit_pull_d  application_type\n",
       "0  1077501    1296599  ...              Jan-16        INDIVIDUAL\n",
       "1  1077430    1314167  ...              Sep-13        INDIVIDUAL\n",
       "2  1077175    1313524  ...              Jan-16        INDIVIDUAL\n",
       "3  1076863    1277178  ...              Jan-15        INDIVIDUAL\n",
       "4  1075358    1311748  ...              Jan-16        INDIVIDUAL\n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_small.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "colab": {
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    },
    "id": "xSN78Ao1yYoO",
    "outputId": "ef41fead-8460-4ec2-b17d-23232cdae550"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           int64\n",
       "member_id                    int64\n",
       "loan_amnt                    int64\n",
       "funded_amnt                  int64\n",
       "funded_amnt_inv            float64\n",
       "term                        object\n",
       "int_rate                   float64\n",
       "installment                float64\n",
       "grade                       object\n",
       "sub_grade                   object\n",
       "emp_title                   object\n",
       "emp_length                  object\n",
       "home_ownership              object\n",
       "annual_inc                 float64\n",
       "verification_status         object\n",
       "issue_d                     object\n",
       "loan_status                 object\n",
       "pymnt_plan                  object\n",
       "purpose                     object\n",
       "title                       object\n",
       "zip_code                    object\n",
       "addr_state                  object\n",
       "dti                        float64\n",
       "delinq_2yrs                  int64\n",
       "earliest_cr_line            object\n",
       "inq_last_6mths               int64\n",
       "mths_since_last_delinq     float64\n",
       "mths_since_last_record     float64\n",
       "open_acc                     int64\n",
       "pub_rec                      int64\n",
       "revol_bal                    int64\n",
       "revol_util                 float64\n",
       "total_acc                    int64\n",
       "initial_list_status         object\n",
       "out_prncp                  float64\n",
       "out_prncp_inv              float64\n",
       "total_pymnt                float64\n",
       "total_pymnt_inv            float64\n",
       "total_rec_prncp            float64\n",
       "total_rec_int              float64\n",
       "total_rec_late_fee         float64\n",
       "recoveries                 float64\n",
       "collection_recovery_fee    float64\n",
       "last_pymnt_d                object\n",
       "last_pymnt_amnt            float64\n",
       "last_credit_pull_d          object\n",
       "application_type            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 484,
     "metadata": {},
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    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dehxHvGKzPGn"
   },
   "source": [
    "## Checking total records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ef2Kd7JMy9Yz",
    "outputId": "d93a71ea-abc4-4d04-eab3-d86d100a905b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999, 47)"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fmwFUHBy0ds"
   },
   "source": [
    "# Part 1: Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nbz0unadz6DB"
   },
   "source": [
    "## Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GkZrlnn-zq0x",
    "outputId": "725e64c3-c643-4672-aecf-941f287a84db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "994    False\n",
       "995    False\n",
       "996    False\n",
       "997    False\n",
       "998    False\n",
       "Length: 999, dtype: bool"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qO3C2xv5z_8T"
   },
   "source": [
    "## Removing 'months' from the column 'term'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "hmtR9c7H0KD-",
    "outputId": "5a6703e9-4941-42eb-e662-946c907765df"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>application_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-85</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr-99</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-01</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.653644</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-96</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12226.302210</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-96</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>766.9</td>\n",
       "      <td>766.9</td>\n",
       "      <td>3242.170000</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3991d918-1704-4570-a123-4bbde2a3f7fe')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-3991d918-1704-4570-a123-4bbde2a3f7fe button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-3991d918-1704-4570-a123-4bbde2a3f7fe');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  "
      ],
      "text/plain": [
       "        id  member_id  ...  last_credit_pull_d  application_type\n",
       "0  1077501    1296599  ...              Jan-16        INDIVIDUAL\n",
