main.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group L MLG382 Assignment 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/chaycwilson/MGL382-Assignment-1\n",
    "\n",
    "Tiaan Theron - 577856   \n",
    "Chay Wilson van Eck - 577502    \n",
    "Christian Anyimadu - 577920     \n",
    "Gregory Avvakoumides - 578199   \n",
    "Tshiamo Mogoai - 576956     \n",
    "Vutomi Monyalue Nkuna- 577359   "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Statement: \n",
    "The company wants to automate the loan eligibility process (real-time) based on customer detail provided while \n",
    "filling out the online application form. \n",
    "These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History \n",
    "and others. To automate this process, they have given a problem identifying the customer segments eligible for \n",
    "loan amounts to target these customers specifically. Here they have provided a partial dataset. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Hypothesis Generation \n",
    "\n",
    "- Income: People with a higher income are more likely to get a loan\n",
    "- Credit History: People with a good credit history are more likely to pay their loan back\n",
    "- Loan Term: A higher chance of approval if the time period is not high\n",
    "- Loan Amount: An amount that is not that high is more likely to be approved \n",
    "- Education: People who are educated are more likely to be aware of loans and get approved for them"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Getting the System Ready and Loading Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns #For Data Visualization\n",
    "import matplotlib.pyplot as plt #For plotting graphs\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = pd.read_csv('./data-for-project-1/raw_data.csv')\n",
    "validation = pd.read_csv('./data-for-project-1/validation.csv')\n",
    "\n",
    "train_original = raw.copy()\n",
    "test_original = validation.copy()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Understanding the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.dtypes #Print the data types for the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.shape, validation.shape #Seeing how many rows and columns each has"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_Status\n",
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the number of approved loans (target variable)\n",
    "raw['Loan_Status'].value_counts(normalize=True) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Loan_Status'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "422 of 614 people got approved with a loan"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independant Variable (All the Categories of object datatype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(221)\n",
    "raw['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender') \n",
    "plt.subplot(222) \n",
    "raw['Married'].value_counts(normalize=True).plot.bar(title= 'Married') \n",
    "plt.subplot(223) \n",
    "raw['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed') \n",
    "plt.subplot(224) \n",
    "raw['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit_History') \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be deduced from the above bar plots that:\n",
    "\n",
    "- 80% of applicants in the dataset are male.\n",
    "- Around 65% of the applicants in the dataset are married.\n",
    "- About 15% of applicants in the dataset are self-employed.\n",
    "- About 85% of applicants have repaid their debts."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other Independant Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(131)\n",
    "raw['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Dependents') \n",
    "plt.subplot(132)\n",
    "raw['Education'].value_counts(normalize=True).plot.bar(title= 'Education') \n",
    "plt.subplot(133) \n",
    "raw['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area') \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information from these graphs deduce:\n",
    "\n",
    "- Most of the applicants don’t have dependents.\n",
    "- About 80% of the applicants are graduates.\n",
    "- Most of the applicants are from semi-urban areas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independant Variable (All of type Integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X9PWvf92jycI3OYfF+xlaVoK/HJZAhsYDAAAAAADg0jpUCH3uuef0k5/8RD/+8Y8VExPTZsyAAQO0dOnSK0oOvYOzEBpi8pefwXBZz2VoPAAAAAAAANqjQ4XQ9evXa8CAAa4OUCeHw6EDBw5owIABMplMuuOOOzySJHxbbQfnB5UkS3BLIfRobYMam+0y+V/RbA8AAAAAAADwUR2qGg0aNEjHjh1rtf3EiRNKSEi44qTQu3R0xXipZSi90c8gh0OqrKErFAAAAAAAAG3rUCHU4XC0ub22tlaBgYFXlBB6nysphPoZDAo700nKPKEAAAAAAAC4kMuqPM2ZM0eSZDAY9Ktf/UrBwcGufTabTVu3btX111/v0QTh+2pPN0nq2NB4qWWe0KpTTcwTCgAAAAAAgAu6rMrT9u3bJbV0hH766acymUyufSaTSd/85jeVn5/v2Qzh82quoCNUksJcCybVeywnAAAAAAAA+JbLqjx98MEHkqQf//jH+t3vfqewsLBOSQq9i3NofGgHO0L7snI8AAAAAAAALqFDlac//elPns4DvZhr1XhzQIee7+wIZY5QAAAAAAAAXEi7C6FZWVlatmyZwsLClJWVddHYVatWXXFi6D2uZLEkqWWOUImOUAAAAAAAAFxYuytPFotFBoPBdR/wBJvdofpGm6QrWyxJoiMUAAAAAAAAF9buytO5w+EZGg9PqWtolkOSQVKwydihYziHxlfWnFazzS5/o5/nEgQAAAAAAIBP6FDFqL6+XqdOnXI93r9/vxYvXqzCwkKPJYbe4dxh8X5nOo4vVx+zv/z9DLI7pMqaBk+mBwAAAAAAAB/RoULo5MmT9corr0iSTp48qW9/+9tauHChJk+erOeee86jCcK3uQqhHRwWL0l+BoOiwwIlMU8oAAAAAAAA2tahQujHH3+s9PR0SdJbb72lmJgY7d+/X6+88op+//vfezRB+LazK8Z3vBAqSdFhZklSZTWFUAAAAAAAALTWoULoqVOnFBoaKkkqLCxUVlaW/Pz8NHLkSO3fv9+jCcK3XemK8U5RoS0doQyNBwAAXa2goEAGg0G5ubmubQ6HQ48++qji4uIUFBSkMWPG6LPPPnN7XkNDg+677z5FRkYqJCREkyZN0sGDB91iqqqqlJOTI4vFIovFopycHJ08edItpqysTLfddptCQkIUGRmp2bNnq7GxsbNeLgBcFpvNpr///e9644039Pe//102m83bKQHoxTpUCB08eLDWrFmjAwcO6K9//asyMjIkSZWVlQoLC/NogvBtniqEOjtCj9ARCgAAutC2bdv04osv6hvf+Ibb9vnz52vRokVasmSJtm3bppiYGN10002qqalxxeTm5mr16tVasWKFNm3apNraWmVmZroVCbKzs7Vjxw6tW7dO69at044dO5STk+Pab7PZdOutt6qurk6bNm3SihUrtHLlSuXl5XX+iweAS1i1apUGDx6ssWPHKjs7W2PHjtXgwYO1atUqb6cGoJfqUCH0V7/6lfLz8zVw4EClpKQoNTVVUkt36A033ODRBOHbPDFHqCRFnZkj9Eg1HaEAAKBr1NbW6oc//KFeeuklhYeHu7Y7HA4tXrxYDz/8sLKyspSYmKiXX35Zp06d0vLlyyVJVqtVS5cu1cKFCzV+/HjdcMMNeu211/Tpp59qw4YNkqRdu3Zp3bp1+uMf/6jU1FSlpqbqpZde0jvvvKPdu3dLarn+3rlzp1577TXdcMMNGj9+vBYuXKiXXnpJ1dXVXf+mAMAZq1at0tSpU5WUlKTi4mLV1NSouLhYSUlJmjp1KsVQAF7RoULo1KlTVVZWpo8++kjr1q1zbR83bpyefvppjyUH31dzukmSJzpCnUPj6QgFAABd495779Wtt96q8ePHu23fu3evKioqXKOmJMlsNmv06NHavHmzJKmkpERNTU1uMXFxcUpMTHTFFBcXy2KxKCUlxRUzcuRIWSwWt5jExETFxcW5YiZMmKCGhgaVlJR4/kUDQDvYbDbl5eUpMzNTK1eu1OnTp/WXv/xFp0+f1sqVK5WZman8/HyGyQPoch2uPsXExCgmJsZt27e//e0rTgi9i6c6Qs8ulkRHKAAA6HwrVqzQxx9/rG3btrXaV1FRIUmKjo522x4dHe2aT7+iokImk8mtk9QZ43x+RUWFoqKiWh0/KirKLeb884SHh8tkMrliztfQ0KCGhrPXTHSOAvC0oqIi7du3TzNnztTXvvY17du3z7Vv4MCBuvvuu/WXv/xFRUVFGjNmjNfyBND7dKgjtK6uTo888ojS0tI0ePBgXXvttW43oL1qG1p+A+ipxZKO0BEKAAA62YEDB/Szn/1Mr732mgIDAy8YZzAY3B47HI5W2853fkxb8R2JOVdBQYFr8SWLxaL4+PiL5gQAl6u8vFyS9NBDD7U5NP7hhx92iwOArtKh6tNPf/pTbdy4UTk5OYqNjb3kBR3QFofDofrGlo7QEJNnOkJPnmrS6SabAgOMV5wfAABAW0pKSlRZWank5GTXNpvNpn/84x9asmSJa/7OiooKxcbGumIqKytd3ZsxMTFqbGxUVVWVW1doZWWl0tLSXDFHjhxpdf6jR4+6HWfr1q1u+6uqqtTU1NSqU9Rp7ty5mjNnjutxdXU1xVAAHuXsZh81apTWrFkjP7+WHqyRI0dqzZo1Gj16tDZt2tRm1zsAdKYOVZ/ee+89vfvuuxo1apSn80EvUn26WXZHy/1g05UVLi1BATL5+6mx2a6jNQ2K7xfsgQwBAABaGzdunD799FO3bT/+8Y/19a9/XQ8++KCuvfZaxcTEaP369a6FRBsbG7Vx40Y9+eSTkqTk5GQFBARo/fr1mjZtmqSWzqjS0lLNnz9fkpSamiqr1aoPP/zQNQXV1q1bZbVaXcXS1NRUPfHEEyovL3cVXQsLC2U2m90Ktecym80ym80eflcAoP0cDoe3UwDQS3WoEBoeHq5+/fp5Ohf0MlV1jZIkk7+f/I0dmqXBxWAwKDrMrAMn6nWk+jSFUAAA0GlCQ0OVmJjoti0kJEQRERGu7bm5uZo3b56GDBmiIUOGaN68eQoODlZ2drYkyWKx6K677lJeXp4iIiLUr18/5efnKykpybX40rBhwzRx4kTNmDFDL7zwgiTp7rvvVmZmpoYOHSpJysjI0PDhw5WTk6OnnnpKJ06cUH5+vmbMmKGwsLCueksAwE1lZaUkadOmTZoyZYrmzp2rxMRElZaWqqCgQP/85z/d4gCgq3So+vQ///M/+tWvfqVTp055Oh/0IidOtRRCQ66wG9TJOU9oZQ0LJgEAAO964IEHlJubq1mzZmnEiBE6dOiQCgsLFRoa6op5+umnNWXKFE2bNk2jRo1ScHCw/vKXv8hoPHtt9PrrryspKUkZGRnKyMjQN77xDb366quu/UajUe+++64CAwM1atQoTZs2TVOmTNGCBQu69PUCwLmcHeoFBQX69NNPlZaWprCwMKWlpam0tFTz5s1ziwOArmJwdKAn/YYbbtCXX34ph8OhgQMHKiAgwG3/xx9/7LEEfV11dbUsFousVmuv+6393z4/op8s+0hX9Q3SvWMHX9GxslMGaNbrJVr7aYV+fdtw/XhUgoeyBAAAl9Kbr2d8AZ8fAE+z2WwaPHiwkpKS9H//9396/vnn9eWXX2rQoEG65557NG3aNJWWlmrPnj1uv/wBgI5q7/VMh4bGT5kypaN5AS4n6pokXfn8oE6uleOr6QgFAAAAAG8xGo1auHChbr/9dvXr10/19fWufQ899JDq6+u1cuVKiqAAulyHCqG//vWvPZ0HeiHnHKEh5itbMd4pOuzM0Pjq0x45HgAAAACg4wwGQ5vb2toOAF2hwyvUnDx5Un/84x81d+5cnThxQlLLkPhDhw55LDn4NuccoZ7qCI0Oa1n9lDlCAQAAAMB7bDab8vLylJmZKavVqg8++EDLly/XBx98oJMnTyozM1P5+fmy2WzeThVAL9OhVrx//etfGj9+vCwWi/bt26cZM2aoX79+Wr16tfbv369XXnnF03nCB510FUI90xF6dmg8HaEAAAAA4C1FRUXat2+f3njjDQUEBGjMmDFu++fOnau0tDQVFRW12gcAnalDHaFz5szRnXfeqT179igwMNC1/eabb9Y//vEPjyUH33airnM6QimEAgAAAID3lJeXS5ISExPb3O/c7owDgK7SoULotm3bNHPmzFbbr7rqKlVUVFxxUugdqs4sluSpOUKjzswRWn26WaebGGIBAAAAAN4QGxsrSSotLW1zv3O7Mw4AukqHCqGBgYGqrq5utX337t3q37//FSeF3qHKw3OEhgX6KzCg5StdycrxAAAAAOAV6enpGjhwoObNmye73e62z263q6CgQAkJCUpPT/dShgB6qw4VQidPnqzHH39cTU0tHX0Gg0FlZWX6xS9+odtvv92jCcJ3eboQajAYzs4TWsPweAAAAADwBqPRqIULF+qdd97RlClTVFxcrJqaGhUXF2vKlCl65513tGDBAhmNnvm/IAC0V4cKoQsWLNDRo0cVFRWl+vp6jR49WoMHD1ZoaKieeOIJT+cIH2S3O1R16szQeA8tliQxTygAAAAAdAdZWVl666239OmnnyotLU1hYWFKS0tTaWmp3nrrLWVlZXk7RQC9UIcqUGFhYdq0aZM++OADlZSUyG6368Ybb9T48eM9nR98VM3pZtnsDkme6wiVzs4TeoSh8QAAAADgVVlZWZo8ebKKiopUXl6u2NhYpaen0wkKwGsuuxBqt9u1bNkyrVq1Svv27ZPBYFBCQoJiYmLkcDhkMBg6I0/4GOeweLO/n/yNHWpMblP0maHxlXSEAgAAAIDXGY1GjRkzxttpAICkyxwa73A4NGnSJP30pz/VoUOHlJSUpOuuu0779+/XnXfeqe9973udlSd8zAkPzw/q5BwaX1lDRygAAAAAAADOuqyO0GXLlukf//iH3n//fY0dO9Zt39/+9jdNmTJFr7zyin70ox95NEn4nqo6ZyHUc/ODSlIUc4QCAAAAAACgDZfVEfrGG2/ooYcealUElaT/+I//0C9+8Qu9/vrrHksOvuvEmUJoiNnDHaHOVeMphAIAAAAAAOAcl1UI/de//qWJEydecP/NN9+sTz755IqTgu87eWbFeM93hDrnCGVoPAAAAAAAAM66rCrUiRMnFB0dfcH90dHRqqqquuKk4Ps6e47QmoZmnWps9nihFQAAAADQfjabjVXjAXQbl9URarPZ5O9/4cKS0WhUc3PzFScF39dZc4T2MfsrKKDlH1W6QgEAAADAe1atWqXBgwdr7Nixys7O1tixYzV48GCtWrXK26kB6KUue9X4O++8U1lZWW3efvKTn1x2As8++6wSEhIUGBio5ORkFRUVXTR+48aNSk5OVmBgoK699lo9//zzrWJWrlyp4cOHy2w2a/jw4Vq9evVln3fVqlWaMGGCIiMjZTAYtGPHjlbHaGho0H333afIyEiFhIRo0qRJOnjw4OW9Ab1UVSd1hBoMBldXKPOEAgAAAIB3rFq1SlOnTlVSUpKKi4tVU1Oj4uJiJSUlaerUqRRDAXjFZRVC77jjDkVFRclisbR5i4qKuqwV4998803l5ubq4Ycf1vbt25Wenq6bb75ZZWVlbcbv3btXt9xyi9LT07V9+3Y99NBDmj17tlauXOmKKS4u1vTp05WTk6NPPvlEOTk5mjZtmrZu3XpZ562rq9OoUaP029/+9oL55+bmavXq1VqxYoU2bdqk2tpaZWZmymaztfs96K2q6lrmCA0xe37ounOe0CM1dIQCAAAAQFez2WzKy8tTZmam1qxZo5EjR6pPnz4aOXKk1qxZo8zMTOXn5/N/ZwBdzuBwOBzeOnlKSopuvPFGPffcc65tw4YN05QpU1RQUNAq/sEHH9Tbb7+tXbt2ubbdc889+uSTT1RcXCxJmj59uqqrq/Xee++5YiZOnKjw8HC98cYbl33effv2KSEhQdu3b9f111/v2m61WtW/f3+9+uqrmj59uiTp8OHDio+P19q1azVhwoR2vQfV1dWyWCyyWq0KCwtr13N8wfhFG/Xvylrd9Z0EDerf54qPl50ywHX/vje26y+fHNYvbx2mn6Zfe8XHBgAAF9dbr2d8BZ8fAE/7+9//rrFjx6q4uFgjR45stb+4uFhpaWn64IMPNGbMmK5PEIDPae/1zGV1hHpSY2OjSkpKlJGR4bY9IyNDmzdvbvM5xcXFreInTJigjz76SE1NTReNcR6zI+dtS0lJiZqamtyOExcXp8TExIsep6GhQdXV1W633sg5R2hIJyxmFB3aMjS+ko5QAAAAAOhy5eXlkqTExMQ29zu3O+MAoKt4rRB67Ngx2Wy2VqvQR0dHq6Kios3nVFRUtBnf3NysY8eOXTTGecyOnPdCuZhMJoWHh1/WcQoKCtymE4iPj2/3OX2F3e7QyfqWwrWn5wiVpCjmCAUAAAAAr4mNjZUklZaWtrnfud0ZBwBdxWuFUCeDweD22OFwtNp2qfjzt7fnmJd73va61HHmzp0rq9Xquh04cOCKz9nT1Jxuls3e8rl1RiE02jlHKIVQAAAAAOhy6enpGjhwoObNmye73e62z263q6CgQAkJCUpPT/dShgB6K68VQiMjI2U0Glt1T1ZWVrbq1nSKiYlpM97f318REREXjXEesyPnvVAujY2NqqqquqzjmM1mhYWFud16mxNnVozvY/aXv9HzX8Go0JZCaGU1Q+MBAAAAoKsZjUYtXLhQ77zzjqZMmeK2avyUKVP0zjvvaMGCBTIaPd8YAwAX47VCqMlkUnJystavX++2ff369UpLS2vzOampqa3iCwsLNWLECAUEBFw0xnnMjpy3LcnJyQoICHA7Tnl5uUpLSy/rOL3RiTPzg/YNDuiU40eHMUcoAAAAAHhTVlaW3nrrLX366adKS0tTWFiY0tLSVFpaqrfeektZWVneThFAL+T5lWouw5w5c5STk6MRI0YoNTVVL774osrKynTPPfdIahlGfujQIb3yyiuSWlaIX7JkiebMmaMZM2aouLhYS5cuda0GL0k/+9nP9N3vfldPPvmkJk+erD//+c/asGGDNm3a1O7zStKJEydUVlamw4cPS5J2794tqaUTNCYmRhaLRXfddZfy8vIUERGhfv36KT8/X0lJSRo/fnynv3c92ckzHaH9QkydcvyoM0PjaxuaVdvQrD5mr37NAQAAAKBXysrK0uTJk1VUVKTy8nLFxsYqPT2dTlAAXuPVCtH06dN1/PhxPf744yovL1diYqLWrl2ra665RlJLh2VZWZkrPiEhQWvXrtX999+vP/zhD4qLi9Pvf/973X777a6YtLQ0rVixQr/85S/1yCOPaNCgQXrzzTeVkpLS7vNK0ttvv60f//jHrsff//73JUm//vWv9eijj0qSnn76afn7+2vatGmqr6/XuHHjtGzZMv5SvwRnR2h4cOcUQvuY/RViMqqu0abK6tPq079Pp5wHAAAAAHBxRqNRY8aM8XYaACBJMjicqw3BK6qrq2WxWGS1WnvNfKEv/uNLzVv7ub53w1X61sB+HjlmdsoAt8f/seDv+upYnd6YMVKpgyI8cg4AANC23ng940v4/AAAQE/X3usZr68aj96n6lSTpM6bI1SSolzzhLJyPAAAAAAAACiEwguqzgyN79dJQ+MlKTqMleMBAAAAAABwFoVQdDnXHKGdtFiSJEWFtnSEHqmmIxQAAAAAAAAUQuEFVac6d7Ek6WxH6JEaOkIBAAAAAABAIRRe4JwjNDykM+cIPVMIpSMUAAAAAAAAohAKL3DNEdqJQ+OjzwyNP0pHKAAA8LDnnntO3/jGNxQWFqawsDClpqbqvffec+13OBx69NFHFRcXp6CgII0ZM0afffaZ2zEaGhp03333KTIyUiEhIZo0aZIOHjzoFlNVVaWcnBxZLBZZLBbl5OTo5MmTbjFlZWW67bbbFBISosjISM2ePVuNjY2d9toBAAB6Mgqh6FJ2u8M1NL4zF0s6tyPU4XB02nkAAEDvc/XVV+u3v/2tPvroI3300Uf6j//4D02ePNlV7Jw/f74WLVqkJUuWaNu2bYqJidFNN92kmpoa1zFyc3O1evVqrVixQps2bVJtba0yMzNls9lcMdnZ2dqxY4fWrVundevWaceOHcrJyXHtt9lsuvXWW1VXV6dNmzZpxYoVWrlypfLy8rruzQAAAOhB/L2dAHqX6tNNsp+pS/btzELomY7QU4021TY0KzSw84bhAwCA3uW2225ze/zEE0/oueee05YtWzR8+HAtXrxYDz/8sLKysiRJL7/8sqKjo7V8+XLNnDlTVqtVS5cu1auvvqrx48dLkl577TXFx8drw4YNmjBhgnbt2qV169Zpy5YtSklJkSS99NJLSk1N1e7duzV06FAVFhZq586dOnDggOLi4iRJCxcu1J133qknnnhCYWFhXfiuAAAAdH90hKJLOecH7WP2l8m/875+IWZ/hZpb6vxHqhkeDwAAOofNZtOKFStUV1en1NRU7d27VxUVFcrIyHDFmM1mjR49Wps3b5YklZSUqKmpyS0mLi5OiYmJrpji4mJZLBZXEVSSRo4cKYvF4haTmJjoKoJK0oQJE9TQ0KCSkpIL5tzQ0KDq6mq3GwAAQG9AIRRd6sSZ+UE7c6Ekp6iwlq7QShZMAgAAHvbpp5+qT58+MpvNuueee7R69WoNHz5cFRUVkqTo6Gi3+OjoaNe+iooKmUwmhYeHXzQmKiqq1XmjoqLcYs4/T3h4uEwmkyumLQUFBa55Ry0Wi+Lj4y/z1QMAAPRMFELRpZwLJYV34rB4p2jnPKE1FEIBAIBnDR06VDt27NCWLVv0//7f/9Mdd9yhnTt3uvYbDAa3eIfD0Wrb+c6PaSu+IzHnmzt3rqxWq+t24MCBi+YFAADgKyiEoks5F0rqikJozJlCaIWVofEAAMCzTCaTBg8erBEjRqigoEDf/OY39bvf/U4xMTGS1Kojs7Ky0tW9GRMTo8bGRlVVVV005siRI63Oe/ToUbeY889TVVWlpqamVp2i5zKbza4V7503AACA3oBCKLqUa8X4kC4ohFqchdD6Tj8XAADo3RwOhxoaGpSQkKCYmBitX7/eta+xsVEbN25UWlqaJCk5OVkBAQFuMeXl5SotLXXFpKamymq16sMPP3TFbN26VVar1S2mtLRU5eXlrpjCwkKZzWYlJyd36usFAADoiVg1Hl3qRF3LYkld0REae6YQWm5laDwAAPCchx56SDfffLPi4+NVU1OjFStW6O9//7vWrVsng8Gg3NxczZs3T0OGDNGQIUM0b948BQcHKzs7W5JksVh01113KS8vTxEREerXr5/y8/OVlJTkWkV+2LBhmjhxombMmKEXXnhBknT33XcrMzNTQ4cOlSRlZGRo+PDhysnJ0VNPPaUTJ04oPz9fM2bMoMsTAACgDRRC0aXOzhHa+YslxViCJEkVLJYEAAA86MiRI8rJyVF5ebksFou+8Y1vaN26dbrpppskSQ888IDq6+s1a9YsVVVVKSUlRYWFhQoNDXUd4+mnn5a/v7+mTZum+vp6jRs3TsuWLZPRaHTFvP7665o9e7ZrdflJkyZpyZIlrv1Go1HvvvuuZs2apVGjRikoKEjZ2dlasGBBF70TAAAAPYvB4XA4vJ1Eb1ZdXS2LxSKr1dorfnN/9ysfqXDnEf1mSqL+a+Q1Wr61zCPHzU4Z0Gpb6SGrMp/ZpP6hZm17eLxHzgMAAFrrbdczvobPDwAA9HTtvZ5hjlB0qa6cI9S5avyx2gY12eydfj4AAAAAAAB0XxRC0aVOnBka37cLhsZHhJgUYDTI4ZAqa1g5HgAAAAAAoDejEIoudfJUy2JJXdER6udncHWFsnI8AAAAAABA70YhFF3GbnecHRrfBavGS6wcDwAAAAAAgBYUQtFlqk83yX5maa6+XVQIda0cTyEUAAAAAACgV6MQii7jnB+0j9lfJv+u+erREQoAAAAAAACJQii6UNWZ+UHDQzp/oSSnGNccoRRCAQAAAAAAejN/byeA3qOqrvPmB12+tazN7XsqayVJnx6yavnWMmWnDPD4uQEAAAAAAND90RGKLnPizEJJXTU/qCRZglq6T6vrm7rsnAAAAAAAAOh+KISiy7g6QkO8UAg93SS7w9Fl5wUAAAAAAED3QiEUXcY1R2gXdoT2MfvLIMnukGobmrvsvAAAAAAAAOheKISiy5ztCO26xZKMfgaFBrZMhcvweAAAAAAAgN6LQii6