loan-eligibility-prediction-updated-on-13-09-2020.ipynb

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    "<div class='text-center'>\n",
    "    <h1>🏧 Loan Eligibility Prediction 💰 using Machine Learning Models 🤖   </h1>\n",
    "</div>\n"
   ]
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   "source": [
    "# Introduction"
   ]
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   "source": [
    "### In this notebook kernal, I'm going to predictions customers are eligible for the loan and check whether what are the missing criteria to know why customer not getting loan to make there own house.\n",
    "\n",
    "\n",
    "<div class=\"text-success \"><h4> We will learning about, Data Analysis Preprocess such as, </h4></div>\n",
    "\n",
    "--- \n",
    "\n",
    "> ### Steps are:\n",
    "\n",
    "\n",
    "1. [Gathering Data](#1)\n",
    "- [Exploratory Data Analysis](#2)\n",
    "- [Data Visualizations](#3)\n",
    "- [Machine Learning Model Decision.](#4)\n",
    "- [Traing the ML Model](#5)\n",
    "- [Predict Model](#6)\n",
    "- [Deploy Model](#7)\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "**Hope** you guys ****Love It**** and get a better **learning experience**.  🙏"
   ]
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   "source": [
    "\n",
    "<img align=\"center\" src=\"https://www.rdccbank.com/uploads/loan-sub-types-template/housing-b.jpg\" alt=\"House Loan\" />\n",
    "\n",
    "---\n",
    "\n",
    "<div class=\"text-danger\" >\n",
    "    <h4>Let's Say, You are the owner of the <b>Housing Finance Company</b> and you want to build your own model to predict the customers are applying for the home loan and company want to check and validate the customer are eligible for the home loan.\n",
    "    </h4>\n",
    "</div>"
   ]
  },
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   "source": [
    "\n",
    "# <div class=\"text-primary\"> The Problem is,  </div>\n",
    "\n",
    "### In a Simple Term, Company wants to make automate the Loan Eligibility Process in a real time scenario related to customer's detail provided while applying application for home loan forms.\n",
    "\n",
    "\n",
    "You will use the training set to build your model, and the test set to validate it. Both the files are stored on the web as CSV files; their URLs are already available as character strings in the sample code.\n",
    "\n"
   ]
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   "source": [
    "First of all, we need to importing the necessary packages to work with the data to solve our problem"
   ]
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   "source": [
    "# Import Packages"
   ]
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   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
   ]
  },
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     "output_type": "stream",
     "text": [
      "/kaggle/input/loan-eligible-dataset/loan-train.csv\n",
      "/kaggle/input/loan-eligible-dataset/loan-test.csv\n"
     ]
    }
   ],
   "source": [
    "# Show the Dataset Path to get detaset\n",
    "\n",
    "import os\n",
    "\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
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   "source": [
    " You can load this data with the `read_csv()` method from `pandas` package. It converts the data set to a python dataframe."
   ]
  },
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    "\n",
    "## Dataset Key Information.\n",
    "  \n",
    "---  \n",
    "  \n",
    "> - `Loan_ID`--------------> Unique Loan ID.\n",
    "- `Gender`   --------------> Male/ Female \n",
    "- `Married`  --------------> Applicant married (Y/N) \n",
    "- `Dependents` ------------> Number of dependents \n",
    "- `Education` -------------> Applicant Education (Graduate/ Under Graduate) \n",
    "- `Self_Employed` ---------> Self-employed (Y/N) \n",
    "- `ApplicantIncome` -------> Applicant income \n",
    "- `CoapplicantIncome` -----> Coapplicant income \n",
    "- `LoanAmount`  -----------> Loan amount in thousands \n",
    "- `Loan_Amount_Term` ------> Term of a loan in months \n",
    "- `Credit_History` --------> Credit history meets guidelines \n",
    "- `Property_Area` ---------> Urban/ Semi-Urban/ Rural \n",
    "- `Loan_Status` -----------> Loan approved (Y/N) "
   ]
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   "source": [
    "<a id=\"1\"></a><br>\n",
    "# 1. Gathering Data"
   ]
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   "source": [
    "#  Create New Variable and stores the dataset values as Data Frame\n",
    "\n",
    "loan_train = pd.read_csv('/kaggle/input/loan-eligible-dataset/loan-train.csv')\n",
    "loan_test = pd.read_csv('/kaggle/input/loan-eligible-dataset/loan-test.csv')"
   ]
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   },
   "source": [
    "- Lets display the some few information from our large datasets\n",
    "\n",
    "Here, We shows the first five rows from datasets"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
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       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
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       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
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   "source": [
    "loan_train.head()"
   ]
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   "source": [
    "- As we can see in the above output, there are too many columns, ( columns known as features as well. )\n",
    "\n",
    "We can also use `loan_train` to show few rows from the first five and last five record from the dataset"
   ]
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       "      <td>LP001006</td>\n",
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       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
       "0    LP001002    Male      No          0      Graduate            No   \n",
       "1    LP001003    Male     Yes          1      Graduate            No   \n",
       "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
       "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
       "4    LP001008    Male      No          0      Graduate            No   \n",
       "..        ...     ...     ...        ...           ...           ...   \n",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0         NaN             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "0               1.0         Urban           Y  \n",
       "1               1.0         Rural           N  \n",
       "2               1.0         Urban           Y  \n",
       "3               1.0         Urban           Y  \n",
       "4               1.0         Urban           Y  \n",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059196,
     "end_time": "2020-09-13T10:27:04.318210",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.259014",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ### Here, we can see there are many rows and many columns, To know how many records and columns are available in our dataset, we can use the `shape` attribute or we can use `len()` to know how many records and how many features available in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:04.444634Z",
     "iopub.status.busy": "2020-09-13T10:27:04.443827Z",
     "iopub.status.idle": "2020-09-13T10:27:04.447093Z",
     "shell.execute_reply": "2020-09-13T10:27:04.447837Z"
    },
    "papermill": {
     "duration": 0.069902,
     "end_time": "2020-09-13T10:27:04.448003",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.378101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:  614\n"
     ]
    }
   ],
   "source": [
    "print(\"Rows: \", len(loan_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.067938,
     "end_time": "2020-09-13T10:27:04.580827",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.512889",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Pandas has inbuild attribute to get all column from the dataset, With the help of this feature we can get the how many column available we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:04.741286Z",
     "iopub.status.busy": "2020-09-13T10:27:04.740511Z",
     "iopub.status.idle": "2020-09-13T10:27:04.744283Z",
     "shell.execute_reply": "2020-09-13T10:27:04.743537Z"
    },
    "papermill": {
     "duration": 0.091128,
     "end_time": "2020-09-13T10:27:04.744408",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.653280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns:  13\n"
     ]
    }
   ],
   "source": [
    "print(\"Columns: \", len(loan_train.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.062952,
     "end_time": "2020-09-13T10:27:04.871572",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.808620",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Also we can get the shape of the dataset using `shape` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:05.006347Z",
     "iopub.status.busy": "2020-09-13T10:27:05.005083Z",
     "iopub.status.idle": "2020-09-13T10:27:05.010555Z",
     "shell.execute_reply": "2020-09-13T10:27:05.009422Z"
    },
    "papermill": {
     "duration": 0.075165,
     "end_time": "2020-09-13T10:27:05.010872",
     "exception": false,
     "start_time": "2020-09-13T10:27:04.935707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape :  (614, 13)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape : \", loan_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.063451,