       "1  1077430    1314167  ...              Sep-13        INDIVIDUAL\n",
       "2  1077175    1313524  ...              Jan-16        INDIVIDUAL\n",
       "3  1076863    1277178  ...              Jan-15        INDIVIDUAL\n",
       "4  1075358    1311748  ...              Jan-16        INDIVIDUAL\n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['term'] = df['term'].str.replace('months', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uWP_D9Hz4DM5"
   },
   "source": [
    "## Removing whitespaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "id": "Q8XtdG5ry3OP"
   },
   "outputs": [],
   "source": [
    "df['term']= df['term'].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rp3ur_jw4GCT"
   },
   "source": [
    "## Changing datatype to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "uZ6DgZ7t1-nP",
    "outputId": "85a95ea5-fbe9-4400-9a90-2a32ddc492b4"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>loan_amnt</th>\n",
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       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
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       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
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       "      <th>annual_inc</th>\n",
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       "      <th>zip_code</th>\n",
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       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
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       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>application_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-85</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr-99</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-01</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.653644</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-96</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12226.302210</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-96</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>766.9</td>\n",
       "      <td>766.9</td>\n",
       "      <td>3242.170000</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
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       "  "
      ],
      "text/plain": [
       "        id  member_id  ...  last_credit_pull_d  application_type\n",
       "0  1077501    1296599  ...              Jan-16        INDIVIDUAL\n",
       "1  1077430    1314167  ...              Sep-13        INDIVIDUAL\n",
       "2  1077175    1313524  ...              Jan-16        INDIVIDUAL\n",
       "3  1076863    1277178  ...              Jan-15        INDIVIDUAL\n",
       "4  1075358    1311748  ...              Jan-16        INDIVIDUAL\n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['term']= df['term'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XCeoyU2A5w9c"
   },
   "source": [
    "## Removing 'years' or 'year' from emp_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "8McnV2ta56-r",
    "outputId": "a1cab9be-246f-4289-e0e3-3d92877ead71"
   },
   "outputs": [
    {
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       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-85</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr-99</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-01</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.653644</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10+</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-96</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12226.302210</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-96</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>766.9</td>\n",
       "      <td>766.9</td>\n",
       "      <td>3242.170000</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  "
      ],
      "text/plain": [
       "        id  member_id  ...  last_credit_pull_d  application_type\n",
       "0  1077501    1296599  ...              Jan-16        INDIVIDUAL\n",
       "1  1077430    1314167  ...              Sep-13        INDIVIDUAL\n",
       "2  1077175    1313524  ...              Jan-16        INDIVIDUAL\n",
       "3  1076863    1277178  ...              Jan-15        INDIVIDUAL\n",
       "4  1075358    1311748  ...              Jan-16        INDIVIDUAL\n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_length'] = df['emp_length'].str.replace('years', '')\n",
    "df['emp_length'] = df['emp_length'].str.replace('year', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OQtg3ywh60Z7"
   },
   "source": [
    "## Filling null values, removing + and < from emp_length column and changing dtype. \n",
    "Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. So\\< 1 means we will replace it by 0 and 10+ means it can be any number greater than 10 so we will replace it by 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TV3DPAFg7Uz6",
    "outputId": "e8933e3a-d8c9-44c1-b57b-3d90187c0c4d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     0\n",
       "2    10\n",
       "3    10\n",
       "4     1\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_length'].fillna(df['emp_length'].mode()[0], inplace = True)\n",