jDfmCJXODo+3UggFAAAAAADotSiEosucPNX1c4RKUhgLJgEAAAAAAPR6FELRZU6cGRrflXOESmcLodZ65ggFAAAAAADorfy9nQB6D9diSV04R6gkWQLPrhwPAAAAAOg6NptNRUVFKi8vV2xsrNLT02U0Gr2dFoBeio5QdAm73XF2aDxzhAIAAACAz1u1apUGDx6ssWPHKjs7W2PHjtXgwYO1atUqb6cGoJeiEIouYa1vkt3Rcr+rF0sKoxAKAAAAAF1q1apVmjp1qpKSklRcXKyamhoVFxcrKSlJU6dOpRgKwCsohKJLHD8zP2hooL9M/l37tTu3I9TurMYCAAAAADqFzWZTXl6eMjMztWbNGo0cOVJ9+vTRyJEjtWbNGmVmZio/P182m83bqQLoZSiEoks4F0qK6OIV46WWQqhBks3u0LHahi4/PwAAAAD0JkVFRdq3b58eeugh+fm5lx38/Pw0d+5c7d27V0VFRV7KEEBvRSEUXeJEXUsBMtwLhVCjn8HVFXrwZH2Xnx8AAAAAepPy8nJJUmJiYpv7ndudcQDQVSiEoksc92JHqCT1DT5TCK2iEAoAAAAAnSk2NlaSVFpa2uZ+53ZnHAB0FQqh6BInas+sGO+lQmj4mQWaDlad8sr5AQAAAKC3SE9P18CBAzVv3jzZ7Xa3fXa7XQUFBUpISFB6erqXMgTQW1EIRZc4ccpZCDV75fzOleoP0REKAAAAAJ3KaDRq4cKFeueddzRlyhS3VeOnTJmid955RwsWLJDRaPR2qgB6GX9vJ4DewZuLJUlSOEPjAQAAAKDLZGVl6a233lJeXp7S0tJc2xMSEvTWW28pKyvLi9kB6K0ohKJLOAuh3hoa35eh8QAAAADQpbKysjR58mQVFRWpvLxcsbGxSk9PpxMUgNdQCEWXOO6cI7SPdztCD52sl8PhkMFg8EoeAAAAANCbGI1GjRkzxttpAIAk5ghFF/H20HhLcIAMkk432V0r2AMAAAAAAKD3oCMUnc7hcHh9aLy/n59CA/1VfbpZB6vqFdnHO4s2AQAAAEBv0tjYqGeffVZffvmlBg0apFmzZslk8s7/CwGAjlB0utqGZjXa7JKkCC+tGi9J4awcDwAAAABd5oEHHlBISIjuv/9+LVmyRPfff79CQkL0wAMPeDs1AL0UHaHodM5u0KAAo4JM3psUu29wgPafYMEkAAAAAOhsDzzwgJ566ilFR0crJydH1157rb766iu9+uqreuqppyRJ8+fP93KWAHobCqHodMe9PCzeydURepKOUAAAAADoLI2NjXr66adlsVgUGBioBQsWuPZdc801slgsevrpp/Wb3/yGYfIAuhRD49HpTtR2r0LoQYbGAwCAK1BQUKBvfetbCg0NVVRUlKZMmaLdu3e7xTgcDj366KOKi4tTUFCQxowZo88++8wtpqGhQffdd58iIyMVEhKiSZMm6eDBg24xVVVVysnJkcVikcViUU5Ojk6ePOkWU1ZWpttuu00hISGKjIzU7Nmz1djI4pAAvOfZZ59Vc3OzrFarjhw54rbvyJEjslqtam5u1rPPPuulDAH0VhRC0elOnOoehdC+wQGSGBoPAACuzMaNG3Xvvfdqy5YtWr9+vZqbm5WRkaG6ujpXzPz587Vo0SItWbJE27ZtU0xMjG666SbV1NS4YnJzc7V69WqtWLFCmzZtUm1trTIzM2Wz2Vwx2dnZ2rFjh9atW6d169Zpx44dysnJce232Wy69dZbVVdXp02bNmnFihVauXKl8vLyuubNAIA27Nmzx3XfYDC47Tv38blxANAVGBqPTuecIzSim3SEHqqql8PhaPUPMgAAQHusW7fO7fGf/vQnRUVFqaSkRN/97nflcDi0ePFiPfzww8rKypIkvfzyy4qOjtby5cs1c+ZMWa1WLV26VK+++qrGjx8vSXrttdcUHx+vDRs2aMKECdq1a5fWrVunLVu2KCUlRZL00ksvKTU1Vbt379bQoUNVWFionTt36sCBA4qLi5MkLVy4UHfeeaeeeOIJhYWFdeE7AwAt7Ha76/5//Md/6JZbblFQUJDq6+u1du1avfvuu63iAKAr0BGKTneim8wRajnTEVrXaNPJU01ezQUAAPgOq9UqSerXr58kae/evaqoqFBGRoYrxmw2a/To0dq8ebMkqaSkRE1NTW4xcXFxSkxMdMUUFxfLYrG4iqCSNHLkSFksFreYxMREVxFUkiZMmKCGhgaVlJS0mW9DQ4Oqq6vdbgDgSc5fwhiNRpWWluree+/VT37yE917770qLS2V0Wh0iwOArkIhFJ3uuHOO0D7eLYQGGP3UP9QsiXlCAQCAZzgcDs2ZM0ff+c53lJiYKEmqqKiQJEVHR7vFRkdHu/ZVVFTIZDIpPDz8ojFRUVGtzhkVFeUWc/55wsPDZTKZXDHnKygocM05arFYFB8ff7kvGwAu6vDhw5Japu8oLy/Xgw8+qC+++EIPPvigysvLXVOAOOMAoKtQCEWnO1HXIMn7Q+Ml6erwIEnSoZPMEwoAAK7cf//3f+tf//qX3njjjVb7zp+Gpz1T85wf01Z8R2LONXfuXFmtVtftwIEDF80JAC7X1VdfLamlG765uVlPPvmkvva1r+nJJ5+UzWaT2Wx2iwOArsIcoeh0Z4fGm72ciXR1eLC2l52kIxQAAFyx++67T2+//bb+8Y9/uP1nPiYmRlJLt2ZsbKxre2Vlpat7MyYmRo2NjaqqqnLrCq2srFRaWpor5vzVliXp6NGjbsfZunWr2/6qqio1NTW16hR1MpvNriIEAHSGyMhISS1Tcdxyyy0aPHiwTp8+rcDAQP373//W2rVr3eIAoKvQEYpOd7ybzBEqSfFnOkL3H6cjFAAAdIzD4dB///d/a9WqVfrb3/6mhIQEt/0JCQmKiYnR+vXrXdsaGxu1ceNGV5EzOTlZAQEBbjHl5eUqLS11xaSmpspqterDDz90xWzdulVWq9UtprS0VOXl5a6YwsJCmc1mJScne/7FA0A7OH8hJEkbNmzQ73//e7344ov6/e9/r/fff7/NOADoCnSEotN1l1XjJSkhMkSStO94nZczAQAAPdW9996r5cuX689//rNCQ0Ndc3FaLBYFBQXJYDAoNzdX8+bN05AhQzRkyBDNmzdPwcHBys7OdsXeddddysvLU0REhPr166f8/HwlJSW5VpEfNmyYJk6cqBkzZuiFF16QJN19993KzMzU0KFDJUkZGRkaPny4cnJy9NRTT+nEiRPKz8/XjBkzWIQEgNdcddVVrvuNjY1u+xoaGtqMA4CuQCEUnep0k02nGlsmwvb2YknS2ULoV0cphAIAgI557rnnJEljxoxx2/6nP/1Jd955pyTpgQceUH19vWbNmqWqqiqlpKSosLBQoaGhrvinn35a/v7+mjZtmurr6zVu3DgtW7bMtZqyJL3++uuaPXu2a3X5SZMmacmSJa79RqNR7777rmbNmqVRo0YpKChI2dnZWrBgQSe9egC4tPT0dFksFlmtVhkMBjkcDtc+Pz8/2e12WSwWpaenezFLAL2RwXHu30joctXV1a5/IHzxt/aHTtZr1G//pgCjQV/85uZWk/Yv31rWpflMuC5ayb/ZIINB2vX4RAUGGC/9JAAAcFG+fj3j6/j8AHhaY2OjgoKCZLfbdcstt2jIkCGqr69XUFCQ9uzZo7Vr18rPz0/19fUymbzfMAOg52vv9QwdoehUVefMD3qpVVK7Qr8Qk0ID/VVzulllJ07pa9Ghl34SAAAAAKDdnn32Wdntdk2YMEGFhYWuxZEkyd/fXxkZGSosLNSzzz6r3Nxc7yUKoNdhsSR0quPdaMV4STIYDLqW4fEAAAAA0Gm+/PJLSdJf//pX+fu791/5+/ursLDQLQ4AugqFUHSqE3UtE2H3CwnwciZnDWTBJAAAAADoNAMHDnTdHz9+vIqLi1VTU6Pi4mLXgnDnxwFAV2BoPDrV8dru1REqnV0waS8doQAAAADgcdddd50kKSAgQC+//LLuuusuffnllxo0aJBefvllxcTEqKmpyRUHAF2FQig61YkzQ+MjQrrPBNiuQigdoQAAAADgcf/85z8lSU1NTYqIiHBt//TTT7VmzRq3uIkTJ3Z1egB6MYbGo1OdOGexpO7CVQg9RiEUAAAAAACgt6AQik51tKZljtD+od1naLxzjtCjNQ2qOd3k5WwAAAAAwLd8+9vfdt03Go1u+859fG4cAHQFCqHoVEdrzxRC+3SfQmhYYIAi+7R0qO4/fsrL2QAAAACAb/njH//oum80GvWDH/xACxcu1A9+8AO3Qui5cQDQFZgjFJ2qO3aESi3D44/VNuqrY3VKvMri7XQAAAAAwGd8+eWXrvuNjY1644039MYbb1w0DgC6Ah2h6DR2u8NVCI0K616F0IERLcPj9zFPKAAAAAB4lMFgcN0PDAx02xcUFNRmHAB0BQqh6DQn65vUbHdIkiJCulchNKE/CyYBAAAAQGdITU113d+/f7+mTJmipKQkTZkyRfv27WszDgC6gtcLoc8++6wSEhIUGBio5ORkFRUVXTR+48aNSk5OVmBgoK699lo9//zzrWJWrlyp4cOHy2w2a/jw4Vq9evVln9fhcOjRRx9VXFycgoKCNGbMGH322WduMWPGjJHBYHC7ff/73+/Au+CbKmtOS2pZMd7k7/WvmpuECAqhAAAAANAZTCaT6350dLTWrFmjTz/9VGvWrFF0dHSbcQDQFbxanXrzzTeVm5urhx9+WNu3b1d6erpuvvlmlZWVtRm/d+9e3XLLLUpPT9f27dv10EMPafbs2Vq5cqUrpri4WNOnT1dOTo4++eQT5eTkaNq0adq6detlnXf+/PlatGiRlixZom3btikmJkY33XSTampq3HKaMWOGysvLXbcXXnjBw+9Sz+WaH7QbLZTkREcoAAAAAHSOlJQUj8YBgKd4tRC6aNEi3XXXXfrpT3+qYcOGafHixYqPj9dzzz3XZvzzzz+vAQMGaPHixRo2bJh++tOf6ic/+YkWLFjgilm8eLFuuukmzZ07V1//+tc1d+5cjRs3TosXL273eR0OhxYvXqyHH35YWVlZSkxM1Msvv6xTp05p+fLlbjkFBwcrJibGdbNYWHjHqbK6e84PKrXMEWowSNb6Jh0/s7I9AAAAAODK9e/f36NxAOApXiuENjY2qqSkRBkZGW7bMzIytHnz5jafU1xc3Cp+woQJ+uijj9TU1HTRGOcx23PevXv3qqKiwi3GbDZr9OjRrXJ7/fXXFRkZqeuuu075+fmtOkbP19DQoOrqarebrzpa2307QgMDjBrQL1iStLvi4p8ZAAAAAKD9Vq1a5dE4APAUf2+d+NixY7LZbG7zg0gt84dUVFS0+ZyKioo245ubm3Xs2DHFxsZeMMZ5zPac1/mzrZj9+/e7Hv/whz9UQkKCYmJiVFpaqrlz5+qTTz7R+vXrL/i6CwoK9Nhjj11wvy9xdoT2D+1+hVBJ+npMqPYfP6VdFTVKGxzp7XQAAAAAwCf8/e9/d92/5ZZbNHjwYJ0+fVqBgYH697//rbVr17aKA4Cu4LVCqJPBYHB77HA4Wm27VPz529tzTE/EzJgxw3U/MTFRQ4YM0YgRI/Txxx/rxhtvbDP/uXPnas6cOa7H1dXVio+PbzO2p3N1hHbbQmiY/vrZEX1e7rtduQAAAADQ1ZwjHxMTE7Vz505X4VOSEhISNHz4cO3cudOnR0gC6J68NjQ+MjJSRqOxVfdnZWVlq05Mp5iYmDbj/f39FRERcdEY5zHbc96YmBhJuqzcJOnGG29UQECA9uzZc8EYs9mssLAwt5uvqqxuWTW+uxZCh8W2vPefMzQeAAAAADzm2muvlSTt3LnTNY2dU2Njoz7//HO3OADoKl4rhJpMJiUnJ7caRr5+/XqlpaW1+ZzU1NRW8YWFhRoxYoQCAgIuGuM8ZnvO6xzufm5MY2OjNm7ceMHcJOmzzz5TU1OTYmNjL/bSew1nR2hUaKCXM2nbsNhQSdIXR2rUbLN7ORsAAAAA8A3f+973JEl2u12HDh1y23fo0CHZ7Xa3OADoKl5dNX7OnDn64x//qP/93//Vrl27dP/996usrEz33HOPpJZh5D/60Y9c8ffcc4/279+vOXPmaNeuXfrf//1fLV26VPn5+a6Yn/3sZyosLNSTTz6pzz//XE8++aQ2bNig3Nzcdp/XYDAoNzdX8+bN0+rVq1VaWqo777xTwcHBys7OliR9+eWXevzxx/XRRx9p3759Wrt2rf7zP/9TN9xwg0aNGtUF7173d7Smew+Njw8PVrDJqIZmu/Ydr/N2OgAAAADgE/77v//bo3EA4ClenSN0+vTpOn78uB5//HGVl5crMTFRa9eu1TXXXCNJKi8vV1lZmSs+ISFBa9eu1f33368//OEPiouL0+9//3vdfvvtrpi0tDStWLFCv/zlL/XII49o0KBBevPNN5WSktLu80rSAw88oPr6es2aNUtVVVVKSUlRYWGhQkNbughNJpPef/99/e53v1Ntba3i4+N166236te//rWMRmNnv3Xd3ukmm2pON0uSosK6ZyHUz8+goTGh2l52UrvKazQ4KtTbKQEAAABAj7d58+Z2x40bN66TswGAswwO52pD8Irq6mpZLBZZrVafmi/0wIlTSp//gcz+fvr8fyZecAGs5VvL2tzeWbJTBrg9nrvqU73xYZnuHTtIP5/w9S7NBQAAX+Gr1zO9BZ8fAE975JFH9Jvf/OaScb/85S/1P//zP12QEQBf197rGa8OjYfvqqw5u1DShYqg3YFzntDPy1kwCQAAAAA8obm5ZXRgSEiI4uPj3fbFx8crJCTELQ4AuopXh8bDdznnB43qpvODOn09hpXjAQAAAMCTTp48KUmqq6vT6NGjlZWVpfr6egUFBWnPnj1au3atWxwAdBUKoegUld18oSSnoTEtHaGHTtbLWt8kS1CAlzMCAAAAAN/x3nvvuQqfkrr1iEEAvo+h8egUZztCA72cycVZggJ0Vd8gSdLn5dVezgYAAAAAer5zFxA+f1mScx+z0DCArkYhFJ2isrpndIRK58wTyvB4AAAAALhiycnJHo0DAE+hEIpOcbS25xRCnfOE7qIjFAAAAACu2IcffujROADwFAqh6BTOVeO7+2JJkpR4VUshdMeBk95NBAAAAAB8wKFDhzwaBwCeQiEUneJoD1ksSZJuHBAuSdp9pEY1p5u8nA0AAAAA9GwUQgF0VxRC4XE2u0PHahsldf/FkiQpKixQ8f2C5HDQFQoAAAAAV8pkMnk0DgA8hUIoPK7qVKNsdocMBimiT8/4hy35TFdoyf4qL2cCAAAAAD1bQ0ODR+MAwFMohMLjnCvGhwebFGDsGV+xG6+hEAoAANrvH//4h2677TbFxcXJYDBozZo1bvsdDoceffRRxcXFKSgoSGPGjNFnn33mFtPQ0KD77rtPkZGRCgkJ0aRJk3Tw4EG3mKqqKuXk5MhischisSgnJ0cnT550iykrK9Ntt92mkJAQRUZGavbs2WpsbOyMlw0A7XL69GmPxgGAp/SMKhV6lMMn6yVJsZbuPyzeyTlP6I6yk7LbHV7OBgAAdHd1dXX65je/qSVLlrS5f/78+Vq0aJGWLFmibdu2KSYmRjfddJNqampcMbm5uVq9erVWrFihTZs2qba2VpmZmbLZbK6Y7Oxs7dixQ+vWrdO6deu0Y8cO5eTkuPbbbDbdeuutqqur06ZNm7RixQqtXLlSeXl5nffiAeASDAaDR+MAwFP8vZ0AfM+hM4XQq/oGeTmT9vt6TKiCTUbVNDRrT2WthsaEejslAADQjd188826+eab29zncDi0ePFiPfzww8rKypIkvfzyy4qOjtby5cs1c+ZMWa1WLV26VK+++qrGjx8vSXrttdcUHx+vDRs2aMKECdq1a5fWrVunLVu2KCUlRZL00ksvKTU1Vbt379bQoUNVWFionTt36sCBA4qLi5MkLVy4UHfeeaeeeOIJhYWFdcG7AQDuzOb2LZrb3jgA8BQKofA4VyE0vOcUQv2Nfro+vq82f3lcJfurKIQCAIAO27t3ryoqKpSRkeHaZjabNXr0aG3evFkzZ85USUmJmpqa3GLi4uKUmJiozZs3a8KECSouLpbFYnEVQSVp5MiRslgs2rx5s4YOHari4mIlJia6iqCSNGHCBDU0NKikpERjx47tmhcNwGfUN9r05dHaKzqGpX+M676fn5/sdnubjy39Y1R6yHpF5xrUv4+CTMYrOgaA3oNCKDyuJ3aESlLyNeGuQmh2ygBvpwMAAHqoiooKSVJ0dLTb9ujoaO3fv98VYzKZFB4e3irG+fyKigpFRUW1On5UVJRbzPnnCQ8Pl8lkcsWcr6GhwW2Bkurq6st5eQB83JdHa5X5zKYrOoa1KdZ1/9wi6PmPdzTFXvG53rnvO0q8ynJFxwDQe1AIhccdqmophF7dgzpCpbMLJn1cxoJJAADgyp0/953D4bjkfHjnx7QV35GYcxUUFOixxx67aB4Aeq9B/fvonfu+c0XHaGz8tr41eFmrIui5/Pz89P7S38pkMl3RuQb173NFzwfQu1AIhced7QgN9nIml+fG+JZC6N5jdTpe26CIPsxXAwAALl9MTMuQ0IqKCsXGnu2KqqysdHVvxsTEqLGxUVVVVW5doZWVlUpLS3PFHDlypNXxjx496nacrVu3uu2vqqpSU1NTq05Rp7lz52rOnDmux9XV1YqPj+/ISwXgg4JMRo90WObl5empp56SwWCQw3F2QVrn0Pi8vDzdmND/is8DAJeDVePhUaebbDpa0zLUqifNESpJluAAfS265beJW7464eVsAABAT5WQkKCYmBitX7/eta2xsVEbN250FTmTk5MVEBDgFlNeXq7S0lJXTGpqqqxWqz788ENXzNatW2W1Wt1iSktLVV5e7oopLCyU2WxWcnJym/mZzWaFhYW53QDA0+bPn6+f//zn8vNzLzv4+fnp5z//uebPn++lzAD0ZhRC4VHl1tOSpKAAo8KDA7yczeVLH9LyG8m/7670ciYAAKA7q62t1Y4dO7Rjxw5JLQsk7dixQ2VlZTIYDMrNzdW8efO0evVqlZaW6s4771RwcLCys7MlSRaLRXfddZfy8vL0/vvva/v27fqv//ovJSUluVaRHzZsmCZOnKgZM2Zoy5Yt2rJli2bMmKHMzEwNHTpUkpSRkaHhw4crJydH27dv1/vvv6/8/HzNmDGDAicAr5s/f75OnTqlB349T6E3ZuqBX89TXV0dRVAAXsPQeHiUc37QuL6Bl5wDqzsaOzRKSzft1d+/ONquebwAAEDv9NFHH7mtyO4can7HHXdo2bJleuCBB1RfX69Zs2apqqpKKSkpKiwsVGhoqOs5Tz/9tPz9/TVt2jTV19dr3LhxWrZsmYzGs6sfv/7665o9e7ZrdflJkyZpyZIlrv1Go1HvvvuuZs2apVGjRikoKEjZ2dlasGBBZ78FANAuJpNJOTNm6c3T31DOjO9c8ZygAHAlKITCow6dPCVJuiq8Z80P6vSthHAFm4w6WtOgzw5Xs/ogAABo05gxY9zmvDufwWDQo48+qkcfffSCMYGBgXrmmWf0zDPPXDCmX79+eu211y6ay4ABA/TOO+9cMmcAAIDejqHx8ChnR+hVfXvW/KBOZn+j0gZFSJI2fnHUy9kAAAAAAADAUyiEwqMOnlkx/uoetlDSucYMjZLEPKEAAAAAAAC+hEIoPOrwyZ7dESpJY4a2LJhUsr9K1lNNXs4GAAAAAAAAnkAhFB51yFkI7cEdoVeHB2twVB/ZHVLRvxkeDwAAAAAA4AsohMJjbHaHyk+eltSzO0IlaeyZrtAPPqcQCgAAAAAA4AsohMJjKmtOq9nukL+fQdFhgd5O54qM/XrLPKHvf35Ejc12L2cDAAAAAACAK+Xv7QTgO5wrxsdYAmX0M3g5m7Yt31p2yZjslAFKSYhQ/1CzjtY0aOMXR3XT8OguyA4AAAAAAACdhY5QeMwhH1goycnoZ9Dkb8ZJklZvP+jlbAAAAAAAAHClKITCYw5W9fyFks415YarJEkbdlXKWs/q8QAAAAAAAD0ZhVB4jLMj9Gof6AiVpOviwvS16D5qbLZrXWm5t9MBAAAAAADAFaAQCo855GMdoQaDwdUVunr7IS9nAwAAAAAAgCvBYknwmP3H6yRJ8f2CvZyJ50y+/irNX7dbW746oUMn631i/lMAAAAAvmPvsTrVNTR7O42L+ndlrdvP7izE7K+EyBBvpwGgk1AIhUecbrKp7MQpSdKQqFAvZ+M5V/UNUkpCP23de0JvbjugOTd9zdspAQAAAICkliLo2AV/93Ya7Zb75g5vp9AuH+SPoRgK+CgKofCIr47Wye6Q+gYHKLKPydvpXJHlW8vcHidEhmjr3hNaWvSVIkJMCjD6KTtlgJeyAwAAAIAWzk7QxdOv1+CoPl7O5sJON9l0sKpeV4cHKTDA6O10LujflbXKfXNHt++wBdBxFELhEXsqayRJQ6L6yGAweDkbz7ouzqK+QRU6Wd+kTw6c1IiB/bydEgAAAAC4DI7qo8SrLN5O46JGDPR2BgDAYknwEOdcL4N9aFi8k9HPoNRBEZKkTf8+JofD4eWMAAAAAAAAcLkohMIj9hxpKYQO6cbDMa7Etwb2k8nfT5U1DT1igm8AAAAAAAC4oxAKj3ANjY/2zUJoYIBRydeES2rpCgUAAAAAAEDPQiEUV6yx2a59x31vxfjzjRoUKYOkPZW1+risytvpAAAAAAAA4DKwWBKu2L7jdbLZHQo1+ys6zOztdDpNvxCTbhwQrpKyKj21breWz0jxuYWhAAAAAPQsBv9q7a3eLb9A3xyd15X2VtfK4F/t7TQAdCIKobhizvlBB0f73orx5xs3LEo7Dp5U8VfH9c9/H9d3hkR6OyUAAAAAvVhA36166MN53k7DZwT0HSfpFm+nAaCTUAjFFXPODzq4v+//BrJvsEkpCf20+cvjeuqvn2vU4FE+X/wFAAAA0H01nUzRwluzNchHF67tSl9W1mr26196Ow0AnYhCKK7YnjOrqPvqQknnGzM0SjsOnNQnB61aV1qhm5NivZ0SAAAAgF7K0RymhLChGh5h8XYqPZ79tFWO5qPeTgNAJ6IQiiv27zND4315oaRz9TH766fp1+r37+/Rb97dpdFD+yvYxB8lAAAAAF2rvskmSSo9ZPVyJhd3usmmg1X1ujo8SIEBRm+nc0H/PtPkA8B3Ub3BFWm22fXVsTNzhPaioRj/b/QgrSw5qEMn6/XM3/6tByd+3dspAQAAAOhlvjxTuPvFqk+9nIlvCTFTKgF8FX+6cUXKTpxSk82hoACjruob5O10ukyQyahHJ12nGa98pJf+8ZWybrhKQ6J7R0csAAAAgO4h47oYSdKgqD4K6uadlrlv7tDi6dd3+waaELO/EiJDvJ0GgE5CIRRX5IsjLQslDYoKkZ+f+6JBy7eWeSOlLnPT8GiNHxalDbsq9cifS/XGjJEsnAQAAACgy/QLMen73x7g7TTabXBUHyVexVymALzHz9sJoGf7aF+VJOkbV/f1biJe8uvbrlNggJ+2fHVCr23Z7+10AAAAAAAAcAEUQnFFtu07IUn69sB+Xs7EO+L7BbvmB31i7S59eZTJtQEAAAAAALojhsajw+oamlV6uFqS9K2E3lUIPXfYf4DRT4Oj+ujflbX60dIPdc/oQTL6GZSd0nOGqAAAAAAAAPg6OkLRYdvLTspmd+iqvkG9aqGk8/kZDLr9xqsVFGDUoZP12rDriLdTAgAAAAAAwHkohKLDPjwzLP5bA8O9nIn3WYICNPn6OEnSxi+OqvSQ1csZAQAAAAAA4FwUQtFh2/aeKYT2smHxF/KNq/tq1KAISdJbJQe1u6LGyxkBAAAAAADAiUIoOqSx2a7tB1pWjO+tCyW1ZWJirK7tH6JGm113v/qRjtc2eDslAADQSzz77LNKSEhQYGCgkpOTVVRU5O2UAAAAuhUKoeiQ0sNWnW6yKzw4QIOj+ng7nW7D6GfQD741QOHBAdp//JR+9L8fylrf5O20AACAj3vzzTeVm5urhx9+WNu3b1d6erpuvvlmlZWVXfrJAAAAvQSrxqNDnMPiRwzsJ4PB4OVsupcQs7/uTEvQq1v26bPD1brzTx/q1btS1MfMHzcAANA5Fi1apLvuuks//elPJUmLFy/WX//6Vz333HMqKCjwcnYAepL6Rpu+PFrr0WP+u7LW7acnDerfR0Emo8ePC8A3UZlBh2w7s1ASw+Lb1j/UrFfvStH3X9yi7WUn9aOlW/Xij0Yoso/Z26kBAAAf09jYqJKSEv3iF79w256RkaHNmze3im9oaFBDw9npe6qrqzs9RwA9x5dHa5X5zKZOOXbumzs8fsx37vuOEq+yePy4AHwThVBcttNNNm1loaRLGhYbplfv+rb+649b9XHZSU35wz+19I5vaWhMqLdTAwAAPuTYsWOy2WyKjo522x4dHa2KiopW8QUFBXrssce6Kj0APcyg/n30zn3f8egxTzfZdLCqXleHBykwwLPdm4P6M1UbgPajEIrL9v6uStWcblacJVDf4DdvF/WNq/tq1axR+unL27Tv+Cnd/txm/fLWYZo2Il5+fkwpAAAAPOf86YocDkebUxjNnTtXc+bMcT2urq5WfHx8p+cHoGcIMhk7pcNyxECPHxIALhuLJeGyrfz4oCTpezdeRTGvHQZH9dGae0cp9doI1TY06xerPtX0F4v1eQXD0AAAwJWLjIyU0Whs1f1ZWVnZqktUksxms8LCwtxuAAAAvQGFUFyWozUN2vjFUUlS1o1XezmbnqNvsEmv3vVt/fLWYQo2GbVtX5UmLi5SztKten/XETXZ7N5OEQAA9FAmk0nJyclav3692/b169crLS3NS1kBAAB0PwyNx2X5845Dstkduj6+L3OxXCZ/o59+mn6tbk6K1bx3d2ltabmK9hxT0Z5jCjEZ9e2EfhoxsJ+ujQzRwMgQRYWa1TfYJCNdtwAA4BLmzJmjnJwcjRgxQqmpqXrxxRdVVlame+65x9upAQAAdBsUQnFZVn18SJJ0ezLdoJeyfGvZBfeNGhypYbFhstY3auXHh3SirlEf7D6qD3YfdYszGKRAf6NCzP4KMRkVfOZniNlfwSajQkz+sgQH6N6xg9UvxNTZLwkAAHRT06dP1/Hjx/X444+rvLxciYmJWrt2ra655hpvpwYAANBtGBwOh8PbSfRm1dXVslgsslqt3X5+pl3l1br5d0UyGf304cPj1Df44oW3ixUC0SI7ZYDsdod2VVTrn/8+pp2Hq7X3+CntP16nk6eaLutY/UPNuj6+r741MFwjBvZTYpxFJn9mvwAAdL6edD2D1vj8AABAT9fe6xk6QtFuz/39S0nSuGFRlyyCov38/Ay6Ls6i6+LcV2Zsstl18lSTXtuyX3UNzaprtOlUY7PqGpw/W7adqGvUibpGHa1p0PqdR7R+5xFJkr+fQVeHByshMlgDI0P08wlDFWzijzwAAAAAAOidqIqgXf7572N6+5PD8jNIs8YM9nY6vUKA0U/9Q82KDgu8ZGxDs00V1tMqO3FK+850lJ5qtGnf8TrtO14n7T6qV4v3K+lqi1ISIpR8Tbi+cbWlXccGAAAAAADwBRRCcUkNzTY98udSSVLOyGuUdLXlEs9Ae3lq+gCzv1HXRITomogQpQ+RHA6HjtU2thRCj9Vp77E6naxv0vayk9pedtL1vOgws5Ku6qtvXG1R0tUW7TlSqz7mi/+1kJ0ywCM5AwAAAAAAdCUKobikPxbt1VdH6xTZx6w5GUO9nQ7awWAwqH+oWf1DzfrWwH6SpO9+LVJbvzqhD/ee0CcHT+qLIzU6Ut2gI9VHtGHXEddz+wYH6OrwYMWHByk+PFhxfYOYaxQAAAAAAPR4FEJxURt2HtHvNuyRJD2SOUyWoAAvZ4SOujo8WFcnB+v25KslSacam7XzcLX+ddCqTw9Z9cnBk/rqaMsiTSdPWVV6yCpJ8jNIMZZADegXooERwRo/LEpRDKkHAAAAAAA9jNfbvJ599lklJCQoMDBQycnJKioqumj8xo0blZycrMDAQF177bV6/vnnW8WsXLlSw4cPl9ls1vDhw7V69erLPq/D4dCjjz6quLg4BQUFacyYMfrss8/cYhoaGnTfffcpMjJSISEhmjRpkg4ePNiBd6F7Wr39oGa+VqJGm123JsVq0jfjvJ0SrsDyrWVutzXbD+uLI7UKDDDqWwP76affuVa/yhyuu76ToAnDozUsNkyhZn/ZHdLhk6e15avjWrHtgL49732NeeoD/fz/+0T/99EB7TtWJ4fD4e2XBwAAAAAAcFFe7Qh98803lZubq2effVajRo3SCy+8oJtvvlk7d+7UgAGt5yHcu3evbrnlFs2YMUOvvfaa/vnPf2rWrFnq37+/br/9dklScXGxpk+frv/5n//R9773Pa1evVrTpk3Tpk2blJKS0u7zzp8/X4sWLdKyZcv0ta99Tb/5zW900003affu3QoNDZUk5ebm6i9/+YtWrFihiIgI5eXlKTMzUyUlJTIajV30Lnre8doGPb/xS71UtFeSlHXjVZp/+zdkMBi8nBk6W2CAUYP699Gg/n0ktfxC4GR9kw6cswhTRfVp7Tve8vj/K2kp/PcPNSsxLkzDYs/eEiJDZPTjO9MV2jPXLHO7AgAAAAB6O4PDi61cKSkpuvHGG/Xcc8+5tg0bNkxTpkxRQUFBq/gHH3xQb7/9tnbt2uXads899+iTTz5RcXGxJGn69Omqrq7We++954qZOHGiwsPD9cYbb7TrvA6HQ3FxccrNzdWDDz4oqaX7Mzo6Wk8++aRmzpwpq9Wq/v3769VXX9X06dMlSYcPH1Z8fLzWrl2rCRMmtOs9qK6ulsVikdVqVVhYWHvfOo873WTTx/ur9LfPK7X8wzKdarRJku5IvUa/vu06+XWgoOWphYDQvdz6jVh9vL9KW/ee0LZ9J/SvgyfVZGv914jZ309Xhwcprm+QYi2BiusbpDhLkCL6mNQ3OECWIOfPAAUYvd6c7jEOh0PNdodsdofsZ+7bzzy22R2yOc65fyamsdmhusZm1TY0q851s6muoVm1je6PW+JsrrhTjTY12ewKMPrJ6GeQv59BAUY/mQP8FGLyV4jZXyFmo8YMjVJEiKnl1sekvsEmn3vvAXhPd7meQcfw+QEAgJ6uvdczXusIbWxsVElJiX7xi1+4bc/IyNDmzZvbfE5xcbEyMjLctk2YMEFLly5VU1OTAgICVFxcrPvvv79VzOLFi9t93r1796qiosLtXGazWaNHj9bmzZs1c+ZMlZSUqKmpyS0mLi5OiYmJ2rx5c7sLoV1h5+FqfV5RrVONNp1usulUo031TS2FlHLraR2sqteXR2vV2Gx3PSfpKovuv2mIxg6NohMUbixBARr79SiN/XqUpJYieukhq3aVV2tneY12lVdrd0WN6pts+vJonb48WnfJY4aYjOobbFJYUIBCTEYFmYwKNhkVFGBUkMlfwWceBwYYZfQzyGgwtPw8/2YwyN9okJ/BIIdaipIOh+TQmZ8Oye5wyCFJZ+432R1qbLaryWZXY/OZm+3sz4YmuxqabWpotquh2a4DJ06pyWZXs82hZnvLzya7Q81nttm89rsl20X3vr+rss3toWZ/WYIDFB58tjBtCQqQ2d8ok7+fTP5+Mp9zM/n7yejnJ4Mk518NBoNkkMF138lgcG51j7E7HGdvdsnmcMjhcMhmP2ef3aFGm12nm+w63dTy/p/78/SZz+XAiVOuwvOFis2BAUYFGP0UYDS4/TT5+7UUjf39FBhgbP0zwE9mf6MCz/wMMBpkMLR8z/wMkp/BID+/1vcNMri+Z87fNTq/h3a7zn43Jcm53SH376okm83u+t41NJ99zS3fybPvR0Oz7cx32NHq+9tks6u+0SY/g0EGZ55nfvobDfL3aymgx/UNPPNZG2Uynv3cXdvO+y447wcY/c5+D2TQ+f9cOP/9aOt7cPZ5LZx/cpzvw9n7zu1n/2y1inGcPYbrvT3n/W19rLa365znthzXcc5xpWb72fe5yXbO3xs2h5psdu05UuP2PWz13bQ7FGwythzDZpfd7mj5Tvqf+U4a/Vp9R52/3Ajwd+53fo+d3+GWz9F537XvvONmXBcjAAAAoLfzWiH02LFjstlsio6OdtseHR2tioqKNp9TUVHRZnxzc7OOHTum2NjYC8Y4j9me8zp/thWzf/9+V4zJZFJ4eHi785daOksbGhpcj63WlgVpqqurL/icK/V/xV/ofzftvWRc/z4mpVwboQnXxWjM0P4yGAyqqanp8HlP1XX8uei+2vqufq2fv77Wr58mX9eyQr3N7tBL//hK1tNNsp5qUvXpRllPNav6dNPZgnxTS0FHkmoapCv4qvUYfgbJ6NdSTPP3k/z8DPI3GORv9FOI2V9BJuOZLs6Wn8Emow5V1csUcLboZD5TmArw95P5TIHDIanZfqYwa3fIZrOrvsmmUw021TW2dI32DQ7QibomVZ1q1Im6BtWcbimcWhska7Xky/3b9d5OoAc4cMTbGaAzmfz99PEjN3XqOZz/NjBndM/k/Nw683oUAACgM7X3etTrq8af323ocDgu2oHYVvz529tzTE/FnO9SMQUFBXrsscdabY+Pj7/ocbvCAUkfS3ruUoHo1WZ4OwEAwGWzLOia89TU1MhisXTNyeAxzl98d4frUQAAgCtxqetRrxVCIyMjZTQaW3VPVlZWturEdIqJiWkz3t/fXxEREReNcR6zPeeNiWkZPlZRUaHY2NgLxjQ2NqqqqsqtK7SyslJpaWkXfN1z587VnDlzXI/tdrtOnDihiIgInxmCXl1drfj4eB04cIB5pnooPsOejc+v5+Mz7Pl622focDhUU1OjuLg4b6eCDoiLi9OBAwcUGhrqM9ejALqX3vbvIoCu197rUa8VQk0mk5KTk7V+/Xp973vfc21fv369Jk+e3OZzUlNT9Ze//MVtW2FhoUaMGKGAgABXzPr1693mCS0sLHQVJ9tz3oSEBMXExGj9+vW64YYbJLXMLbpx40Y9+eSTkqTk5GQFBARo/fr1mjZtmiSpvLxcpaWlmj9//gVft9lsltlsdtvWt2/fC79RPVhYWBj/yPVwfIY9G59fz8dn2PP1ps+QTtCey+//b+/eo6Iq9/+Bv5FhYEZpEDiKV/CCoHnBA4qYJy94QfGIUQalqOViZSdM0kQNTWuFYieO4rFjRw+CmjZ0vEVIrpSEFQiiyKgJgZpKuSjKBu+BA5/fH33dxy3g7SdC+H6tNWuxn+cz+3n284E1w2f2nt2iBTp27NjY0yCix8Dj9LpIRI/evbwfbdRL4+fMmYOwsDD4+PjAz88P69atQ2lpKWbOnAng97Mnz58/j02bNgH4/Q7xa9aswZw5cxAeHo6cnBwkJCQod4MHgNmzZ+Ppp5/GihUrEBQUhM8++wz79u1DVlbWPY9rZWWFyMhILFu2DO7u7nB3d8eyZcug1+vx4osvAvh9cWfMmIG5c+fCyckJjo6OePPNN9GnTx+MHDnyUS0hERERERERERER3YNGLYSGhITgwoULePfdd1FWVobevXsjLS0Nrq6uAH4/w7K09H+38OjSpQvS0tLwxhtv4MMPP0T79u2xevVqPPvss0rM4MGDYTQasWjRIixevBjdunVDcnIyfH1973lcAIiKisL169fxt7/9DWazGb6+vvjyyy9hb2+vxKxcuRIajQbPP/88rl+/Dn9/fyQlJcHa2rohl42IiIiIiIiIiIjuk5Xw9p70kFVWVmL58uVYuHBhra8BoD8G5vCPjfn742MO//iYQyIiov/h6yIRNRUshBIREREREREREVGz16KxJ0BERERERERERETU0FgIJSIiIiIiIiIiomaPhVAiIiIiIiIiIiJq9lgIJSIiIiIiInoMLF26FF5eXsr29OnTMXHixEabDxHRo8ZCKD10//rXv9ClSxfY2dnB29sbX3/9dWNPqdlbvnw5BgwYAHt7e7Rp0wYTJ05EcXGxKkZEsHTpUrRv3x46nQ7Dhg3DiRMnVDGVlZWYNWsWnJ2d0bJlS0yYMAE//PCDKsZsNiMsLAwGgwEGgwFhYWGoqKhQxZSWluKvf/0rWrZsCWdnZ7z++uuoqqpqkGNvjpYvXw4rKytERkYqbcxf03f+/HlMmTIFTk5O0Ov18PLyQn5+vtLPHDZtFosFixYtQpcuXaDT6dC1a1e8++67qKmpUWKYQyIiamgHDhyAtbU1AgICHsl48fHxSEpKeiRj3TRs2DDV+1wAOHv2LKysrGAymR7pXIjo8cNCKD1UycnJiIyMRHR0NAoKCvCXv/wFY8eORWlpaWNPrVnLzMzEa6+9htzcXOzduxcWiwWjR4/G1atXlZj3338f//jHP7BmzRocOnQILi4uGDVqFC5fvqzEREZGYufOnTAajcjKysKVK1cwfvx4VFdXKzEvvvgiTCYT9uzZgz179sBkMiEsLEzpr66uRmBgIK5evYqsrCwYjUZs374dc+fOfTSL8Qd36NAhrFu3Dn379lW1M39Nm9lsxlNPPQUbGxt88cUXKCwsRFxcHBwcHJQY5rBpW7FiBT766COsWbMGRUVFeP/99/H3v/8d//znP5UY5pCIiBrahg0bMGvWLGRlZT2S/6EMBoPq/QoRUbMnRA/RwIEDZebMmao2T09PWbBgQSPN6PFUXl4uACQzM1NERGpqasTFxUViY2OVmN9++00MBoN89NFHIiJSUVEhNjY2YjQalZjz589LixYtZM+ePSIiUlhYKAAkNzdXicnJyREA8u2334qISFpamrRo0ULOnz+vxHzyySdia2srFy9ebLiDbgYuX74s7u7usnfvXhk6dKjMnj1bRJi/P4L58+fLkCFD6u1nDpu+wMBAefnll1VtwcHBMmXKFBFhDomIqOFduXJF7O3t5dtvv5WQkBB55513lL79+/cLAElNTZW+ffuKra2tDBw4UI4dO6bEJCYmisFgkJ07d4q7u7vY2trKyJEjpbS0VIlZsmSJ9OvXT9meNm2aBAUFKdvV1dUSGxsr3bp1E61WK506dZL33ntP6Y+KihJ3d3fR6XTSpUsXWbRokVRVVdXa/6ZNm8TV1VWeeOIJCQkJkUuXLinjAVA9zpw5I2fOnBEAUlBQoDreffv2ibe3t+h0OvHz81NeK2/67LPPxNvbW2xtbcXJyUmeeeYZpe/XX3+VsLAwcXBwEJ1OJwEBAVJSUlJrvT7//HPp0aOH6HQ6efbZZ+XKlSuSlJQkrq6u4uDgIBEREWKxWJTnVVZWyrx586R9+/ai1+tl4MCBsn///nvMMhE1Np4RSg9NVVUV8vPzMXr0aFX76NGjceDAgUaa1ePp4sWLAABHR0cAwJkzZ/Djjz+qcmNra4uhQ4cqucnPz8eNGzdUMe3bt0fv3r2VmJycHBgMBvj6+ioxgwYNgsFgUMX07t0b7du3V2LGjBmDyspK1WXCVNtrr72GwMBAjBw5UtXO/DV9KSkp8PHxwaRJk9CmTRv0798f69evV/qZw6ZvyJAhSE9PR0lJCQDg6NGjyMrKwrhx4wAwh0RE1PCSk5Ph4eEBDw8PTJkyBYmJiRARVcy8efPwwQcf4NChQ2jTpg0mTJiAGzduKP3Xrl1DTEwMNm7ciOzsbFy6dAmhoaH3PIeFCxdixYoVWLx4MQoLC7F161a0bdtW6be3t0dSUhIKCwsRHx+P9evXY+XKlap9nD59Grt27UJqaipSU1ORmZmJ2NhYAL9fiu/n54fw8HCUlZWhrKwMnTp1qnc+0dHRiIuLw+HDh6HRaPDyyy8rfbt370ZwcDACAwNRUFCA9PR0+Pj4KP3Tp0/H4cOHkZKSgpycHIgIxo0bV2u9Vq9eDaPRiD179iAjIwPBwcFIS0tDWloaNm/ejHXr1mHbtm3Kc1566SVkZ2fDaDTi2LFjmDRpEgICAnDy5Ml7Xmciajyaxp4ANR+//PILqqurVS+UANC2bVv8+OOPjTSrx4+IYM6cORgyZAh69+4NAMr615Wbc+fOKTFarRatW7euFXPz+T/++CPatGlTa8w2bdqoYm4fp3Xr1tBqtfw9uAOj0YgjR47g0KFDtfqYv6bvu+++w9q1azFnzhy89dZbyMvLw+uvvw5bW1tMnTqVOfwDmD9/Pi5evAhPT09YW1ujuroaMTExeOGFFwDw75CIiBpeQkICpkyZAgAICAjAlStXkJ6ervqQfMmSJRg1ahQAYOPGjejYsSN27tyJ559/HgBw48YNrFmzRvnAbePGjejZsyfy8vIwcODAO45/+fJlxMfHY82aNZg2bRoAoFu3bhgyZIgSs2jRIuVnNzc3zJ07F8nJyYiKilLaa2pqkJSUBHt7ewBAWFgY0tPTERMTA4PBAK1WC71eDxcXl7uuSUxMDIYOHQoAWLBgAQIDA/Hbb7/Bzs4OMTExCA0NxTvvvKPE9+vXDwBw8uRJpKSkIDs7G4MHDwYAbNmyBZ06dcKuXbswadIkZb3Wrl2Lbt26AQCee+45bN68GT/99BNatWqFXr16Yfjw4di/fz9CQkJw+vRpfPLJJ/jhhx+UDyzffPNN7NmzB4mJiVi2bNldj4mIGhcLofTQWVlZqbZFpFYbNZyIiAgcO3YMWVlZtfoeJDe3x9QV/yAx9D/ff/89Zs+ejS+//BJ2dnb1xjF/TVdNTQ18fHyUN7/9+/fHiRMnsHbtWkydOlWJYw6bruTkZHz88cfYunUrnnzySZhMJkRGRqJ9+/bKP4MAc0hERA2juLgYeXl52LFjBwBAo9EgJCQEGzZsUBVC/fz8lJ8dHR3h4eGBoqIipU2j0ajOivT09ISDgwOKioruWggtKipCZWUl/P39643Ztm0bVq1ahVOnTuHKlSuwWCx44oknVDFubm5KERQA2rVrh/Ly8rusQN1u/d78du3aAQDKy8vRuXNnmEwmhIeH13ssGo1GdQWGk5NTrfXS6/VKERT4/cNLNzc3tGrVStV2c/5HjhyBiKBHjx6q8SorK+Hk5PRAx0hEjxYvjaeHxtnZGdbW1rXOVikvL691Zgs1jFmzZiElJQX79+9Hx44dlfabn7beKTcuLi6oqqqC2Wy+Y8xPP/1Ua9yff/5ZFXP7OGazGTdu3ODvQT3y8/NRXl4Ob29vaDQaaDQaZGZmYvXq1dBoNMq6MX9NV7t27dCrVy9VW8+ePZWbHPBvsOmbN28eFixYgNDQUPTp0wdhYWF44403sHz5cgDMIRERNayEhARYLBZ06NBBeT+4du1a7Nixo9bryu1u/5Csrg/N7uWDNJ1Od8f+3NxchIaGYuzYsUhNTUVBQQGio6NRVVWlirOxsak1dk1NzV3Hr8ut+7p5DDf3daf53v6VAre237oWdc31TvOvqamBtbU18vPzYTKZlEdRURHi4+Pv48iIqLGwEEoPjVarhbe3N/bu3atq37t3r3I5AjUMEUFERAR27NiBr776Cl26dFH1d+nSBS4uLqrcVFVVITMzU8mNt7c3bGxsVDFlZWX45ptvlBg/Pz9cvHgReXl5SszBgwdx8eJFVcw333yDsrIyJebLL7+Era0tvL29H/7BNwP+/v44fvy46s2Uj48PJk+eDJPJhK5duzJ/TdxTTz2F4uJiVVtJSQlcXV0B8G/wj+DatWto0UL9tsja2lr5x4c5JCKihmKxWLBp0ybExcWp3g8ePXoUrq6u2LJlixKbm5ur/Gw2m1FSUgJPT0/Vvg4fPqxsFxcXo6KiQhVTH3d3d+h0OqSnp9fZn52dDVdXV0RHR8PHxwfu7u7K18PcD61Wi+rq6vt+3u369u1b71x79eoFi8WCgwcPKm0XLlxASUkJevbs+cBj9u/fH9XV1SgvL0f37t1Vj3u51J+ImoBHd18mehwYjUaxsbGRhIQEKSwslMjISGnZsqWcPXu2safWrL366qtiMBgkIyNDysrKlMe1a9eUmNjYWDEYDLJjxw45fvy4vPDCC9KuXTvlDo4iIjNnzpSOHTvKvn375MiRIzJixAjp16+f6i6JAQEB0rdvX8nJyZGcnBzp06ePjB8/Xum3WCzSu3dv8ff3lyNHjsi+ffukY8eOEhER8WgWo5m49a7xIsxfU5eXlycajUZiYmLk5MmTsmXLFtHr9fLxxx8rMcxh0zZt2jTp0KGDpKamypkzZ2THjh3i7OwsUVFRSgxzSEREDWHnzp2i1WqloqKiVt9bb70lXl5eyl3Un3zySdm3b58cP35cJkyYIJ07d5bKykoR+f0u6DY2NjJw4EDJzc2V/Px88fPzk0GDBin7u9td45cuXSqtW7eWjRs3yqlTpyQnJ0f+85//iIjIrl27RKPRyCeffCKnTp2S+Ph4cXR0FIPBUO/+RURWrlwprq6uynZ4eLgMGDBAzpw5Iz///LNUV1fXe9d4s9msPK+goEC5y/zNmBYtWsjbb78thYWFcuzYMVmxYoUSHxQUJL169ZKvv/5aTCaTBAQESPfu3ZW73N+8a/yt6pr/7Ws0efJkcXNzk+3bt8t3330neXl5EhsbK7t37xYiavpYCKWH7sMPPxRXV1fRarXy5z//WTIzMxt7Ss0egDofiYmJSkxNTY0sWbJEXFxcxNbWVp5++mk5fvy4aj/Xr1+XiIgIcXR0FJ1OJ+PHj5fS0lJVzIULF2Ty5Mlib28v9vb2MnnyZNUbFBGRc+fOSWBgoOh0OnF0dJSIiAj57bffGurwm6XbC6HMX9P3+eefS+/evcXW1lY8PT1l3bp1qn7msGm7dOmSzJ49Wzp37ix2dnbStWtXiY6OVv65FGEOiYioYYwfP17GjRtXZ19+fr4AkLi4OAEgn3/+uTz55JOi1WplwIABYjKZlNibhb3t27dL165dRavVyogRI1QnpdytEFpdXS3vvfeeuLq6io2NjXTu3FmWLVum9M+bN0+cnJykVatWEhISIitXrrzvQmhxcbEMGjRIdDqdUth8kEKoiMj27dvFy8tLtFqtODs7S3BwsNL366+/SlhYmBgMBtHpdDJmzBgpKSmptV63updCaFVVlbz99tvi5uYmNjY24uLiIs8884wcO3ZMiKjpsxKp58sziIiIiIiIiKjRZWRkYPjw4TCbzXBwcKgzJikpCZGRkaioqHikcyMi+iPhd4QSERERERERERFRs8dCKBERERERERERETV7vDSeiIiIiIiIiIiImj2eEUpERERERERERETNHguhRERERERERERE1OyxEEpERERERERERETNHguhRERERERERERE1OyxEEpE9IgsXboUXl5eyvb06dMxceLERpsPERERERER0eOEhVAiov9z4MABWFtbIyAg4JGMFx8fj6SkpEcy1k3Dhg1DZGSkqu3s2bOwsrKCyWR6pHMhIiIiIiIiepRYCCUi+j8bNmzArFmzkJWVhdLS0gYfz2AwwMHBocHHISIiIiIiIiIWQomIAABXr17Fp59+ildffRXjx49XnamZkZEBKysr7N69G/369YOdnR18fX1x/PhxJSYpKQkODg7YtWsXevToATs7O4waNQrff/99vWPefml8TU0NVqxYge7du8PW1hadO3dGTEyM0j9//nz06NEDer0eXbt2xeLFi3Hjxg2l/+al95s3b4abmxsMBgNCQ0Nx+fJlZbzMzEzEx8fDysoKVlZWOHv2bK153Tze9PR0+Pj4QK/XY/DgwSguLlbFpaSkwMfHB3Z2dnB2dkZwcLDSZzabMXXqVLRu3Rp6vR5jx47FyZMna61XamoqPDw8oNfr8dxzz+Hq1avYuHEj3Nzc0Lp1a8yaNQvV1dXK86qqqhAVFYUOHTqgZcuW8PX1RUZGRr1rTERERERERHQTC6FERACSk5Ph4eEBDw8PTJkyBYmJiRARVcy8efPwwQcf4NChQ2jTpg0mTJigKkReu3YNMTEx2LhxI7Kzs3Hp0iWEhobe8xwWLlyIFStWYPHixSgsLMTWrVvRtm1bpd/e3h5JSUkoLCxEfHw81q9fj5UrV6r2cfr0aezatQupqalITU1FZmYmYmNjAfx+Kb6fnx/Cw8NRVlaGsrIydOrUqd75REdHIy4uDocPH4ZGo8HLL7+s9O3evRvBwcEIDAxEQUGBUjS9afr06Th8+DBSUlKQk5MDEcG4ceNqrdfq1athNBqxZ88eZGRkIDg4GGlpaUhLS8PmzZuxbt06bNu2TXnOSy+9hOzsbBiNRhw7dgyTJk1CQECAqshKREREREREVCchIiIZPHiwrFq1SkREbty4Ic7OzrJ3714REdm/f78AEKPRqMRfuHBBdDqdJCcni4hIYmKiAJDc3FwlpqioSADIwYMHRURkyZIl0q9fP6V/2rRpEhQUJCIily5dEltbW1m/fv09z/n9998Xb29vZXvJkiWi1+vl0qVLStu8efPE19dX2R46dKjMnj1btZ8zZ84IACkoKFAd7759+5SY3bt3CwC5fv26iIj4+fnJ5MmT65xXSUmJAJDs7Gyl7ZdffhGdTieffvqpiPxvvU6dOqXEvPLKK6LX6+Xy5ctK25gxY+SVV14REZFTp06JlZWVnD9/XjWev7+/LFy4sP6FIiIiIiIiIhIRTeOVYImImobi4mLk5eVhx44dAACNRoOQkBBs2LABI0eOVOL8/PyUnx0dHeHh4YGioiKlTaPRqM6K9PT0hIODA4qKijBw4MA7zqGoqAiVlZXw9/evN2bbtm1YtWoVTp06hStXrsBiseCJJ55Qxbi5ucHe3l7ZbteuHcrLy++yAnXr27evaj8AUF5ejs6dO8NkMiE8PLzeY9FoNPD19VXanJycaq2XXq9Ht27dlO22bdvCzc0NrVq1UrXdnP+RI0cgIujRo4dqvMrKSjg5OT3QMRIREREREdHjg4VQInrsJSQkwGKxoEOHDkqbiMDGxgZms/mOz7Wysrrjdn1tt9PpdHfsz83NRWhoKN555x2MGTMGBoMBRqMRcXFxqjgbG5taY9fU1Nx1/Lrcuq+bx3BzX3ear9z2lQK3tt+6FnXN9U7zr6mpgbW1NfLz82Ftba2Ku7V4SkRERERERFQXfkcoET3WLBYLNm3ahLi4OJhMJuVx9OhRuLq6YsuWLUpsbm6u8rPZbEZJSQk8PT1V+zp8+LCyXVxcjIqKClVMfdzd3aHT6ZCenl5nf3Z2NlxdXREdHQ0fHx+4u7vj3Llz9328Wq1WdfOhB9W3b99659qrVy9YLBYcPHhQabtw4QJKSkrQs2fPBx6zf//+qK6uRnl5Obp37656uLi4PPB+iYiIiIiI6PHAM0KJ6LGWmpoKs9mMGTNmwGAwqPqee+45JCQkKDckevfdd+Hk5IS2bdsiOjoazs7Oqru+29jYYNasWVi9ejVsbGwQERGBQYMG3fWyeACws7PD/PnzERUVBa1Wi6eeego///wzTpw4gRkzZqB79+4oLS2F0WjEgAEDsHv3buzcufO+j9fNzQ0HDx7E2bNn0apVKzg6Ot73PgBgyZIl8Pf3R7du3RAaGgqLxYIvvvgCUVFRcHd3R1BQEMLDw/Hvf/8b9vb2WLBgATp06ICgoKAHGg8AevTogcmTJ2Pq1KmIi4tD//798csvv+Crr75Cnz59MG7cuAfeNxERERERETV/PCOUiB5rCQkJGDlyZK0iKAA8++yzMJlMOHLkCAAgNjYWs2fPhre3N8rKypCSkgKtVqvE6/V6zJ8/Hy+++CL8/Pyg0+lgNBrveS6LFy/G3Llz8fbbb6Nnz54ICQlRvh8zKCgIb7zxBiIiIuDl5YUDBw5g8eLF9328b775JqytrdGrVy/86U9/Qmlp6X3vAwCGDRuG//73v0hJSYGXlxdGjBihOgM0MTER3t7eGD9+PPz8/CAiSEtLq3Xp+/1KTEzE1KlTMXfuXHh4eGDChAk4ePAgOnXq9P+1XyIiIiIiImr+rKS+L3MjIiIAQEZGBoYPHw6z2QwHB4c6Y5KSkhAZGYmKiopHOjciIiIiIiIiujc8I5SIiIiIiIiIiIiaPRZCiYiIiIiIiIiIqNnjpfFERERERERERETU7PGMUCIiIiIiIiIiImr2WAglIiIiIiIiIiKiZo+FUCIiIiIiIiIiImr2WAglIiIiIiIiIiKiZo+FUCIiIiIiIiIiImr2WAglIiIiIiIiIiKiZo+FUCIiIiIiIiIiImr2WAglIiIiIiIiIiKiZo+FUCIiIiIiIiIiImr2/h9xHHBN5XjrygAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(121) \n",
    "sns.distplot(raw['ApplicantIncome']); \n",
    "plt.subplot(122) \n",
    "raw['ApplicantIncome'].plot.box(figsize=(16,5)) \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the data in the distribution of applicant income are towards the left which means it is not normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(121)\n",
    "df=raw.dropna() \n",
    "sns.distplot(raw['LoanAmount']); \n",
    "plt.subplot(122)\n",
    "raw['LoanAmount'].plot.box(figsize=(16,5)) \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of outliers are present\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical Independent Variable vs Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender=pd.crosstab(raw['Gender'],raw['Loan_Status']) \n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Loan Status is about the same for both male and female"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remaining categorical variables vs the target variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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evHlNrmvqeWKDBg2ymSIhIhlIqmr4r5QvC7/F8QZLRNQhJCUlobCwUOwy7MaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaaIOQBAEsUvokNrj586QJpKxTp06AQAuXbokciUdU+PPvfHvoTVuvWskiajFlEolunfvjrKyMgBA586dm70ZWpvUSXikfuXKTd+lIAi4dOkSysrK0L17dyiVyla/F0OaSOY8PT0BwBLUN8Qfv9+4926rP4tF23X37t0tP//WYkgTyZxCoYCXlxfc3d1RW1t7Y3byzqM35n3bw/x8UXbbqVOnNo2gGzGkiToIpVLZLqHRpIslN+Z924NGI3YFbcIDh0REEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYS1KqQzMjLg6+sLjUaDoKAgHDhwoNm+e/fuhUKhsFmOHTvW6qKJiDoKu0M6KysLsbGxSExMhF6vR1hYGCIiImAwGK653fHjx2E0Gi1L//79W100EVFHYXdIp6WlITo6GnPmzIG/vz/S09Oh1WqxZs2aa27n7u4OT09Py3LDnlpMRCQjdoV0TU0NCgoKEB4ebtUeHh6OvLy8a247YsQIeHl5Yfz48dizZ4/9lRIRdUCO9nQuLy9HfX09PDw8rNo9PDxQWlra5DZeXl7IzMxEUFAQTCYTPvzwQ4wfPx579+7F2LFjm9zGZDLBZDJZXldXV9tTJhGRbNgV0o0UCoXVa0EQbNoaDRw4EAMHDrS8DgkJQUlJCd54441mQzo1NRXJycmtKY2ISFbsmu7o1asXlEqlzai5rKzMZnR9LaNGjcKJEyeaXZ+QkICqqirLUlJSYk+ZRESyYVdIq1QqBAUFQafTWbXrdDqEhoa2+H30ej28vLyaXa9Wq+Hs7Gy1EBF1RHZPd8TFxWH69OkIDg5GSEgIMjMzYTAYEBMTA6BhFHz27Fls3rwZAJCeng4fHx8EBASgpqYGH330EXbs2IEdO3a07ychIpIhu0M6MjISFRUVSElJgdFoRGBgILKzs+Ht7Q0AMBqNVudM19TUID4+HmfPnoWTkxMCAgLw5ZdfYsqUKe33KYiIZEohCIIgdhHXU11dDRcXF1RVVXHqwx5JLmJX0LykKrEroPbE75rdWpprvHcHEZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJY0gTEUkYQ5qISMIY0kREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLWqpDOyMiAr68vNBoNgoKCcODAgRZtd/DgQTg6OmL48OGt2S0RUYdjd0hnZWUhNjYWiYmJ0Ov1CAsLQ0REBAwGwzW3q6qqwowZMzB+/PhWF0tE1NHYHdJpaWmIjo7GnDlz4O/vj/T0dGi1WqxZs+aa2z311FOYNm0aQkJCWl0sEVFHY1dI19TUoKCgAOHh4Vbt4eHhyMvLa3a7Dz74AKdOncLy5ctbtB+TyYTq6mqrhYioI7IrpMvLy1FfXw8PDw+rdg8PD5SWlja5zYkTJ7BkyRJ8/PHHcHR0bNF+UlNT4eLiYlm0Wq09ZRIRyUarDhwqFAqr14Ig2LQBQH19PaZNm4bk5GQMGDCgxe+fkJCAqqoqy1JSUtKaMomIbnktG9r+f7169YJSqbQZNZeVldmMrgHgwoULyM/Ph16vx/z58wEAZrMZgiDA0dER//rXv3DPPffYbKdWq6FWq+0pjYhIluwaSatUKgQFBUGn01m163Q6hIaG2vR3dnbGTz/9hMLCQssSExODgQMHorCwEHfddVfbqicikjm7RtIAEBcXh+nTpyM4OBghISHIzMyEwWBATEwMgIapirNnz2Lz5s1wcHBAYGCg1fbu7u7QaDQ27UREZMvukI6MjERFRQVSUlJgNBoRGBiI7OxseHt7AwCMRuN1z5kmotbxWfKl2CU06bRG7ArkSyEIgiB2EddTXV0NFxcXVFVVwdnZWexybh1JLmJX0LykKrEruCVJN6SniV1C8yT6XWtprvHeHUREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJY0gTEUkYQ5qISMIY0kREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWtVSGdkZMDX1xcajQZBQUE4cOBAs31zc3MxevRouLq6wsnJCYMGDcJbb73V6oKJiDoSR3s3yMrKQmxsLDIyMjB69GisXbsWEREROHr0KPr27WvTv0uXLpg/fz6GDh2KLl26IDc3F0899RS6dOmCJ598sl0+BBGRXCkEQRDs2eCuu+7CHXfcgTVr1lja/P398eCDDyI1NbVF7/HQQw+hS5cu+PDDD1vUv7q6Gi4uLqiqqoKzs7M95XZsSS5iV9C8pCqxK7gl+Sz5UuwSmnRaM03sEpon0e9aS3PNrumOmpoaFBQUIDw83Ko9PDwceXl5LXoPvV6PvLw83H333c32MZlMqK6utlqIiDoiu0K6vLwc9fX18PDwsGr38PBAaWnpNbft06cP1Go1goOD8cwzz2DOnDnN9k1NTYWLi4tl0Wq19pRJRCQbrTpwqFAorF4LgmDTdrUDBw4gPz8f7733HtLT07Fly5Zm+yYkJKCqqsqylJSUtKZMIqJbnl0HDnv16gWlUmkzai4rK7MZXV/N19cXADBkyBD89ttvSEpKwuOPP95kX7VaDbVabU9pRESyZNdIWqVSISgoCDqdzqpdp9MhNDS0xe8jCAJMJpM9uyYi6pDsPgUvLi4O06dPR3BwMEJCQpCZmQmDwYCYmBgADVMVZ8+exebNmwEA7777Lvr27YtBgwYBaDhv+o033sCzzz7bjh9DXNI94i52BUTUVnaHdGRkJCoqKpCSkgKj0YjAwEBkZ2fD29sbAGA0GmEwGCz9zWYzEhISUFxcDEdHR9x+++1YsWIFnnrqqfb7FEREMmX3edJikPp50tIdSfPcVbnhd60VJPpduyHnSRMR0c3FkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJY0gTEUkYQ5qISMIY0kREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSVirQjojIwO+vr7QaDQICgrCgQMHmu27c+dOTJw4EW5ubnB2dkZISAi+/vrrVhdMRNSR2B3SWVlZiI2NRWJiIvR6PcLCwhAREQGDwdBk//3792PixInIzs5GQUEBxo0bh/vvvx96vb7NxRMRyZ3dIZ2Wlobo6GjMmTMH/v7+SE9Ph1arxZo1a5rsn56ejkWLFuHOO+9E//798eqrr6J///7YtWtXm4snIpI7u0K6pqYGBQUFCA8Pt2oPDw9HXl5ei97DbDbjwoUL6NmzZ7N9TCYTqqurrRYioo7IrpAuLy9HfX09PDw8rNo9PDxQWlraovd488038eeff+Kxxx5rtk9qaipcXFwsi1artadMIiLZaNWBQ4VCYfVaEASbtqZs2bIFSUlJyMrKgru7e7P9EhISUFVVZVlKSkpaUyYR0S3P0Z7OvXr1glKptBk1l5WV2Yyur5aVlYXo6Gh8+umnmDBhwjX7qtVqqNVqe0ojIpIlu0bSKpUKQUFB0Ol0Vu06nQ6hoaHNbrdlyxZERUXhk08+wb333tu6SomIOiC7RtIAEBcXh+nTpyM4OBghISHIzMyEwWBATEwMgIapirNnz2Lz5s0AGgJ6xowZWLVqFUaNGmUZhTs5OcHFxaUdPwoRkfzYHdKRkZGoqKhASkoKjEYjAgMDkZ2dDW9vbwCA0Wi0Omd67dq1qKurwzPPPINnnnnG0j5z5kxs3Lix7Z+AiEjG7A5pAJg3bx7mzZvX5Lqrg3fv3r2t2QUREYH37iAikjSGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJY0gTEUkYQ5qISMIY0kREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSVirQjojIwO+vr7QaDQICgrCgQMHmu1rNBoxbdo0DBw4EA4ODoiNjW1trUREHY7dIZ2VlYXY2FgkJiZCr9cjLCwMERERMBgMTfY3mUxwc3NDYmIihg0b1uaCiYg6ErtDOi0tDdHR0ZgzZw78/f2Rnp4OrVaLNWvWNNnfx8cHq1atwowZM+Di4tLmgomIOhK7QrqmpgYFBQUIDw+3ag8PD0deXl67FWUymVBdXW21EBF1RHaFdHl5Oerr6+Hh4WHV7uHhgdLS0nYrKjU1FS4uLpZFq9W223sTEd1KWnXgUKFQWL0WBMGmrS0SEhJQVVVlWUpKStrtvYmIbiWO9nTu1asXlEqlzai5rKzMZnTdFmq1Gmq1ut3ej4joVmXXSFqlUiEoKAg6nc6qXafTITQ0tF0LIyIiO0fSABAXF4fp06cjODgYISEhyMzMhMFgQExMDICGqYqzZ89i8+bNlm0KCwsBABcvXsTvv/+OwsJCqFQqDB48uH0+BRGRTNkd0pGRkaioqEBKSgqMRiMCAwORnZ0Nb29vAA0Xr1x9zvSIESMsfy4oKMAnn3wCb29vnD59um3VExHJnN0hDQDz5s3DvHnzmly3ceNGmzZBEFqzGyKiDo/37iAikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJY0gTEUkYQ5qISMIY0kREEsaQJiKSMIY0EZGEMaSJiCSMIU1EJGEMaSIiCWNIExFJGEOaiEjCGNJERBLGkCYikjCGNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwloV0hkZGfD19YVGo0FQUBAOHDhwzf779u1DUFAQNBoN+vXrh/fee69VxRIRdTR2h3RWVhZiY2ORmJgIvV6PsLAwREREwGAwNNm/uLgYU6ZMQVhYGPR6Pf7+979jwYIF2LFjR5uLJyKSO7tDOi0tDdHR0ZgzZw78/f2Rnp4OrVaLNWvWNNn/vffeQ9++fZGeng5/f3/MmTMHs2fPxhtvvNHm4omI5M7Rns41NTUoKCjAkiVLrNrDw8ORl5fX5DaHDh1CeHi4VdukSZOwfv161NbWolOnTjbbmEwmmEwmy+uqqioAQHV1tT3l3jRm0yWxS2hStUIQu4TmSfTvUur4XWsFiX7XGvNMEK79s7MrpMvLy1FfXw8PDw+rdg8PD5SWlja5TWlpaZP96+rqUF5eDi8vL5ttUlNTkZycbNOu1WrtKbfDcxG7gGtZIenqyE6S/tuU+HftwoULcHFpvka7QrqRQqGwei0Igk3b9fo31d4oISEBcXFxltdmsxmVlZVwdXW95n7ov6qrq6HValFSUgJnZ2exyyEZ43etdQRBwIULF9C7d+9r9rMrpHv16gWlUmkzai4rK7MZLTfy9PRssr+joyNcXV2b3EatVkOtVlu1de/e3Z5S6f9zdnbmLw7dFPyu2e9aI+hGdh04VKlUCAoKgk6ns2rX6XQIDQ1tcpuQkBCb/v/6178QHBzc5Hw0ERH9l91nd8TFxWHdunXYsGEDioqKsHDhQhgMBsTExABomKqYMWOGpX9MTAzOnDmDuLg4FBUVYcOGDVi/fj3i4+Pb71MQEcmU3XPSkZGRqKioQEpKCoxGIwIDA5GdnQ1vb28AgNFotDpn2tfXF9nZ2Vi4cCHeffdd9O7dG6tXr8bDDz/cfp+CbKjVaixfvtxm2oiovfG7dmMphOud/0FERKLhvTuIiCSMIU1EJGEMaSIiCWNIExFJGEOaiOxWV1eHb775BmvXrsWFCxcAAOfOncPFixdFrkx+eHaHzJw6dQoffPABTp06hVWrVsHd3R27d++GVqtFQECA2OWRDJw5cwaTJ0+GwWCAyWTCzz//jH79+iE2NhZXrlzh/eLbGUfSMrJv3z4MGTIE3377LXbu3GkZ1fz4449Yvny5yNWRXDz33HMIDg7G+fPn4eTkZGmfOnUqcnJyRKxMnhjSMrJkyRK8/PLL0Ol0UKlUlvZx48bh0KFDIlZGcpKbm4sXX3zR6jsGAN7e3jh79qxIVckXQ1pGfvrpJ0ydOtWm3c3NDRUVFSJURHJkNptRX19v0/7rr7+iW7duIlQkbwxpGenevTuMRqNNu16vx2233SZCRSRHEydORHp6uuW1QqHAxYsXsXz5ckyZMkW8wmSKIS0j06ZNw+LFi1FaWgqFQgGz2YyDBw8iPj7e6qZXRG3x1ltvYd++fRg8eDCuXLmCadOmwcfHB2fPnsVrr70mdnmyw7M7ZKS2thZRUVHYunUrBEGAo6Mj6uvrMW3aNGzcuBFKpVLsEkkmLl++jK1bt6KgoABmsxl33HEHnnjiCasDidQ+GNIy9Msvv+D777+H2WzGiBEj0L9/f7FLIhnZv38/QkND4ehofRPNuro65OXlYezYsSJVJk8MaRlJSUlBfHw8OnfubNV++fJlrFy5EsuWLROpMpITpVIJo9EId3d3q/aKigq4u7s3eVCRWo8hLSP85aGbwcHBAb/99hvc3Nys2n/++WcEBwdbnoJN7aNVD6IlaWrugcA//PADevbsKUJFJCcPPfQQgIazOaKioqxu8l9fX48ff/yx2cfoUesxpGWgR48eUCgUUCgUGDBggFVQ19fX4+LFi5bHmxG1VuNDUwVBQLdu3awOEqpUKowaNQpz584VqzzZ4nSHDGzatAmCIGD27NlIT0+3egKxSqWCj48PQkJCRKyQ5CQ5ORnx8fHo0qWL2KV0CAxpGdm3bx9CQ0P5FHYiGWFIy9Tly5dRW1tr1ebs7CxSNSQ327dvx7Zt22AwGFBTU2O17vvvvxepKnniFYcycunSJcyfPx/u7u7o2rUrevToYbUQtYfVq1dj1qxZcHd3h16vx8iRI+Hq6opffvkFERERYpcnOwxpGXnhhRfwf//3f8jIyIBarca6deuQnJyM3r17Y/PmzWKXRzKRkZGBzMxMvPPOO1CpVFi0aBF0Oh0WLFiAqqoqscuTHU53yEjfvn2xefNm/OUvf4GzszO+//57+Pn54cMPP8SWLVuQnZ0tdokkA507d0ZRURG8vb3h7u4OnU6HYcOG4cSJExg1ahTvuNjOOJKWkcrKSvj6+gJomH+urKwEAIwZMwb79+8XszSSEU9PT0sQe3t74/DhwwCA4uJicMzX/hjSMtKvXz+cPn0aADB48GBs27YNALBr1y50795dvMJIVu655x7s2rULABAdHY2FCxdi4sSJiIyMbPJ+5tQ2nO6QkbfeegtKpRILFizAnj17cO+996K+vh51dXVIS0vDc889J3aJJANmsxlms9lyg6Vt27YhNzcXfn5+iImJsXliC7UNQ1rGDAYD8vPzcfvtt2PYsGFil0NErcCQJiK7XO/4Bm9V2r4Y0jKSkpJyzfW8VSm1BwcH20NZV98vhtoPQ1pGRowYYfW6trYWxcXFcHR0xO23384rwahdXH0udG1tLfR6PZYuXYpXXnkF48ePF6kyeeJd8GREr9fbtFVXVyMqKopH3and/O8NvBpNnDgRarUaCxcuREFBgQhVyRdH0h3AkSNHcN9991lOzyO6EYqKinDnnXfi4sWLYpciKxxJdwB//PEHL9eldvPjjz9avRYEAUajEStWrOBZRDcAQ1pGVq9ebfW68Zfnww8/xOTJk0WqiuRm+PDhUCgUNlcXjho1Chs2bBCpKvnidIeMNF4S3sjBwQFubm645557kJCQgG7duolUGcnJmTNnrF43fs80Go1IFckbQ5qISMI43UFE13X1VNq1LFiw4AZW0vFwJH2La3yCc0vs3LnzBlZCcnb1VNrvv/+OS5cuWW7c9ccff6Bz585wd3fHL7/8IkKF8sW74N3iXFxcLIuzszNycnKQn59vWV9QUICcnJwmz20laqni4mLL8sorr2D48OEoKipCZWUlKisrUVRUhDvuuAMvvfSS2KXKDkfSMrJ48WJUVlbivffeg1KpBNBwie68efPg7OyMlStXilwhycHtt9+O7du321zhWlBQgEceeQTFxcUiVSZPHEnLyIYNGxAfH28JaABQKpWIi4vjqVHUboxGo81DjoGGAcFvv/0mQkXyxpCWkbq6OhQVFdm0FxUVwWw2i1ARydH48eMxd+5c5OfnW86Vzs/Px1NPPYUJEyaIXJ388OwOGZk1axZmz56NkydPYtSoUQCAw4cPY8WKFZg1a5bI1ZFcbNiwATNnzsTIkSPRqVMnAA0DhEmTJmHdunUiVyc/nJOWEbPZjDfeeAOrVq2C0WgEAHh5eeG5557D888/bzUNQtRWP//8M44dOwZBEODv748BAwaIXZIsMaRlqrq6GkDDA2mJ6NbFkCYiu/3666/45z//CYPBgJqaGqt1aWlpIlUlT5yTlpnt27dj27ZtTf7y8Kb/1B5ycnLw17/+Fb6+vjh+/DgCAwNx+vRpCIKAO+64Q+zyZIdnd8jI6tWrMWvWLLi7u0Ov12PkyJFwdXXFL7/8goiICLHLI5lISEjA888/jyNHjkCj0WDHjh0oKSnB3XffjUcffVTs8mSH0x0yMmjQICxfvhyPP/44unXrhh9++AH9+vXDsmXLUFlZiXfeeUfsEkkGunXrhsLCQtx+++3o0aMHcnNzERAQgB9++AEPPPAAHy7RzjiSlhGDwYDQ0FAAgJOTEy5cuAAAmD59OrZs2SJmaSQjXbp0gclkAgD07t0bp06dsqwrLy8XqyzZYkjLiKenJyoqKgAA3t7eOHz4MICG+y7wH0zUXkaNGoWDBw8CAO699148//zzeOWVVzB79mzL+fnUfnjgUEbuuece7Nq1C3fccQeio6OxcOFCbN++Hfn5+XbdLY/oWtLS0izPMUxKSsLFixeRlZUFPz8/vPXWWyJXJz+ck5YRs9kMs9kMR8eG//du27YNubm58PPzQ0xMDFQqlcgV0q2uvr4eubm5GDp0KHr06CF2OR0CQ1om6urqLP/k1Gq1YpdDMqbRaFBUVGRzj2m6MTgnLROOjo5YuXIl6uvrxS6FZG7IkCG8sf9NxJCWkQkTJmDv3r1il0Ey98orryA+Ph5ffPEFjEYjqqurrRZqX5zukJG1a9ciKSkJTzzxBIKCgtClSxer9X/9619FqozkxMHhv2M7hUJh+bMgCFAoFPzXXDtjSMvI//7yXI2/PNRe9u3bd831d999902qpGNgSBMRSRjPk5aBy5cvIycnB/fddx+AhnsrNF4RBjQcVExJSYFGoxGrRJKJ6upqy+1vs7OzUVdXZ1mnVCpx7733ilWabHEkLQNr167FF198gV27dgFouLdCQEAAnJycAADHjh3DCy+8gLi4ODHLpFvcF198gaVLl0Kv1wNo+J79+eeflvUKhQJZWVl45JFHxCpRlnh2hwx8/PHHmD17tlXbJ598gj179mDPnj1YuXIlPv30U5GqI7nIzMzE/PnzrdpOnjxpuYgqNTWVDzy+ARjSMvDzzz9bPbpIo9FYHUQcOXIkjh49KkZpJCM//vgjhg0b1uz6iIgI5Ofn38SKOgbOSctAVVWV5VJwAPj999+t1pvNZqs5aqLWKC0thaurq+X1nj17rK5u7dq1K6qqqsQoTdY4kpaBPn364MiRI82u//HHH9GnT5+bWBHJUc+ePa1uSxocHGx5WjgAnDhxAj179hSjNFljSMvAlClTsGzZMly5csVm3eXLl5GcnMyj7tRmY8eOxerVq5tdv3r1aowdO/YmVtQx8OwOGfjtt98wfPhwqFQqzJ8/HwMGDIBCocCxY8fwzjvvoK6uDnq9Hh4eHmKXSrcwvV6PkJAQ3H///Vi0aJHlOMjx48fx2muv4csvv0ReXh6fc9jOGNIyUVxcjKeffho6nc5yg3+FQoGJEyciIyMD/fr1E7lCkoN//OMfmDNnDiorK63ae/TogXXr1uHBBx8UpzAZY0jLTGVlJU6ePAkA8PPz4xwhtbtLly7h66+/xokTJwAA/fv3R3h4uM29Yqh9MKSJiCSMBw6JiCSMIU1EJGEMaSIiCWNIExFJGEOaiOyiVCpRVlZm015RUQGlUilCRfLGkCYiuzR3QpjJZIJKpbrJ1cgfb7BERC3SeEm4QqHAunXr0LVrV8u6+vp67N+/H4MGDRKrPNniedJE1CK+vr4AgDNnzqBPnz5WUxsqlQo+Pj5ISUnBXXfdJVaJssSQJiK7jBs3Djt37kSPHj3ELqVDYEgTUav9731i6MbggUMistvmzZsxZMgQODk5wcnJCUOHDsWHH34odlmyxAOHRGSXtLQ0LF26FPPnz8fo0aMhCAIOHjyImJgYlJeXY+HChWKXKCuc7iAiu/j6+iI5ORkzZsywat+0aROSkpJQXFwsUmXyxOkOIrKL0WhEaGioTXtoaCiMRqMIFckbQ5qI7OLn54dt27bZtGdlZaF///4iVCRvnJMmIrskJycjMjIS+/fvx+jRo6FQKJCbm4ucnJwmw5vahnPSRGS3goICvPXWWygqKoIgCBg8eDCef/55jBgxQuzSZIchTUQkYZyTJiKSMM5JE1GLODg4XPfKQoVCgbq6uptUUcfAkCaiFvnss8+aXZeXl4e333672duYUutxTpqIWu3YsWNISEjArl278MQTT+Cll15C3759xS5LVjgnTUR2O3fuHObOnYuhQ4eirq4OhYWF2LRpEwP6BmBIE1GLVVVVYfHixfDz88N//vMf5OTkYNeuXQgMDBS7NNninDQRtcjrr7+O1157DZ6entiyZQseeOABsUvqEDgnTUQt4uDgACcnJ0yYMOGaD5zduXPnTaxK/jiSJqIWmTFjBm/uLwKOpImIJIwHDomIJIwhTUQkYQxpIiIJY0gTEUkYQ5pueQqFAp9//rnYZSAqKgoPPvig2GWQzDCkSVKioqKgUChslsmTJ4tdmsXp06ehUChQWFho1b5q1Sps3LhRlJpIvnieNEnO5MmT8cEHH1i1qdVqkappORcXF7FLIBniSJokR61Ww9PT02rp0aMHAODEiRMYO3YsNBoNBg8eDJ1OZ7Xt3r17oVAo8Mcff1jaCgsLoVAocPr0aUvbwYMHcffdd6Nz587o0aMHJk2ahPPnzwMAdu/ejTFjxqB79+5wdXXFfffdh1OnTlm29fX1BQCMGDECCoUCf/nLXwDYTneYTCYsWLAA7u7u0Gg0GDNmDP7973/b1JqTk4Pg4GB07twZoaGhOH78eHv8GEkmGNJ0yzCbzXjooYegVCpx+PBhvPfee1i8eLHd71NYWIjx48cjICAAhw4dQm5uLu6//37U19cDAP7880/ExcXh3//+N3JycuDg4ICpU6fCbDYDAL777jsAwDfffAOj0djsZdCLFi3Cjh07sGnTJnz//ffw8/PDpEmTUFlZadUvMTERb775JvLz8+Ho6IjZs2fb/ZlIxgQiCZk5c6agVCqFLl26WC0pKSnC119/LSiVSqGkpMTS/6uvvhIACJ999pkgCIKwZ88eAYBw/vx5Sx+9Xi8AEIqLiwVBEITHH39cGD16dItrKisrEwAIP/30kyAIglBcXCwAEPR6vU3tDzzwgCAIgnDx4kWhU6dOwscff2xZX1NTI/Tu3Vt4/fXXrWr95ptvLH2+/PJLAYBw+fLlFtdH8saRNEnOuHHjUFhYaLU888wzKCoqQt++fdGnTx9L35CQELvfv3Ek3ZxTp05h2rRp6NevH5ydnS3TGwaDocX7OHXqFGprazF69GhLW6dOnTBy5EgUFRVZ9R06dKjlz15eXgCAsrKyFu+L5I0HDklyunTpAj8/P5t2oYnbzFx9wx8HBwebvrW1tVZ9nJycrrn/+++/H1qtFu+//z569+4Ns9mMwMBA1NTUtPgzNO7/6voEQbBp69Spk+XPjesap1aIOJKmW8bgwYNhMBhw7tw5S9uhQ4es+ri5uQEAjEajpe3qU+WGDh2KnJycJvdRUVGBoqIivPjiixg/fjz8/f0tBxQbqVQqALDMYTfFz88PKpUKubm5lrba2lrk5+fD39//Gp+SyBpH0iQ5JpMJpaWlVm2Ojo6YMGECBg4ciBkzZuDNN99EdXU1EhMTrfr5+flBq9UiKSkJL7/8Mk6cOIE333zTqk9CQgKGDBmCefPmISYmBiqVCnv27MGjjz6Knj17wtXVFZmZmfDy8oLBYMCSJUustnd3d4eTkxN2796NPn36QKPR2Jx+16VLFzz99NN44YUX0LNnT/Tt2xevv/46Ll26hOjo6Hb8aZHsiTslTmRt5syZAgCbZeDAgYIgCMLx48eFMWPGCCqVShgwYICwe/duqwOHgiAIubm5wpAhQwSNRiOEhYUJn376qdWBQ0EQhL179wqhoaGCWq0WunfvLkyaNMlysFGn0wn+/v6CWq0Whg4dKuzdu9dmH++//76g1WoFBwcH4e6777bU3njgUBAE4fLly8Kzzz4r9OrVS1Cr1cLo0aOF7777zrK+JQc5iXg/aSIiCeOcNBGRhDGkiYgkjCFNRCRhDGkiIgljSBMRSRhDmohIwhjSREQSxpAmIpIwhjQRkYQxpImIJIwhTUQkYQxpIiIJ+39RbLKKi598LgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married=pd.crosstab(raw['Married'],raw['Loan_Status']) \n",
    "Dependents=pd.crosstab(raw['Dependents'],raw['Loan_Status']) \n",
    "Education=pd.crosstab(raw['Education'],raw['Loan_Status']) \n",
    "Self_Employed=pd.crosstab(raw['Self_Employed'],raw['Loan_Status']) \n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4)) \n",
    "plt.show() \n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show() \n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", figsize=(4,4)) \n",
    "plt.show() \n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float),axis=0).plot(kind=\"bar\",figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The proportion of married applicants is higher for the approved loans.\n",
    "- The distribution of applicants with 1 or 3+ dependents is similar across both the categories of Loan_Status.\n",
    "- There is nothing significant we can infer from Self_Employed vs Loan_Status plot."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remaining categorical independent variables and Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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//1z9+/fX4MGD9e233+qtt97SV199pSFDhvzu8AAAoOqrcBmZPXu2Bg8erCFDhqh58+ZKTU1VaGio5s6dW+b6W7ZsUXh4uEaOHKmIiAi1b99ejzzyiLZuNXsLWwAAcG2oUBkpKirStm3bFB8f7zAeHx+vTZs2lblN27Zt9eOPP2rNmjWyLEs//fST3n77bXXr1u2i71NYWKiCggKHBwAAqJ4qVEby8vJUXFysoKAgh/GgoCDl5OSUuU3btm21dOlS9enTR15eXmrQoIECAwP14osvXvR9UlJSFBAQYH+EhoZWJCYAAKhCrmgCq+0334ZoWVapsQv27NmjkSNHavLkydq2bZs++OADZWRkaOjQoRd9/fHjxys/P9/+yMrKupKYAACgCqjQt/bWrVtX7u7upc6C5ObmljpbckFKSoratWunxx9/XJJ00003qWbNmoqLi9OMGTMUHBxcahtvb295e3tXJBoAAKiiKnRmxMvLS9HR0UpPT3cYT09PV9u2bcvc5vTp03Jzc3wbd3d3SefPqAAAANdW4cs0SUlJWrBggRYtWqS9e/dq9OjRyszMtF92GT9+vPr3729fv3v37lqxYoXmzp2rAwcOaOPGjRo5cqT++Mc/KiQkpPL2BAAAVEkVukwjSX369NHRo0c1bdo0ZWdnq2XLllqzZo3CwsIkSdnZ2Q73HElMTNTJkyf1z3/+U4899pgCAwPVqVMnzZw5s/L2AgAAVFk2qwpcKykoKFBAQIDy8/Pl7+9vOs7FJQeYTlB9JOebTlB9cFxWDo7JysMxWXmu8eOyvL+/+W4aAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGEUZAQAARl1RGZkzZ44iIiLk4+Oj6Ohobdiw4ZLrFxYWasKECQoLC5O3t7euv/56LVq06IoCAwCA6sWjohssX75co0aN0pw5c9SuXTu9/PLLSkhI0J49e9S4ceMyt+ndu7d++uknLVy4UFFRUcrNzdW5c+d+d3gAAFD1VbiMzJ49W4MHD9aQIUMkSampqfrwww81d+5cpaSklFr/gw8+0Pr163XgwAHVrl1bkhQeHv77UgMAgGqjQpdpioqKtG3bNsXHxzuMx8fHa9OmTWVu8+677yomJkazZs1Sw4YN1bRpU40ZM0a//vrrRd+nsLBQBQUFDg8AAFA9VejMSF5enoqLixUUFOQwHhQUpJycnDK3OXDggD7//HP5+Pho5cqVysvL07Bhw3Ts2LGLzhtJSUnR1KlTKxINAABUUVc0gdVmszk8tyyr1NgFJSUlstlsWrp0qf74xz/q7rvv1uzZs5WWlnbRsyPjx49Xfn6+/ZGVlXUlMQEAQBVQoTMjdevWlbu7e6mzILm5uaXOllwQHByshg0bKiAgwD7WvHlzWZalH3/8UU2aNCm1jbe3t7y9vSsSDQAAVFEVOjPi5eWl6OhopaenO4ynp6erbdu2ZW7Trl07HTlyRL/88ot97Pvvv5ebm5saNWp0BZEBAEB1UuHLNElJSVqwYIEWLVqkvXv3avTo0crMzNTQoUMlnb/E0r9/f/v6ffv2VZ06dTRw4EDt2bNHn332mR5//HENGjRINWrUqLw9AQAAVVKFP9rbp08fHT16VNOmTVN2drZatmypNWvWKCwsTJKUnZ2tzMxM+/p+fn5KT0/XiBEjFBMTozp16qh3796aMWNG5e0FAACosmyWZVmmQ1xOQUGBAgIClJ+fL39/f9NxLi454PLroHyS800nqD44LisHx2Tl4ZisPNf4cVne3998Nw0AADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjrqiMzJkzRxEREfLx8VF0dLQ2bNhQru02btwoDw8PtW7d+kreFgAAVEMVLiPLly/XqFGjNGHCBO3YsUNxcXFKSEhQZmbmJbfLz89X//791blz5ysOCwAAqp8Kl5HZs2dr8ODBGjJkiJo3b67U1FSFhoZq7ty5l9zukUceUd++fRUbG3vFYQEAQPVToTJSVFSkbdu2KT4+3mE8Pj5emzZtuuh2ixcv1v79+zVlypQrSwkAAKotj4qsnJeXp+LiYgUFBTmMBwUFKScnp8xtfvjhB40bN04bNmyQh0f53q6wsFCFhYX25wUFBRWJCQAAqpArmsBqs9kcnluWVWpMkoqLi9W3b19NnTpVTZs2Lffrp6SkKCAgwP4IDQ29kpgAAKAKqFAZqVu3rtzd3UudBcnNzS11tkSSTp48qa1bt2r48OHy8PCQh4eHpk2bpl27dsnDw0OffPJJme8zfvx45efn2x9ZWVkViQkAAKqQCl2m8fLyUnR0tNLT09WzZ0/7eHp6unr06FFqfX9/f33zzTcOY3PmzNEnn3yit99+WxEREWW+j7e3t7y9vSsSDQAAVFEVKiOSlJSUpL/85S+KiYlRbGys5s+fr8zMTA0dOlTS+bMahw8f1quvvio3Nze1bNnSYfv69evLx8en1DgAAHBNFS4jffr00dGjRzVt2jRlZ2erZcuWWrNmjcLCwiRJ2dnZl73nCAAAwAU2y7Is0yEup6CgQAEBAcrPz5e/v7/pOBcVPu590xGqjYN/72Y6QvWRHGA6QfWQnG86QfXBMVl5rvHjsry/v/luGgAAYBRlBAAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRlBEAAGAUZQQAABhFGQEAAEZRRgAAgFGUEQAAYBRlBAAAGOVhOgAAwLWEn3nddIRq46DpAJWEMyMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADDKw3QAAFdX+JnXTUeoFg6aDgBUY5wZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGUUYAAIBRV1RG5syZo4iICPn4+Cg6OlobNmy46LorVqzQnXfeqXr16snf31+xsbH68MMPrzgwAACoXipcRpYvX65Ro0ZpwoQJ2rFjh+Li4pSQkKDMzMwy1//ss8905513as2aNdq2bZs6duyo7t27a8eOHb87PAAAqPoqXEZmz56twYMHa8iQIWrevLlSU1MVGhqquXPnlrl+amqqxo4dq1tuuUVNmjTR008/rSZNmmj16tW/OzwAAKj6KlRGioqKtG3bNsXHxzuMx8fHa9OmTeV6jZKSEp08eVK1a9e+6DqFhYUqKChweAAAgOqpQmUkLy9PxcXFCgoKchgPCgpSTk5OuV7j2Wef1alTp9S7d++LrpOSkqKAgAD7IzQ0tCIxAQBAFXJFE1htNpvDc8uySo2V5Y033lBycrKWL1+u+vXrX3S98ePHKz8/3/7Iysq6kpgAAKAKqNAX5dWtW1fu7u6lzoLk5uaWOlvyW8uXL9fgwYP11ltvqUuXLpdc19vbW97e3hWJBgAAqqgKnRnx8vJSdHS00tPTHcbT09PVtm3bi273xhtvKDExUa+//rq6det2ZUkBAEC1VKEzI5KUlJSkv/zlL4qJiVFsbKzmz5+vzMxMDR06VNL5SyyHDx/Wq6++Kul8Eenfv7+ef/553XbbbfazKjVq1FBAQEAl7goAAKiKKlxG+vTpo6NHj2ratGnKzs5Wy5YttWbNGoWFhUmSsrOzHe458vLLL+vcuXN69NFH9eijj9rHBwwYoLS0tN+/BwAAoEqrcBmRpGHDhmnYsGFlLvttwVi3bt2VvAUAAHARfDcNAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAoygjAADAKMoIAAAwijICAACMoowAAACjKCMAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAo66ojMyZM0cRERHy8fFRdHS0NmzYcMn1169fr+joaPn4+CgyMlLz5s27orAAAKD6qXAZWb58uUaNGqUJEyZox44diouLU0JCgjIzM8tcPyMjQ3fffbfi4uK0Y8cOPfnkkxo5cqTeeeed3x0eAABUfRUuI7Nnz9bgwYM1ZMgQNW/eXKmpqQoNDdXcuXPLXH/evHlq3LixUlNT1bx5cw0ZMkSDBg3SM88887vDAwCAqs+jIisXFRVp27ZtGjdunMN4fHy8Nm3aVOY2mzdvVnx8vMNY165dtXDhQp09e1aenp6ltiksLFRhYaH9eX5+viSpoKCgInGdrqTwtOkI1ca1/t+6KuG4rBwck5WHY7LyXOvH5YV8lmVdcr0KlZG8vDwVFxcrKCjIYTwoKEg5OTllbpOTk1Pm+ufOnVNeXp6Cg4NLbZOSkqKpU6eWGg8NDa1IXFRhAammEwCOOCZxLaoqx+XJkycVEBBw0eUVKiMX2Gw2h+eWZZUau9z6ZY1fMH78eCUlJdmfl5SU6NixY6pTp84l3weXV1BQoNDQUGVlZcnf3990HIBjEtccjsnKY1mWTp48qZCQkEuuV6EyUrduXbm7u5c6C5Kbm1vq7McFDRo0KHN9Dw8P1alTp8xtvL295e3t7TAWGBhYkai4DH9/f/6Q4ZrCMYlrDcdk5bjUGZELKjSB1cvLS9HR0UpPT3cYT09PV9u2bcvcJjY2ttT6H330kWJiYsqcLwIAAFxLhT9Nk5SUpAULFmjRokXau3evRo8erczMTA0dOlTS+Uss/fv3t68/dOhQHTp0SElJSdq7d68WLVqkhQsXasyYMZW3FwAAoMqq8JyRPn366OjRo5o2bZqys7PVsmVLrVmzRmFhYZKk7Oxsh3uOREREaM2aNRo9erReeuklhYSE6IUXXlCvXr0qby9Qbt7e3poyZUqpy2CAKRyTuNZwTDqfzbrc520AAACuIr6bBgAAGEUZAQAARlFGAACAUZQRAABgFGUEAAAYRRkBAABGXdF30wDA71VSUqJ9+/YpNzdXJSUlDstuv/12Q6kAmEAZqabatGlT7i8V3L59+1VOAzjasmWL+vbtq0OHDpX6anGbzabi4mJDyeDqioqKyizIjRs3NpTINVBGqql7773XdATgooYOHaqYmBi9//77Cg4O5tu4YdwPP/ygQYMGadOmTQ7jF76VnoJ8dXEHVgBOV7NmTe3atUtRUVGmowCSpHbt2snDw0Pjxo0rsyC3atXKUDLXwJkRAE536623at++fZQRXDN27typbdu2qVmzZqajuCTKiAsoLi7Wc889pzfffFOZmZkqKipyWH7s2DFDyeCqRowYoccee0w5OTm68cYb5enp6bD8pptuMpQMrqpFixbKy8szHcNlcZnGBUyePFkLFixQUlKSJk2apAkTJujgwYNatWqVJk+erJEjR5qOCBfj5lb6rgI2m43r8zDmk08+0cSJE/X000+XWZD9/f0NJXMNlBEXcP311+uFF15Qt27dVKtWLe3cudM+tmXLFr3++uumI8LFHDp06JLLw8LCnJQEOO9CQf7tXBEKsnNwmcYFXDgVLkl+fn7Kz8+XJN1zzz2aNGmSyWhwUZQNXGs+/fRT0xFcGmXEBTRq1EjZ2dlq3LixoqKi9NFHH+nmm2/WV199JW9vb9Px4ML27NlT5jymP/3pT4YSwVV16NDBdASXRhlxAT179tTHH3+sW2+9VX/729/0wAMPaOHChcrMzNTo0aNNx4MLOnDggHr27KlvvvnGPldE+v+nyDklDlNOnz5dZkFmUvXVxZwRF/TFF19o48aNioqK4l+gMKJ79+5yd3fXK6+8osjISH355Zc6evSoHnvsMT3zzDOKi4szHREu5ueff9bAgQO1du3aMpdTkK8uviivmjt79qwGDhyoAwcO2MduvfVWJSUlUURgzObNmzVt2jTVq1dPbm5ucnNzU/v27ZWSksKnu2DEqFGjdPz4cW3ZskU1atTQBx98oCVLlqhJkyZ69913Tcer9igj1Zynp6dWrlxpOgbgoLi4WH5+fpKkunXr6siRI5LOT2z97rvvTEaDi/rkk0/03HPP6ZZbbpGbm5vCwsL04IMPatasWUpJSTEdr9qjjLiAnj17atWqVaZjAHYtW7bU119/Len8mbpZs2Zp48aNmjZtmiIjIw2ngys6deqU6tevL0mqXbu2fv75Z0nSjTfeyJeJOgETWF1AVFSUpk+frk2bNik6Olo1a9Z0WM5pcTjbxIkTderUKUnSjBkzdM899yguLk516tTR8uXLDaeDK7rhhhv03XffKTw8XK1bt9bLL7+s8PBwzZs3T8HBwabjVXtMYHUBERERF11ms9kc5pMAphw7dkzXXXcd3+ALI5YuXaqzZ88qMTFRO3bsUNeuXXX06FF5eXkpLS1Nffr0MR2xWqOMADAqKytLNptNjRo1Mh0FsDt9+rT+93//V40bN1bdunVNx6n2mDMCwOnOnTunSZMmKSAgQOHh4QoLC1NAQIAmTpyos2fPmo4HF2dZlmrUqKGbb76ZIuIkzBlxAYMGDbrk8kWLFjkpCXDe8OHDtXLlSs2aNUuxsbGSzn/cNzk5WXl5eZo3b57hhHBFCxcu1HPPPacffvhBktSkSRONGjVKQ4YMMZys+qOMuIDjx487PD979qx2796tEydOqFOnToZSwZW98cYbWrZsmRISEuxjN910kxo3bqz777+fMgKnmzRpkp577jmNGDHCoSCPHj1aBw8e1IwZMwwnrN6YM+KiSkpKNGzYMEVGRmrs2LGm48DFBAUFad26dWrevLnD+N69e3X77bfbP1YJOEvdunX14osv6oEHHnAYf+ONNzRixAjl5eUZSuYamDPiotzc3DR69Gg999xzpqPABT366KOaPn26CgsL7WOFhYV66qmnNHz4cIPJ4KqKi4sVExNTajw6Olrnzp0zkMi1cJnGhe3fv58/ZHCa++67z+H5f/7zHzVq1EitWrWSJO3atUtFRUXq3LmziXhwcQ8++KDmzp2r2bNnO4zPnz9f/fr1M5TKdVBGXEBSUpLDc8uylJ2drffee0+JiYlmQsHlBAQEODzv1auXw/PQ0FBnxgEc/m602WxasGCBPvroI912222SpC1btigrK0v9+/c3FdFlMGfEBdxxxx0ON5Jyc3NTvXr11KlTJ3Xr1k0NGzY0mA6uxrIsZWZmql69evL19TUdBy6sY8eO5VrPZrPpk08+ucppXBtlxEXl5OToqaee0oIFC/Trr7+ajgMXUlJSIh8fH3377bdq0qSJ6TgArgFMYK3GTpw4oX79+qlevXpq2LChXnjhBZWUlGjKlCm6/vrrtWXLFu4xAqdzc3NTkyZNdPToUdNRAEnnb8Ln4eGh3bt3m47isigj1diTTz6pzz77TAMGDFBgYKBGjx6te+65Rxs2bNCaNWv01VdflfoYG+AMs2bN0uOPP85f/rgmeHh4KCwsTMXFxaajuCwu01RjYWFhWrhwobp06aIDBw4oKipKI0eOVGpqqulocHHXXXedTp8+rXPnzsnLy0s1atRwWH7s2DFDyeCqFi9erLfeekuvvfaaateubTqOy6GMVGOenp46dOiQQkJCJEm+vr768ssv1bJlS8PJ4OqWLFlyyeUDBgxwUhLgvDZt2mjfvn06e/aswsLCVLNmTYfl27dvN5TMNfDR3mqspKREnp6e9ufu7u6l/oABJlA2cK3p0aOHw6cO4VycGanG3NzclJCQIG9vb0nS6tWr1alTp1KFZMWKFSbiwYVlZmZecnnjxo2dlATAtYAyUo0NHDiwXOstXrz4KicBHLm5uV3yX6FMJISzXOxY9Pf31w033KCxY8eWunswKh9lBIDT7dq1y+H52bNntWPHDs2ePVtPPfUUf/nDaf7973+XOX7ixAl9+eWXWrx4sZYsWaI///nPTk7mWigjAK4Z77//vv7xj39o3bp1pqMAkqSXXnpJr776qr744gvTUao17jMC4JrRtGlTffXVV6ZjAHbx8fH6/vvvTceo9vg0DQCnKygocHh+4csbk5OTuUU8rim//vqrfHx8TMeo9igjAJwuMDCw1KRBy7IUGhqqZcuWGUoFlPbKK6+oTZs2pmNUe5QRAE736aefOjy/8E3SUVFR8vDgryU4T1JSUpnj+fn52rp1q/bv368NGzY4OZXrYQIrAMBldezYscxxf39/NWvWTMOGDVNYWJiTU7keyggAp3j33XeVkJAgT09Pvfvuu5dc909/+pOTUgG4FlBGADiFm5ubcnJyVL9+fbm5XfyDfDabjZueAS6GMgIAAIziPiMAAMAopq0DMOLLL7/UunXrlJubq5KSEodls2fPNpQKgAmUEQBO9/TTT2vixIm64YYbFBQU5HDPEb7GHXA9zBkB4HRBQUGaOXOmEhMTTUcBcA1gzggAp3Nzc1O7du1MxwBwjaCMAHC60aNH66WXXjIdA8A1gss0AJyupKRE3bp10/fff68WLVrI09PTYfmKFSsMJQNgAhNYATjdiBEj9Omnn6pjx46qU6cOk1YBF8eZEQBOV6tWLS1btkzdunUzHQXANYA5IwCcrnbt2rr++utNxwBwjaCMAHC65ORkTZkyRadPnzYdBcA1gMs0AJyuTZs22r9/vyzLUnh4eKkJrNu3bzeUDIAJTGAF4HT33nuv6QgAriGcGQEAAEYxZwSAESdOnNCCBQs0fvx4HTt2TNL5yzOHDx82nAyAs3FmBIDTff311+rSpYsCAgJ08OBBfffdd4qMjNSkSZN06NAhvfrqq6YjAnAizowAcLqkpCQlJibqhx9+kI+Pj308ISFBn332mcFkAEygjABwuq+++kqPPPJIqfGGDRsqJyfHQCIAJlFGADidj4+PCgoKSo1/9913qlevnoFEAEyijABwuh49emjatGk6e/asJMlmsykzM1Pjxo1Tr169DKcD4GxMYAXgdAUFBbr77rv17bff6uTJkwoJCVF2drZiY2O1du1a1axZ03REAE5EGQFgzCeffKLt27erpKRE0dHR6ty5s+lIAAzgMg0Ap/niiy+0du1a+/NOnTqpXr16mjNnjh544AE9/PDDKiwsNJgQgAmUEQBOk5ycrK+//tr+/JtvvtFDDz2kO++8U+PGjdPq1auVkpJiMCEAE7hMA8BpgoODtXr1asXExEiSJkyYoPXr1+vzzz+XJL311luaMmWK9uzZYzImACfjzAgApzl+/LiCgoLsz9evX6+77rrL/vyWW25RVlaWiWgADKKMAHCaoKAgZWRkSJKKioq0fft2xcbG2pefPHlSnp6epuIBMIQyAsBp7rrrLo0bN04bNmzQ+PHj5evrq7i4OPvyr7/+Wtdff73BhABM8DAdAIDrmDFjhu677z516NBBfn5+WrJkiby8vOzLFy1apPj4eIMJAZjABFYATpefny8/Pz+5u7s7jB87dkx+fn4OBQVA9UcZAQAARjFnBAAAGEUZAQAARlFGAACAUZQRAABgFGUEqAYSExNls9lks9nk6empyMhIjRkzRqdOnTIdrUxpaWkKDAy8Kq/9448/ysvLS82aNbsqrw+g8lFGgGrirrvuUnZ2tg4cOKAZM2Zozpw5GjNmTKn1zp49ayCd894/LS1NvXv31unTp7Vx40bjeQBcHmUEqCa8vb3VoEEDhYaGqm/fvurXr59WrVql5ORktW7dWosWLVJkZKS8vb1lWZYyMzPVo0cP+fn5yd/fX71799ZPP/1kf70L27388ssKDQ2Vr6+v/vznP+vEiRMO77t48WI1b95cPj4+atasmebMmWNfdvDgQdlsNr355pu644475OPjo9dee00DBw5Ufn6+/WxOcnKypk2bphtvvLHUfkVHR2vy5Mnl+hlYlqXFixfrL3/5i/r27auFCxc6LL9YnsvthyQ98cQTatq0qXx9fRUZGalJkyZRZIDKYgGo8gYMGGD16NHDYWzEiBFWnTp1rClTplg1a9a0unbtam3fvt3atWuXVVJSYrVp08Zq3769tXXrVmvLli3WzTffbHXo0MG+/YXtOnXqZO3YscNav369FRUVZfXt29e+zvz5863g4GDrnXfesQ4cOGC98847Vu3ata20tDTLsiwrIyPDkmSFh4fb1zl06JCVmppq+fv7W9nZ2VZ2drZ18uRJKysry3Jzc7O+/PJL++vv2rXLstls1v79+8v1c/j444+tBg0aWOfOnbN2795t1axZ0yooKLAvLyvP4cOHL7sflmVZ06dPtzZu3GhlZGRY7777rhUUFGTNnDmzIv+ZAFwEZQSoBn5bRr744gurTp06Vu/eva0pU6ZYnp6eVm5urn35Rx99ZLm7u1uZmZn2sW+//daSZC8DU6ZMsdzd3a2srCz7OmvXrrXc3Nys7Oxsy7IsKzQ01Hr99dcdskyfPt2KjY21LOv///JPTU11WGfx4sVWQEBAqf1ISEiw/vrXv9qfjxo1yrrjjjvK/XPo27evNWrUKPvzVq1aWa+88or9+cXyXG4/yjJr1iwrOjq63NkAXByXaYBq4r333pOfn598fHwUGxur22+/XS+++KIkKSwsTPXq1bOvu3fvXoWGhio0NNQ+1qJFCwUGBmrv3r32scaNG6tRo0b257GxsSopKdF3332nn3/+WVlZWRo8eLD8/PzsjxkzZmj//v0O2WJiYsq1Dw899JDeeOMNnTlzRmfPntXSpUs1aNCgcm174sQJrVixQg8++KB97MEHH9SiRYtKrfvfecq7H2+//bbat2+vBg0ayM/PT5MmTVJmZma5sgG4NL4oD6gmOnbsqLlz58rT01MhISHy9PS0L6tZs6bDupZlyWazlXqNi41fcGGZzWZTSUmJJOmVV17Rrbfe6rDeb79z5rfvfzHdu3eXt7e3Vq5cKW9vbxUWFqpXr17l2vb111/XmTNnHLJYlqWSkhLt2bNHLVq0KDNPefZjy5Ytuv/++zV16lR17dpVAQEBWrZsmZ599tlyZQNwaZQRoJqoWbOmoqKiyrVuixYtlJmZqaysLPvZkT179ig/P1/Nmze3r5eZmakjR44oJCREkrR582a5ubmpadOmCgoKUsOGDXXgwAH169evQlm9vLxUXFxcatzDw0MDBgzQ4sWL5e3trfvvv1++vr7les2FCxfqscceU2JiosP4yJEjtWjRIj3zzDNlblee/di4caPCwsI0YcIE+9ihQ4fKlQvA5VFGABfUpUsX3XTTTerXr59SU1N17tw5DRs2TB06dHC4hOHj46MBAwbomWeeUUFBgUaOHKnevXurQYMGks5/4mbkyJHy9/dXQkKCCgsLtXXrVh0/flxJSUkXff/w8HD98ssv+vjjj9WqVSv5+vraS8eQIUPshag8H82VpJ07d2r79u1aunRpqfuLPPDAA5owYYJSUlIuuv3l9iMqKkqZmZlatmyZbrnlFr3//vtauXJlubIBuDzmjAAuyGazadWqVbruuut0++23q0uXLoqMjNTy5csd1ouKitJ9992nu+++W/Hx8WrZsqXDR16HDBmiBQsWKC0tTTfeeKM6dOigtLQ0RUREXPL927Ztq6FDh6pPnz6qV6+eZs2aZV/WpEkTtW3bVjfccEOpyyYXs3DhQrVo0aLMG53de++9OnbsmFavXn3R7S+3Hz169NDo0aM1fPhwtW7dWps2bdKkSZPKlQ3A5dksy7JMhwBw7UlOTtaqVau0c+dOp76vZVlq1qyZHnnkkUueXQFQfXCZBsA1Izc3V//61790+PBhDRw40HQcAE5CGQFwzQgKClLdunU1f/58XXfddQ7L/Pz8Lrrd2rVrFRcXd7XjAbhKuEwDoErYt2/fRZc1bNhQNWrUcGIaAJWJMgIAAIzi0zQAAMAoyggAADCKMgIAAIyijAAAAKMoIwAAwCjKCAAAMIoyAgAAjKKMAAAAo/4f5H+k51Sp3ycAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Credit_History=pd.crosstab(raw['Credit_History'],raw['Loan_Status']) \n",
    "Property_Area=pd.crosstab(raw['Property_Area'],raw['Loan_Status']) \n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4)) \n",
    "plt.show() \n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It seems people with a credit history of 1 are more likely to get their loans approved.\n",
    "- The proportion of loans getting approved in semi-urban areas is higher as compared to that in rural or urban areas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine the Applicant Income and Co-applicant Income and see the combined effect of Total Income on the Loan_Status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvtO7776rr7/+WhEREYWcHAAAFFWGlpsFCxYoPDxcERERCgkJUVRUlAIDA7V06dJcl//vf/+rWrVqacSIEQoKCtIDDzygp59+Wnv33v5S0JmZmcrIyLB7AAAA8zKs3Fy7dk379u1T586d7cY7d+6sXbt25bpOWFiYTp06pY0bN8pqteqXX37Rv//9b3Xv3v227zNv3jz5+vraHoGBgU79HAAAoGgxrNycPXtWWVlZ8vf3txv39/dXampqruuEhYVpzZo16tu3rzw8PFSlShX5+fnptddeu+37TJo0Senp6bbHyZMl9F4gAACUEIYfUPzH25pbrdbb3ur80KFDGjFihKZNm6Z9+/Zp06ZNOn78uIYNG3bb7Xt6esrHx8fuAQAAzMuwu4JXrFhRrq6uOWZp0tLScszm3DJv3jy1adNG48aNkyTdc889Kl26tNq2bas5c+aoatWqdz03AAAo2gybufHw8FBoaKji4uLsxuPi4hQWFpbrOleuXJGLi33kW7eit1qtdycoAAAoVgzdLRUZGamVK1dq9erVOnz4sEaPHq2kpCTbbqZJkyZp4MCBtuV79Oih2NhYLV26VMeOHdPOnTs1YsQItWzZUgEBAUZ9DAAAUIQYtltKkvr27atz585p1qxZSklJUePGjbVx40bVrFlTkpSSkmJ3zZvBgwfr4sWLWrx4scaMGSM/Pz899NBDevHFF436CAAAoIixWEvY/pyMjAz5+voqPT3dmIOLZ/gW/nsWBTPSjU5gDL7vkoXvu2Th+y5U+fn7bfjZUgAAAM5EuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKZCuQEAAKbicLk5evSopkyZon79+iktLU2StGnTJn333XdOCwcAAJBfDpWb+Ph4NWnSRHv27FFsbKwuXbokSTp48KCmT5/u1IAAAAD54VC5mThxoubMmaO4uDh5eHjYxjt06KDdu3c7LRwAAEB+OVRuvvnmG/Xq1SvHeKVKlXTu3LkChwIAAHCUQ+XGz89PKSkpOcYTEhJUrVq1AocCAABwlEPlpn///powYYJSU1NlsViUnZ2tnTt3auzYsRo4cGC+trVkyRIFBQXJy8tLoaGh2r59+x2Xz8zM1OTJk1WzZk15enqqTp06Wr16tSMfAwAAmJCbIys9//zzGjx4sKpVqyar1aqGDRsqKytL/fv315QpU/K8nfXr12vUqFFasmSJ2rRpo+XLl6tr1646dOiQatSokes6ffr00S+//KJVq1apbt26SktL040bNxz5GAAAwIQcKjfu7u5as2aNZs2apYSEBGVnZ6tZs2YKDg7O13YWLFig8PBwRURESJKioqL02WefaenSpZo3b16O5Tdt2qT4+HgdO3ZM5cuXlyTVqlXLkY8AAABMyqFyc0udOnVUp04dh9a9du2a9u3bp4kTJ9qNd+7cWbt27cp1nY8++kjNmzfX/Pnz9fbbb6t06dJ69NFHNXv2bHl7e+e6TmZmpjIzM23PMzIyHMoLAACKB4fKTWRkZK7jFotFXl5eqlu3rh577DHb7Epuzp49q6ysLPn7+9uN+/v7KzU1Ndd1jh07ph07dsjLy0vvv/++zp49q+HDh+v8+fO3Pe5m3rx5mjlzZh4/GQAAKO4cKjcJCQnav3+/srKyVL9+fVmtVh05ckSurq5q0KCBlixZojFjxmjHjh1q2LDhHbdlsVjsnlut1hxjt2RnZ8tisWjNmjXy9fWVdHPX1t/+9je9/vrruc7eTJo0ya6MZWRkKDAwML8fGQAAFBMOnS312GOP6eGHH1ZycrL27dun/fv36/Tp0+rUqZP69eun06dPq127dho9evRtt1GxYkW5urrmmKVJS0vLMZtzS9WqVVWtWjVbsZGkkJAQWa1WnTp1Ktd1PD095ePjY/cAAADm5VC5eemllzR79my7ouDj46MZM2Zo/vz5KlWqlKZNm6Z9+/bddhseHh4KDQ1VXFyc3XhcXJzCwsJyXadNmzZKTk623e5Bkn788Ue5uLioevXqjnwUAABgMg6Vm/T0dNvNMn/vzJkztgN2/fz8dO3atTtuJzIyUitXrtTq1at1+PBhjR49WklJSRo2bJikm7uUfn/dnP79+6tChQoaMmSIDh06pC+//FLjxo3T0KFDb3tAMQAAKFkcOubmscce09ChQ/XKK6+oRYsWslgs+uqrrzR27Fj17NlTkvTVV1+pXr16d9xO3759de7cOc2aNUspKSlq3LixNm7cqJo1a0qSUlJSlJSUZFu+TJkyiouL07PPPqvmzZurQoUK6tOnj+bMmePIxwAAACZksVqt1vyudOnSJY0ePVpvvfWW7QJ6bm5uGjRokBYuXKjSpUsrMTFRktS0aVNn5i2wjIwM+fr6Kj093ZDjb2pN/E+hv2dRcOKF7kZHMMYM3z9fxoxmpBudwBh83yUL33ehys/fb4dmbsqUKaM33nhDCxcu1LFjx2S1WlWnTh2VKVPGtkxRKzUAAKBkKNBF/MqUKaN77rnHWVkAAAAKzOFy8/XXX+vdd99VUlJSjgOHY2NjCxwMAADAEQ6dLbVu3Tq1adNGhw4d0vvvv6/r16/r0KFD2rJli901aAAAAAqbQ+Vm7ty5WrhwoT755BN5eHho0aJFOnz4sPr06XPbu3kDAAAUBofKzdGjR9W9+82zXzw9PXX58mVZLBaNHj1aK1ascGpAAACA/HCo3JQvX14XL16UJFWrVk3ffvutJOnChQu6cuWK89IBAADkk0MHFLdt21ZxcXFq0qSJ+vTpo5EjR2rLli2Ki4tTx44dnZ0RAAAgzxwqN4sXL9bVq1cl3bxFgru7u3bs2KG//vWvmjp1qlMDAgAA5IdD5aZ8+fK2n11cXDR+/HiNHz/eaaEAAAAc5dAxN66urrneOPPcuXNydXUtcCgAAABHOVRubnc7qszMTHl4eBQoEAAAQEHka7fUq6++KkmyWCxauXKl3b2ksrKy9OWXX6pBgwbOTQgAAJAP+So3CxculHRz5mbZsmV2u6A8PDxUq1YtLVu2zLkJAQAA8iFf5eb48eOSpA4dOig2NlblypW7K6EAAAAc5dDZUlu3bnV2DgAAAKdwqNxkZWUpJiZGmzdvVlpamrKzs+1e37Jli1PCAQAA5JdD5WbkyJGKiYlR9+7d1bhxY1ksFmfnAgAAcIhD5WbdunXasGGDunXr5uw8AAAABeLQdW48PDxUt25dZ2cBAAAoMIfKzZgxY7Ro0aLbXswPAADAKA7tltqxY4e2bt2qTz/9VI0aNZK7u7vd67GxsU4JBwAAkF8OlRs/Pz/16tXL2VkAAAAKzKFyEx0d7ewcAAAATuHQMTeSdOPGDX3xxRdavny5Ll68KElKTk7WpUuXnBYOAAAgvxyaufn555/1yCOPKCkpSZmZmerUqZPKli2r+fPn6+rVq9xfCgAAGMahmZuRI0eqefPm+vXXX+Xt7W0b79WrlzZv3uy0cAAAAPnl8NlSO3fulIeHh914zZo1dfr0aacEAwAAcIRDMzfZ2dnKysrKMX7q1CmVLVu2wKEAAAAc5VC56dSpk6KiomzPLRaLLl26pOnTp3NLBgAAYCiHdkstXLhQHTp0UMOGDXX16lX1799fR44cUcWKFbV27VpnZwQAAMgzh8pNQECAEhMTtW7dOu3bt0/Z2dkKDw/XgAED7A4wBgAAKGwOlRtJ8vb21pAhQzRkyBBn5gEAACgQh465mTdvnlavXp1jfPXq1XrxxRcLHAoAAMBRDpWb5cuXq0GDBjnGGzVqxAX8AACAoRwqN6mpqapatWqO8UqVKiklJaXAoQAAABzlULkJDAzUzp07c4zv3LlTAQEBBQ4FAADgKIcOKI6IiNCoUaN0/fp1PfTQQ5KkzZs3a/z48RozZoxTAwIAAOSHQ+Vm/PjxOn/+vIYPH65r165Jkry8vDRhwgRNmjTJqQEBAADyI9/lJisrSzt27NCECRM0depUHT58WN7e3goODpanp+fdyAgAAJBn+S43rq6u6tKliw4fPqygoCC1aNHibuQCAABwiEMHFDdp0kTHjh1zdhYAAIACc6jcPP/88xo7dqw++eQTpaSkKCMjw+4BAABgFIcOKH7kkUckSY8++qgsFott3Gq1ymKxKCsryznpAAAA8smhcrN161Zn5wAAAHAKh8pN+/btnZ0DAADAKRw65kaStm/frieeeEJhYWE6ffq0JOntt9/Wjh07nBYOAAAgvxwqN++99566dOkib29v7d+/X5mZmZKkixcvau7cuU4NCAAAkB8OlZs5c+Zo2bJleuONN+Tu7m4bDwsL0/79+50WDgAAIL8cKjc//PCD2rVrl2Pcx8dHFy5cKGgmAAAAhzlUbqpWraqffvopx/iOHTtUu3btAocCAABwlEPl5umnn9bIkSO1Z88eWSwWJScna82aNRo7dqyGDx/u7IwAAAB55vBdwTMyMtShQwddvXpV7dq1k6enp8aOHat//vOfzs4IAACQZ/kqN1euXNG4ceP0wQcf6Pr16+rRo4fGjBkjSWrYsKHKlClzV0ICAADkVb7KzfTp0xUTE6MBAwbI29tb77zzjrKzs/Xuu+/erXwAAAD5kq9yExsbq1WrVunxxx+XJA0YMEBt2rRRVlaWXF1d70pAAACA/MjXAcUnT55U27Ztbc9btmwpNzc3JScnOz0YAACAI/JVbrKysuTh4WE35ubmphs3bjg1FAAAgKPytVvKarVq8ODB8vT0tI1dvXpVw4YNU+nSpW1jsbGxzksIAACQD/mauRk0aJAqV64sX19f2+OJJ55QQECA3Vh+LFmyREFBQfLy8lJoaKi2b9+ep/V27twpNzc3NW3aNF/vBwAAzC1fMzfR0dFOffP169dr1KhRWrJkidq0aaPly5era9euOnTokGrUqHHb9dLT0zVw4EB17NhRv/zyi1MzAQCA4s2hKxQ7y4IFCxQeHq6IiAiFhIQoKipKgYGBWrp06R3