     "end_time": "2020-09-13T10:27:05.141082",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.077631",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ### *After we collecting the data, Next step we need to understand what kind of data we have.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.064942,
     "end_time": "2020-09-13T10:27:05.272592",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.207650",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Also we can get the column as an list(array) from dataset\n",
    "\n",
    "> **Note: DataFrame.columns returns the total columns of the dataset,\n",
    "> Store the number of columns in variable `loan_train_columns`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:05.408266Z",
     "iopub.status.busy": "2020-09-13T10:27:05.407152Z",
     "iopub.status.idle": "2020-09-13T10:27:05.411279Z",
     "shell.execute_reply": "2020-09-13T10:27:05.411999Z"
    },
    "papermill": {
     "duration": 0.075226,
     "end_time": "2020-09-13T10:27:05.412170",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.336944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train_columns = loan_train.columns # assign to a variable\n",
    "loan_train_columns # print the list of columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.064397,
     "end_time": "2020-09-13T10:27:05.541917",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.477520",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Now, Understanding the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.081669,
     "end_time": "2020-09-13T10:27:05.691528",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.609859",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- First of all we use the `loan_train.describe()` method to shows the important information from the dataset\n",
    "- It provides the `count`, `mean`, `standard deviation (std)`, `min`, `quartiles` and `max` in its output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:05.872715Z",
     "iopub.status.busy": "2020-09-13T10:27:05.871293Z",
     "iopub.status.idle": "2020-09-13T10:27:05.906052Z",
     "shell.execute_reply": "2020-09-13T10:27:05.905274Z"
    },
    "papermill": {
     "duration": 0.12133,
     "end_time": "2020-09-13T10:27:05.906189",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.784859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.081316,
     "end_time": "2020-09-13T10:27:06.067575",
     "exception": false,
     "start_time": "2020-09-13T10:27:05.986259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### As I said the above cell, this the information of all the methamatical details from dataset. Like `count`, `mean`, `standard deviation (std)`, `min`, `quartiles(25%, 50%, 75%)` and `max`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.07252,
     "end_time": "2020-09-13T10:27:06.206958",
     "exception": false,
     "start_time": "2020-09-13T10:27:06.134438",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ### Another method is `info()`, This method show us the information about the dataset, Like\n",
    "\n",
    "1. What's the type of culumn have?\n",
    "- How many rows available in the dataset?\n",
    "- What are the features are there?\n",
    "- How many null values available in the dataset?\n",
    "- Ans so on..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:06.355931Z",
     "iopub.status.busy": "2020-09-13T10:27:06.355042Z",
     "iopub.status.idle": "2020-09-13T10:27:06.359141Z",
     "shell.execute_reply": "2020-09-13T10:27:06.358435Z"
    },
    "papermill": {
     "duration": 0.086725,
     "end_time": "2020-09-13T10:27:06.359273",
     "exception": false,
     "start_time": "2020-09-13T10:27:06.272548",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "loan_train.info()\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.065011,
     "end_time": "2020-09-13T10:27:06.490604",
     "exception": false,
     "start_time": "2020-09-13T10:27:06.425593",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see in the output.\n",
    "\n",
    "1. There are `614` entries\n",
    "- There are total 13 features (0 to 12)\n",
    "- There are three types of datatype `dtypes: float64(4), int64(1), object(8)`\n",
    "- It's Memory usage that is, `memory usage: 62.5+ KB`\n",
    "- Also, We can check how many missing values available in the `Non-Null Count` column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.066217,
     "end_time": "2020-09-13T10:27:06.624952",
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     "start_time": "2020-09-13T10:27:06.558735",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"2\"></a><br>\n",
    "# 2. Exploratory Data Analysis\n",
    "\n",
    "In this section, We learn about extra information about data and it's characteristics.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.064723,
     "end_time": "2020-09-13T10:27:06.754711",
     "exception": false,
     "start_time": "2020-09-13T10:27:06.689988",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- First of all, We explore object type of data\n",
    "So let's make a function to know how many types of values available in the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:06.892581Z",
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     "shell.execute_reply": "2020-09-13T10:27:06.894485Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def explore_object_type(df ,feature_name):\n",
    "    \"\"\"\n",
    "    To know, How many values available in object('categorical') type of features\n",
    "    And Return Categorical values with Count.\n",
    "    \"\"\"    \n",
    "    if df[feature_name].dtype ==  'object':\n",
    "        print(df[feature_name].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.117976,
     "end_time": "2020-09-13T10:27:07.088245",
     "exception": false,
     "start_time": "2020-09-13T10:27:06.970269",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- After defined a function, Let's call it. and check what's the output of our created function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:07.237335Z",
     "iopub.status.busy": "2020-09-13T10:27:07.236438Z",
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     "shell.execute_reply": "2020-09-13T10:27:07.240202Z"
    },
    "papermill": {
     "duration": 0.083989,
     "end_time": "2020-09-13T10:27:07.241770",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.157781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      489\n",
      "Female    112\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Now, Test and Call a function for gender only\n",
    "explore_object_type(loan_train, 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.06672,
     "end_time": "2020-09-13T10:27:07.379594",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.312874",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Here's one little issue occurred, Suppose in your datasets there are lots of feature to defined like this above code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:07.544667Z",
     "iopub.status.busy": "2020-09-13T10:27:07.541953Z",
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     "shell.execute_reply": "2020-09-13T10:27:07.553402Z"
    },
    "papermill": {
     "duration": 0.100866,
     "end_time": "2020-09-13T10:27:07.555043",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.454177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\"Loan_ID's\" Values with count are :\n",
      "LP002501    1\n",
      "LP001046    1\n",
      "LP001972    1\n",
      "LP002422    1\n",
      "LP002106    1\n",
      "           ..\n",
      "LP002116    1\n",
      "LP002705    1\n",
      "LP002319    1\n",
      "LP001333    1\n",
      "LP002692    1\n",
      "Name: Loan_ID, Length: 614, dtype: int64\n",
      "\n",
      "\"Gender's\" Values with count are :\n",
      "Male      489\n",
      "Female    112\n",
      "Name: Gender, dtype: int64\n",
      "\n",
      "\"Married's\" Values with count are :\n",
      "Yes    398\n",
      "No     213\n",
      "Name: Married, dtype: int64\n",
      "\n",
      "\"Dependents's\" Values with count are :\n",
      "0     345\n",
      "1     102\n",
      "2     101\n",
      "3+     51\n",
      "Name: Dependents, dtype: int64\n",
      "\n",
      "\"Education's\" Values with count are :\n",
      "Graduate        480\n",
      "Not Graduate    134\n",
      "Name: Education, dtype: int64\n",
      "\n",
      "\"Self_Employed's\" Values with count are :\n",
      "No     500\n",
      "Yes     82\n",
      "Name: Self_Employed, dtype: int64\n",
      "\n",
      "\"Property_Area's\" Values with count are :\n",
      "Semiurban    233\n",
      "Urban        202\n",
      "Rural        179\n",
      "Name: Property_Area, dtype: int64\n",
      "\n",
      "\"Loan_Status's\" Values with count are :\n",
      "Y    422\n",
      "N    192\n",
      "Name: Loan_Status, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Solution is, Do you remember we have variable with name of `loan_train_columns`, Right,  let's use it\n",
    "# 'Loan_ID', 'Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status'\n",
    "\n",
    "for featureName in loan_train_columns:\n",
    "    if loan_train[featureName].dtype == 'object':\n",
    "        print('\\n\"' + str(featureName) + '\\'s\" Values with count are :')\n",
    "        explore_object_type(loan_train, str(featureName))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.078997,
     "end_time": "2020-09-13T10:27:07.712553",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.633556",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ## *Note: Your output maybe shorter or longer, It's totally depend upon your dataset's columns* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.074144,