    "df['emp_length'] = df['emp_length'].str.replace('< 1', '0')\n",
    "df['emp_length'] = df['emp_length'].str.replace('+', '')\n",
    "df['emp_length'] = df['emp_length'].str.strip()\n",
    "df['emp_length'] = df['emp_length'].astype(int)\n",
    "df['emp_length'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OwPiHekR5Hab"
   },
   "source": [
    "## Checking for columns with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fd2L4wV85K1H",
    "outputId": "385968d8-eca3-4556-ec54-8a34660ccf6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "member_id                    0\n",
       "loan_amnt                    0\n",
       "funded_amnt                  0\n",
       "funded_amnt_inv              0\n",
       "term                         0\n",
       "int_rate                     0\n",
       "installment                  0\n",
       "grade                        0\n",
       "sub_grade                    0\n",
       "emp_title                   51\n",
       "emp_length                   0\n",
       "home_ownership               0\n",
       "annual_inc                   0\n",
       "verification_status          0\n",
       "issue_d                      0\n",
       "loan_status                  0\n",
       "pymnt_plan                   0\n",
       "purpose                      0\n",
       "title                        0\n",
       "zip_code                     0\n",
       "addr_state                   0\n",
       "dti                          0\n",
       "delinq_2yrs                  0\n",
       "earliest_cr_line             0\n",
       "inq_last_6mths               0\n",
       "mths_since_last_delinq     774\n",
       "mths_since_last_record     979\n",
       "open_acc                     0\n",
       "pub_rec                      0\n",
       "revol_bal                    0\n",
       "revol_util                   0\n",
       "total_acc                    0\n",
       "initial_list_status          0\n",
       "out_prncp                    0\n",
       "out_prncp_inv                0\n",
       "total_pymnt                  0\n",
       "total_pymnt_inv              0\n",
       "total_rec_prncp              0\n",
       "total_rec_int                0\n",
       "total_rec_late_fee           0\n",
       "recoveries                   0\n",
       "collection_recovery_fee      0\n",
       "last_pymnt_d                 1\n",
       "last_pymnt_amnt              0\n",
       "last_credit_pull_d           0\n",
       "application_type             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QbNG4UXu5Mat"
   },
   "source": [
    "## Impute or replace missing values\n",
    "We will fill numeric columns with the mean value and object columns using forward and backward fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "id": "94lxfmNcP1mT",
    "outputId": "c4a24932-ab64-4962-dd2b-3f127b090e0c"
   },
   "outputs": [
    {
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       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>application_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-85</td>\n",
       "      <td>1</td>\n",
       "      <td>41.115556</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13648</td>\n",
       "      <td>83.7</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5861.071414</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>0</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Apr-99</td>\n",
       "      <td>5</td>\n",
       "      <td>41.115556</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1687</td>\n",
       "      <td>9.4</td>\n",
       "      <td>4</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1008.710000</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-13</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-13</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-01</td>\n",
       "      <td>2</td>\n",
       "      <td>41.115556</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2956</td>\n",
       "      <td>98.5</td>\n",
       "      <td>10</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3003.653644</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-14</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>10</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-96</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5598</td>\n",
       "      <td>21.0</td>\n",
       "      <td>37</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12226.302210</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>357.48</td>\n",
       "      <td>Jan-15</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-96</td>\n",
       "      <td>0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>27783</td>\n",
       "      <td>53.9</td>\n",
       "      <td>38</td>\n",
       "      <td>f</td>\n",
       "      <td>766.9</td>\n",
       "      <td>766.9</td>\n",
       "      <td>3242.170000</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>67.79</td>\n",
       "      <td>Jan-16</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31f546f9-7242-4fb2-81f1-1e84488c887a')\"\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        id  member_id  ...  last_credit_pull_d  application_type\n",