Xe/rpp9W/f3+1bt26kJICAIDiwrByc+3aNe3bt0+dO3e2G+/cubN27dp12/Wio6N19OhRTZ8+PU/vk5mZqYyMDLsHAAAwL8PKzdmzZ5WVlSV/f3+7cX9/f6Wmpua6zpEjRzRx4kStWbNGbm5526M2b948u+OBAgMDC5wdAAAUXYbulpIki8Vi99xqteYYk26eht6/f3/NnDlT9erVy/P2J02apPT0dNvj5MmTBc4MAACKLodunOkMFStWlKura45ZmrS0tByzOZJ08eJF7d27VwkJCbabc2ZnZ8tqtcrNzU2ff/65HnrooRzreXp62p26DgAAzM2wmRsPDw+FhoYqLi7ObjwuLk5hYWE5lvfx8dE333yjxMRE22PYsGGqX7++EhMT1apVq8KKDgAAijDDZm4kKTIyUk8++aSaN2+u1q1ba8WKFUpKStKwYcMk3dyldPr0ab311ltycXFR48aN7davXLmyvLy8cowDAICSy9By07dvX507d06zZs1SSkqKGjdurI0bN6pmzZqSpJSUFCUlJRkZEQAAFDMWq9VqNTpEYcrIyJCvr6/S09Pl4+NT6O9fa+J/Cv09i4ITL3Q3OoIxZuTvit2mMSPd6ATG4PsuWfi+C1V+/n4bfrYUAACAM1FuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqVBuAACAqbgZHQAAgOKo1tV3jI5giBNGB8gDZm4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpcONMAHASbqQIFA3M3AAAAFOh3AAAAFOh3AAAAFOh3AAAAFOh3AAAAFOh3AAAAFPhVHDgLuLUYAAofMzcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAU6HcAAAAUzG83CxZskRBQUHy8vJSaGiotm/ffttlY2Nj1alTJ1WqVEk+Pj5q3bq1Pvvss0JMCwAAijpDy8369es1atQoTZ48WQkJCWrbtq26du2qpKSkXJf/8ssv1alTJ23cuFH79u1Thw4d1KNHDyUkJBRycgAAUFQZWm4WLFig8PBwRUREKCQkRFFRUQoMDNTSpUtzXT4qKkrjx49XixYtFBwcrLlz5yo4OFgff/xxIScHAABFlWHl5tq1a9q3b586d+5sN965c2ft2rUrT9vIzs7WxYsXVb58+dsuk5mZqYyMDLsHAAAwL8PKzdmzZ5WVlSV/f3+7cX9/f6WmpuZpG6+88oouX76sPn363HaZefPmydfX1/YIDAwsUG4AAFC0GX5AscVisXtutVpzjOVm7dq1mjFjhtavX6/KlSvfdrlJkyYpPT3d9jh58mSBMwMAgKLLzag3rlixolxdXXPM0qSlpeWYzfmj9evXKzw8XO+++64efvjhOy7r6ekpT0/PAucFAADFg2EzNx4eHgoNDVVcXJzdeFxcnMLCwm673tq1azV48GC988476t69+92OCQAAihnDZm4kKTIyUk8++aSaN2+u1q1ba8WKFUpKStKwYcMk3dyldPr0ab311luSbhabgQMHatGiRbr//vttsz7e3t7y9fU17HMAAICiw9By07dvX507d06zZs1SSkqKGjdurI0bN6pmzZqSpJSUFLtr3ixfvlw3btzQM888o2eeecY2PmjQIMXExBR2fAAAUAQZWm4kafjw4Ro+fHiur/2xsGzbtu3uBwIAAMWa4WdLAQAAOBPlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmIrh5WbJkiUKCgqSl5eXQkNDtX379jsuHx8fr9DQUHl5eal27dpatmxZISUFAADFgaHlZv369Ro1apQmT56shIQEtW3bVl27dlVSUlKuyx8/flzdunVT27ZtlZCQoOeee04jRozQe++9V8jJAQBAUWVouVmwYIHCw8MVERGhkJAQRUVFKTAwUEuXLs11+WXLlqlGjRqKiopSSEiIIiIiNHToUL388suFnBwAABRVbka98bVr17Rv3z5NnDjRbrxz587atWtXruvs3r1bnTt3thvr0qWLVq1apevXr8vd3T3HOpmZmcrMzLQ9T09PlyRlZGQU9CM4JDvziiHvazSjft9G4/suWfi+Sxa+b2Pe12q1/umyhpWbs2fPKisrS/7+/nbj/v7+Sk1NzXWd1NTUXJe/ceOGzp49q6pVq+ZYZ968eZo5c2aO8cDAwAKkR375RhmdAIWJ77tk4fsuWYz+vi9evChfX987LmNYubnFYrHYPbdarTnG/mz53MZvmTRpkiIjI23Ps7Ozdf78eVWoUOGO72M2GRkZCgwM1MmTJ+Xj42N0HNxlfN8lC993yVJSv2+r1aqLFy8qICDgT5c1rNxUrFhRrq6uOWZp0tLScszO3FKlSpVcl3dzc1OFChVyXcfT01Oenp52Y35+fo4HL+Z8fHxK1P8MJR3fd8nC912ylMTv+89mbG4x7IBiDw8PhYaGKi4uzm48Li5OYWFhua7TunXrHMt//vnnat68ea7H2wAAgJLH0LOlIiMjtXLlSq1evVqHDx/W6NGjlZSUpGHDhkm6uUtp4MCBtuWHDRumn3/+WZGRkTp8+LBWr16tVatWaezYsUZ9BAAAUMQYesxN3759de7cOc2aNUspKSlq3LixNm7cqJo1a0qSUlJS7K55ExQUpI0bN2r06NF6/fXXFRAQoFdffVW9e/c26iMUG56enpo+fXqOXXQwJ77vkoXvu2Th+/5zFmtezqkCAAAoJgy//QIAAIAzUW4AAICpUG4AAICpUG4AAICpUG4AAICpGH77BQDOc+PGDW3btk1Hjx5V//79VbZsWSUnJ8vHx0dlypQxOh4AB124cEFfffWV0tLSlJ2dbffa768Hh5s4FRwwiZ9//lmPPPKIkpKSlJmZqR9//FG1a9fWqFGjdPXqVS1btszoiAAc8PHHH2vAgAG6fPmyypYta3dfRIvFovPnzxuYrmii3JjcgAED1L59ez344IOqV6+e0XFwF/Xs2VNly5bVqlWrVKFCBR04cEC1a9dWfHy8IiIidOTIEaMjwsmysrIUExOjzZs35/ov+i1bthiUDM5Ur149devWTXPnzlWpUqWMjlMssFvK5MqUKaMFCxZo2LBhqlKlitq3b28rOw0aNDA6Hpxox44d2rlzpzw8POzGa9asqdOnTxuUCnfTyJEjFRMTo+7du6tx48Z2/6KHeZw+fVojRoyg2OQD5cbkli9fLklKTU3Vtm3btG3bNi1atEjPPPOMKleurJSUFIMTwlmys7OVlZWVY/zUqVMqW7asAYlwt61bt04bNmxQt27djI6Cu6hLly7au3evateubXSUYoNyU0KULVtW5cqVU7ly5eTn5yc3NzdVqVLF6Fhwok6dOikqKkorVqyQdHNf/KVLlzR9+nT++JmUh4eH6tata3QM3AUfffSR7efu3btr3LhxOnTokJo0aSJ3d3e7ZR999NHCjlfkccyNyU2YMEHx8fE6cOCAGjdurHbt2ql9+/Zq166d/Pz8jI4HJ0pOTlaHDh3k6uqqI0eOqHnz5jpy5IgqVqyoL7/8UpUrVzY6IpzslVde0bFjx7R48WJ2SZmMi0vertRisVhynbEt6Sg3Jufi4qJKlSpp9OjReuyxxxQSEmJ0JNxFv/32m9auXav9+/crOztb9913nwYMGCBvb2+jo8FJ/vrXv9o937Jli8qXL69GjRrl+Bd9bGxsYUYDigzKjckdOHBA8fHx2rZtm7Zv3y5XV1fbAcUPPvggZQcoZoYMGZLnZaOjo+9iEqDootyUMAcOHFBUVJT+9a9/3fYAVBRPv99H/3sWi0VeXl6qW7eugoKCCjkVgIJ69dVXcx3//f/b7dq1k6urayEnK7ooNyVAQkKC7Uyp7du3KyMjQ02bNlWHDh300ksvGR0PTuLi4iKLxaI//i99a8xiseiBBx7QBx98oHLlyhmUEkB+BQUF6cyZM7py5YrKlSsnq9WqCxcuqFSpUipTpozS0tJUu3Ztbd26VYGBgUbHLRK4t5TJlStXTi1bttSaNWsUHByst956S+fPn9fevXspNiYTFxenFi1aKC4uTunp6UpPT1dcXJxatmypTz75RF9++aXOnTunsWPHGh0VTtKsWTPdd999OR6hoaFq06aNBg0apK1btxodEwU0d+5ctWjRQkeOHNG5c+d0/vx5/fjjj2rVqpUWLVqkpKQkValSRaNHjzY6apHBzI3JffLJJ2rXrp18fHyMjoK7rHHjxlqxYoXCwsLsxnfu3KmnnnpK3333nb744gsNHTpUSUlJBqWEM02aNElLly5VkyZN1LJlS1mtVu3du1cHDx7U4MGDdejQIW3evFmxsbF67LHHjI4LB9WpU0fvvfeemjZtajeekJCg3r1769ixY9q1a5d69+7Ntcv+h+vcmNxf/vIX28+nTp2SxWJRtWrVDEyEu+Xo0aO5llgfHx8dO3ZMkhQcHKyzZ88WdjTcJWfPntWYMWM0depUu/E5c+bo559/1ueff67p06dr9uzZlJtiLCUlRTdu3MgxfuPGDaWmpkqSAgICdPHixcKOVmSxW8rksrOzNWvWLPn6+qpmzZqqUaOG/Pz8NHv27Bz3oUHxFhoaqnHjxunMmTO2sTNnzmj8+PFq0aKFJOnIkSOqXr26URHhZBs2bFC/fv1yjD/++OPasGGDJKlfv3764YcfCjsanKhDhw56+umnlZCQYBtLSEjQP/7xDz300EOSpG+++YYTBn6HcmNykydP1uLFi/XCCy8oISFB+/fv19y5c/Xaa6/l+NceirdVq1bp+PHjql69uurWravg4GBVr15dJ06c0MqVKyVJly5d4ns3ES8vL+3atSvH+K5du+Tl5SXp5j9wPD09CzsanGjVqlUqX768QkND5enpKU9PTzVv3lzly5fXqlWrJN28j+Arr7xicNKig2NuTC4gIEDLli3LcXnuDz/8UMOHD+eGiiZjtVr12Wef6ccff5TValWDBg3UqVOnPF/tFMXLnDlzNHfuXP39739XixYtZLFY9NVXX2nlypV67rnnNHnyZC1cuFAbN25UXFyc0XFRQN9//73d/9v169c3OlKRRbkxOS8vLx08eFD16tWzG//hhx/UtGlT/fbbbwYlA+AMa9as0eLFi227nurXr69nn31W/fv3l3TzqtW3rocClBSUG5Nr1aqVWrVqleMiUM8++6y++uor7dmzx6BkuBsuX76s+Ph4JSUl6dq1a3avjRgxwqBUAPIrMjJSs2fPVunSpRUZGXnHZRcsWFBIqYoPzpYyufnz56t79+764osv1Lp1a1ksFu3atUsnT57Uxo0bjY4HJ0pISFC3bt105coVXb58WeXLl9fZs2dVqlQpVa5cmXIDFCMJCQm6fv267efb4YapuWPmpgRITk7W66+/ru+//15Wq1UNGzbUU089pRkzZmj16tVGx4OTPPjgg6pXr56WLl0qPz8/HThwQO7u7nriiSc0cuTIHDdcRPFUvnx5/fjjj6pYsaLKlSt3xz9u58+fL8RkQNFBuSmhDhw4oPvuu497S5mIn5+f9uzZo/r168vPz0+7d+9WSEiI9uzZo0GDBun77783OiKc4M0339Tjjz8uT09PxcTE3LHcDBo0qBCTAUUHu6UAk3B3d7f9ofP391dSUpJCQkLk6+vLFYlNZNCgQcrIyFBmZiazcSXE5cuX9cILL2jz5s1KS0vLcY2yWxfpxP9HuQFMolmzZtq7d6/q1aunDh06aNq0aTp79qzefvttNWnSxOh4cCI/P788HWvBzKw5REREKD4+Xk8++aSqVq3KcTZ5wG6pEordUuazd+9eXbx4UR06dNCZM2c0aNAg7dixQ3Xr1lV0dLTuvfdeoyPCSeLj420/W61WdevWTStXrsxxa5X27dsXdjTcBX5+fvrPf/6jNm3aGB2l2GDmxqT+bLr6woULhRMEhcJqtapSpUpq1KiRJKlSpUqcDWdifywtrq6uuv/++1W7dm2DEuFuKleunMqXL290jGKFy5aalK+v7x0fNWvW1MCBA42OCSexWq0KDg7WqVOnjI4CwMlmz56tadOm6cqVK0ZHKTaYuTGp6OhooyOgELm4uCg4OFjnzp1TcHCw0XEAFFCzZs3sjq356aef5O/vr1q1asnd3d1u2f379xd2vCKPcgOYxPz58zVu3DgtXbpUjRs3NjoOChkHmZpLz549jY5QrHFAMWAS5cqV05UrV3Tjxg15eHjI29vb7nUu6GYefzym7uOPP9ZDDz2k0qVL243HxsYWZiygyGDmBjCJqKgooyOgkPj6+to9f+KJJwxKAhRNzNwAAABT4WwpwESOHj2qKVOmqF+/fkpLS5Mkbdq0Sd99953ByQCg8FBuAJOIj49XkyZNtGfPHsXGxurSpUuSpIMHD2r69OkGpwOAwkO5AUxi4sSJmjNnjuLi4uTh4WEb79Chg3bv3m1gMgAFsW3bNqMjFDuUG8AkvvnmG/Xq1SvHeKVKlXTu3DkDEgFwhkceeUR16tTRnDlzdPLkSaPjFAuUG8Ak/Pz8lJKSkmM8ISEhxz2HABQfycnJGjlypGJjYxUUFKQuXbpow4YNunbtmtHRiizKDWAS/fv314QJE5SamiqLxaLs7Gzt3LlTY8eO5VYbQDFWvnx5jRgxQvv379fevXtVv359PfPMM6patapGjBihAwcOGB2xyOFUcMAkrl+/rsGDB2vdunWyWq1yc3NTVlaW+vfvr5iYGLm6uhodEYATJCcna8WKFXrhhRfk5uamq1evqnXr1lq2bJnt5rklHeUGMJmjR48qISFB2dnZatasGfeaAkzg+vXr+vDDD7V69WrFxcWpefPmCg8PV79+/XT+/HlNmDBBiYmJOnTokNFRiwTKDWAS8fHxat++vdExADjZs88+q7Vr10q6eTXqiIiIHPePS0pKUq1atZSdnW1ExCKHcgOYhIeHh6pUqaL+/fvriSee4OaZgEl07NhRERER6t27t91lHn7vxo0b2rlzJ//A+R8OKAZMIjk5WePHj9f27dt1zz336J577tH8+fN16tQpo6MBcND169dVo0YNtWrV6rbFRpLc3NwoNr/DzA1gQsePH9c777yjtWvX6vvvv1e7du20ZcsWo2MBcICfn5/279+v2rVrGx2l2KDcACaVlZWlTz/9VFOnTtXBgweVlZVldCQADhgyZIiaNGmiyMhIo6MUG25GBwDgXDt37tSaNWv073//W1evXtWjjz6quXPnGh0LgIPq1q2r2bNna9euXQoNDVXp0qXtXh8xYoRByYouZm4Ak3juuee0du1anT59Wp06ddKAAQPUs2dPlSpVSomJiWratKnREQE4ICgo6LavWSwWHTt2rBDTFA+UG8AkwsLCNGDAAPXt21cVK1ZUenq61qxZo1WrVikxMZHdUgBKDMoNYDJbtmzR6tWrFRsbq5o1a6p3797q3bu3mjVrZnQ0AAVw7do1HT9+XHXq1JGbG0eV3AmnggMmcOrUKc2ZM0d16tRRv379VK5cOV2/fl3vvfee5syZQ7EBirErV64oPDxcpUqVUqNGjZSUlCTp5rE2L7zwgsHpiibKDVDMdevWTQ0bNtR3332nV199VcnJyXrttdeMjgXASSZNmqQDBw5o27Zt8vLyso0//PDDWr9+vYHJii7mtYBi7vPPP9eIESP0j3/8g/tIASb0wQcfaP369br//vtlsVhs4w0bNtTRo0cNTFZ0MXMDFHPbt2/XxYsX1bx5c7Vq1UqLFy/WmTNnjI4FwEnOnDmjypUr5xi/fPmyXdnB/0e5AYq51q1b64033lBKSoqefvpprVu3TtWqVVN2drbi4uJ08eJFoyMCKIAWLVroP//5j+35rULzxhtvqHXr1kbFKtI4WwowoR9++EGrVq3S22+/rQsXLqhTp0766KOPjI4FwAG7du3SI488ogEDBigmJkZPP/20vvvuO+3evVvx8fEKDQ01OmKRw8wNYEL169e33TRz7dq1RscB4IDExERJN69htXPnTl25ckV16tTR559/Ln9/f+3evZticxvM3AAAUAS5uLioWbNmioiIUP/+/eXr62t0pGKDmRsAAIqgnTt36r777tPEiRNVtWpVPfnkk9q6davRsYoFZm4AACjCfvvtN23YsEHR0dHavn27atWqpaFDh2rQoEGqXr260fGKJMoNAADFxNGjRxUdHa233npLKSkp6tSpkzZu3Gh0rCKHcgMAQDFy6dIlrVmzRs8995wuXLjATXFzwRWKAQAoBuLj47V69Wq99957cnV1VZ8+fRQeHm50rCKJmRsAAIqokydPKiYmRjExMTp+/LjCwsIUHh6uPn36qHTp0kbHK7KYuQEAoAjq1KmTtm7dqkqVKmngwIEaOnSo6tevb3SsYoFyAwBAEeTt7a333ntPf/nLX+Tq6mp0nGKF3VIAAMBUuIgfAAAwFcoNAAAwFcoNAAAwFcoNAAAwFcoNgCKnVq1aioqKMjoGgGKKcgPgtiwWyx0fgwcP/tP1P/jgg7uakSIE4I+4zg2A20pJSbH9vH79ek2bNk0//PCDbczb29uIWABwR8zcALitKlWq2B6+vr6yWCx2Y++8847q1KkjDw8P1a9fX2+//bZt3Vq1akmSevXqJYvFYnt+9OhRPfbYY/L391eZMmXUokULffHFF07LbLFYtHLlSvXq1UulSpVScHCwPvroI7tlvvvuO3Xv3l0+Pj4qW7as2rZtq6NHj0qSsrOzNWvWLFWvXl2enp5q2rSpNm3aZFv3xIkTslgs2rBhg9q2bStvb2+1aNFCP/74o77++ms1b95cZcqU0SOPPKIzZ87YvW90dLRCQkLk5eWlBg0aaMmSJU773AD+P8oNAIe8//77GjlypMaMGaNvv/1WTz/9tIYMGaKtW7dKkr7++mtJN/+gp6Sk2J5funRJ3bp10xdffKGEhAR16dJFPXr0UFJSktOyzZw5U3369NHBgwfVrVs3DRgwQOfPn5cknT59Wu3atZOXl5e2bNmiffv2aejQobpx44YkadGiRXrllVf08ssv6+DBg+rSpYseffRRHTlyxO49pk+frilTpmj//v1yc3NTv379NH78eC1atEjbt2/X0aNHNW3aNNvyb7zxhiZPnqznn39ehw8f1ty5czV16lS9+eabTvvcAP7HCgB5EB0dbfX19bU9DwsLs/7973+3W+b//u//rN26dbM9l2R9//33/3TbDRs2tL722mu25zVr1rQuXLgwT7n+uKwk65QpU2zPL126ZLVYLNZPP/3UarVarZMmTbIGBQVZr127luv2AgICrM8//7zdWIsWLazDhw+3Wq1W6/Hjx62SrCtXrrS9vnbtWqsk6+bNm21j8+bNs9avX9/2PDAw0PrOO+/YbXf27NnW1q1b5+lzAsg7Zm4AOOTw4cNq06aN3VibNm10+PDhO653+fJljR8/Xg0bNpSfn5/KlCmj77//3qkzN/fcc4/t59KlS6ts2bJKS0uTJCUmJqpt27Zyd3fPsV5GRoaSk5Pz9Ll+/x7+/v6SpCZNmtiN3XrPM2fO6OTJkwoPD1eZMmVsjzlz5th2hwFwHg4oBuAwi8Vi99xqteYY+6Nx48bps88+08svv6y6devK29tbf/vb33Tt2jWn5fpjcbFYLMrOzpaUt4Og8/K5fv8et17749it97z13zfeeEOtWrWy2w43RAScj5kbAA4JCQnRjh077MZ27dqlkJAQ23N3d3dlZWXZLbN9+3YNHjxYvXr1UpMmTVSlShWdOHGiMCJLujnjsn37dl2/fj3Haz4+PgoICPjTz5Vf/v7+qlatmo4dO6a6devaPYKCghzeLoDcMXMDwCHjxo1Tnz59dN9996ljx476+OOPFRsba3fmU61atbR582a1adNGnp6eKleunOrWravY2Fj16NFDFotFU6dOtc1sFIZ//vOfeu211/T4449r0qRJ8vX11X//+1+1bNlS9evX17hx4zR9+nTVqVNHTZs2VXR0tBITE7VmzZoCve+MGTM0YsQI+fj4qGvXrsrMzNTevXv166+/KjIy0kmfDoDEzA0AB/Xs2VOLFi3SSy+9pEaNGmn58uWKjo7Wgw8+aFvmlVdeUVxcnAIDA9WsWTNJ0sKFC1WuXDmFhYWpR48e6tKli+67775Cy12hQgVt2bJFly5dUvv27RUaGqo33njDtktpxIgRGjNmjMaMGaMmTZpo06ZN+uijjxQcHFyg942IiNDKlSsVExOjJk2aqH379oqJiWHmBrgLLFar1Wp0CAAAAGdh5gYAAJgK5QZAkbVmzRq7U6d//2jUqJHR8QAUUeyWAlBkXbx4Ub/88kuur7m7u6tmzZqFnAhAcUC5AQAApsJuKQAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCr/DxFm1dnW4faOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['Total_Income']=raw['ApplicantIncome']+raw['CoapplicantIncome']\n",
    "bins=[0,2500,4000,6000,81000] \n",
    "group=['Low','Average','High', 'Very high'] \n",
    "raw['Total_Income_bin']=pd.cut(raw['Total_Income'],bins,labels=group)\n",
    "Total_Income_bin=pd.crosstab(raw['Total_Income_bin'],raw['Loan_Status']) \n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('Total_Income') \n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we can see that the chances of getting a loan approved with a low total_income is unlikely as opposed to average, high and very high total_income."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Loan Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,100,200,700] \n",
    "group=['Low','Average','High'] \n",
    "raw['LoanAmount_bin']=pd.cut(raw['LoanAmount'],bins,labels=group)\n",
    "LoanAmount_bin=pd.crosstab(raw['LoanAmount_bin'],raw['Loan_Status']) \n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True) \n",
    "plt.xlabel('LoanAmount') \n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data supports the hypothesis where a higher loan amount would lead to a lower chance of getting a loan approved. Low and average amounts are about the same chances."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation between all type integer variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# categorical_cols = ['Gender', 'Married', 'Education', 'Self_Employed', 'Property_Area', 'Loan_Status']\n",
    "# raw = pd.get_dummies(raw, columns=categorical_cols, drop_first=True)\n",
    "# raw['Dependents'] = raw['Dependents'].replace({'3+': 3}).astype(float)\n",
    "# raw = raw.dropna()\n",
    "\n",
    "# raw['Loan_Status_Y'] = raw['Loan_Status_Y'].astype(int)\n",
    "# raw['Loan_Amount_Term'] = pd.to_numeric(raw['Loan_Amount_Term'], errors='coerce').fillna(raw['Loan_Amount_Term'].median())\n",
    "\n",
    "\n",
    "# matrix = raw.corr() \n",
    "# f, ax = plt.subplots(figsize=(9, 6)) \n",
    "# sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\", annot=True)\n",
    "# plt.title('Heatmap of Correlation Matrix')\n",
    "# plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Missing Value and Outlier Treatment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now impute missing values and address outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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",
       "Total_Income          0\n",
       "Total_Income_bin      0\n",
       "LoanAmount_bin       22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data above shows where there is missing values e.g(Gender, Married,Dependents etc)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values above can be filled. for categorical data we can employ iputation using mode and numerical data we can employ imputation using meadian and meam. below mode is being used to fill the features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw['Gender'].fillna(raw['Gender'].mode()[0], inplace=True)\n",
    "raw['Married'].fillna(raw['Married'].mode()[0], inplace=True)\n",
    "raw['Dependents'].fillna(raw['Dependents'].mode()[0], inplace=True)\n",
    "raw['Self_Employed'].fillna(raw['Self_Employed'].mode()[0], inplace=True)\n",
    "raw['Credit_History'].fillna(raw['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fill the Loan_Amount_Term the value count of the Loan_Amount_Term will be utilized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_Amount_Term\n",
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "240.0      4\n",
       "84.0       4\n",
       "120.0      3\n",
       "60.0       2\n",
       "36.0       2\n",
       "12.0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the values above the mode is 360 therefore the missing values in this variable will be replaced using the mode of this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw['Loan_Amount_Term'].fillna(raw['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to adress the LoanAmount we can utilize the median and mean to fill the null values since it is a numerical variable. Given that the LoanAmount has outliers as seen from the previous data the median is better suited for this instance as the mean is affected by the presence of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender                0\n",