     "end_time": "2020-09-13T10:27:07.862915",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.788771",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.072221,
     "end_time": "2020-09-13T10:27:08.006205",
     "exception": false,
     "start_time": "2020-09-13T10:27:07.933984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "- We need to fill null values with `mean` and `median` using `missingno` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:08.165000Z",
     "iopub.status.busy": "2020-09-13T10:27:08.164150Z",
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     "shell.execute_reply": "2020-09-13T10:27:09.409470Z"
    },
    "papermill": {
     "duration": 1.319617,
     "end_time": "2020-09-13T10:27:09.409671",
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     "start_time": "2020-09-13T10:27:08.090054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:09.568274Z",
     "iopub.status.busy": "2020-09-13T10:27:09.567237Z",
     "iopub.status.idle": "2020-09-13T10:27:09.573413Z",
     "shell.execute_reply": "2020-09-13T10:27:09.574414Z"
    },
    "papermill": {
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     "end_time": "2020-09-13T10:27:09.574701",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of how many percentage values are missing\n",
    "loan_train\n",
    "\n",
    "loan_train.isna().sum()\n",
    "# round((loan_train.isna().sum() / len(loan_train)) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:09.724287Z",
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     "exception": false,
     "start_time": "2020-09-13T10:27:09.646218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6676616c10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.bar(loan_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:10.969400Z",
     "iopub.status.busy": "2020-09-13T10:27:10.968632Z",
     "iopub.status.idle": "2020-09-13T10:27:11.434426Z",
     "shell.execute_reply": "2020-09-13T10:27:11.433743Z"
    },
    "papermill": {
     "duration": 0.54212,
     "end_time": "2020-09-13T10:27:11.434552",
     "exception": false,
     "start_time": "2020-09-13T10:27:10.892432",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f66742e3a90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(loan_train )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.073564,
     "end_time": "2020-09-13T10:27:11.583839",
     "exception": false,
     "start_time": "2020-09-13T10:27:11.510275",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- As we can see here, there are too many columns missing with small amount of null values so we use `mean` amd `mode` to replace with `NaN` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:11.761049Z",
     "iopub.status.busy": "2020-09-13T10:27:11.760205Z",
     "iopub.status.idle": "2020-09-13T10:27:11.763939Z",
     "shell.execute_reply": "2020-09-13T10:27:11.763146Z"
    },
    "papermill": {
     "duration": 0.106392,
     "end_time": "2020-09-13T10:27:11.764077",
     "exception": false,
     "start_time": "2020-09-13T10:27:11.657685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loan_train['Credit_History'].fillna(loan_train['Credit_History'].mode(), inplace=True) # Mode\n",
    "loan_test['Credit_History'].fillna(loan_test['Credit_History'].mode(), inplace=True) # Mode\n",
    "\n",
    "\n",
    "loan_train['LoanAmount'].fillna(loan_train['LoanAmount'].mean(), inplace=True) # Mean\n",
    "loan_test['LoanAmount'].fillna(loan_test['LoanAmount'].mean(), inplace=True) # Mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.071377,
     "end_time": "2020-09-13T10:27:11.906484",
     "exception": false,
     "start_time": "2020-09-13T10:27:11.835107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### # convert Categorical variable with Numerical values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.074134,
     "end_time": "2020-09-13T10:27:12.056663",
     "exception": false,
     "start_time": "2020-09-13T10:27:11.982529",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`Loan_Status` feature boolean values, So we replace `Y` values with `1` and `N` values with `0`\n",
    "and same for other `Boolean` types of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:12.230511Z",
     "iopub.status.busy": "2020-09-13T10:27:12.216226Z",
     "iopub.status.idle": "2020-09-13T10:27:12.235073Z",
     "shell.execute_reply": "2020-09-13T10:27:12.234203Z"
    },
    "papermill": {
     "duration": 0.106717,
     "end_time": "2020-09-13T10:27:12.235217",
     "exception": false,
     "start_time": "2020-09-13T10:27:12.128500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loan_train.Loan_Status = loan_train.Loan_Status.replace({\"Y\": 1, \"N\" : 0})\n",
    "# loan_test.Loan_Status = loan_test.Loan_Status.replace({\"Y\": 1, \"N\" : 0}) \n",
    "\n",
    "loan_train.Gender = loan_train.Gender.replace({\"Male\": 1, \"Female\" : 0})\n",
    "loan_test.Gender = loan_test.Gender.replace({\"Male\": 1, \"Female\" : 0})\n",
    "\n",
    "loan_train.Married = loan_train.Married.replace({\"Yes\": 1, \"No\" : 0})\n",
    "loan_test.Married = loan_test.Married.replace({\"Yes\": 1, \"No\" : 0})\n",
    "\n",
    "loan_train.Self_Employed = loan_train.Self_Employed.replace({\"Yes\": 1, \"No\" : 0})\n",
    "loan_test.Self_Employed = loan_test.Self_Employed.replace({\"Yes\": 1, \"No\" : 0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:12.397267Z",
     "iopub.status.busy": "2020-09-13T10:27:12.387959Z",
     "iopub.status.idle": "2020-09-13T10:27:12.400598Z",
     "shell.execute_reply": "2020-09-13T10:27:12.399827Z"
    },
    "papermill": {
     "duration": 0.093803,
     "end_time": "2020-09-13T10:27:12.400724",
     "exception": false,
     "start_time": "2020-09-13T10:27:12.306921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "loan_train['Gender'].fillna(loan_train['Gender'].mode()[0], inplace=True)\n",
    "loan_test['Gender'].fillna(loan_test['Gender'].mode()[0], inplace=True)\n",
    "\n",
    "loan_train['Dependents'].fillna(loan_train['Dependents'].mode()[0], inplace=True)\n",
    "loan_test['Dependents'].fillna(loan_test['Dependents'].mode()[0], inplace=True)\n",
    "\n",
    "loan_train['Married'].fillna(loan_train['Married'].mode()[0], inplace=True)\n",
    "loan_test['Married'].fillna(loan_test['Married'].mode()[0], inplace=True)\n",
    "\n",
    "loan_train['Credit_History'].fillna(loan_train['Credit_History'].mean(), inplace=True)\n",
    "loan_test['Credit_History'].fillna(loan_test['Credit_History'].mean(), inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "tags": []
   },
   "source": [
    "* Here, `Property_Area`, `Dependents` and `Education` has multiple values so now we can use `LabelEncoder` from `sklearn` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:12.702443Z",
     "iopub.status.busy": "2020-09-13T10:27:12.701521Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "feature_col = ['Property_Area','Education', 'Dependents']\n",
    "le = LabelEncoder()\n",
    "for col in feature_col:\n",
    "    loan_train[col] = le.fit_transform(loan_train[col])\n",
    "    loan_test[col] = le.fit_transform(loan_test[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.070154,
     "end_time": "2020-09-13T10:27:12.928346",
     "exception": false,
     "start_time": "2020-09-13T10:27:12.858192",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ### Finally, We have all the features with numerical values,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.073412,
     "end_time": "2020-09-13T10:27:13.074146",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"3\"></a><br>\n",
    "# 3. Data Visualizations\n",
    "\n",
    "\n",
    "In this section, We are showing the visual information from the dataset, For that we need some pakages that are `matplotlib` and `seaborn`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:13.228214Z",
     "iopub.status.busy": "2020-09-13T10:27:13.227420Z",
     "iopub.status.idle": "2020-09-13T10:27:13.231414Z",
     "shell.execute_reply": "2020-09-13T10:27:13.230637Z"
    },
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     "end_time": "2020-09-13T10:27:13.231554",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('dark')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:13.394524Z",
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     "shell.execute_reply": "2020-09-13T10:27:13.412939Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
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       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>LP001005</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
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       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0    LP001002     1.0      0.0           0          0            0.0   \n",
       "1    LP001003     1.0      1.0           1          0            0.0   \n",
       "2    LP001005     1.0      1.0           0          0            1.0   \n",
       "3    LP001006     1.0      1.0           0          1            0.0   \n",
       "4    LP001008     1.0      0.0           0          0            0.0   \n",
       "..        ...     ...      ...         ...        ...            ...   \n",
       "609  LP002978     0.0      0.0           0          0            0.0   \n",
       "610  LP002979     1.0      1.0           3          0            0.0   \n",
       "611  LP002983     1.0      1.0           1          0            0.0   \n",
       "612  LP002984     1.0      1.0           2          0            0.0   \n",
       "613  LP002990     0.0      0.0           0          0            1.0   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0  146.412162             360.0   \n",
       "1               4583             1508.0  128.000000             360.0   \n",
       "2               3000                0.0   66.000000             360.0   \n",
       "3               2583             2358.0  120.000000             360.0   \n",
       "4               6000                0.0  141.000000             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0   71.000000             360.0   \n",
       "610             4106                0.0   40.000000             180.0   \n",
       "611             8072              240.0  253.000000             360.0   \n",
       "612             7583                0.0  187.000000             360.0   \n",
       "613             4583                0.0  133.000000             360.0   \n",
       "\n",
       "     Credit_History  Property_Area  Loan_Status  \n",
       "0               1.0              2            1  \n",
       "1               1.0              0            0  \n",
       "2               1.0              2            1  \n",
       "3               1.0              2            1  \n",
       "4               1.0              2            1  \n",
       "..              ...            ...          ...  \n",
       "609             1.0              0            1  \n",
       "610             1.0              0            1  \n",
       "611             1.0              2            1  \n",
       "612             1.0              2            1  \n",
       "613             0.0              1            0  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_train"
   ]
  },
  {
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_train.plot(figsize=(18, 8))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:14.365583Z",
     "iopub.status.busy": "2020-09-13T10:27:14.364714Z",
     "iopub.status.idle": "2020-09-13T10:27:15.068890Z",
     "shell.execute_reply": "2020-09-13T10:27:15.068210Z"
    },
    "papermill": {
     "duration": 0.906364,
     "end_time": "2020-09-13T10:27:15.069017",
     "exception": false,
     "start_time": "2020-09-13T10:27:14.162653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "\n",
    "loan_train['ApplicantIncome'].hist(bins=10)\n",
    "plt.title(\"Loan Application Amount \")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.grid()\n",
    "plt.hist(np.log(loan_train['LoanAmount']))\n",
    "plt.title(\"Log Loan Application Amount \")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:15.238355Z",
     "iopub.status.busy": "2020-09-13T10:27:15.232696Z",
     "iopub.status.idle": "2020-09-13T10:27:15.624534Z",
     "shell.execute_reply": "2020-09-13T10:27:15.623896Z"
    },
    "papermill": {
     "duration": 0.477497,
     "end_time": "2020-09-13T10:27:15.624673",
     "exception": false,
     "start_time": "2020-09-13T10:27:15.147176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Nd/MQRPn5K8xOpCSzM5mfv6KVW9Z2cI0aZrPZlJe3SnZ7uhyOQiUlxcvhKJTdnm6OkAAAAABgTSGdmiFJ9957r2w2m2677TbddtttOn78uBITEyVJiYmJOnHihCTJ6XRq8ODB5nZJSUlyOp2N7js21qaEhM6ha3yQxcbGWKq9LWEYhqqrz8rhKFTHju21Zs1jWrBgvhyOQs2ZM1fx8RdZsjMZzBhG6jUKpoKCdXI4Cr1et/SaRNNzGKmIofURQ+sjhtZHDK2PGFpfNMcwpImIp59+WklJSTp+/LhmzJih73//+z4/29BQa38djpoaQ5WVZ1vcznBJSOhsqfa2VHZ2nqqrz6ugYJ0KCtZJkuz2dGVn58nl+rqVW9c8wY5hJF6jYHGPDvE0Z87cFo+IiLbnMBIRQ+sjhtZHDK2vrcXQMAyvf9/rv8aF2loM0XTREMOePbs1+H5Ip2YkJSVJknr06KGJEyfq3XffVY8ePVRRUSFJqqioUPfu3SVJvXr10rFjx8xtnU6nOXIC1uQeXu+JYfXeuEYN86wJYbeny+l0mdM0PKeyAAAA62OqKhB9QpaIOHv2rE6fPm3+/+uvv64BAwYoNTVVxcXFkqTi4mKNHz9eUt0KG6WlpTp37pwOHTqkzz//XIMGDQpV8xAGDX2jTSfSG9eoYTabTXFx8V41Idw1I+Li4qM+UQMAQKSgQDUQnUI2NeP48eOaNWuWJKmmpkY33HCDrr76ag0cOFDz5s3Tpk2b1Lt3b61du1aSNGDAAE2ZMkXXX3+9YmNjtXTpUsXGxoaqeQix+t9o5+WtMl9LfOsvcY38ycjI8hqW6U5GRPM1AQAg0niODnU4Cs3fgyhQDUQ2m2HhNOP58zWWmlMTDXOAPOXnr1BVlcv8R8Td8Y6Li1dGRlbQjxeOuYXBjmG4rxHqYnjy5Bmf9wpzVNu+aPu7NBIRQ+sjhtbX1mJoGIaSkuLN106ni39//WhrMUTTRUMMfdWIIBERRtFwo9UXrk5duDr0oYghHd/wWrfuUVVUfNXgvSKJxJAFROPfpZGGGFofMbS+thRDz1GiboyI8K8txRDNEw0xbJVilUD9fzxC8Y+J1ecWhuMaoY5hGHK5Gr5XXG81mToAACAASURBVK5Knz+zwn0EAIAVUaAaiE4hXb4TCAfmFiJQNptNa9Y8purq8w3eK3Wf4T4CACBcfBWolkSBaiCCMTUjjKJh6E1rCsfcQmJofe4aEb7uFeaotn08h9ZHDK2PGFpfW4shU1Wbrq3FEE0XDTFkagYiGstgIlCN3SvcRwAAtA6mqgLRJfbhhx9+uLUb0Vy1tYa++eZ8azcjYJ06tbdUe62i/tzCLVvKzJoRVVUuXXvthKD9Y0YMrc0wDGVnZ6qw8IkL7hWXq1Ivv1ymoqLQ30doGZ5D6yOG1kcMrY8YWh8xtL5oiGGXLh0bfJ8aEbA85hYiUDabTfHxvu8VSdxHAAAAQIhRIyKMomEOUGsKx9xCYmh97hoRvu4V5qi2fTyH1kcMrY8YWh8xtD5iaH3REENqRCDiMbcQgWrsXuE+AgAAAEKLRAQAAAAAAAgbEhEAAAAAACBsSEQAAAAAAICwIREBAAAAAADChkQEAAAAAAAIGxIRAAAAAAAgbEhEAAAAAACAsCERAQAAAAAAwoZEBAAAAAAACBsSEUAUMgyj0dcAAAAAECokIoAok5+/Qjk5mWbywTAM5eRkKj9/RSu3DAAAAEA0IBEBRBHDMFRV5ZLDUWgmI3JyMuVwFKqqysXICAAAAAAh1661GwAgfGw2m/LyVkmSHI5CORyFkiS7PV15eatks9las3kAAAAAogAjIoAo45mMcCMJAQAAACBcSEQAUcY9HcOTZ80IAAAAAAglEhFAFPGsCWG3p8vpdMluT/eqGQEAAAAAoeQ3EXH33XcH9B6Ats9msykuLt6rJkRe3irZ7emKi4tnegYAAACAkPNZrLK6ulpff/21Tp48KZfru2r6p0+fVkVFRdgaCCC4MjKyZBiGmXRwJyNIQgAAAAAIB5+JiGeeeUa/+93vVFFRoZtvvtlMRHTt2lU/+9nPwtZAAMFXP+lAEgIAAABAuPhMRNx99926++679Yc//EF33nlnONsEAAAAAAAilM9EhNudd96pffv26fDhw6qpqTHfnz59ekgbBgAAAAAAIo/fRMTChQt16NAhXX755YqNjZVUN4ybRAQAAAAAAGgqv4mIgwcPasuWLcwhB1qZZ4HJhl4DAAAAgBX4Xb5zwIAB+vLLL8PRFgA+5OevUE5Oplk01jAM5eRkKj9/RSu3DAAAAACaxu+IiJMnT2rq1KkaNGiQ2rdvb76/fv36kDYMQB3DMFRV5ZLDUShJystbpZycTDkchbLb0y07MoIRHgAAAEB08puImDNnTosOUFNTo5/85CdKSkrShg0bVFlZqQcffFCHDx9Wnz599Otf/1rx8fGSpA0bNmjTpk2KiYlRdna2xo4d26JjA5HAZrMpL2+VJMnhKDQTEnZ7uvLyVlmy856fv0JVVS6z/e4RHnFx8crIyGrt5gEAAAAIIb9TM0aMGNHgn0D9/ve/V//+/c3XDodDo0aN0vbt2zVq1Cg5HA5J0ieffKLS0lKVlpZq48aNWrZsmdcqHUA080xGuFk1CeE5wsM93cQ9wqOqymVOPwEAAAAQmfwmIlJSUjRkyBANGTJEAwcO1BVXXKEhQ4YEtPNjx47p1Vdf1S233GK+V1ZWZq64MX36dO3YscN8f+rUqerQoYP69u2rfv366d13323OOQERx91Z9+RZM8JK3EkVuz1dDkehkpLizWkmVk2uAAAAAAic36kZ+/fv93q9Y8eOgBMEK1as0MKFC3XmzBnzvePHjysxMVGSlJiYqBMnTkiSnE6nBg8ebH4uKSlJTqez0f3HxtqUkNA5oLa0BbGxMZZqLy7UGjE0DEMLFsyXw1GoOXPmas2ax7RgwXwVFKxTx47ttWbNY5bsvBcUrDOnmbhfh+M8eA6tjxhaHzG0PmJofcTQ+oih9UVzDP0mIuqbMGGCOZ2iMa+88oq6d++u5ORk7dmzx+/nG/pm11+npKbGUGXlWb/7bisSEjpbqr24UGvFsGPHzrLb05WdnSeX62tlZ+epuvq8OnbsLJfr67C3p6UaGuExZ87csIyI4Dm0PmJofcTQ+oih9RFD6yOG1hcNMezZs1uD7/tNRGzfvt38/9raWh08eDCgjsK+ffv08ssva+fOnaqurtbp06e1YMEC9ejRQxUVFUpMTFRFRYW6d+8uSerVq5eOHTtmbu90Os2RE0C0y8jI8lpVwj29wYojITxrQrinY7hfS9atfQEAAAAgMH4TEa+88or5/7GxserTp4+efPJJvzueP3++5s+fL0nas2ePfvvb32rNmjVavXq1iouLZbfbVVxcrPHjx0uSUlNTNX/+fM2YMUNOp1Off/65Bg0a1NzzAiJO/c65VTvrNptNcXHxXjUh3IU44+LiLXteAAAAAALjNxGxcuXKoB7Qbrdr3rx52rRpk3r37q21a9dKkgYMGKApU6bo+uuvV2xsrJYuXarY2NigHhtA2xBJIzwAAAAANI3N8FN2/9ixY8rLy9O+fftks9k0dOhQLVmyRL169QpXG306f77GUnNqomEOkJV5dowbei0Rw0hADK2PGFofMbQ+Ymh9xND6iKH1RUMMfdWI8Lt85+LFi5Wamqpdu3Zp586duvbaa7V48eKgNxAItfo5N8/X+fkrvJbDdNcxyM9fEdY2AgAAAECk85uIOHHihH7yk5+oXbt2ateunW6++WZzyU3AKhpLNBiGoaoqlxyOQvMz7uKJVVWuBld0AQAAAAA0j99ExMUXX6ySkhLV1NSopqZGJSUlSkhICEfbgKDwl2iQ6lZqsNvT5XAUKikp3mtFB+oWAAAAAEDw+K0RceTIEeXm5uqdd96RzWZTSkqKlixZoj59+oSrjT5RIwKB8kw+uNVPNBiGoaSkePPnTqeLGhERyAoxDKReSTSzQgzROGJofcTQ+oih9RFD64uGGPqqEeE3EdGWkYhAUzSWaAgkUSERw0jQ1mOYn79CVVUu895z35txcfHKyMhq7ea1CW09hvCPGFofMbQ+Ymh9xND6oiGGzS5WeejQIa1cuVKzZ8/WzJkzzT+Albg7c57c0zQ8kxB2e7qcTpc5TcOzrgQQatQrAQAAQDRo5+8Ds2bN0i233KJrr71WMTF+8xZAm1M/0ZCXt8pr9ENe3irFxcV7jYDIy1slSYqLi2dIPMLG895zOArNe5R6JQAAAIgkfqdm3Hrrrfrzn/8crvY0CVMzEKhAhrsHMi+fGFqfFWIYSL2SaGaFGKJxxND6iKH1EUPrI4bWF0gMrV47zNfUDL8jIu666y49/vjjGj16tDp06GC+f+WVVwavdUCIZWRkeT207m+ePR/i+g+0lR5wRA5f04gYEQEAABBdIrl2mN9ExEcffaSSkhK9+eabXp243//+9yFvHBBMJBrQ1gUyjYj7FgAAIPJ51g6T5PV7od2ebrmREfX5TUT87//+r3bs2OE1GgIAEHw2m416JQAAAIj42mF+a0TMmzdPOTk56tGjR7jaFDBqRCDciKH1WSGGVp8LGGpWiCEaRwytjxhaHzG0PmJofYHWiLBy7bBm14g4fvy4pkyZooEDB6p9+/bm++vXrw9e66IQHQ00BfdLdGEaEQAAACK5dpjfRMScOXPC0Y6oEslFRxB83C8AAABAdIn02mEx/j4wYsQIrz+xsbHaunVrONoWkTyLjuTkZHrdYFVVLvmZKYMow/0CAAAARB9ftcPs9vSIqB3mt0aEJH3wwQd64YUXtG3bNvXp00eTJ0/WHXfcEY72NcqqNSI8O5NukVJ0JNK1xlw87pfgYj6l9RFD6yOG1kcMrY8YWh8xtL5Aa0RYeYq2rxoRPhMRf//731VaWqrS0lIlJCTo+uuv129/+1u98sorIW1oU1g1ESFZv+hItGqtv/C5X4KHf7StjxhaHzG0PmJofcTQ+oih9UVDDH0lInxOzZgyZYrefPNNrV+/Xk8//bTuvPNOxcT4ncmBAPgqOsIwezSE+wUAAABAJPGZWSgoKNAll1yiu+66S9nZ2dq9ezcdnyCoX3TE6XTJbk/3qgEAuHG/AAAAAIg0PlfNmDhxoiZOnKizZ89qx44deuqpp3T8+HE99NBDmjhxosaMGRPOdkYMX0VHJEVE0REEF/cLAAAAgEgTULFKt8rKSm3btk1btmzR73//+1C2KyBWrxFh5aIjLWXV82/NGhFWvF5tUTTMxYt0xND6iKH1EUPrI4bWRwytLxpi2OQaEQ1JSEjQ7bff3iaSEFZXvxMZTZ3K/PwVXtMK3NMP8vNXtHLL2q5ovl8AAAAARBaqTyKsDMNQVZXLq8aBuwZCVZWLmgcAAAAAEOF81ogAQsGzxoHDUSiHo1CSZLenKzd3Jd/0AwAAAECEY0QEws5dgNFTbu5KLV26mOkZAAAAABDh/I6I2L59u9asWaPjx4/LMAyzSN6+ffvC0T5EoNraWm3bVur13oQJY3Xw4AHZ7ekUYhTFKQEAAABELr+JiEcffVTr169X//79w9EeRDjDMLR06WIdPHhAyckDdfDgAUkyXzM9o66YZ1WVy1yu011HIy4uXhkZWa3dPAAAAABoEb9TM3r06EESAkHjnpZht6drx45dXj+bPPl6xcRE92whinkCAAAAiHR+R0QkJydr3rx5mjBhgjp06GC+P2nSpJA2DJHHMAw9+uhKVVW5tGzZCuXkLPb6+alTVZaeghCM6RSNFfN0j5AAAAAAACvzm4g4c+aMLrroIr3++ute75OIQFPk56+Qy+WSZKioaL3eeOMvOnjwgHr2TNSXX1YoOXmg2em2Yoc7mNMp3MkI9/WQrHlNAAAAAKAhfhMRK1euDEc7EMHc0w2KigqVlpautLSZKipaL0n68ssKpaXNVF7eKi1dulhxcfEh7XCHogik53QKqS5p4J5O0Zzim+4khqecnEySEQAAAAAigt9ERHV1tTZt2qSPP/5Y1dXV5vskKBCo+tMN6lu+fLX5mVB2tENVBDKY0yk8a0K4t3e/loI7MoKVOQAAAAC0Br+VARcuXKgvv/xSf/nLXzRixAg5nU516dIlHG1DBPHsrNfnLsoY6pEQoSwC2dD5NSdp4FnM0719Xt4q2e3pQR0tkp+/wrwO0ncJkPz8FUHZPwAAAAD44ndExBdffKF169aprKxMP/7xj3XDDTfo3nvvDUfbEEEMw1B2tvd0g7S0dElGWGpDhLoIZDCnU2RkZHklZoI9WiTQqSSMkAAAAAAQCn5HRLRrV5eriIuL00cffaRTp07p8OHDfndcXV2tW265RTfddJOmTp2qdevWSZIqKys1Y8YMTZo0STNmzPi/AoZ1NmzYoIkTJ2ry5MnatWuXr13DYtyd9KKiQg0ZMkxpaTOVlpauoqJCSTalpc0MeW0IKXijFuqrP53C6XTJbk/3Gn3RnLY29rolPEdZOByFSkqKl8NRqOTkgcrNXek1bYUREgAAAACCze+IiNtuu00ul0sPPPCA0tPTdfbsWT3wwAN+d9yhQwf97ne/U5cuXXT+/Hn99Kc/1dVXX63t27dr1KhRstvtcjgccjgcWrhwoT755BOVlpaqtLRUTqdTM2bM0EsvvaTY2NignChaj+d0A3dHt+59KS4uXgsXLg7LN++hKgLpazqFpLAkWJqjoZU5Dh48oKVLF7e42CYAAAAANMbviIhbb71V8fHxGjFihMrKyrR7927dfvvtfndss9nMWhLffvutvv32W9lsNpWVlWn69OmSpOnTp2vHjh2SpLKyMk2dOlUdOnRQ37591a9fP7377rstOTe0IRkZWerWLU5Lly6WVHd/5OauVFWVS48+GvrCp6EYteApIyPLK6Hh7ui3pAhmKDWUlHEvoeoeIRGsaSsAAAAA4MnviIhTp06poKBA5eXlkqSRI0fq/vvvV7du3fzuvKamRjfffLO++OIL/fSnP9XgwYN1/PhxJSYmSpISExN14sQJSZLT6dTgwYPNbZOSkuR0Ohvdf2ysTQkJnf22o62IjY2xTHuDvaKCYRg6d+5rORyF6tixvdaseUwLFsyXw1GoOXPmKj7+opB3eBMTL9GcOXO1Zs1jstlsKihYp44d2ys+Pl4XX9wloHO2Ugx9MQzD69q7Y1FQsM7rcwUF6yIyCREJMYx2xND6iKH1EUPrI4bWRwytL5pj6DcRkZWVpQEDBmjt2rWSpJKSEi1evFiPP/64353HxsaqpKREVVVVmjVrlj766COfn23oG2l/naCaGkOVlWf9tqOtSEjobIn2hmqZy+zsPFVXn1dBwTqz02u3pys7O08u19fBar5Pc+culGEYXsfKzs6TzWZTVlZ2QOdslRj607FjZ69rv2RJrl555RUdPHjA/MycOXMjckREpMQwmhFD6yOG1kcMrY8YWh8xtL5oiGHPng0PYPA7NeOLL77Q3Llz1bdvX/Xt21ezZ8/WoUOHmnTwuLg4jRw5Urt27VKPHj1UUVEhSaqoqFD37t0lSb169dKxY8fMbZxOpzlyAuETymUuQ1UssqltqP861Et7utXfT7D22xyeU0kMw9DSpYt18OCBkExbAQAAAABPfhMRnTp1MqdlSNLbb7+tTp06+d3xiRMnVFVVJUn65ptv9MYbb+j73/++UlNTVVxcLEkqLi7W+PHjJUmpqakqLS3VuXPndOjQIX3++ecaNGhQs04KzedrRYVg1AvwVSyytTu6oTxnt/z8FV7n2hZWpfCsZ9FQsU27Pb3NFtsEAAAAYF02w08v8MMPP1RGRoZOnz4tqW50w6pVq3T55Zc3uuMPP/xQmZmZqqmpkWEYuu666zR79mydPHlS8+bN09GjR9W7d2+tXbtWCQkJkqTCwkI9++yzio2NVVZWlsaNG9foMc6fr7HUUBYrDb0xDENJSfHma6fTFZQkhGcHv/7r1u7wBnLOzYmhFc7d3c5g1gVpq6z0HKJhxND6iKH1EUPrI4bWRwytLxpi6Gtqht9EhJs7EdG1a1c99dRTuueee4LWuOYiEdG45nYsPTvObsHoMIeq9oQvTTn/QM+5uTEM1TVF00XDX/iRjhhaHzG0PmJofcTQ+oih9UVDDJtdI8Kta9eu6tq1qyTpqaeeCkqjEDrNnQoQymUuw7nEZVPOP9RLe0ptoz4GAAAAALQFASciPLX2nH40riXFF0NdL6ChYpHB4pl0aMr51z9nSV7nHKy2tcX6GKHSlgpzAgAAAGhbAp6a4emaa67Rq6++GoLmNA1TM3xr6VQAq9ULqD/to7a2VhMmjPVajtLf+RuGoUcfXWnux81z+kgk14gIlnBPwWmqaBgCF+mIofURQ+sjhtZHDK2PGFpfNMTQ19SMdr42SElJabBzZBiGqqurg9cyhIR7JINnIqIpHd5QjlwINs8REFLdebqXo/QUyPnX349nwqC53+r7GmUiKeJWpWgoFvWvYSSdLwAAAICma9aIiLaCERG+tXZxxHCPqGjofJOTBzZpRISv/Xhu15IYWm2USXO19r3nTzRkniMdMbQ+Ymh9xND6iKH1EUPri4YYtrhYJawjHMUXG+OvUGQo6gc0VAzy4MEDTT7/UBaVtNIok5agMCcAAACAxpCIiEChLjjZGH+FIpu7mkcgx61fDDI5eaByc1c26fyjrahkKHANAQAAADQm9uGHH364tRvRXLW1hr755nxrNyNgnTq1D1t7R48eq2uvneC1VOa1107QmDFXh/S47uO4kxFr1qzS22+Xy25PV27uSm3bVmomJa69doKZpEhOHujV3qaoPwJky5YyVVW5tHVrqXmcQM7f134829upU3tVV3/rtU20ftPf0FQTSX6vYWtfr3A+hwgNq8QwWqZjNYdVYgjfiKH1EUPrI4bWFw0x7NKlY4PvUyMijKJhDpCbYRhKSvpu6ctjxyoVExPTYP2AtLSZWr58tfkLenN+WW/KSg2NdQ4a248kVVefVXZ2nrkyx9Kli81jRFMnw991YtUMhJIVYtjWV49pbVaIIRpHDK2PGFofMbS+aIhhk1fNQGQKxjd0/vbR0ND88ePHqKzsL4qJiVFu7kqvRIR04bZN/WW9fiLAPR2j/rn56xz42o/03Tf91dXn1a1bnF56aYtZh6J+UiKS+VsZw329/MUCiFSsHgMAANA4akREkWDUZwikEKXnL9zHjlWqZ89EvffeQY0fP0Y1NTUaP36M1z6LigqVnX1hPYmmDtZxJxZ8vfZXv8L92YaKSro703PmzJXDUajHHlutgwcPmHUoli5d3Ox2W41nzQ2Ho1BJSfFeSQj39aq/DRAtAnlGAAAAohmJiCgRaCe8pftoqFDm9Ok3S5Lee++geve+WO+9d1BS3ZSM++77uaS6ZERLf1n3lyRpaefAZrNpzZrHvN47ePCAevVKiLpOBitjAI3jGQEAAPCNYpVh1JrFSBorIhnoL8eB7sOzUKbNZlNq6gS5XJXat6/c3Nd99/1cjzySr/HjJ17wsy1bypo1XSSQQpjuc1iz5rsOQqDHMwxD2dmZeuutPQ3+vDnttip3kuftt7+LW1spRulPNBQFinRWiKGVn5FwsEIM0ThiaH3E0PqIofVFQwx9FatkREQUCcY3dIHuw98+33zzDY+RC94/a85Sj4GMdjAMo9lLS7q3KyhYZ045SU4e2OJ2W1H96TdOp8u87tFyDYDG8IwAAAA0jhERYdTaGa9gfEPX1H3UjSJYpKKi9V7vV1RUaOvWF/XRRx9p48a6n6WlpWvo0GEqKlrfrG8OGxrtUFq6w1ytIzt7kR59dJVefPH5RpeWbKiQnM1mU3n5Xg0dOkQPPfSIli5drK1bS5WcPFB33fX/lJw8sE0tURlK7muRnDzQTPK4R8rExcWHfInYlmrt5xAt19ZjaPVnJBzaegzhHzG0PmJofcTQ+qIhhr5GRLBqRpSo/w2dZxV3wzACWj6zsX1IvkdXuJMWaWnpWr58lZYsWaSNG9frvfcOetSLqPuZJLPORHOmZ9Qf7TBhwljt2LFLS5cuVlHReg0ZMkxpad+NkujWLU7JyQPN4zW2akdGRpbi4y+Sy/W1WQcjN3elmeiQ1Kx2txVNWVEl0FVKgGjFMwIAAOAbiQgLacnSmw0VkczLW6Xy8r1eoxsa64j72ofkuwPurhExdOhwLV9et80jj6yWJHMkhCTzZ1LTp4u4kwDupEhdomGlJkwYaxaTlHTB0pKGYejUqSodPHhAP/rRmAsSLb5GRkiR18nwt6xpQ1gZA2gczwgAAEDDSERYRHM6ivXV7zxL0tChw1VUVDdvOZC17pvTAW/ouPU/7z5+Q0s/NsbzusTFxSstLV2SoTVrVmnHjl1mEkK6MMHhmUhxOArNkR1NWUWjsddW4bkaiqSA7gMAAAAAaC6KVVpAMJbedKvfEV++vOnLWTbUAa/fhvqvPad95ORkqqio5UXc6l+XhQsXSzJUVLReLlelcnIWe32+of0He4k9f9ehLWrpsqYAAAAA0BQ2wwo9JR/On69RZeXZ1m5GwBISOje7vZ7JB7dgdRQNw1BSUrz52ul0NXtUQiCjNYIxusOz7fWvS1raTEk2M9lR/xt+z2vW1OvaWAyDeV6toaX3gVW05DlE20AMrY8YWh8xtD5iaH3E0PqiIYY9e3Zr8H1GRFhEsL+5d2vucpae2zd1tEZGRpZX293n5u6sN2VUQUPXZfny1YqPv7CWhd2e7lXLIphL7AVz1EpraOl9AAAAAACBYvnOMGrJ8izBWHrT1z7dHXFfy1k2xnNZOoejUGvWrNLbb5c3a3qHVDeqYNu2Uq+lNHNyMlVevlejR4/1eQ71r0t29jKlpk7wSnZce+0Er2XzbDab9u59SwMHDjLbes014xtdYs9XDJt7HdqCYNwHVhINyyRFOmJofcTQ+oih9RFD6yOG1hcNMfS1fCcjIiwgmN/ce/K1Ckb9kQOB7CcYozWaOqrA33VpqJ2e8vNX6NSpKuXmrjSTHkuXLm5wKkUgozQauw5tuXZEsO6DQLTl6wAAAAAgPFg1wwKas2xmoIKxDKWvYf1N3U9TV7FoyXVpykoRnrUfPM+3fsLC13Xo1i1Op05VtenaEeFYjtTqNTQAAAAABAdTM8KoJUNvRo8e6zVEvqGpBs3VkmUogz2s331ea9Z8N7Jgy5Yyn/to7nUJdCqFYRjatq3UPJ8pU6Zo4cL5cjgKlZw88IIpJA1dh1OnqrR1a6l5Pdyf89y+LQjlcqT1r2NrXodoGAIX6Yih9RFD6yOG1kcMrY8YWl80xNDX1AxWzQijSK2K2pxvuj2/ffd8HcrVQXy1w99KEYG2qbHr4Dn6ItTn1FaFO7a+ROpzGE2IofURQ+sjhtZHDK2PGFpfNMTQ16oZJCLCKNAbzVcnPRyae+ymbOerw+6ewuC5zKavZTeDoSkd40CXtmwswRINS2P60xauQzT8hR/piKH1EUPrI4bWRwytjxhaXzTEkOU7LSI/f4VXAUp3Zzk/f0WbPnagw/obK0h56lRVWIsmBloA1DAMZWcv8to+O3uRz4KV9V+zNGYdrgMAAAAAiWKVbUpTCiha+dj+ClKGumiie7+BFLo0DENTpozXvn3lSkubqccfL9Ds2XNUVLReb79drq1bfdevcG9ff1SH5yiMaJmewXUAAAAA4EYiog1p6qoRVju255SMvLxVXlMi3Meora1VTIz3QJ1gHLuhKRNNXynCVu+//oVyxRMr4ToAAAAAcKNGRBg1pUZEa82j93VsXyMiAq0N4fmNeFpauiRDRUXrzZ+npc1Ubu5KTZx4tbp1i1NJydagnVNLl42sm5qRqaKi7xInaWnpWr488ARNa9b9aEvawnWIhrl4kY4YWh8xtD5iaH3E0PqIofVFQwypEWERrTmP3texa2trG6wV0ZSaEu5vwNPS0lVUVOiVhLj44otVVLReV17ZXwcPHtCpU1Wqra0Nyjk3VpOiqsoV0DFsNpuWL1/l9V5TkhDufTT2OlpwHQAAAACQiGhDmlJAMZTHTk4eKElKTh4oh6NQEyaMvaDjz3ovaQAAIABJREFU3pwOvs1mU3x8vNd7P/zhlTp58qQk6cSJE7ryymTt2LFLNpstKEU6PQteOhyFSkqKb/IqHBRZBAAAAIDgIRHRhviaR++5akT9zm9TO8O+tvc89o4du2S3p+vgwQOSpIMHD5ht8mxrQx38tDTvDr7n8Wpra7VtW6nX8evXg/jRj8bIZrMpO/vCpEYg59rQ+XnWI3BrahLCnbyorj4ftuQQAAAAAESikNWIOHr0qDIyMvTVV18pJiZG//mf/6m7775blZWVevDBB3X48GH16dNHv/71r81vyTds2KBNmzYpJiZG2dnZGjt2bKPHiOQaEQ3No29prYNAtncfq6FaEZIa/Lzn59LSZmr58tUX7H/hwsVeIy7cSY7G9OyZqAMHPlJMTExA59rY+XmuCCI1rQin534vvriLTp4806TrjrYlGubiRTpiaH3E0PqIofURQ+sjhtYXDTEMe42I2NhYZWZmauvWrfrv//5v/elPf9Inn3wih8OhUaNGafv27Ro1apQcDock6ZNPPlFpaalKS0u1ceNGLVu2TDU1NaFqXpvW0Dz6ltY6CHR797Gys72nImRnZyo7e5HX5xuaslBUtF7Z2Ysu2L8krxEXnjp16iRJ6t69u9f7X35ZoZycxQGda2Pnt21baYumu2RkZHklLdwjLAItdNnYawAAAACINmFbNSM9PV133HGHcnNz9Yc//EGJiYmqqKjQnXfeqZdeekkbNmyQJP385z+XJN17772aPXu2UlJSfO4zUkdE+OLZuXZrTq2DxrYPZHWL5ctXyzAMLV262KveQv2VJerv3528cG/ndt99MyUZ2rhxg9e29UdOeE4PaWjEiK/z69YtTqdOVTV7JImnpsSwpSNYrKItrITRFNGQeY50xND6iKH1EUPrI4bWRwytLxpi6GtERFgSEf/85z91xx136MUXX9Q111yj8vJy82fDhw/X3r17lZubq8GDB2vatGmSpKysLF199dW67rrrfO63trZWNTXW+YY5NjZGNTW1LdqHYRjq2LG9+bq6+rzfTp9nxzCQ7XNzl8nlcmnNmsck6YLPS9KCBfP1zjvv6KqrrtKaNY+ZHe3583+hxx8vuGD/ubnLVFlZKUl6/PECzZ49R5K0Z88e7d37lgYPHqy//vWv5nazZ8/x2o97X3l5uWbb3MdcsGC+4uPjtXTpQz7Prymd5cY+G2gM3e0qKFinOXPmas2axy543ZY764HyvFcaikdbFIznEK2LGFofMbQ+Ymh9xND6iKH1RUMM27ePbfD9dqE+8JkzZzR37lxlZWWpa9euPj/na5WFxtTUGJbKIAVrRISnOXPmXjAiwrPjnJ+/Qi5XpZYvXy1Jys5e5Hf7uXMXyjDqrm39z9clEGwqKqobCbFkSa5crq/N47oTFZ77z81dqYqKr+RwFGrIkGFKS5up6upvVVRUqLS0mRo8eIhefLHEa7tvvjl3wYgIz2PXJSVWeRWSPHHitJYuXez3/HwxDEOPPrrSHMXg5jmKoSkxzM7OU3X1eRUUrFNBwTpJdSM0srPzzGtmZYZhmHFtKB4nT55pk8mWaMg8RzpiaH3E0PqIofURQ+sjhtYXDTH0NSIipImI8+fPa+7cubrxxhs1adIkSVKPHj1UUVFhTs1w1wXo1auXjh07Zm7rdDqVmJgYyuZZSv3VGzw7fdJ3q0B4TgcwDEMuV6WKitbr7bfLNWTIUHP6g3vahef2bu7OY3b2IhUVrVda2ky5EwDuaRr33TfTq4NfU1Ojhx7K+r/Ppys3d4UeeihLDkehDENatuwRSZLDUah9++pGxAwZMuz/pmtIx44d1X33zZTNJu3d+5ZXO5cvX+UxXWSm0tLqajy42263pys3d6VycjJVVLTeY6rIogvOr6GOsTsB4XK55J6KUpcYs+ntt/dq375y2e3pPlfk8PXaXUvCc6qI5zSN1pzOEIzje65GUj8egSZ/AAAAAESfkE3NMAxDixYtUnx8vJYsWWK+v3r1al188cWy2+1yOByqrKxURkaGPv74Y82fP1+bNm2S0+nUPffco+3btys2tuGhHFL01YjwV3PAM1kxZMgwDR06TJK8ajxI33XuH310pbZtK9XkydfLZrPJ5aqUZFN8fN0qF1OmjJdkaOvWlyWp3uoYdfuw2WxavfoRvfTSFnXrFqfq6moZhqHz589p0qQpOnXqlJ5//jnV1tbqzjvv0S9/mW/uY/DgFP31r/vVs2eiEhMT1bFjR+3b97aSkwfqq6++0qWX9tHWrWUNrsDh2ZZjxypls9k0ZUqqDh06pLvv/n+SZCYW4uMTzFU76tdnqBsx8l0CIimplyorK1Vd/Y35mf/f3p1HR1Wm+QP/VgVDQEggIQu26Blb7ZFUUBIYZVMJWSBhc5kZe/qntk0qJGeEnlaIBJIAAYJE1BFaE1JRW9v59fQ2ihACSFCh2SSLJpXgTxxFwM5GQqoSspHU/f0R7qVuLUllq6qb+n7O8WDdunXrrXoSDu9z3/d5/P0D8LOf/SP27i2UYuhoB5LhrlkxEENdu8JWhxV3TkJ4QuZ5pGMMlY8xVD7GUPkYQ+VjDJXPE2Lo9K4ZJSUl2Lt3L06fPo1ly5Zh2bJl+Pzzz5GYmIgTJ04gJiYGJ06cQGJiIgDgnnvuwaJFixAXF4eEhARkZGT0moTwRH11bxAfJyQkobS0GDpdLnS6XCQkrJRdZ8uW7QAAo9EAvb4CRqNRWjmh0+XAYDAgLe0llJYWo6OjAyaTyUZ3jBykpa2DyWTCoUMHoNdXoLnZiAcemI6yshLo9RU4fLgQ3d1dqK2tQX19Hd56a5fsGl9/XYVJkyahvr4OlZV6KQmh11dgyZLlOHDgiNVnXbs21Wq7yIIFc2+MtwT19XVoamq68XlyAKhkrUPNu26InTbE81asWIna2hpZEgIAGhsb0NxshMlkkgpu9tWBxHIFi3m3jkOHDgy4+8lgDbb7iq3rWf5sONqNhIiIiIiIPJPTumYMB09bEdEbcWm9eIffsnuFOXE1AwCrO/bmxowZg7a2NoSGalBZqZf+DAwMRH19vexcy3oOlnx8fNDefnOC7+XlZbc9q7jVQq2W58nkHT2SANha7XFzy4i4tcT8urbqafT2HQA9bUX1+m/h5eWFrVvTMXr0WKxZs86q+4fl9XtbeSAmA3ob23AZbPcVW9ex3C7kztszPCHzPNIxhsrHGCofY6h8jKHyMYbK5wkxdGnXjOHCREQPccLbUych1W4Sorr6KuLjo1FaWiwlIwRBQEjIhH69n1abBEGQt9usqWnq9TovvJAi25bRm+rqq9i4cb3NrQLmk3tAvl1EfJ+Wlmab34W9LQOWWwts0WjC8NBDc5Cff7Nmhp+fH3buvFlbw9b17dVicPV2hqF6fyW2KPWEv/BHOsZQ+RhD5WMMlY8xVD7GUPk8IYYuKVZJw898qb1Y+NGeuLgoRETMQGlpMUpKzkIQBERFzbN7fmioBgBQWamXjmm1ydiypWeSb6636wDAe++97cCnEd/3bjQ2NiAxMRkmk0m2MmLNmnWyYpqWfve7fDQ2NuLkyb9ZrdBIS1sn1bUQCYKAtDT51oLRo32stmfo9RXQ6yvw/POrpI4fGk2Y7Jz09HVWqwAsJ/fmk/W+XjtchvL9xdokllto3HElBBERERERuQevTZs2bXL1IAbKZBLQ3n697xPdhI/PLUM+XpVKhfnzo27UOsiVOlKYmzo1FPX19aipqUZExEyEh0egoGAfdu58GXV1ddBowrBkyWMoKyuRva6+vg719XWyY+HhEfj006NS+86CgiM4eLAAen3Fjessl64TGBiEO+64A1euXEFbm+PtKtva2hAYGISZMx/EoUMHMH9+lLTtJCMjFTU11di2bRMKCvYBAKZPj8DixctQWlosvU9d3c1xa7VJiIiYCZ2upw6CeD1xQq7T9RT3jItbgosXL6C5uRkBAQH4t397BpcuXURr680s5RdffIHS0mJpK0piYjIOHCiSkkHm17fFcjtDf147FIbj/W0lW9zZcPweknMxhsrHGCofY6h8jKHyMYbK5wkxvPXW0TaPc0XECGCrTaS5qqpKrFiRiDNnTkGvr8CsWXNkzx869BnuvfdOqNVqmEymXt8rP38PHnggHBpNGHx9/aBWqxEbGwcAWLgwHkBPsqKzsxOjRt2CL78shbe3Nzo7O6VrBARMQkPDFdl1/f390djYKD2eMmUKmpuNUo2HzMztUsIDAG65xVs6d8aMmb0WR9y6dceN7wnw9fWT3b339fWT6hkAQGVlBYD/h1/+MgFr16ZCrVZZ1aEAgNjYOMyePVe6+y++3vz6tli+Z39eOxRc/f5ERERERESsEeFE9vYA2asj0J/HaWnrZPUQAgIC0NDQID0W7+CHh0egqqpSVjjSMlFgaepUDaqq9NI1AgODUF9fJ5vMmm+hEAQBO3ZsQ3NzMwTBJKslYU6jCUNsbBwOHiyQbf8AbhadtCwI2VtRTLGYpiXzRIO9GhHmx7u7u6FWq6WVAwkJK6FSqWXfr7hFxXzbiOV1etNXTIebq9/flTxhL95IxxgqH2OofIyh8jGGyscYKp8nxNBejQhuzXAiW0tvsrOzcPBggdV2geLis5gzZ16fz5tvL9BqkwEIqK6uttoKUVdXh4SElfj+++9QXV0te85e9woAmDRpEk6fLoPRaMTBgwUAgNbWa7IkhCAIUKvV0mR2x45tOHy4EAcPFqCzs1O2TUKk0YThk0+OYe7ch/HNN9/ItpRotUnQ6XKlApzmBSHLy7+RPTZXX9+zzUTcbiL+WVhYcGPbwQKbk23LY2q1GiqVCsXFZxEaqpGSEFptMmbPnoWuLhMKCj622srQn4l8fxJNw0Fp2ymGkicsgRvpGEPlYwyVjzFUPsZQ+RhD5fOEGHJrhhsyLzQJwKr9oclk6vV5ccJqvtS+ty4Yp06dwPjxvnZXDthy5cqVG4Up5QtnxCSE2DVBbEm5eXMWDh8uhF5fgYCAALurF265xVtaTWBd10IFrTYJvr5+yMhwvCimRhOGmJhFmD17LsaP9wVwcwvF+PG+yMhI7Vc3B7EQ4yuvbJe+XzFrKXaGGOwEXoldJ4iIiIiIiAZD3fcpNFzE/fmJicnIy8tBcLCflGTYsuVlqNXqXp8XJ8Fr16ZKWw8sJ+7mKiv1mDbtfhw+/LlD4wsICAAA5OfnWm2vSEtbJ0uUHDxYgLy8HERHPywlIcy3hgDAffdNBdCzyqKsrARpaS9hw4aXpHaitbWGG6shehIvBkOT9HlrapqkbRkaTRgSEpKk62q1ydBqk6DXV6ClpRmbN2ehudkIvb4Czc1GZGZuR3OzUSrI2J/dSCqVCikp66XvW/xvy5aXpUSFuf5c2zwRlZ6+TlZIsr/jJCIiIiIiUgrWiHCi3mpEBAf7SY9raw1WS/XtPS/eUc/M3C7VUwgMDMK1ay2ybg+ikJDJ6OzskBWGtGXixIm4evWq7FhCwkr4+U3AoUMHbtSbmIGCgk+k5ENfxETCmDFj8G//9jSqqirR0dGOCxe+xz33/Awff3wQaWnrUFJyVtpGYb5aQNymEhsbB5VKBYOhCYAKfn5+WLs2VbaSQDzXfFwaTRgWLowf1EoD8xgOxWoG8+SDyDLRREPLE/bijXSMofIxhsrHGCofY6h8jKHyeUIM7dWI4NYMFxMnoubS09dZTW5tPQ9AtnVj/HhfqZCkPfX1dVJNiEmTAnH1aqNVjYixY8daJSEA4IMP3oNarZYSHJcu/YD09FSHkhAAoNdXwMfHB21tbTh9+iRmz54rdaQ4f/4bbNiQgvz8PUhISILRaISfn59sQp6Ssh5r1qyTFcUEbtY3MP/OjEaD1bj0+grMnj13QDUYLF/j6LaZvtjqeMIkBBERERERjWQsVulElsVIzO+GJyYm48CBImlyazQa8OijC6RVDraej4yMQmRklHTs1KkTaG29BgCYOjUU9fX1svcfNWqU1BFCEAS0trZaLf+/776pqK7+u+zY1Kka1NfXoaurC9ev3xx/a2urjfoOvZsx45/Q1taKH374QfbatrY2lJaWQKtNgkqlgk6XA40mDJGRUbJJueX/23pOpVLh0UcX4ODBAlmhTI0mDO+++1+yTheOMC8YOmaMN9raOqV6ExpNGPLycrBz58soKSnu92oG8WegpOTmd2FZBJOGlicUBRrpGEPlYwyVjzFUPsZQ+RhD5fOEGNorVskaES5kWWjSvGaEr68f1Gp1r8+b1yswl5CQhKqqSqv36+rqglqthslk6tc4q6ocK2zpiJaWZqvaEeZ0utwbHSqSBrwyQBAEZGRYr9TQ6yuQkZEqJV8c2ZXUVx2HzMztsvMHkoQQE021tQapHoj4XkozmJoZROQ6/N0lIiIiZ+LWDBcTCx6a38233I7Q2/O2tm6UlJy1+359JSHOnasa8GeZPj0C7e1tVtcICAhARcV5bNq0AXl5OfDx8UF7e3sfV1PhlVe2D6ieg0qlQnn5V1YFM318fPDVV1/2q56DeaInLy9H2kKRmJgs1eUwZ76txpFx2ko0ARiSjhzOxg4gRMrE310iIiJyNq6IcAOWE05HHguCAEEQkJbWc0ddq01CTU0TfvWrRJSVlcjO9/PzgzOUlZVYJSH8/XuSATExjyAjYwsCAgIcSEIAOl0ODAaDlDgxvzvX1507k8kkrbzQaMJQXX1Vet9vv/0G3d3d/epOYWvVSWbm9iFZzWDekcP8vQbyj39X3tEcCR1AeEeYPNFI+N0lIiIi5WHXDCcaqqqo4t2r8eN98emnRQAEXLx4Ea2trejoaLcqPulKK1asxJkzJ3H16lXExS1GeflX+Pbbb2QrFby9vdHZ2WnztWq1Gn5+fjAaDfD17UmoOHLnzlbXDMsVEo7WcxAEAYsWLZDVtNBqk26sPFGhsLDI5XcR3eGOplI6gNj6PXSH748c5wkVpp3JFb+7jKHyMYbKxxgqH2OofJ4QQ3tdM7giQmHM714dOnQApaXFuHDhe1y5Uo/W1mtuk4SYOjUUWm0S3n57D0aNugUNDVeg0+Xi2rUWNDQ0ICAgAEBP547Ozk5MnRpqdY2PP/4QOl0ODh4sQF5eDgyGJhgMjt25S0lZjyNHjsuup9d/K3vsaBIiLe0lKQnx/POroNUmQafLRWlpCSIiZkjnDmY1w2C4yx1NWytH3C0JYYu7fH9ErqLU310iIiJSLnbNcKKhqIqqUqkwf35Pp4zCwgIAPR0nnCUhYSUeeCAcZWWlvZ5XX1+P8PAIRETMxNmzZ2A0GgBA6mIhjvnnP/8/6OhoR2NjI1paWmTXEDuA1NXVQaMJg0YzDenpm6VJY2+dKsSClebdKA4dOiDrouFIdwqVSoWSkrMIDdUgImImcnPflJIS4eEz8M47v7fb1cNZzH8mBtPBY7CU0gHE8vfQXb4/cpwnVJh2Jlf87jKGyscYKh9jqHyMofJ5Qgztdc3g1gwnGsqlN4IgIDjYObUfAMDLywuhoWEoL/8S/v7+aGxstHtuaKgGlZXyThsaTZhVF4sVKxIBAG+/nQcA0GqTsXnzNtx2m7/N64oTQwCyz15ba7BKQpjXb8jM3I6oqHnQ6yug0YThyJHjsraojq6M6Ot9bb3GclzDOam1/Jnoa3xD/d7m3/mWLS9bPXaXCb2930NXfn/UP56wjNFZXPW7yxgqH2OofIyh8jGGyucJMeTWDDcwVMXwbHXKGG7d3d24dOkiAgMDe01CAEBtbS0SEpJkx8aNs/4BfPvtPCkJodGEYdy4cYiJecTmNQMCArB5cxYAIC3tJdlzlsUhLbtRqNVqLFwYD40mDLGxcVCr1VZtUB1h+Z33VpQyOztL9rwYs+zsLIfeq79s/Uw4swVoX61o3X1C7+rvj8hVlP67S0RERMrErRlOkp2dhcLCAjz88HxZMbzi4rOYM2eew9cxv3ul0YTJthoMt/b2Ntx9972ora2xe86ECRPR2NiA77//X1l3jOrqv9ud1CUkJOGddz7A5s1p0OsrEBAQYLXdpK2tDYcOHcA33/w/5OfnAuhZQRERMQM6Xa7VMuI5c+ZZPX766ecwb15PokNcjj937sN9fm7z73zVqtXYt++wtIzf1vJlQRCkuhbi8+YxG+rlzpZ3NA8cKOp1fMPF8jvvz3fsTJZL4Nzl+yPHecIyRmdyxe8uY6h8jKHyMYbKxxgqnyfE0N7WjFFOHodHMi+G19Fx3Wrpa3+W7JvfvRJXGVhueRhO5eVf2n1u1KhRuO2229Daeg1Xr16Vjo8ZM6bXOhYlJV9ArVbD23s0AgODUF9vnVzx9/eHXl8hfVatNhlbt/Zs0xC/k77anqrVatl3LSaE+vruzb/znTtfhcHQJm0Rsfe+4vN5eTlSJfrhWuZs746mvfENp75i4I7c6fsjchUl/u4SERGRcrFGhJMIgoCtW9Oxe/cu6dhAJqbmrTubm43YtGkboqMftqrJ4Are3qPR2dkhTfBFwcEhva6iAHq6bFRVVWLFipV4++090vHq6qvIyEiFTpcrO7+mpglqdc/OIpPJJP0/YL8Ww2BbNAqCgIkTb5V+5vpKYji75oCza1IoVW81Ivj9KYMn7Kcc6RhD5WMMlY8xVD7GUPk8IYasEeFiKpUKO3e+KjvW3ySEZevOvLwc3H//P7pFEgIAOjs7AFjXvrhypR4AcMst3nZfW1VViYSElTh16m+y4xkZqSguPmt1flTUPOzYsQ3Z2VnIyEjtsxbDULRo7M8dQ1fUHBhJdzSHqp5Kf4yk74+IiIiIyJ0xEeEkgiBgzZoXZcf6OzE1LyImblGwtY3B3XR3dwMArl/v7PW8U6dOoKqqEqGhGqnYpU6Xi7KyEgA93Tiqq68iICAAen0FDh4sgMHQ5FBywfy7y8vLQXCw37BVhbesOVBba5DelwUQ++bsQp9ERERERORcTEQ4gTiR2r1716Anpub714ebeS2FwRo7dmyf51RW6hEaqkFR0d+wbdsO2XP+/v6orNRj8uSJaGhoQEBAABYujO9XcsHWd+fMmg2sQt+3oVi5QkTO54pVTERERKRcLFbpBOLEdNWq1UhL2zKoYnjObN1pfkd6sFpb5XufvL290dlpvUJi1qy5AKxbZVq2DK2oOI+NGzcgLi4K8+cvkD1nL7lgb7vEcCQjUlLWWxXGHI73GWmcXeiTiAZvsPV3iIiIyPOwfaeTzJkzD0uXxqOjowvAwNqjmd8dFltc+vtbt7pUAnG7hpeXlyzRcfHiBezfvxf7938stVJsampCaWmx7PWHDh3AwYMFqK7+O5qbjbI2pvZaag5Fi8b+tNhhzYGBEX83du68uXrlwIGiIfv+PKFN0kjHGLqPgbYrZgyVjzFUPsZQ+RhD5fOEGNpr38lEhBONGeMtG6/4jzNHq/WrVCoUF5+FRhOGBx+cheZmI3744cKwj3ug/P39ZUkSH58x6Orqkp0jJiE0mjCYTCZcuXIFXV3d+MUvnsbWrTsgCAK2bdtkVQtDTDyEhmpQWanvM7lg/t2Jd+3mz4+C0WiAr6+fwwkhT/jLwtXEpFFJyc3kU3+SRX1hDJWPMXQf5n+X5uXlYOfOl1FSUtznKibGUPkYQ+VjDJWPMVQ+T4ihvUQE23c6ka32LLaWtKalrYOf380lrZaJiR07tqG52YiCgn348cfLTv0M/aXRhEmtRsUCm70JDdVg0aLFWLs2FQCkO2uhoRocOXIckydPlJ2/Zs26fi0J7k+LRlvnmrfvpKFnuXJly5aXrR4PNhnhCW2SRjrG0P30t10xY6h8jKHyMYbKxxgqnyfE0F77TtaIcCHzwnxAT22DRYsWoLS0GFptsrRawDwxIQgCmpuNyMvLgZeXlyuH36uQkMmoqanGQw/NwbZtO2AymRAd/XCfyYgjR47LPpdY9HHz5iykp6danW8wNMkmp33VYnB0u4S9Pc9BQZOwevXaXj8D9S/hY85eoU+g//VUiMg5nFl/h4iIiEYGJiJcyF5hPgCyJIROl4Pw8BlYuzYVKpUKmzdn4eTJvzm0wsBVFi9eCkCFCRMmAAA2blwPvb4CWm0SABV0uhybr4uKmodFixZLqxlSUtbDZDIhLi5KqhMhtvbMz8+FTpcLANi6dceQdfmwlSAS78qvWrXa4Um1pxps4ToW+iRSjt5WMQHD05mIiIiIlI/tO13MXjvO/PxcBAf7SRP2iIgZAHomeRs3rkdsbJxTx+mIgIBJSEhIQnj4DOTn7wEgSMkTX18/aLVJ2Lp1B3x9fREaGiZ7rUYThoCASais1OPgwQKYTCYpGWP+j1iNJgxbt978h21gYJDsTvlQ7DQyb7dp2RZ0585X+Y/qXgxV+00W+iRSBrYrJiIiooFgjQgnsrUHyHyiJtJowmSrHbTaJOkfeObVyN1tRcSKFYnYti0bALBo0QKoVCoUFhZZ1b4QJ6rTp0egs7MDlZV66RpTp4ZKKyLM76KLrzdfSREeHoHw8BnYti17WFrG2drzzBoRfbP1M+1O7Tc9YS/eSMcYup/+bsdiDJWPMVQ+xlD5GEPl84QY2qsRMWwrIlJTUzFr1iwsXrxYOtbU1ITnnnsOMTExeO6552AwGKTn9uzZg+joaMTGxuL48ePDNSy3YrmktbbWAK022SrBUFJSLO2/zczc7pZJCB8fH2l7RFraS7h8+RJKS3vGbTKZkJ7ek0QwGg0YN248EhOTcfDgURQV/U12nTlz5klJCPO76CqVClu3yleORETMRH7+nkHdebfH3p5nBeftnMbWKh93SUIQ0fDgKiYiIiLqj2FLRDz++OPIz8+XHcvLy8OsWbNw+PBhzJo1C3l5eQCAb7/9FgUFBSgoKEB+fj42b96M7u7u4RqaS5lPZC2XtFo+LyotLYZOl4u0tJegUqnw3XffyZ6/776pGDXKteU+2tvbMW3az7BhQwry8/egvr4OGk0Y8vJyEBIyQUq2ZGa+rnRjAAAgAElEQVRuR0tLM3x9e1YaZGTIC1DqdLmyrRCWdQbkVNBqk6y2Twx20msrQSRu01iz5kUmI/rAJA4REREREfVm2BIRM2fOhJ+fn+xYUVERli9fDgBYvnw5jhw5Ih2Pj4+Ht7c3pkyZgjvvvBPl5eXDNTSXyczcLJuQifvpx4/3lSbOYkFGrTbpxgqJnsKMgYFB0iS9tfWa7LrnzlWhq6vLiZ/Etvr6uhu1IQCtNhmffHJM9nxm5nZkZKQiLy8HBoMBaWkvWawGSZKdb5mEsEwM9GzTUNl8zWD0tufZz2/k7Xm2TBAMJmHQWxKHyQgiIiIiIgKc3DWjoaEBQUFBAICgoCA0NjYCAGpra3H//fdL5wUHB6O2ttaZQxt2giDAYLDdiSExMVnafhAZGYWIiBnSNoetW3cA6Gld+OqrO1z5Efply5btVqsdQkJ6OmiIE/xXXtkuWw1imVQwb/9mKzEgCEBJyVm7rxkMe50bRlqNiMF2uLDE9ptERERERNQXt2jfaesuqSMTFi8vFSZMGDscQxoWr7/+OgBg9+5dUkJi1arVsk4MWVlbrYp87d69C2vWvOj8AQ9CbOwj+Oqrr/D886sgCALefPO30nO7dr0BtVotfVYAWLPmReh0OXj++VV49dXXsGbNi9i9exdGj74FO3e+avW9CIKA0aNHobS0WPoOLV8zHJNeLy+1on7meiMIAjo6WpGXlyN9Z2vWvCi1KfXzGzOg79Dez7C7JCFGUgw9FWOofIyh8jGGyscYKh9jqHyeHEOnJiICAgJQV1eHoKAg1NXVwd/fHwAQEhKCmpoa6bza2lpp5URvursFRd2dnjBhLNLStmD37l3SsbS0LTAY2uy+xnypu1bbs3IiPz/XGcMdEH//ADQ2NuCrr76CRhOGtrYOvP12nuyc1at/bbVqYfTosUhMTEZ6+lYYDG1IS9uCjo7rGD16rN3vx8fnViQmJkvfoSOvGayRVtlW/M52794l/Vyaf6cj0UiLoSdiDJWPMVQ+xlD5GEPlYwyVzxNiaK9rxrC277x8+TKSkpKwf/9+AMCOHTswceJEJCYmIi8vD01NTUhJScH58+fx4osv4i9/+Qtqa2vxy1/+EocPH4aXl1ev11da+04/vzFYtWp1v9sami+f7+rqwk9+EuCM4do0dWooqqoqbT4XGqrBJ58cQ3T0w7KWnEBPzQhxu4a9opL9bf820NcMxkj8y8JWm9LBfofOjkt/jMQYehrGUPkYQ+VjDJWPMVQ+xlD5PCGGTm/f+cILL+Cpp57C999/j4cffhh//vOfkZiYiBMnTiAmJgYnTpxAYmIiAOCee+7BokWLEBcXh4SEBGRkZPSZhFAaQRCkZe/9LeKXkrJe2me/adMGZw1ZZtKkQCQkJKGqqhKTJgVi9OjRVud4e4+Gl5eXVUtOrTYZW7e+DLVaLRV9tFUvwN7j3oopumvLuKEsADmchqPDRXZ2llVR1vT0dcjOzhrUWImIiIiIaGQYtq0Zr732ms3j7733ns3jycnJSE5OHq7huJxKpYKf3+CK+KWnr4NOl4uxY8eitdW5mbMrV+oBCAgPj0BVVSU6OjqszikrK8H69SlQqy3zW/LEQX+KSQ51MUVnUMqYLTtcmBdQBQbWgUTsBNNXUVYiIiIiIvJcblGs0lNkZGzE1avXrDoxODIxE7sRaLXJOHv2NL78smxYxjhmzBh0dHTAZDJJxzSaMHh7e0utOcXxCIKA0FANKiv1GDNmDNra2vCHP/wera2tVhNb88/q6ERUiZNaJY15ODpcmF8jLy9H+h4c2YJERERERESeYVhrRAw3pdWIGIo9QN3d3Zg27Weor6+DRhOGqKhY5OT8Fh0d7bLzRo8ebXPVQl/UajVMJhPUajXCwqbBy6unM4VWmwydLsfu68SEREjIZCxZskxqPzrY1QDmd+1FrpzUOhJDdxtzX4ajnsNw1J0YKp6wF2+kYwyVjzFUPsZQ+RhD5WMMlc8TYuj0GhE0PLy8vHD33fdAownDJ58cw7Vr19DR0W5VU2Og+SVxJYTJZMLMmQ+ioOATaDRh2Lfvo15fV1mph1abhK+++lpKQgA375APdEuC+R120XBN6IeqroMzxzwUhrrOxnDUnSAiIiIiopGDiQgF2ru3EEeOHMerr+5ASclZaDRh6O7uRmDgzZannZ2d8PcfeHcNf3//G9sKUqHXV6CmphpabRKqq69i6tRQm68RExDiSgjRYCa2zprUDmWBRU+eiFvWnehPUVYiIiIiIvIMTEQolEqlgtFoQGlpMUaNugUrVqxEYGCg7JzGxoYBX7+xsRGTJ09Efn4upk+PgFabhMzM7QgLu9du+860tJcgCII0Gd2xY5vs+f5OQp01qTWv6yBeV3xfo9HQr/dR4kR8KDt82Ks7Ya9TChEREREReR4Wq1QolUqFzMztKC4+i9LSYnz5ZemwvVdExExs2fIy0tLW3eieAfj7B6CxsUH6EwB0ulwAKgACdLpcaDRhWLs2FWq1ekC1IoajmKK99xmqAovOGvNQGY4OHykp62V1JvrbKYWIiIiIiEY2Fqt0oqEsRpKdnQWDoQmCIMi6WUyaFCglC0R+fhNgMDQNyfuGh89AR0cHKisrpGOhoT1dNcrKSqRjGk0Y9PoKq+4ZA5ncD0cxRXvv01eBRUdj6KwxD0Zv7TvdubjmYHlCUaCRjjFUPsZQ+RhD5WMMlY8xVD5PiKG9YpVcEaFA4lYCnS4XoaEa2XOWSQgADichAgODYDQa0NHRAS8vL/z61y/i8OFC6PU3kw6FhUUQBAEhIROkY0VFx6FSqWST+CNHjiMjI3VIWjgOdTFFW+zVdRjoZNwZYx4sttokIiIiIiJX8Nq0adMmVw9ioEwmAe3t1109DIf5+NwyJONVqVR49NEFOHBgn916DQPx7beXcPz456ivr8P169dx6tQJ1NXVyc4xGAz49NMjKC0tNjvWhKNHi2THjEYDMjO3Y+fOm90jDhwocsvJreXKgAMHiqSaEUajAfPnR0njHqoYuguVSoX586MUEaehMtJi6IkYQ+VjDJWPMVQ+xlD5GEPl84QY3nrraJvHmYhwoqH8QVu+PA6XL19Ca+s1m8+LdRn6w2g0ICgoGF98ccbqOa02GeHhM5Cfn4vS0mJotUkoLCyCwdAEnU48lozCwpuT+IMHC2SJDMtJvbtQqVQoLu7pPiKuBJg/PwpGowG+vn6YO/dh6dyR9peFmIQpKZEnkdwxTkNlpMXQEzGGyscYKh9jqHyMofIxhsrnCTFkIsINDNUPmslkQn5+Li5c+N7uOY4mIby8vCAIAgIDg3D8+OeIiJiB9vY21Nff3OKh0YTh3Xc/QFRUDI4ePYLJk2/DO+/8HiqVCpGRUVbHHn10AQ4eLJBqRPS2wsBdzJkzTzYuMRlhnoQARtZfFv1ZCSKe7+51LxwxkmLoqRhD5WMMlY8xVD7GUPkYQ+XzhBjaS0SwRoQCqVQqPPjgLFntBltGjx6Njo4Ou897eXkhJGQyJk6ciJiYRWhubkZJyRdW2z30+gpERz+M2Ng4FBYWSWMQ/7Q8plarsXBhPGbPnquIzhEiJdR1GEr96fAxHN01iIiIiIjIMzERoUCvvLIdBQX7cN99U3HuXJXd83pLQgBAd3c32tra8OOPlzFr1lxs2bIdcXELrM4LCAiAXl+B2bPnAnBsws4WjsrgSJzE4qhiMUvL7hpKXRlBRERERESuwUSEwgiCAIOhCTU11aipqR7UtcaMGYNnn/0VWlpaoNPlQKfLkZ7z9/dHY2MjAKChoQEBAQEYN2681YSzt+X6nrbCQKn6ihO7axARERER0VBSu3oA1D8qlQqbN2dh9Giffr3uV7/SWh1ra2tDS0sLBMEkOx4aqpGSEKKGhga0tDTLak9kZ2chPX2ddExcrp+dndWvsZH7M09GiJiEICIiIiKigWAiQoFeey0bEydO7PWc0aPlRUH+7//9PUJDNVbn6XQ5yM/fIzs2a9Zcq3M1mjBkZm6XHpsv1xeTEeJyfaPRIEtYWBbO7G83D3I9Mb7mzJNQREREREREjmIiQmHMt2bYIxapHDNmjHSsvb0dlZV6JCQkoaamCRpNmOw1Wm0yamqaoNUmIz8/F5WVetnzen0F0tNTkZb2ErKzs6Q75ImJycjLy0FwsJ9UM8D8TjlXTSifZXeN2lqDFHcmI4iIiIiIqL+YiFCg3uZ9EyZMlIpUtrW1YcWKlVixYiUAYOzYsdi69WWkp6daddwoLv4C6emp2LJlO8LDZyAwMFD2vEYThpKSs9DpcqUVD30t1+/PqgnqP2etNLHXXSMxMdmtu6AQEREREZF7YrFKhVGpVPDz80NgYBDq6+uk4/7+AWhsbEBT01XZ+VlZ2dLr8vNzERIyQXouIWEltm3Lxvr1a/H223koKysBICA8PAKlpcUAejpmNDQ0SIkLjSZMmnzaW64vTlZZ5HD4OLudJrugEBERERHRUOGKCIUwv9udkrIet98+Rfb8Y489YfN1YqJg27YdsuNiEgIA1GovhIdHIDx8BnS6XKlmhEYThoaGBtnr9PoKGI0GmEwmh5brs8jh0HPVShN2QSEiIiIioqHAFREKYH73GwDS0tahrKwE06dHoLCwCOnp66DT5cpeo9UmAxCQl5dzY2IqnzSePn0SgiAgIyMVOl0OtNpkbNmyXbZi4pNPjiE6+mHZNg6xaKVarba5XB+AbLl+X6smqP+40oSIiIiIiJSMKyLcnPnd77S0lwAAJSVnAQARETNunNUz8QwJmQytNglabTJ0uhwAKiQkrERJSTF0up6VC2KhSr2+AiEhE6QVDZmZWcjISJW9t2USAuhZEZGRkQpBEJCSsl428RUnyOLWABY5HD5caUJERERERErltWnTpk2uHsRAmUwC2tuvu3oYDvPxuaXf41WpVJg/PwpHjx5BQcHH2LnzZVRX/x0JCSuhUqlRWloMHx8fhIaG4c9//giRkdGIjIyC0WiAn58f0tMzUV39d2g0Ydiy5WWo1WpUV1fj1KkT0nvs3/8JoqMfRmFhARITk3HgQBGMRgMKCwsQEBCAtrY26VyNJgxTp2owd+7D0vgsx2v+/8XFZ6X3Fj+L0WiAr6+fdA0lGUgMh4OY5CkpKZaOGY0GzJ8fxWREH9wlhjRwjKHyMYbKxxgqH2OofIyh8nlCDG+9dbTN49yaoRARETOlApIAoFKppVUO4p1x8wmo+d1x80KDJpMJhw8Xyq4trnzQaMKweXNPa87Nm7Nw8uTfoNdXSO8hrm6YPXuurHChPeKqCfFc804bnCwPnOVKE/PYAFwZQURERERE7o2JCMWQb2O4WdfB9qTT1koFsSaEmHQQt13o9RUICAjAoUOfYePG9fD19YPRaMD48b591oCwx9ldHTyJvXaagGOxISIiIiIiciUmItycOIHX6XKh1SZZFKXsX40F8wns5s1ZmDx5ovRcQ0MDYmMflSUpempHbO93y0bzuhYAZHfsExOTHVpNQb1jO00iIiIiIlIqJiLcnJg80GqTYNn5wrw+gKNSUtbDZDJZFaYEIFshYa8DgyMTXXZ1cA620yQiIiIiIiVi1ww3JwgC1q5NBaCStmPU1hqg1SajtLS4390nxO0Z4uqEmpomm+cNNmHArg5ERERERERkCxMRbiw7Owvp6esAAH5+ftBqkwEIeOWV7di69WUkJib3uyaA+faMzMztNldGABh0e01xS8lQXpOIiIiIiIiUj4kIN2VeZyE9fd2NVRECdLpcGI0GAD0rDAZS+DElZb2UhMjLy4FGEwYAsj/F9x1I4sCyq0NtrQGJicmDuiYRERERERGNDKwR4aYGWmfBshCkvcKQarVaWhkxfrwvZs+eKyUnfH39MHv23AF3YGBXByIiIiIiIrJHJSj49vT1691oamp19TAcNmHC2H6PVxAEBAf7SY9raw12J/IDaZkpJirs/TkYjiZFlGQgMST3whgqH2OofIyh8jGGyscYKh9jqHyeEMPAwPE2j3NrhhvrT50Fy60c5tsjjEaD3e0Q5u0fbf05GOzqQERERERERJaYiHBT/a2zIG5/EM8JDvaTXstuFUREREREROQu3C4RcezYMcTGxiI6Ohp5eXmuHo7L2Kuz0FunDLbMJCIiIiIiInfnVsUqu7u7kZmZiXfffRfBwcF48sknERkZibvvvtvVQ3OJlJT1sroKYqKht0KVtrZyMBlBRERERERE7sKtVkSUl5fjzjvvxJQpU+Dt7Y34+HgUFRW5elgu5WidBbbMJCIiIiIiIiVwqxURtbW1CAkJkR4HBwejvLzchSNSDrbMJCIiIiIiIiVwq0SErbv2vU2gvbxUmDBh7HAOaUh5eamHdbxZWVutWmTu3r2LSYghNNwxpOHHGCofY6h8jKHyMYbKxxgqH2OofJ4cQ7dKRISEhKCmpkZ6XFtbi6CgILvnd3cLiuq76gl9Ykc6xlD5GEPlYwyVjzFUPsZQ+RhD5WMMlc8TYhgYON7mcbeqEREWFoYLFy7g0qVL6OzsREFBASIjI109LCIiIiIiIiIaIm61ImLUqFHIyMhAQkICuru78cQTT+Cee+5x9bCIiIiIiIiIaIi4VSICAB555BE88sgjrh4GEREREREREQ0Dt9qaQUREREREREQjGxMRREREREREROQ0TEQQERERERERkdMwEUFERERERERETsNEBBERERERERE5DRMRREREREREROQ0KkEQBFcPgoiIiIiIiIg8A1dEEBEREREREZHTMBFBRERERERERE7DRAQREREREREROQ0TEURERERERETkNExEEBEREREREZHTMBFBRERERERERE7DRISTHDt2DLGxsYiOjkZeXp6rh+PRUlNTMWvWLCxevFg61tTUhOeeew4xMTF47rnnYDAYpOf27NmD6OhoxMbG4vjx49JxvV6PJUuWIDo6Glu3boXYCbezsxP/8R//gejoaPzzP/8zLl++7LwP5yGqq6vx9NNPY9GiRYiPj8d7770HgHFUko6ODjz55JNYunQp4uPjsWvXLgCMoRJ1d3dj+fLlWLlyJQDGUGkiIyOxZMkSLFu2DI8//jgAxlBpjEYjVq9ejYULF2LRokUoKytjDBXku+++w7Jly6T/wsPD8bvf/Y4xVJDf/e53iI+Px+LFi/HCCy+go6OD8XOEQMOuq6tLWLBggXDx4kWho6NDWLJkiXD+/HlXD8tjffHFF4Jerxfi4+OlYzt27BD27NkjCIIg7NmzR8jOzhYEQRDOnz8vLFmyROjo6BAuXrwoLFiwQOjq6hIEQRCeeOIJobS0VDCZTMKKFSuEzz77TBAEQfjggw+E9PR0QRAEYf/+/cKvf/1rZ348j1BbWyvo9XpBEAShublZiImJEc6fP884KojJZBJaWloEQRCEzs5O4cknnxTKysoYQwV65513hBdeeEFITEwUBIF/nyrN/PnzhYaGBtkxxlBZUlJShD/96U+CIAhCR0eHYDAYGEOF6urqEmbPni1cvnyZMVSImpoaYf78+UJbW5sgCIKwevVq4a9//Svj5wCuiHCC8vJy3HnnnZgyZQq8vb0RHx+PoqIiVw/LY82cORN+fn6yY0VFRVi+fDkAYPny5Thy5Ih0PD4+Ht7e3pgyZQruvPNOlJeXo66uDi0tLZg+fTpUKhWWL18uxfTo0aN47LHHAACxsbE4deqUlNGkoREUFITQ0FAAwLhx43DXXXehtraWcVQQlUqFW2+9FQDQ1dWFrq4uqFQqxlBhampq8Nlnn+HJJ5+UjjGGyscYKkdLSwvOnj0r/Q56e3vD19eXMVSoU6dOYcqUKfjJT37CGCpId3c32tvb0dXVhfb2dgQFBTF+DmAiwglqa2sREhIiPQ4ODkZtba0LR0SWGhoaEBQUBKBnktvY2AjAfuwsj4eEhEgxra2txeTJkwEAo0aNwvjx43H16lVnfRSPc/nyZZw7dw73338/46gw3d3dWLZsGWbPno3Zs2czhgqUlZWFtWvXQq2++c8JxlB5VqxYgccffxx//OMfATCGSnLp0iX4+/sjNTUVy5cvx4YNG9Da2soYKlRBQYG0dZgxVIbg4GD86le/wvz58zF37lyMGzcOc+fOZfwcwESEE9jKWKlUKheMhPrLXux6iynj7TzXrl3D6tWrsX79eowbN87ueYyje/Ly8sLevXvx+eefo7y8HN98843dcxlD9/Ppp5/C398fGo3GofMZQ/f0hz/8AR9++CF0Oh3+67/+C2fPnrV7LmPofrq6ulBVVYWf//zn+OijjzBmzJhea5Exhu6rs7MTR48excKFC3s9jzF0LwaDAUVFRSgqKsLx48fR1taGvXv32j2f8buJiQgnCAkJQU1NjfS4trZWypCRewgICEBdXR0AoK6uDv7+/gDsx87yeE1NjRTTkJAQVFdXA+j5B0JzczMmTJjgrI/iMa5fv47Vq1djyZIliImJAcA4KpWvry8efPBBHD9+nDFUkNLSUhw9ehSRkZF44YUXcPr0aaxZs4YxVJjg4GAAPX9/RkdHo7y8nDFUkJCQEISEhOD+++8HACxcuBBVVVWMoQIdO3YMoaGhmDRpEgD+m0YpTp48idtvvx3+/v645ZZbEBMTg7KyMsbPAUxEOEFYWBguXLiAS5cuobOzEwUFBYiMjHT1sMhMZGQkPvroIwDARx99hAULFkjHCwoK0NnZiUuXLuHChQuYNm0agoKCcOutt+LLL7+EIAhWr/nwww8BAIcOHcJDDz00IrKW7kQQBGzYsAF33XUXnnvuOek446gcjY2NMBqNAID29nacPHkSd911F2OoIC+++CKOHTuGo0eP4rXXXsNDDz2EnTt3MoYK0traipaWFun/T5w4gXvuuYcxVJDAwECEhITgu+++A9BTY+CnP/0pY6hABQUFiI+Plx4zhspw22234auvvkJbWxsEQeDvYH8MUxFMsvDZZ58JMTExwoIFC4S33nrL1cPxaL/5zW+EOXPmCFOnThXmzZsn/OlPfxIaGxuFZ555RoiOjhaeeeYZ4erVq9L5b731lrBgwQIhJiZGql4rCIJQXl4uxMfHCwsWLBA2b94smEwmQRAEob29XVi1apUQFRUlPPHEE8LFixed/hlHurNnzwr33nuvsHjxYmHp0qXC0qVLhc8++4xxVJBz584Jy5YtExYvXizEx8cLu3fvFgRBYAwV6vTp01LXDMZQOS5evCgsWbJEWLJkiRAXFyf9+4QxVJaqqirhscceExYvXiwkJycLTU1NjKHCtLa2Cv/0T/8kGI1G6RhjqBxvvPGGEBsbK8THxwtr1qwROjo6GD8HqARhBJTcJCIiIiIiIiJF4NYMIiIiIiIiInIaJiKIiIiIiIiIyGmYiCAiIiIiIiIip2EigoiIiIiIiIichokIIiIiIiIiInIaJiKIiIg8wCeffIKf/exn+N///d9BXWfdunU4ePAgAGDDhg349ttvh2J4ktzcXLvPRUZGorGxcUjfj4iIiJyPiQgiIiIPsH//fkRERODAgQNDds1t27bh7rvvHrLrAcCePXuG9HpERETkfka5egBEREQ0vK5du4bS0lK8//77SE5OxqpVqwAAZ86cwa5duzBhwgR8//33mDFjBjZt2gS1Wo3p06fjX//1X3HmzBn4+vri9ddfh7+/v+y6Tz/9NFJSUhAWFoZjx47h9ddfR3d3NyZOnIj33nsP5eXlyMrKQnt7O3x8fJCVlYW77roL//M//4OjR4+ira0Nly5dQlRUFFJSUrBz5060t7dj2bJluPvuu/Hqq6/a/DyXL1+GVqtFREQEysrKEBwcjLfeegs+Pj744YcfsHHjRjQ2NsLLywtvvPEGpkyZguzsbBw/fhwqlQrJycmIi4vDmTNnsHv3bgQEBODrr79GdHQ07r33Xrz//vvo6OjAm2++iTvuuAONjY3YuHEj/v73vwMA1q9fj4iIiOENGhER0QjGFRFEREQj3JEjRzBv3jz8wz/8AyZMmIDKykrpufLycqxbtw779u3DpUuXcPjwYQBAa2srpk6dig8//BAzZ87Eb3/7W7vXb2xsRHp6Onbt2oWPP/4Yb7zxBgDgrrvuwgcffICPPvoIq1evxuuvvy695ty5c/jP//xP7Nu3D4WFhaiursaaNWvg4+ODvXv32k1CiH744Qf84he/QEFBAcaPH49Dhw4BANasWYNf/OIX+Pjjj/Hf//3fCAwMxOHDh/H1119j7969ePfdd5GdnY26ujoAwNdff40NGzZg37592Lt3Ly5cuIC//OUvePLJJ/H73/8eQM/Kj2effRZ//etfsXv3bqSlpQ0gCkRERCTiiggiIqIRrqCgAM8++ywAIC4uDvv370doaCgAYNq0aZgyZQoAID4+HiUlJVi4cCHUajXi4uIAAMuWLcPzzz9v9/pffvklZsyYIV1nwoQJAIDm5ma89NJL+OGHH6BSqXD9+nXpNbNmzcL48eMBAD/96U/x448/YvLkyQ5/pttvvx333XcfACA0NBQ//vgjWlpaUFtbi+joaADA6NGjAQAlJSWIj4+Hl5cXJk2ahJkzZ6KiogLjxo1DWFgYgoKCAAB33HEH5syZAwC49957cebMGQDAyZMnZbUwWlpa0NLSgnHjxjk8XiIiIrqJiQgiIqIR7OrVqzh9+jTOnz8PlUqF7u5uqFQqpKSkAABUKpXsfMvHfR0HAEEQbD7/xhtv4MEHH8Sbb76Jy5cv45lnnpGe8/b2lv7fy8sL3d3d/fpclq/v6OjodXyOXEetVkuP1Wq1NCaTyYQ//vGP8PHx6dcYiYiIyDZuzSAiIhrBDh06hOXLl+PTTz/F0aNH8fnnn+P2229HSUkJgJ6tGZcuXYLJZEJhYaFU+8BkMknbHfbt29drTYTp06fj7NmzuHTpEgCgqakJQM+KiODgYADAhx9+6NB4R40aJVs50R/jxo1DSEgIjhw5AtvBUzMAAAFsSURBVADo7OxEW1sbZs6cicLCQnR3d6OxsRHFxcWYNm2aw9edO3cuPvjgA+nxuXPnBjQ+IiIi6sFEBBER0QhWUFCAqKgo2bGYmBjs27cPAPDAAw/g1VdfxeLFi/GTn/xE2tYwduxYnD9/Ho8//jhOnz6Nf//3f7f7Hv7+/sjMzMSqVauwdOlS/OY3vwEAJCQk4LXXXsNTTz3l8IqHf/mXf8HSpUvx4osvDuTjIjs7G++//z6WLFmCp556CleuXJGKUC5btgzPPvss1q5di8DAQIevuWHDBuj1eixZsgRxcXH4wx/+MKCxERERUQ+V0Nt6RSIiIhqxzpw5g3feecdmy8zp06ejrKzMBaMiIiKikY4rIoiIiIiIiIjIabgigoiIiIiIiIichisiiIiIiIiIiMhpmIggIiIiIiIiIqdhIoKIiIiIiIiInIaJCCIiIiIiIiJyGiYiiIiIiIiIiMhpmIggIiIiIiIiIqf5/zwnG24smiSNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "plt.title(\"Relation Between Applicatoin Income vs Loan Amount \")\n",
    "\n",
    "plt.grid()\n",
    "plt.scatter(loan_train['ApplicantIncome'] , loan_train['LoanAmount'], c='k', marker='x')\n",
    "plt.xlabel(\"Applicant Income\")\n",
    "plt.ylabel(\"Loan Amount\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:15.844718Z",
     "iopub.status.busy": "2020-09-13T10:27:15.838101Z",
     "iopub.status.idle": "2020-09-13T10:27:16.220921Z",
     "shell.execute_reply": "2020-09-13T10:27:16.221550Z"
    },
    "papermill": {
     "duration": 0.486153,
     "end_time": "2020-09-13T10:27:16.221706",
     "exception": false,
     "start_time": "2020-09-13T10:27:15.735553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(loan_train['Loan_Status'], loan_train['LoanAmount'])\n",
    "plt.title(\"Loan Application Amount \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:16.402250Z",
     "iopub.status.busy": "2020-09-13T10:27:16.401450Z",
     "iopub.status.idle": "2020-09-13T10:27:17.306216Z",
     "shell.execute_reply": "2020-09-13T10:27:17.306852Z"
    },
    "papermill": {
     "duration": 0.999952,
     "end_time": "2020-09-13T10:27:17.307019",
     "exception": false,
     "start_time": "2020-09-13T10:27:16.307067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(loan_train.corr(), cmap='coolwarm', annot=True, fmt='.1f', linewidths=.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.087901,