       "0  1077501    1296599  ...              Jan-16        INDIVIDUAL\n",
       "1  1077430    1314167  ...              Sep-13        INDIVIDUAL\n",
       "2  1077175    1313524  ...              Jan-16        INDIVIDUAL\n",
       "3  1076863    1277178  ...              Jan-15        INDIVIDUAL\n",
       "4  1075358    1311748  ...              Jan-16        INDIVIDUAL\n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mths_since_last_delinq'].fillna(df['mths_since_last_delinq'].mean(), inplace=True) \n",
    "df['mths_since_last_record'].fillna(df['mths_since_last_record'].mean(), inplace = True)\n",
    "df['last_pymnt_d'] = df['last_pymnt_d'].fillna(method='ffill')\n",
    "df['emp_title'] = df['emp_title'].fillna(method='bfill')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GsyXRfvKT6GD",
    "outputId": "fc6c8826-05d4-4c5b-82c1-51376b4206b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "member_id                  0\n",
       "loan_amnt                  0\n",
       "funded_amnt                0\n",
       "funded_amnt_inv            0\n",
       "term                       0\n",
       "int_rate                   0\n",
       "installment                0\n",
       "grade                      0\n",
       "sub_grade                  0\n",
       "emp_title                  0\n",
       "emp_length                 0\n",
       "home_ownership             0\n",
       "annual_inc                 0\n",
       "verification_status        0\n",
       "issue_d                    0\n",
       "loan_status                0\n",
       "pymnt_plan                 0\n",
       "purpose                    0\n",
       "title                      0\n",
       "zip_code                   0\n",
       "addr_state                 0\n",
       "dti                        0\n",
       "delinq_2yrs                0\n",
       "earliest_cr_line           0\n",
       "inq_last_6mths             0\n",
       "mths_since_last_delinq     0\n",
       "mths_since_last_record     0\n",
       "open_acc                   0\n",
       "pub_rec                    0\n",
       "revol_bal                  0\n",
       "revol_util                 0\n",
       "total_acc                  0\n",
       "initial_list_status        0\n",
       "out_prncp                  0\n",
       "out_prncp_inv              0\n",
       "total_pymnt                0\n",
       "total_pymnt_inv            0\n",
       "total_rec_prncp            0\n",
       "total_rec_int              0\n",
       "total_rec_late_fee         0\n",
       "recoveries                 0\n",
       "collection_recovery_fee    0\n",
       "last_pymnt_d               0\n",
       "last_pymnt_amnt            0\n",
       "last_credit_pull_d         0\n",
       "application_type           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() #to check if we have filled all null values or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FAhbJqLGoj6N"
   },
   "source": [
    "## Coverting column loan_status into binary attribute\n",
    "Fully Paid and current is good status, while charged off, late and default is bad status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27g63ufXmw74",
    "outputId": "dbd4806d-76be-4b37-cf12-bdce776005c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid            729\n",
       "Charged Off           176\n",
       "Current                91\n",
       "Late (31-120 days)      2\n",
       "Default                 1\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "id": "iPtQEggXo6df"
   },
   "outputs": [],
   "source": [
    "df['loan_status'] = df['loan_status'].replace(['Fully Paid', 'Current'], 'Good')\n",
    "df['loan_status'] = df['loan_status'].replace(['Charged Off', 'Late (31-120 days)', 'Default'], 'Bad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "do1f0ve8mjo3"
   },
   "source": [
    "# Part 2: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZFECdQcvvxhc"
   },
   "source": [
    "## Summary Tables Using Grouping & Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 833
    },
    "id": "3U9YxZXK7ebA",
    "outputId": "efa04106-43b0-46e0-841b-0c2b8c17539d"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>purpose</th>\n",
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       "               purpose loan_status  Total Records\n",
       "0                  car         Bad              5\n",
       "1                  car        Good             17\n",
       "2          credit_card         Bad             32\n",
       "3          credit_card        Good            220\n",
       "4   debt_consolidation         Bad            100\n",
       "5   debt_consolidation        Good            427\n",
       "6     home_improvement         Bad              9\n",
       "7     home_improvement        Good             28\n",
       "8                house         Bad              2\n",
       "9                house        Good              5\n",
       "10      major_purchase         Bad              2\n",
       "11      major_purchase        Good             17\n",
       "12             medical         Bad              2\n",
       "13             medical        Good              9\n",
       "14              moving         Bad              1\n",
       "15              moving        Good              4\n",
       "16               other         Bad             13\n",
       "17               other        Good             50\n",
       "18    renewable_energy        Good              1\n",
       "19      small_business         Bad             10\n",
       "20      small_business        Good             25\n",
       "21            vacation         Bad              1\n",
       "22            vacation        Good              9\n",
       "23             wedding         Bad              2\n",
       "24             wedding        Good              8"
      ]
     },
     "execution_count": 497,
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   "source": [
    "df.groupby(['purpose', 'loan_status'])['loan_status'].count().reset_index(name='Total Records')"
   ]
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    "From the above table we can observe the total number of people who took loan for different purposes and whats their loan status. For example there are total of 427 people whose purpose of loan is debt_consolidation and have good loan status while 100 of them with the same purpose but bad loan status. A total of 22 people took loan for the purpose of car out of which 5 have bad loan status(either defualter, late of charged off). This can be observed for other categories as well "
   ]
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       "                               loan_amnt    annual_inc   int_rate\n",
       "loan_status home_ownership                                       \n",
       "Bad         MORTGAGE        14663.068182  72068.272727  14.144318\n",
       "            OWN             13450.000000  51629.714286  14.935000\n",
       "            RENT            12574.173554  50371.991405  14.417934\n",
       "Good        MORTGAGE        14363.556338  75117.411972  12.468979\n",
       "            OWN             11652.868852  57764.622951  12.397705\n",
       "            RENT            11677.105263  57286.007537  12.789284"
      ]
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   "source": [
    "df.groupby(['loan_status', 'home_ownership'])[['loan_amnt', 'annual_inc', 'int_rate']].mean()"
   ]
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    "From the table above we can observe that average amount of loan taken by people with bad loan status is from 1200s to 1400s whether they have their own home, rented or mortgaged. Similarly, for people who have fully paid or whose loan is currently running have taken around the same range of average loan amount which is from 1100s to 1400s whether they have their own home, rented or mortgaged. We can also observe that the average annual income for people with good loan status is higher than that of bad loan status which makes sense as those with higher income can pay off their debts easily. Moreover, we can also observe that the average int_rate for people with bad loan status is slightly higher than that of good loan status. "
   ]
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       "      <th>grade</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>12.507407</td>\n",
       "      <td>2.21</td>\n",
       "      <td>27.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>13.155882</td>\n",
       "      <td>0.78</td>\n",
       "      <td>24.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>14.112308</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">B</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>14.739623</td>\n",
       "      <td>3.17</td>\n",
       "      <td>29.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>16.397083</td>\n",
       "      <td>7.44</td>\n",
       "      <td>26.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>14.719732</td>\n",
       "      <td>0.72</td>\n",
       "      <td>28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>14.508060</td>\n",
       "      <td>1.99</td>\n",
       "      <td>25.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>13.308889</td>\n",
       "      <td>3.16</td>\n",
       "      <td>23.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>14.946885</td>\n",
       "      <td>0.89</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">D</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>14.959512</td>\n",
       "      <td>3.59</td>\n",
       "      <td>24.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>15.328889</td>\n",
       "      <td>4.96</td>\n",
       "      <td>22.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>15.609773</td>\n",
       "      <td>1.00</td>\n",
       "      <td>24.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">E</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>17.312500</td>\n",
       "      <td>4.29</td>\n",
       "      <td>26.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>18.286000</td>\n",
       "      <td>12.75</td>\n",
       "      <td>21.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>15.232326</td>\n",
       "      <td>2.65</td>\n",