     "end_time": "2020-09-13T10:27:17.482083",
     "exception": false,
     "start_time": "2020-09-13T10:27:17.394182",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In this heatmap, we can clearly seen the relation between two variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.090033,
     "end_time": "2020-09-13T10:27:17.660121",
     "exception": false,
     "start_time": "2020-09-13T10:27:17.570088",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"4\"></a><br>\n",
    "# 4. Choose ML Model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-09-13T10:27:17.839872",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "* In this step, We have a lots of Machine Learning Model from sklearn package, and we need to decide which model is give us the better performance. then we use that model in final stage and send to the production level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:18.037546Z",
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   "outputs": [],
   "source": [
    "# import ml model from sklearn pacakge\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-09-13T10:27:18.393569",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First of all, we are use `LogisticRegression` from `sklearn.linear_model` package. Here is the little information about `LogisticRegression`.\n",
    "\n",
    "`Logistic Regression` is a **classification algorithm**. It is used to predict a binary outcome (`1 / 0`, `Yes / No`, and `True / False`) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables. You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as the dependent variable.\n",
    "\n",
    "![](https://www.analyticsvidhya.com/wp-content/uploads/2015/10/logit.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-09-13T10:27:18.573009",
     "exception": false,
     "start_time": "2020-09-13T10:27:18.485411",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Let's build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:18.764005Z",
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "logistic_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.088454,
     "end_time": "2020-09-13T10:27:18.947640",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "<a id=\"5\"></a><br>\n",
    "# 5. Traing the ML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-09-13T10:27:19.125339",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "> ### **Before fitting the model, We need to decide how many feature are available for testing and training, then after complete this step. fitt the model** \n",
    "\n",
    "Currently, we are using `Credit_History', 'Education', 'Gender` features for training so let's create train and test variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:19.309931Z",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "train_features = ['Credit_History', 'Education', 'Gender']\n",
    "\n",
    "x_train = loan_train[train_features].values\n",
    "y_train = loan_train['Loan_Status'].values\n",
    "\n",
    "x_test = loan_test[train_features].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:19.502934Z",
     "iopub.status.busy": "2020-09-13T10:27:19.501983Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.092563,
     "end_time": "2020-09-13T10:27:19.710441",
     "exception": false,
     "start_time": "2020-09-13T10:27:19.617878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.09073,
     "end_time": "2020-09-13T10:27:19.889521",
     "exception": false,
     "start_time": "2020-09-13T10:27:19.798791",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"6\"></a><br>\n",
    "# 6. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:20.077684Z",
     "iopub.status.busy": "2020-09-13T10:27:20.076480Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Predict the model for testin data\n",
    "\n",
    "predicted = logistic_model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:20.272404Z",
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of model : [[ 3.316164   -0.3059193   0.09398266]]\n"
     ]
    }
   ],
   "source": [
    "# check the coefficeints of the trained model\n",
    "print('Coefficient of model :', logistic_model.coef_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:20.464936Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept of model [-1.98307795]\n"
     ]
    }
   ],
   "source": [
    "# check the intercept of the model\n",
    "print('Intercept of model',logistic_model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:20.657737Z",
     "iopub.status.busy": "2020-09-13T10:27:20.656923Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy_score overall : 0.8094462540716613\n",
      "accuracy_score percent : 80.94\n"
     ]
    }
   ],
   "source": [
    "# Accuray Score on train dataset\n",
    "# accuracy_train = accuracy_score(x_test, predicted)\n",
    "score = logistic_model.score(x_train, y_train)\n",
    "print('accuracy_score overall :', score)\n",
    "print('accuracy_score percent :', round(score*100,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:20.853669Z",
     "iopub.status.busy": "2020-09-13T10:27:20.852862Z",
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     "shell.execute_reply": "2020-09-13T10:27:20.856757Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target on test data [1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 0 1 0 1 1 1 1\n",
      " 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1\n",
      " 1 1 1 1 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0\n",
      " 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1\n",
      " 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
      " 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# predict the target on the test dataset\n",
    "predict_test = logistic_model.predict(x_test)\n",
    "print('Target on test data',predict_test) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.088581,
     "end_time": "2020-09-13T10:27:21.038295",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"7\"></a><br>\n",
    "# 7. Deploy Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.091887,
     "end_time": "2020-09-13T10:27:21.222449",
     "exception": false,
     "start_time": "2020-09-13T10:27:21.130562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "- Finally, we are done so far. The last step is to deploy our model in production map. So we need to export our model and bind with web application API. \n",
    "\n",
    "Using pickle we can export our model and store in to `logistic_model.pkl` file, so we can ealy access this file and calculate customize prediction using Web App API.\n",
    "\n",
    "\n",
    "#### A little bit information about pickle:\n",
    "\n",
    "`Pickle` is the standard way of serializing objects in Python. You can use the pickle operation to serialize your machine learning algorithms and save the serialized format to a file. Later you can load this file to deserialize your model and use it to make new predictions\n",
    "\n",
    "\n",
    ">>  Here is example of the Pickle export model\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "model.fit(X_train, Y_train)\n",
    "# save the model to disk\n",
    "filename = 'finalized_model.sav'\n",
    "pickle.dump(model, open(filename, 'wb'))\n",
    "\n",
    "# some time later...\n",
    "\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "result = loaded_model.score(X_test, Y_test)\n",
    "print(result)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:21.447631Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-13T10:27:21.646508Z",
     "iopub.status.busy": "2020-09-13T10:27:21.645656Z",
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   "outputs": [],
   "source": [
    "# save the model to disk\n",
    "filename = 'logistic_model.pkl'\n",
    "pkl.dump(logistic_model, open(filename, 'wb')) # wb means write as binary\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.091397,
     "end_time": "2020-09-13T10:27:21.842790",
     "exception": false,
     "start_time": "2020-09-13T10:27:21.751393",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Now, You can check your current directory. You can see the file with named \"logistic_model.pkl\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.093642,
     "end_time": "2020-09-13T10:27:22.033019",
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     "start_time": "2020-09-13T10:27:21.939377",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- To read model from file\n",
    "\n",
    "```\n",
    "# load the model from disk\n",
    "loaded_model = pkl.load(open(filename, 'rb')) # rb means read as binary\n",
    "result = loaded_model.score(X_test, Y_test)\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
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    "tags": []
   },
   "source": [
    "--- \n",
    "---\n",
    "\n",
    "<div class=\"text-center\">\n",
    "    <h1>That's it Guys,</h1>\n",
    "    <h1>🙏</h1>\n",
    "    \n",
    "        \n",
    "        I Hope you guys you like and enjoy it, and learn something interesting things from this notebook, \n",
    "        \n",
    "        Even I learn a lots of things while I'm creating this notebook\n",
    "    \n",
    "        Keep Learning,\n",
    "        Regards,\n",
    "        Vikas Ukani.\n",
    "    \n",
    "</div>\n",
    "\n",
    "---\n",
    "---\n",
    "\n",
    "<img src=\"https://static.wixstatic.com/media/3592ed_5453a1ea302b4c4588413007ac4fcb93~mv2.gif\" align=\"center\" alt=\"Thank You\" style=\"min-height:20%; max-height:20%\" width=\"90%\" />\n",
    "\n"
   ]
  }
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