       "      <td>23.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>12.536250</td>\n",
       "      <td>6.83</td>\n",
       "      <td>20.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN</th>\n",
       "      <td>11.135000</td>\n",
       "      <td>5.55</td>\n",
       "      <td>16.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>14.014545</td>\n",
       "      <td>6.10</td>\n",
       "      <td>24.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">G</th>\n",
       "      <th>MORTGAGE</th>\n",
       "      <td>12.960000</td>\n",
       "      <td>12.96</td>\n",
       "      <td>12.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT</th>\n",
       "      <td>14.322500</td>\n",
       "      <td>7.02</td>\n",
       "      <td>20.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                            dti              \n",
       "                           mean    min    max\n",
       "grade home_ownership                         \n",
       "A     MORTGAGE        12.507407   2.21  27.99\n",
       "      OWN             13.155882   0.78  24.29\n",
       "      RENT            14.112308   0.93  29.85\n",
       "B     MORTGAGE        14.739623   3.17  29.44\n",
       "      OWN             16.397083   7.44  26.34\n",
       "      RENT            14.719732   0.72  28.32\n",
       "C     MORTGAGE        14.508060   1.99  25.71\n",
       "      OWN             13.308889   3.16  23.92\n",
       "      RENT            14.946885   0.89  26.53\n",
       "D     MORTGAGE        14.959512   3.59  24.98\n",
       "      OWN             15.328889   4.96  22.86\n",
       "      RENT            15.609773   1.00  24.84\n",
       "E     MORTGAGE        17.312500   4.29  26.82\n",
       "      OWN             18.286000  12.75  21.25\n",
       "      RENT            15.232326   2.65  23.21\n",
       "F     MORTGAGE        12.536250   6.83  20.46\n",
       "      OWN             11.135000   5.55  16.72\n",
       "      RENT            14.014545   6.10  24.82\n",
       "G     MORTGAGE        12.960000  12.96  12.96\n",
       "      RENT            14.322500   7.02  20.88"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['grade', 'home_ownership']).agg({'dti':['mean', 'min', 'max']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UgoaKbA-duOb"
   },
   "source": [
    "From the above table we can observe the mean, min and max dti for each grade and home ownership. For example we can observe that for grade A, people whose home is owned has avg dti of 13.156. Similarly we can observe for other grades and home ownership categories as well.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rWEWyUFIvrC3"
   },
   "source": [
    "## Univariate Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "rdVArBdHnIA8",
    "outputId": "1ca036a1-32d9-42ed-910a-1ccf5b901f48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = df['loan_status'].value_counts()*100/len(df['loan_status'])\n",
    "plot = ax1.plot(kind='barh')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Loan Status')\n",
    "plt.title(\"Bar chart for loan status of loan takers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KC_k9fM-9T7l"
   },
   "source": [
    "From the above plot we can see that around 82% people have good loan_status which means that they have either fully paid the loan or their loans are currently running. On the other hand we can see that around 17% of the people have bad loan status which means that they are either defaulters, are late in payment or charged off which means that the debt has gone unpaid for very long.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "id": "6r4G8DQc_ZN6",
    "outputId": "259feacb-6d21-48f4-9ebd-e1f8a1cdfdbb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = df['term'].value_counts()*100/len(df['term'])\n",
    "plot = ax2.plot(kind='bar')\n",
    "plt.xlabel('Term')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title(\"Bar chart for term (number of payments on the loan)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GJbdxkTv-W2A"
   },
   "source": [
    "From the above plot we can see that around 72-73% people have loan payments for 36 month term while the rest 26-27% people for 60 month term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "oxOP2dd60NGH",
    "outputId": "6fa685a0-b188-4674-d4d5-d56857550ca3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram for debt to income ratio for each loan status')"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax3 = sns.histplot(data=df, x='dti', kde=True, hue='loan_status')\n",
    "ax3.set(xlabel='debt-to-income ratio')\n",
    "plt.title(\"Histogram for debt to income ratio for each loan status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pz1cx23bAH1T"
   },
   "source": [
    "From the above plot we can observe that the debt to income ratio (dti) is not skewed as the median and mean are very close to each other. The shape is close enough to be called symmetric and the mean dti is around 15% which means on average this much income of the people goes to debt payments. We can also observe that people with max dti(around 30) are the people with bad loan status which makes sense as they are unable to manage their monthly debt payments effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qnUa89Tv-WvA"
   },
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "NlYuQ_1--dut",
    "outputId": "90ddf912-1a66-4eac-e560-24f69abd23aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot between int_rate and annual income for each loan status')"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax4 = sns.scatterplot(x='int_rate', y='annual_inc', data=df)\n",
    "plt.title(\"Scatterplot between int_rate and annual income for each loan status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j4BvzSRARvQf"
   },
   "source": [
    "From the above scatterplot we can observe that its mostly straight lines which means that there is no signifant relationship between the int_rate and annual income for each loan status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 313
    },
    "id": "UjOBPdbt_eti",
    "outputId": "4a1b4ea5-3d46-4912-d653-40356c6431ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot between annual income and loan amount for each loan status')"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax5 = sns.scatterplot(x='loan_amnt', y='annual_inc', data=df)\n",
    "plt.title(\"Scatterplot between annual income and loan amount for each loan status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YIaqufZuSjzm"
   },
   "source": [
    "From the above scatterplot we can observe that the relationship between annual income and loan amount is positive as the trend seems to move a bit uphill and this makes sense as people with higher annual income might tend to have more amount of loan as they can pay it off easily. However other factors other than annual income affect the amount of loan therefore the relationship though is positive its not too strong as we can observe there are some straight lines as well.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "0QMTXHl-ACuc",
    "outputId": "3e98d36f-1291-4ff5-e4c3-9dfc8488d387"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Box plot between term and int_rate for each loan_status')"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax6 = sns.boxplot(x='term', y='int_rate', data=df, order=sorted(df['term'].unique()))\n",
    "plt.title(\"Box plot between term and int_rate for each loan_status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sr2XTI4Bb5DI"
   },
   "source": [
    "From the above plot we can see that int_rate increases with the term on loan as observed that int_rate is higher for 60 months term on loan and as a result we can say that chances for defualters also increases. The highest int_rate for term of 36 months is around 21 while for 60 months its 24-25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "BWG6mkFaD0ji",
    "outputId": "290c4bdf-d7bb-4f53-fbb9-15deb20ec1d0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "continuous_f = [\"annual_inc\", \"term\", \"int_rate\", \"loan_status\", \"loan_amnt\", \"dti\"]\n",
    "loan_corr = df[continuous_f].corr()\n",
    "sns.heatmap(loan_corr,vmin=-1.0,vmax=1.0,annot=True, cmap=\"YlGnBu\")\n",
    "plt.title(\"Correlation Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d-4mlelqijDs"
   },
   "source": [
    "From the correlation map we can observe that the loan_amnt is positively correlated with the annual income and term which means that as annual income or term increases the loan amount also increases which makes sense as debts can be then payed off easily with higher income. Moreover annual income and int_rate are very weakly correlated as we also observed in the scatterplot above that most plot were staright lines. further the int_rate and term are positively correlated with the value of 0.54 which we also observed above in the box plot that as term increases the int_rate also increases. Further we can observe that debt-to-income ratio is weakly negatively correlated with annual income and have little to no affect on term, int_rate and loan_amnt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4fMPnHoB6-R"
   },
   "source": [
    "## Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "OXUq4dkXB_GN",
    "outputId": "21c9223f-adb8-4c87-8ff3-019774b93cfe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Box plot between term and int_rate for each loan_status')"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax7 = sns.boxplot(x='term', y='int_rate', data=df, order=sorted(df['term'].unique()), hue='loan_status')\n",
    "plt.title(\"Box plot between term and int_rate for each loan_status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U_7eMDGoDHzp"
   },
   "source": [
    "From the above plot we are observing three variables together: int_rate, term and loan_status. We can see that int_rate increases with the term on loan as observed that int_rate is higher for 60 months term on loan and as a result we can say that chances for defualters also increases.\n",
    "Morover we can see that people with bad loan status have higher int_rate than that of good loan status for both terms which makes sense as with increased int_rate people will face difficulty in paying off the loan."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "DSHW3.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}