       "Married               0\n",
       "Dependents            0\n",
       "Education             0\n",
       "Self_Employed         0\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term      0\n",
       "Credit_History        0\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "Total_Income          0\n",
       "Total_Income_bin      0\n",
       "LoanAmount_bin       22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw['LoanAmount'].fillna(raw['LoanAmount'].median(), inplace=True)\n",
    "\n",
    "raw.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw data is now filled and the same can also be done for the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation['Gender'].fillna(raw['Gender'].mode()[0], inplace=True) \n",
    "validation['Dependents'].fillna(raw['Dependents'].mode()[0], inplace=True) \n",
    "validation['Self_Employed'].fillna(raw['Self_Employed'].mode()[0], inplace=True) \n",
    "validation['Credit_History'].fillna(raw['Credit_History'].mode()[0], inplace=True) \n",
    "validation['Loan_Amount_Term'].fillna(raw['Loan_Amount_Term'].mode()[0], inplace=True) \n",
    "validation['LoanAmount'].fillna(raw['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the effect of log transformation for both the raw data and validation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['LoanAmount_log'] = np.log(raw['LoanAmount']) \n",
    "raw['LoanAmount_log'].hist(bins=20) \n",
    "validation['LoanAmount_log'] = np.log(validation['LoanAmount'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization looks similar to a normal distribution curve. showing that the extreme values have been reduced."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluation Metrics for classification problem"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without assessing the model's performance, the model-building process is not finished. How can we determine whether the model's predictions are true, assuming we have access to them? We may determine the difference between the predicted and real values by plotting the findings and comparing them with the actual values. The more accurate the predictions are, the smaller this distance is. Given that this is a classification problem, we are able to assess our models using any combination of the following metrics:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- True Positive: Targets that we have projected to be true (Y) but are in fact true (Y)\n",
    "\n",
    "- True Negative: Targets that we expected to be false(N) but are in fact false(N)\n",
    "\n",
    "- False Positive: Targets that we projected to be true (T) but are in fact false (N).\n",
    "\n",
    "- False Negative: Targets that we have predicted as false(N), but are in fact true (T)."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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Z2TKv68o2egjx4Q9/eHnQbEgIzSZElOV92yo8qBbZPyLBg3C2eci+yawR0iGC7S+VJY82icNDWITRdLiXfbP1Z+3l1gZvetObyhJ5vC5LryzLArLFMvK2qtR1c2gvD6YuCcSc7RyytrbIaO8hjAeTR5njN7zhDYuUxyTP3mQTt4Atm/D4ySbqzLW6ud61BfFuiwXbkA02MQWbtTdaee15NlElcE0++QPt5A+c9OtvTNuzXOOzHoQ02Tn11FPL9otxDwwmSTI5KZaTZJ4Q2SU/La/KWvs2ifrYf//9y19E80CTbOmkPOQhDyl7ieM6BIb9m5afieLYIkGEuz+R7IgtIDX2/fb3IBOihKOH5gRwIllw9mARATmX2H5AMMvEE6Tx4BRhJItsP2mIJ5k64gMmHr5do25fh29EMImw/3Rc/QPbPwiR+rOWuE00ZOuJynjdw1WRAZzvaB9t6kHBum4O7XXQQQcV+7N/OLLMM0Vb6BdbY2yfsfVnnIhVFn2mTZ/xjGcsUh72dcghhzRvfetbyxYDWxNsJ7L9QjbWxMVqA9i1/b8mUe4b2zMCNkuQs4s4XP9DH/rQ6Iwlx7jRtraDGGvEsgkBQU8Qm4QSu1ZLlJdg9tNksG9rDhNdk1JjzIqPTLNrPPjBDy5+wnts3x98MYHzrT0y1UuaIU+SFY0Uy0kyD5F98pBbHbj7hyA4KYItEVt/nqAYt8+VQKmzcX0svQviRHO9TcO+Z9f1TQG+SkvAttRsz6XldqJnLrAEr71sRbBv1X5a20ks3xMF9rTaYtJHltRfCqzbJA7ilpiKScQ43NO1688pg4yeyYkH0OJ159WTkvmOB95kOOu69Q/9HVsUZoI2IGRlhV1DFtvKR3ybwzhsTdCm48oRh0wrsUhcxqTTQ3ke1vTtJg5fx2jc2EevfoFJlocWbU1ipwSnLDD7Hre6siSwRfZA1Ku77LEVHYKWTdmv7L0aNsVex9XbYWVHpj5WfExefU2f101iTSRsQ/LXPNXRfZMkmZwUy0kyRxCTMpO2NdQQo7Kfll7r7RSyTsSHz9kqUD/8s7QgEGRgCU5HZGFrBFpZQEFfEK8FIOFqD6mvHZMFJ3A8eGTLwlyJZcg8yqzZA+pbHHx3s6V7wqh+iE/5QjjLMBIpyyvsiS1pdzZYt7/Jjq8R87MPGzTJiC0Qc4UJHLGsb0yo9Fd/DzSh6n3lMfmZBGNLBlndiEZjiUj21Wts2qRRlpYdENTaxnYF57ifr2b0VW3Eta9f840bs4n6GBey8vrABM9Y98Cs7UuRrTeu2KuJwpL4AyskvvPdViA24GvyjMkkSSYnxXKSzBGxt1eQi+0NDoLOkqul59gyAEFUto1YkZH1rQ2Cm88E/u9acb3ZhtBda621SmbZHsr4tgv46b6+JYLItA3E9gcTApm8fpkEecEek2ZU4zzXmUn9ZG2JYgLRsj7hQXjZzlLvvfb7ox71qHKebDfh37+PeqhPv+2XNWRZHepClMXER52IRt/NO25vtm88ISRtYWGHNT6rXWJ7xJK2D3FoJUJ21V782MMLtmBbhQcC/e6bMyJjHER5or9gX7qvpJOd9UdHfO2cb9SIw/5dYy2uQ4i6vwmX99iICaP3Y6z1UZ7aVmeKMWMbCl+gXjLgbFJ72E4E2X1lYcPslD/olyXKqD+iHHV7xGsy+fENGHXZkySZjBTLSTJHCPKyZzKdITyJTH+Q4IADDhib1UP8oQTZWN93XAdIX/Um8+WY6R+FmAT7cAVsKCPhGdlFosJeXuUiqD1EFNgPbb9y/OEGhBgV8G35mGr/L2QBiXWBnHgg6GYCcW7p2V5lZfTT9+bWwsBERNltsdAPjrrM8MCUh6nG/UXAZQkZSv0JmVKZYmhb+8llc8dtfZBJ9Y0rhOWrXvWq0atXwxbOOOOMIuC8v6SZZ2XUR4Sqsoy7HtuxakBIe3i0tnvjSaaU4PaX9GDrhD42EfUHW/oPxTmserBnEOTEpMx2/d3h7MJ3NfurjH3cw5Ybk0EZ71rkT4KxoF4m0x70c1+TPXu5661NzpMJNzHV3jLENcp44oknlm0WUQZfPSmLzOfEdirvWW1xvcc97nGlvZMkmZyVD+wY/Z4kSYeMpD+EILgQDcQjIRcI6h4aIiQtI9ubS6j1kdmyDC5Q+QonX6lGnBGBgr/slcye4CVQBu5lL7H7+FYMmVzZMXsNCVCf8/CVh4Bkn4aQYYt6EE2ewJ/u+1Vdm1AncIlN4lzQFYCVQfbPg12CsweRInNM5HiYShmJFz/9GV5BmfiXzSUCCFdt4GEqWWnfayyjBnVxjnv6yjeiTDZNPYgFYs9rvkdWXYiVWoDLrmkz7Swr6jMyhfGHTuBeBIq9oSYhlsCJrCizgwDz7RlElczsTLJw7IA9+Gx/i8pM8S0R6uRatX1A+xBPhKMHNX3jRx+2pzyyqCZdssv6kj25rv2s2ld/u0fs4dU+2kw2XmbZpCHahg2wKdlnNmz1JPpvHCYb8Qd0XN/napQxjviLfvrVfv3YD03IsnX/P/nkk8tX20V/sSXj0BhiE8qjbvqQ/bFR4t+KjTZ0KJMJg763P1gbsTu2Q7gqh2vHHwJRbvdwDZM592B3xgobMhm2UmTfs3HiawzZDfHNFn1Thz7qP2PAP/jDIbGf30Oh6lH7Evajf5RT27i+e0V/sF1tS1CbmBuPyuZcD/2xEZ9RF/WVzbYfn/1P5TuSJFmYFMtJ0kMwtYwp4AnAhCbBEUSml/AkGASeeLCmRjAUWF1HMBTIHLJeApblVpk1Wc76mxKIAiLT52WGiBaB2eEzzhdUx92zxj2VVfAl+IlTwmM6BGcPC4XwgHsTFMrpgSECIoQyfLuAfaX2YUdZCS5imMgg/kM4qpN6KBNRGoJXfdTdT4HcNZTdtYmFaHdlIO60US1G/a6M+o9QjkmI69XnubbrxcON6hhldviMz7pv/blJUC/3XlKhDG1JALHB2j5gVUJZ3cs3K+izcehD9XA+O/QZ/aLtfT8zMagPCHwTpMBntK+20l/RNv5PDPqmFuLPa1OhL0xulF892GAf13A/dq6+HmSzpzfsy09lI5htW2DDUR797bMe1Iy9viajRL19ucaZSYEsrnHnYFP+GqAH6tTd59UpxHlc25g31tgwMc8mnOee0L/K5Vzv+6kO6ula7NX/TbjjT5AHYavGjMmx9idk+yI2zouH/kxKo3wO/W4Fyx+Pifbil1zThMprzlMXbW/ySPBP4geSJLmGlTpntuyuMyZJkiRJhRUOXzMnq+yr7gjVGpleWyt8/d1uu+1WvrXEBCJJkmSI3LOcJEmSLDdYKbDlRNbVqkI/HyTT7UC9jSdJkmSI9BRJkiTJcoOtD7ZH2N8e+7prfJ+yr4mzVcHXqtmqkiRJMhW5DSNJkiRZbrCv2YNv8YCkLRb1fmEP3nkg0nct266RJEkyHSmWkyRJkuUKD7eecsop5ZstPChZQyjHN08kSZJMQorlJEmSJEmSJBkg9ywnSZIkSZIkyQAplpMkSZIkSZJkgBTLSZIkSZIkSTJAiuUkSZIkSZIkGSDFcpIkSZIkSZIMMKNvw8gvzkiSJEmSJEmWBVZaaaXRb0vGjMTyRX+7ovnOLy9rvvuLy5or/pvCOUmSJEmSJJlfPG3tWzYPuMuNmhuuMjsbKGYklv/xnyub319yefOHv1zeXHlViuUkSZIkSZJkfnGf26/a3OZm12+ut/K1kFlOkiRJkiRJkhWJfMAvSZIkSZIkSQZIsZwkSZIkSZIkA6RYTpIkSZIkSZIBUiwnSZIkSZIkyQAplpMkSZIkSZJkgBTLSZIkSZIkSTJAiuUkSZIkSZIkGSDFcpIkSZIkSZIMkGI5SZIkSZIkSQbIv+A3zzjvvPOac845p/nzn//crL322s2d73zn5oY3vOHo3an52c9+1vzkJz9p/v3vfzePecxjmlvf+tbNKqusMno3SeYPl112WfPrX/+6+c53vtPc5S53ae5///s3t7/97UfvJrPF3/72twXtzJ/c/e53b25yk5uM3k2Evx/84AfNL37xi2a11VZrHvjABzZ3vOMdR+9OzVVXXdWceeaZzW9+85vmNre5TfPgBz+4ueUtbzl6N5ktrrzyytLOF1xwQbHfddZZZ/ROsjT561//WsbJD3/4w9IPD3jAA5pb3epWo3eXf6YUy9664oorijP4+9//Pnp1aoizm9/85s3tbne70SvzG3W79NJLy4DsQ6Te9ra3bW584xs317nO0knCf/jDH26OO+645vvf/37z6le/unn6059e2lL5/vnPf5bAx6kz0n7QO/7445s3vvGNzUUXXVR+f+QjH9nc7GY3G7277PKXv/ylufjii5t//OMfo1em5kY3ulEJXtpnpZVm5+/CzxVE44UXXlh+Xu961yuCUbmve93rjs64ht/+9rfFVr0voC8tm1xcfve73xUHa/wQxDW/+tWvmve///3N//7v/zbPeMYzmpe85CXN4x73uNG7Kw7G9b/+9a8iBP7zn/+MXl0YdnHTm9609PlM7VlwO/XUU0s78yfPec5zmnvd616jd5c+6sqHiSvjQg+x6Zg0QbCkaP/Xvva1zbvf/e6SmNh7772bJz/5yeW9//73v8XvOPhRPuX6179+eQ/e32233ZqPfexjRcAdcMABzYMe9KDRuysWl1xySUnwDPlodsu3ace6DSdB8kc7f/KTn2ye+9znNkcfffTonaUPmzUB5bPV9QY3uEGJz3zcyiuvPDrrGiS/xG31vsMd7jB6dX5iLPDZ6neLW9xikfL+9Kc/LePkiCOOaDbddNPmf/7nf5qHPvSho3eXf6aMthrvD3/4Q7PTTjuVrMQkxyabbNK87W1vG11h/nPggQc2T3jCE8bWZZtttmm+8pWvFMd+bSPIfPe7320e//jHFyP95je/OXpn+ee0005rdtxxx7F9NO7Yfvvtm6997WslmM13vvWtbzW77757KfdTn/rUEhBMDsZx5JFHNhtuuGERPfPBJqdDeTfaaKMyxpLxGNcym5ttttkidhzH0572tOawww4bfWLZ5txzz22e+cxnNuuuu+7Yur7mNa8pq2PzAeNQLHvKU57SHH744SUJkYzn4x//eLPDDjuM7VOH/ia0fv/7348+sWwipogtYox6mXx+/vOfH5wk7LfffiUZcOyxx45emb/wRQcddFCzwQYbLBPlXdpMKZbNlMya3vCGNxQDqY+HPexhZaa47bbbLvT6KaecUl5bVrCUJhPJgdf1kKE1s9p///2b173udSUbsjR47GMf2xxzzDHNpz71qTLIzPACs1qDVZnHZWWcf9JJJxXHJassA7088KQnPal505vetFD/aKNHPOIRzRprrFGExBlnnLHgvf/7v/8r9R+XnZ1v6Ef9GRNTwvL8888fvbswcZ6fywJTldfYev7zn998/etfL5m9hzzkIaN3VjzYgHZ69KMfXSYYtZ07rDaZIC8vqCu/JjBHHb/61a82r3jFK0r26uCDD25OPPHE0dlzi9UZSZH3ve99RSA8/OEPH71zNWyXz1XmPuLjPvvsU/ztoYceeq1m7K9twobvfe97lxWjL33pSwvZ8Be/+MVmiy22WC62WkVdHSZ/7FVWfRxx3rLks0Nj9Lnb3e7WvOhFLyoJRGP3fve73+idFYMpxbKlE9sqCBLCpD4sK1iCuNOd7rTQ65ahvLYsQVTZblHXQ0aMSLM0ahnTPmLLKXONIMIILW/0l/2mw/n23BEe+mdZEIuTYMtJ3wZXX331st3H8td973vfEuTiPftfvTfft2DU2FphGdikTHbZ/vPlGb6DYNZv97nPfRbZUrQiYuyz5drOHWuttVYJVMuSPU9FxBXCqq7n8573vDLOf/nLXxYBavvDXK8OKYvtLeIWv2u7y6T4rL2b/C0blnRZ0dEG9m7X/tjh/4Ty8vQMjfEq5v785z9vPvKRjwwmOZYXVl111RKj9KWJ4Ypm73Oipv70pz813/ve98rSxKMe9aiy9GKp0d47Tt/GcLMz++nMzCxn1A+ymdUQpl/+8pcXzFb7sxgzuR/96EdlT5BzEOLd/rGZ7ovqw4ESrIQXx21PofrUe+mUz57nyy+/fPTK1cJO/e5xj3uMXrmGP/7xj2UrheW8mLkpryUqhsf5yi4afH7KNHlIzyA866yzymfdyz1tTbCHlQErp/bT7kS9fa2EPsfvc7I1rvPEJz5xbLt8+tOfLu1tANR77gg2n7UfLeAcBAbHTNFn+lv5lW9ob+6SYouCNv7c5z7X3POe9yx9og7qAvZx17vetZQntrPYL6uNathXPPjjM9FHgf249ivWAZ196A/9qm8mRSCxWqOcn/jEJ0pf2Oc76TWUQX1kavvbOGzdYc/9vrc3TX8Ym0MQamyZLbnuRz/60YWyDspH4ITztPfamPRA2Y9//OPyrIN7vOMd7yjna2M25if7lXUiEI1xWT7nWoZ3X0KkL17sqWOX6mp82N+qXvHAoDHCtwQCmhWXcfu7v/GNb5SxVS+h2pJFxM/Ef6ijh17sYzT22dxcot+0gXvWsBftNpOHzPQ9ccqWA+3Kfvv7hp3nvvxYYFLON7OBJcG4ch1jgM/ix9yHTYWPMDZiyTtsUD/xScbnuAmXMcyf1vUTb/gv8SLiEN/gXtpPndgG+1UWe2a9L/tsEm4s6Wdj9uyzzy7twse4rvFgjFjSNqF3vT7sxBhXbu+7HtQpHmSLxIx2ETPFIefXuIdyeb4lUD7t6N6Tog3cT53FUnXr71edbfgSvldbhP/Ul1ay9eWkq6LagL+ofZg2UP++ZoC4Kt7V6O/oh5mMe+ON3/vsZz9bEmpsStKNXU2CWK7+fGDtg9i7+MhH0kw17Fh9jY9x+Kz4I75pQ9eX5de/MWaMaz5dYo29uiYbtiWQ/9WmfAufzW8qh2tKTIVvN975bHUwvjyDUt+3ho/lN4wjfcsnK6f7GnvGUA3bsxWkj7hufIgBygj9pa34vH5bzTZzIpY1gE3gguHLX/7y0rGWEhmxhyc0BuOwZeNd73pX2acmk1uLZVkF2x80sP1BteETQgzsve99b+ncGGw6gJP3oIXzl3QbAucr8FluExCVxb38rm62BnCodSBmfDa/28vEcAKOntHaPsBwYylnzTXXLE7abE1nMyoP533hC19o3v72t5ftBO5hMHKKjJNhCpgGKSPxcBQD5NhtQXANDlt7ylJ6jWELRM6L8rq/ulhCdD3bZ0IsGzwf+tCHms985jMLxJe6cwaMc+utty7OZZwIGYJIfs973lMChfIpUwTC2cRqAIey8847NxtvvHEJNAKvrRpgk4Kb9vI7Tj755EXE8umnn17sk2gj6LSn4OX62oTTYGv6XN8ZzAavvifQ2E5fcAzh3vqaM//2t79dri0jwxlPB7s0ITUm2AwHqV31l9/tm7Mfmj1GZoe4ZE8f+MAHFoxNuA4hGpm/F7/4xWUccU7KZJsAtIP76v/111+/2I42MlFzniVtY8R9BPTXv/715XMcNKfr+sr7whe+sOxHN8a1FdvgD172spc1W2211SJiWdsYH4TxCSecUMao8cTh2rpk8mK8Kh/b9nn1FhzUw3v6Tz3f/OY3l7Gl34xHP/kee1RNPieFLdiPyZG/9KUvnVOx7F4CmXFdB319pm3ZvPEpYKvrEPwIP2q7A5EkcMX5fK9taTEh1jYCnjFrAqpfA/fh0/UjUbekEKAEEx+mTHyU/lI/5XzlK1+5oH/hd/7TODZ++Dcosz42rpWZrUIfy2DbIy5GOM8WELagz12fzfC/hDnbZV/EmOsZ55Ib7E452au+j2yb8e8BNHHJmCA86yCuLsad/b3sbN999y1+VDxUZ3ukxcuIEcawcWjLkkkvvxW26jz9J46G7+Kf+TxlizE9Ha4nPolnxjkbnkuxzO4IM2UXy7SJeiqHch9yyCElOz1V/I420D98ND+mbeAa7FdbRBu4B7u2HYQ/9Hr4AjbDHmgQNjCp2NVvbC6SYBKE+ruO+0PoX2PKlgZ97v9RHragPeokh/rypXwrm2ab4RvjSwqMAyKWHmCfzudj9av4HmPJ9ayG7LnnnmWM63tJlre85S3FxpVFfYhScYQm0Kbak+Yw1tyDfRtX2l4dbFkV+/r9po6Et/6yvVW5ldc9+DFxNsaI1/lPOo4/EVu1C7ukK4866qhS37AXdfFZyS7lm0smVzqLiUbl6Oxj0mic0bis60zgnARTRkrMaXSHTvR/e6MIobmAA+d87S020O2zi/s7GKlyKF+NIO4hEeKFwTmXQxZotAkDHYLzI4IYM0Ncb731itG5BkPz/lA2SQAlyAU4h8ESuKdBZ7CYiZsZBoIuZ0YIRd20rYAgsNinFbO7+YzsP2dq0Ec9DOi+MJ4JnIhvLCHytttuuzLgXVcfE3gewPM+Rz4TlMm3FshYcIoc3SSY2JgQKY+JGhEV/bX55psXu1MezjkQrExGCTyCX5bHZ5wPDst7RBBBrM/tz+SoOVbnCtIyAOySMxS8OXcTKdc3DjlZ3+jifIe+YL/jEKBNENyPcBBU+xAa7iMgCagEtjHAHq0AcdxRPs9acLauKUhG9kb/eaiS0LFf1bnqSgw6+Kn5ijKbsAgg0aaOvfbaq4xv/kgbTeVPwHeaJPOl+jB8koOvjollYM80G5KBq+/Lt7AN958rBEeTLbao3/Vx3J8Q0N/Ekb4LTHriWy74NecaUyZUVuDY7hDsjw83sTemiCk+I8YV38umxkHE8+kEgf7o27C6GBvuIYbEpMwEiPAiCMQD91EvfSFzx6eodyCO6D92vcceeyxoD/FW3/ezp/MJSS7x8VnPetaC+K28Yo6JCx9oUjwVhJJ+1C58POEXbWCCb6Js9TXg+zzbYq8tWxb3nPvBD36wTC4J6JiEzwSCzpgTf/kN15wEk3QJBeNG/8X4k6ASc30rC9vlq0Acur7y0x3qHfV1PiTDxAx+l4jk/9TZucazc9mQSYHkGz8iRkkG7bLLLuX63lMXPtz54gIhPG6FBMYj4a186hSJtRr2zu7dhx5RNttW1MUEQ9w0EXY/Nk3kmyh4LzQGn28Sp53YjnPZitjDB8yV3quZM7FsFiNLZlZC9RNZZgBES2QFFgdGrpEEYUHeEorrOszqfMUMcc7RLOlDeWZZOkcWhsC3DMa4BAeiVcBlqHF/h9dsd2DQMnYhTi1fMALnC/DO5YQJCdlms8IhOFbtJkDW/497+v9Qm8r26QMOlFMhjANlkzFRLjNM5zF2g1j5lY2YiPtoc+U3cIiL+Ox8RlCR9fEtLVEP7bgkNkiccT6CG3FMILquwMfxclrOIVpmAmEX+/0EAtfgJGL5bBwmb/pCsHnBC15QArDsXPQXB+P/zqlFIKdqnOhLky3ZFJ/R59qKbbB1mQP1kwkjHoxjtupctsW5QiZfmbWr9vV+2KWf/u9gt/4/DtflUNWfsDEJqCE8CBCZadsl3Ie4MEY5a2Nf8IryqZs24Yu0D9EBopl/4ORl7ZxrTHL8gkt8ddh8hLA1yfGgTbSpw9YJ9mg8GpfTTWS1AZvQXjKsJmpxLRNzKyNgI4K2QOibWHx1V31fIpV/N7nRDzJYS4Kgp/zGAiHupwkCUWhsyCzpp7g/eyRe2bo+FZgD44/tsoM434SMWDLJHaK24drnxjWmsmGv23rovmyVHdewYW3lPBlq5SEKtLFxTiTpR/dhx/qHj3G+uslYO4/d8+UycJa2o2zsYMstt5zXNiyLq758pUmFcvNRtrHoK36H356Kug0kF+o2MBnXBq4fEKHsyt8fsLIQy/aSAnwJ7UBI6puZQAfwI3wWf0Wf9P1WH2W2ykyI8qv6O8YfnUE883GSaTFpkJWNCb+68gNRX2NSXPae8vOFMue+xcwEQFxXzqgv+5SFN0Ezvvs+G37G9aeyd+V0TfdQvhD30Edigm1FtJP7ek1MM0nS9ybAkc13L2NCwkddCGqfhTjHN+hb/e1cdZLko8eGJq+zyZyJZTM/AVcwNYAnXY6eDsutRDgnwUB1ZKBD3EuHcFS1MJwp7iPAEsyyCpwyIyR6BQ6dZHk/ln4Chs+pCxqyGIQ2iA6GU++x1uHK7Dp1PWaT2Ffl/gZeHcz0jxmaCYfzGD5RTaRx1OrI6QdmhES162nbqb4bdr5ACBCQZrSzgUHLIfnJSbLD2NpgwIfTN9hl72YymeCQ9IGMC0HABmVHwl7GIagIGpavOBL310/wM7aQ6Fez+4AdGifKzwZj8sBpEx7eU3b3Vi/2IZARL+E4jWn3MwbYtjIsCRw2WzTZYFfKWwdN2SFLqQIBIaXMxINJMcdp7ClrlE+5jEV18lmBAeqm/3zOT/iMz7J5518bCOYc/6677rrQYWVAYOVTlY1N648a/UB48TfqGn5nCP2qv9ioCXI9jrVtLGmyAYHd+bFPsUZ7eY1f0ReL6w/Uja1LhAiiJnDGgvubiPkpILuffgrYC2FvvAj+hGfgM3xU2BB7MVYJksjozjbuwdewPW1rz3SNccLu9KFxpi76i7BXN4c6gU0ab+KcFUtC2+dhMkRAaXf3CYwNPvvasmFjVmac4KttWOY3Jt9s1bgkWCPu8VWSR/wVGxJf+NCp4NPE5KE20Afsin2zIdd1X+NE7IX25QP5cvfUvtPdt0b53Y8Adz/x1HiZCvqFoDZG+Wx9VZeHfbIP5aE/wGfx8+4XdQvCBxujbN652tOYMJaNo9pn8/c+Q9jKwC8JyuPe2tW9az+rPJJu6uGebNtr7MB5+l+srH22chk/fAB/EjFFP+tH13MfiBd0iomOPphr5kwsByqu02YLzk9nMDhLKCeeeGLzzne+sxxmhgQhJ8KZTBcwAoPIbCeu4zAjM8DMcizFGVA6icNyXcbBgfVhAIyZ8etsxgFOwOsGk1m1e8g6zPVWBmUUWDkIDplTUX5Oy0BkuARwBF/vaQvvK2vdJg5CmtDihDiVqF8fn9d+9Wctk7ufAWrpxdfceV17CJT6bbbheByzNZgMfPbnJ4cXdYiDDWpXddEGi9O/ZteyB9pWu7iPa41DWdik9/WvZdu6PA4C0/uCS8ChIkRyELYxKRwWx+Yzk463qeD8ZCA4eI7RGAqIIQ6TLZsAKLtJMYcqyMnq2OYR9bZlRHYGbDXK5x6ClHYzoXWuVSBBd8ieA76C2OF74j7a3JhhE1Zv4nWHbEsdyKdCQGAvxkd9hDCaCiJQgFR+fmq6ephcSCy4Phuz/1sbaMf6Xu5tzKo3+7J9oa6f9hPE2ZNrTXdfhO8gKuprWe4lFggPE0awXe3KHgTbPuoswPLP7EBcCGy/UB424PpsQ/yYCz8TsEnih88Bm43+UL4QE5aZ2SHUj00RDPxi3SbimxUA5yi3/nAPsYTgNx5kTdVNX4S4mg6xx3aIaBc2rGwEFB9fl8EEpvYdU8EOnFvbbxzseyoIL/FKO6jnVH5IG+hzBxup26C2AbYWNuToxySHuGRCFeNvJv4Pym11hfDjQ2zTFCOH7My9lIVd1P0QhxU840M56rKoi3o7ambqe5XXeHKP2RgL/L94RfTyg+oO7UlDsAe2akywD3bGHvjuvq1pC6ue6uicKJ/4LXtsMhZjRF87J2LZXDM36cw5hHPgdGSEPEQ4DsJUQJ00m22ACAQxaw8sZdV/LYgR6HhCOJajxyEQMaAag0mZDehXvepVZbBYKrIMwYg42JhdzTYcivszMALETIzzlvUQyAUVSynwOhFhYDLG/t7FgFhhwAbeOAxgWZ66j7ym/zgJ7SDQGRT6yQzYElnMsOcrBifhwAELwvWyb43MnNl0LOPOBO2qLYgUgcoytH1o44jAxEkTO9GmfZRFJiswORI42Lz+YLM+xx44c//XL2GT7MK9OCu2ERgTtUNfUtxThlj7mSQoI/EMokHbm9zFBNyYMn4F+fjGjT4yH5y1AAFtYf+17UbErb2uzvF/dk18DY3tmFibTMcSIYfOHrSPMVPvNedD3Ldu+yGMQxPzcU+C17iPceQI/N73X1OhDexvVW6+j6jns+wjtIJg+V+5BTzXZWNW2ob2c2s3bTjkD/qwOXat/Wvc/9nPfnb5nZ05jxCQbZ0q6SIrWWec2fK2225bruEetiOxFfs4TZT4xMjgzgWxT5r9GjP8K3uJyZ8tLZFsYb/a2DhiV0NtqP7GpTHJX9pmYNy7DxuW+bfv14OD+rdujz78umXusCE2TGRqL8K5tv942HOSeMpurIQQ83zREGxYe7Cv8CfaSXn4mungV600GF9skx9WH21g/61tCZGx1UZsyEQiVi/GoX0lu2bqs/WHz9l+yWcRwPzAkM9WXkJVnNXW+nucz+YzYoXAPegabcQ/uwZbAN+n3fSZPqqv5bwQr4Eyun/tx5cE44iQNQbFLJNn9s+m2Jmy8w/GnH6nffhR+kMbjIP9ilFhc+zK9ilbVk1u6AcrWrak+SlemGjPJcucWIbA5+l+G+uH0EGTGr0gYdDpiBrXmC10KIdtD7BMqgeyImvgoSp71SZxRouDQSdAuK+BI0AaUIK+WZkZMWOscT6nwwEMYZAPtZE2VS/70wLLzDEj9DVMAkgYuPLMd6FcY2WB4BKwhoIb+1scsQztzrlzDG9961tLJmoqjAlZbf04FCTrssgqCjAcFnHH/r3PuRFPgjnRFAGPk7Mn1EpLncXgcAU8k9PZgA0ItJZFlYtjD5SVU+SI+1iK105DsK+oPxGovvYq2mdrj65Aa6+2w7Kxyco4fFYg9tlAhiS2Slh+jockMRM/NCkEmImBctcQGZOuoBh3bFhGzmqcaxmb9lDKEvGttWgnzgSrqYS8ek5aV/3ooUz2VDOp2J4O/W2FxhYGex3VzQOKnikxltTT9rq5QkKCcONv9RebJg4JG+KC/dW+k9gySRAHhsaSOtXtYy+zLLykhJ/u4+E219F/U2090w7sHGIAX6N/CXY2XNu3cs5WvwTKqvzaI/wJX8KGZ5Ip9CCa6/BlvjGEjzBh0LceBNMHAZ8X/T/ETGy4j61DhKLJmUkPHzoV+ocGYBtDsS/Kov31CR/DPxPkEmAwiZUE0++uyU4C55k81YTPni2xbFVL/LF9ztZBgh760tgjpG017UMLSRwOUdu78WHS5pkamWXty3ers1UoNsufzSVzk8qcY2QbZAl00tDB+CYVuxFIx11jHIzA7M7saBwy38QFkRplCIej0804LWf6ihwzR4GaUanXXCCAGkQMOmamnBShbiAJXP22MlNVv36b1Ic2qwdmTRh6fb72jM/4vX5vKCM6X9E2+lm563rUh/ovbp20k2VnzkAgIxwjkzkOAUYGRT+OK4sjHA84XWNIMBckTY5kcL0egUYfKb9Jlb20/rogEW/SJavuMMOXAZgqizQT3M+hLCZchKhAYIsFm5ShqANgMF1/eD0Cj+v73Wvu46vqbKvwfAXhIdgNtXV8dty1vTfO5if1Q5Ng2w+/od88Oa5d9APR6+v3JkVZlUvZCWHiih8iIvkGmWZZojhXBoioqevWPya1d+c4+j7AEX3UJzJSQ8jM+nxki+v6CdREqH6VbbZ6ItDWW3xmG1lKtioBQtzIHrMtmT7PUIS9BLJw6uC1fpvEoS61LWlvrxHmMpTswPg0qbWtZir6dho27Pr9fgk/MBsYw7bbSMS4l0lf+BK27U+7R8Z0EqINZJm1AbslImWPCdEaEwHtX9etf0xqw+PQfib5JpS21bC3qVZ7CFYxv98X9eG9ONeqDj9na4+JDt/l4B9NFEyUtKny2+pgVde4Nvk99dRTF7SzpIJyuv5s4H76gJj3k8/2MLVtVjLY7NNKfB9jQv37dY7DtcLe3UP7et19POjLXowl9xEf6+03c8HsefEZIvvFkQjygnbsTQGjZuyxUbxGoJTdIfoYBOG6tLDMQHDKyugcA78PYaNcOtmMKow98H+ZA9kxBkssG1CEzlzVhbOzrMGwBAiDjUELijKjBHygXyyDC44G/FwJ+PmAuloKZWe1/XFMXhPctFGNvnMI3oTlXNofByPzRjRaPpSN6SMrQaxyPL5+Z9L+EsAt28mk+mYFT1Q7BDGZGvcOR8WeZW6IjFjiDEfNho3jOHe2YIPa2XhSL4dlfgLE5C7wIAzbZq+yGjPNlhBXriGg8C0hymZza8lswu8Yu1aqTKSiH2RQ47mDmcKf6m9tbjWB/WuDWOr1uqBHoA8tm84Vlm5ti+EfZcn66CdiweTGWNAufdTBVjeZZhMDtmp1TZ3mCjFCm1o9k/lTPuUU7PVXjfo518qbMTZT21M/8UT93E//sZH5CEHD96inbDhhGzasDsZ2P2ZOAhu2QqoN9DV/rg2ILDFbG7NpSam5RFLKpFs/m3zq8z4mUnwbm+ZX+e7pEGecy9fFc1Ths22nkuCoV4f5bOOFRjEBrtvZxEJMmU2fTWPQF7QEbSY2amvjl8+O7SQErzaSwBNbJWJminuYAPN5xjv9GMm9ueRaE8uEp4Y0MOIBBjAgjWyWOG6moEMMBlkP59SOJWbnlvgth9TLxbMBYWXvlnS/AS+Ah5gi+gUXWwwYAMNloGZHHIMgx9hrnO9w3dhfNYR24hAcjMPnZgJBQ+RoX5kHjtmAFgjrvXscbzxco8yMvoYY81n7Y2e7fZc2BiyhpC3ZiwAKGUrZU+3Un7ARbJyNn/pf+2jTwIAlamUVYjlqceEULG2xeTP1cdcjEDgf5wrKvqlF+Wsss7O9OoAKqPqPw9T/cRCbHLy6xXIoZ+5gyxw9uwEhq43cb9ykQZnYnTGqHWciZAk/Y02wk1GRMeIYiQrXDIxFfchZGnvGfz3xUTZBS7voJ/arf9hvnakM32EM8k3G2XxE+fWdccoGA/7IMQn60qT5xBNPXMi+ow1cX985jBEBWbvI4OjvGn1rkmK7kLLN5uRR3/OLBDwfGeMtRC77NSZssVAnEzdB1Bg00ff1VAR+jTHODtVLP08FO2cH6i4W1XY1CWIA+9Q+MnrGn9VG/qOGXdv2o6xsmNAJlFUfWUGI+GFcagvjOmJknKfu6kYgzkfYizKaTIjjEfP0i3HJLieJK+yMLdZtoF2iDUJf8G8EZkzqaQ2fqQUqe5JAYt9i9ZJA7BNx4iodM+56fCifrQ18Rz/fXsdz40/diN368+qsbWiB2mc73zc02bseqJ+2lhByv2hnPjsmZf2xarzxKT4jaTAuGTgEERzfvOS+4p+2Zv/11kBlN6nh25XFCo8JeF0WK9+2WqiTvqF3jCHPONQTXG3Bblybrxp6zmS2uNbEsk6JfV0MhhMU0DSwhjITHzdoGKLZowHhC6kFCJ9zMAJOxaZvjavTZhsdL3hw4sqp7O5tsDFwT3Mqmz1pnDzj4NDtJ+XUo6wOdRacDCxGNlVnM4jISPuMuroGg5okSPm8TLd2d19CmLPqP7ihDJbCiDT7v4jGuszqaDJiKW0Sp1ajDGb47ul3g/PahNC0bKlMBi3bUUfO1P5G9lO3TaAdZeQFT1+IbhUk2kc2Wl+feuqps7LMyxn6rmCZAE6R82dTgWDOIbE3Ipdgrsvj8HQ1MVNnOdgbh2kbhfFSH77E3oOtJn2crgAQWQsikxN3XQKALbD3ENY1ysWBCmDOZU/GwqQZPQFFHxEGPk8U90UAUW0vooBIUMvAC1JRd/7EciS75fw5XtfzByvqdjKefF49iZlJHsgLBCI2JKunn+YS/lJbK2/4TIdtGAJUBMWp0P7ayB9/MBGJa3iNwBR4+Bn+gf3xB3xZ2Hac7zBWJC0s7Wrb6fzQ4qB/JR4ICn+cRP9FeX3zgP3W2sWkkr2xWcLL2IwJZBz6nI8jnqbLxBPo+pTYUHf26xpseJLYEsKYkOAPjAOTvf59ldl2OGNS/GDDUV73ZPv8rbpCGzvHufqrPo8AIb6ne8ahD7sxtpRtLkVHnSTTNxG/2aFYOqlY5gvVXRv4RoVoAz6cXxNj2EzANkxI2KiHPE08oo39rn8c4xISM0W/2xYhuaCv+j6bjxBfZXkl/egIfirKY2zbRqN+UR42ZCLoGQ5bV8b5bM9BeV/78Yf6k68wAQu9oH2Izr5AhTLSVz7Hjjy/YhyZ5NWTi6ngN0zW1MdBGNcrgerBr+gPsZXNir1RPof7GufEvwSHceM1fpy/ifOMf76AjnRE9nrO6Gaki8UGG2zQdgO/PfDAA0evXEPnkNr11luv7Yyl7Tpq9OqidAbfdrOqthNw5dxOQLVdR7WdobXdbLHtBEnbdV7bieLRJ66mM4a26/S2M/62Ewvlc3F0wa7cv2vktjOU0SeG6YRI2znEdpdddhm9MjWdgbXdrKvtjLztnPiCcjtWWWWVdrvttmu7ji1lDNSzc3TtVltttVBZO4Nud95557ZzvAvK2gXsdsstt2y74Fvargvw5XV0oqTtxHHbieu2c27lGp3htZ3jLOd1AbPtRFPbOby2M6RSzsD1ld21uwBY+q4Llm0XOEdnXE2cp0yHHHJIKUddZnXcfPPN227wTNS+Na6rDj7r95l+vkb/d0G87SZObTf4F2rvbvLQdgOw7QZju/HGG5e2H4cydBOPtnM2xQbUTR21jWsec8wx5fqu0Tmtcn58rhvAbSdA2y6wLPicoxMa7frrr992wbX0+3Qo54YbblhsqQsWbSfAR+9cjTZynd13372MjS4IlDGnHQPl6Wbj7UknndSuvvrqC5XHwcY6p7JQeQ477LC2c1jtHnvsUeykPrrA3K666qrlWlGPLpi1m2666ULX7SYNpSw77rhj24mpthPeo6tfjboce+yxbSd6F5Spm2iW8dEFtLYLCG3noMvY6xzj6FPX0AWxdqeddmq7YCMl3XaibKHxELhP50Tb3XbbbaHyOfSl+7ifdnJ0gaA99NBDS5/HefyIsdQJ0EX6YDpcM+x6pp9lq9q2mwi0m2yySdsJiNE743Gffffdt+0C0EL9vNdee7UHHHBA24mz0t/nnXdeOb8TH20XSEv7+en/bIq96K9OoC24hutp624ivVA9nK9uhx9+eNsFpQXnx8GndEG6tMN0Y7oLiqXdHaeccsro1alx3S64t93ErO2C7UL17oRmqZfxGfatDPwBO15nnXUWnMum+cZu8rjAX7h2Jz7aLqiX9u/EW3k9uOSSS9pOkLXdxH4hG1YP9+MjtaGY1YmK0aeuRjm6iWWJST7Xibf25JNPHr17Dc5zrU7ItZ1objtBuaDMDvc9/vjjF4x55/t97733Xsg/O2+zzTYrcWMmduh62oHPd9R+YlKMsU78tZ1YartJ+UL+qcZ9OlFbYo++jLJ3Ar/deuut227SVep05JFHthdffHH5DNvbYYcdyuv8FaIN9txzz0XaQP90QnOhNnDfsPn6vo5O3Ja+Z8PT1Z3diA3igvgjDvVRNvqDtjBO11xzzfaoo44avXs17nPBBRe0xx13XNtNHhay6U5Ml/F89tlnLygPv7z//vuX2H/wwQcv4rNf85rXtKuttlrxyeeff375HB20/fbbL9AKjk6kl1ii/dmZMRUotzY76KCDij9SJmXZZpttShxQ3qOPPrpcZ5999ikaqA9ftsUWW5Tx0k02S/toiz7aURygl2oN5egmsqVcxoN+c/Bb++23X+mrOI+Oca73FsdmZ8pK/imqeYZQ9p0RlxlMf6ZsNmBJScbTQ0pTLXeZoZtVdJ1U/m+GK5vROdOS2eoaqsxM+pme+h51Fczm3NOsxSxmOiyBuJbZkHtOQmdUZfYWn61RVoesRI3lYDPees+fmZzZUH3fOM9PWQjXiVlp3DeyyVBfmQQZRp+xJKVftIFlnn4b+KzlZ+3s3mb5Q+0ku2+GGX0TyE7L8l2byBDYAtMNkjIT1n+RqWYz2sesmC3JyPs5DnWz7KOenfMtr3VOorQ929Rf2l8faev6HvpeZkC/BLII+ky2aJIMTdTD9fRFXY8aWWEzbNePfvN7oB1cyxKu/q8xdhyW7tVJ9sbTw64jAyJzWGO27pthZDxkjmWp1FEbxVYVuJ6xrQ21t3bmD2rYo6Nz6OX/spbOY5syvba/KJsx43o1MonGggNsTj/GeKiRLbXioow1+lJmULuyeRhD+ly5os+1uXPdo1+OuUS/xzYgPktbKOtU6GNtUo9LGXjjmI3wBeyjCyjFFvSZz2gHY1fb+2zYC5uosSIwrg20rftGXwbsSEZe+02HfjJmoDw+Ownsm3+LzwbGZNid3wMxIfq4XuJ2jsyWdkB9nvLzJXXdY1zVeyvZsPOMb1k3n9fm6tNvA/f2WXbGL+jfoTobY+rHr7CLGj7eGKmx1SS2zQTq5R5D/m6uUA6HOhhD2rn2TzXsh03WWVztJuPqYJPsWZ8as64pY8pfqJ/+C/hF7T9JGzjHNcTAGv5E3/Dx09mwsrAHsdHnlHNcW+s/ZRNrtYW6OLeGH1IeOqYey3wRm9bffAIbsnLtL5Tac89n91cOZGetpFgx9jWC8eyBPgn/CTarjZXdeFbnvj2qmzblO5RFeyq7vghf0k1QFpSvhu0aS+Imf2TFx3gapzGcG3ZT45o0JT8Yn+OjlEmZo6/Zl3NpnUl8z5Ky2GI5SZJlD6LWkudee+1VlsJ8zRIHXGP5y7df2KJhmdNedw42SZIkWboQqLYrSHD4qjxfudZPVtmmYOugLRbEsu0US3PSvyJwre1ZTpJk6WNuLDvip4y2LEKNzKTMi+yzrIMsWmRkkyRJkqVL+GzIxsvc1sjiWpHgy61ADK2+JUvGygf68tQkSVYIOF3Lfx6O8ECM5S0Pw8heODzs4oEZy57+oIwHB2PJOkmSJFm62NJhS4QVP9tfbU2wTSF8tq0XfLbf+WwPrdsWlMwuuQ0jSVYgIkth39kBBxxQ/jhOnTn2nu8ctuTnCX17wYb2HiZJkiRzC59tn669zf4CoW9FqTPHfLakhj9K4g928OfjnntJlowUy0myAuKBI863/3AF7E/uPzSZJEmSXDuQaXy2B4Hr74cP4iv5lvaDnSsSKZaTJEmSJEmSZIBcX02SJEmSJEmSAVIsJ0mSJEmSJMkAKZaTJEmSJEmSZIAUy0mSJEmSJEkyQIrlJEmSJEmSJBkgxXKSJEmSJEmSDJBiOUmSJEmSJEkGSLGcJEmSJEmSJAOkWE6SJEmSJEmSAVIsJ0mSJEmSJMkAKZaTJEmSJEmSZIAUy0mSJEmSJEkyQIrlJEmSJEmSJBkgxXKSJEmSJEmSDJBiOUmSJEmSJEkGSLGcJEmSJEmSJAOkWE6SJEmSJEmSAVIsJ0mSJEmSJMkAKZaTJEmSJEmSZIAUy0mSJEmSJEkyQIrlJEmSJEmSJBlL0/w/BqRrwQ5qKFoAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these values, we can calculate the accuracy of the model. The accuracy is given by:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precision is a metric that quantifies the degree of accuracy attained in true prediction, meaning that it indicates how many observations that are reported as true actually are.\n",
    "\n",
    "    Precision = TP / (TP + FP)\n",
    "\n",
    "- The number of real observations that are correctly predicted, or the number of observations of the true class that have the correct label, is known as recall (sensitivity). I call it \"Sensitivity\" as well.\n",
    "\n",
    "    Recall = TP / (TP + FN)\n",
    "\n",
    "- Specificity is the ratio of accurately identified erroneous class observations to total observations.\n",
    "\n",
    "    Specificity = TN / (TN + FP)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Model building: part 1 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Separation as Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw = raw.drop('Loan_ID',axis=1) \n",
    "validation = validation.drop('Loan_ID',axis=1)\n",
    "\n",
    "X = raw.drop('Loan_Status', axis=1)\n",
    "y = raw.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)\n",
    "raw = pd.get_dummies(raw)\n",
    "validation = pd.get_dummies(validation)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X, y, test_size =0.3, random_state=100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying the model to make a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7567567567567568"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_cv = model.predict(x_cv)\n",
    "\n",
    "accuracy_score(y_cv, pred_cv)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prediction is about 70,8% correct "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluting the Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n1 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "n2 of kfold 5\n",
      "accuracy_score 0.8292682926829268\n",
      "n3 of kfold 5\n",
      "accuracy_score 0.7560975609756098\n",
      "n4 of kfold 5\n",
      "accuracy_score 0.7886178861788617\n",
      "n5 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n"
     ]
    }
   ],
   "source": [
    "validation = pd.get_dummies(validation)\n",
    "\n",
    "validation = validation.reindex(columns=X.columns, fill_value=0)\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True) \n",
    "for train_index,test_index in kf.split(X,y):\n",
    "    print('n{} of kfold {}'.format(i,kf.n_splits))\n",
    "    xtr,xvl = X.iloc[train_index],X.iloc[test_index]\n",
    "    ytr,yvl = y.iloc[train_index],y.iloc[test_index]\n",
    "    model = LogisticRegression(random_state=1)\n",
    "    model.fit(xtr, ytr)\n",
    "    pred_test = model.predict(xvl)\n",
    "    score = accuracy_score(yvl,pred_test)\n",
    "    print('accuracy_score',score)\n",
    "    i+=1\n",
    "\n",
    "pred_test = model.predict(validation)\n",
    "pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Total_Income', ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['Total_Income']=raw['ApplicantIncome']+raw['CoapplicantIncome']\n",
    "validation['Total_Income']=validation['ApplicantIncome']+validation['CoapplicantIncome']\n",
    "sns.distplot(raw['Total_Income'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now Doing a Log Transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['Total_Income_log'] = np.log(raw['Total_Income']) \n",
    "sns.distplot(raw['Total_Income_log']) \n",
    "validation['Total_Income_log'] = np.log(validation['Total_Income'])\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now the distribution looks much closer to normal and the effect of extreme values has been significantly subsided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='EMI', ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['EMI']=raw['LoanAmount']/raw['Loan_Amount_Term'] \n",
    "validation['EMI']=validation['LoanAmount']/validation['Loan_Amount_Term']\n",
    "sns.distplot(raw['EMI'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now adding a balance income feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Balance Income', ylabel='Density'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QQq7GY2JiEBkZidWrV0vHBg8ejClTpiAtLa1e/bx587Bt2zacPHlSOpacnIycnBwYDAYAQEJCAkwmE3bs2CHVjBs3Dr6+vti4cWOz2z137hzCwsKQnZ2NYcOGOTx3//33Y9iwYXj33Xedvr+TJ08iPDwcBw4cQExMDADgwIEDiI2NxU8//YSBAwc24VMCTCYTdDodjEYjfHx8mvSaruazTOcB3m56TO926gkREZFNU39/yzYyZjabkZWVhbi4OIfjcXFx2L9/v9PXGAyGevXx8fE4fPgwLBZLozX2c7ak3cZs2LAB/v7+uOeee/DSSy+hvLzcob86nU4KYgAwYsQI6HS6Rtuqrq6GyWRyeNCd+Swzr9EHERGRXNzlari4uBhWqxWBgYEOxwMDA1FYWOj0NYWFhU7ra2pqUFxcjKCgoAZr7OdsSbsNmTFjBsLCwqDX6/Hjjz9iwYIFyMnJQUZGhtTfgICAeq8LCAhotK20tDQsWbKkWX0hIiKijkm2MGanUCgcfhZC1Dt2u/pbjzflnM1t15mkpCTpzxERERgwYACio6Nx5MgRREZGOm2nKW0tWLAAqamp0s8mkwkhISHN6hsRERF1DLJNU/r7+0OpVNYbISoqKqo3amWn1+ud1ru7u8PPz6/RGvs5W9JuU0VGRkKlUuH06dNSXy5fvlyv7sqVK422pdFo4OPj4/AgIiKizkm2MKZWqxEVFSVN6dllZGRg5MiRTl8TGxtbr37Xrl2Ijo6GSqVqtMZ+zpa021THjx+HxWJBUFCQ1Bej0YiDBw9KNZmZmTAajXfcFhEREXUOsk5TpqamIjExEdHR0YiNjcWaNWuQl5eH5ORkALbpuvz8fKxfvx6A7crJFStWIDU1FUlJSTAYDFi7dq10lSQAzJkzB2PGjMHy5csxefJkfPnll9i9ezf27dvX5HYBoKSkBHl5ebh06RIA4NSpUwBso116vR5nz57Fhg0bMGHCBPj7++PEiRN48cUXMXz4cIwaNQqA7QrNcePGISkpSdry4tlnn8XEiRObfCUlERERdW6yhrGEhARcvXoVS5cuRUFBASIiIrB9+3b06dMHAFBQUOCw91dYWBi2b9+OuXPnYuXKlQgODsb777+Pxx57TKoZOXIkNm3ahIULF2LRokXo168fNm/e7HBF4+3aBYBt27bh6aefln62b+a6ePFivPbaa1Cr1fj666/x3nvv4dq1awgJCcHDDz+MxYsXQ6lUSq/bsGEDZs+eLV29OWnSJKxYsaKVP0kiIiLqqGTdZ4yahvuM3d6dbk/BfciIiKi1ufw+Y0RERETEMEZEREQkK4YxIiIiIhkxjBERERHJiGGMiIiISEYMY0REREQyYhgjIiIikhHDGHVaNbW1+Pv+c9h4MA+13E6PiIhcFMMYdVqHcktw6nI5juUbcabomtzdISIicophjDql6horvjl1Rfp535liGXtDRETUMIYx6pT2nSlGRXUNdB4qKACcKbqGAmOl3N0iIiKqh2GMOp2K6hrsO20bCRsfoUfEXToAwPdnrsrZLSIiIqcYxqjT+anQhOqaWuh9tIi4S4f7+vsDAHIulKG8yiJz74iIiBwxjFGnc9lUDQAI6+kFN4UCIT08EazTwioEfimukLl3REREjhjGqNMpKq8CAAR4a6Rjd/l62J4zVcnSJyIiooYwjFGnYx8ZC/TWSscCfbQOzxEREbkKhjHqVKosVhgrbevC7AHs5j9f5sgYERG5GIYx6lSulNtGvry17vBQK6Xj9inLkgozzDW1svSNiIjIGYYx6lTsI183rxcDgG4ad3iqlRC4EdiIiIhcAcMYdSpFdUEr4KYpSgBQKBScqiQiIpfEMEadiv1KypsX79tJYaycYYyIiFwHwxh1KtKVlD6aes/Zj3FkjIiIXAnDGHUaN19JGeBsZMyb21sQEZHrYRijTqOogSsp7ezTlMZKC6os1nbtGxERUUMYxqjTsO+u72y9GAB4qJXw0boD4FQlERG5DoYx6jTsI2M9nawXs+NO/ERE5GoYxqjTKL1uBgD4eakbrLHvP3aFV1QSEZGLYBijTsNUt3jfR6tqsMa3LqiV1dUSERHJjWGMOg1TVQ0AQOfRcBjrXveckWGMiIhcBMMYdQq1QqC8yhawvOsW6Tuj86wbGbvOMEZERK6BYYw6hYrqGtQKQAHAu5FpSvvI2LXqGlisvGE4ERHJj2GMOgX7FGU3jTuUbooG6zzVSqiUtudNnKokIiIXwDBGnYI9WHl7NDxFCdhuGK7z4CJ+IiJyHbKHsVWrViEsLAxarRZRUVHYu3dvo/V79uxBVFQUtFot+vbtiw8++KBeTXp6OsLDw6HRaBAeHo6tW7c2u90tW7YgPj4e/v7+UCgUOHr0qMPzJSUleOGFFzBw4EB4enqid+/emD17NoxGo0NdaGgoFAqFw2P+/PlN/HSoqUxVt7+S0q67p62G68aIiMgVyBrGNm/ejJSUFLz66qvIzs7G6NGjMX78eOTl5Tmtz83NxYQJEzB69GhkZ2fjlVdewezZs5Geni7VGAwGJCQkIDExETk5OUhMTMTUqVORmZnZrHYrKiowatQoLFu2zGlfLl26hEuXLuHPf/4zjh07hnXr1mHnzp145pln6tUuXboUBQUF0mPhwoUt/cioAaZK2zSlTyNXUtrZ142VVZrbtE9ERERNoRBCCLkaj4mJQWRkJFavXi0dGzx4MKZMmYK0tLR69fPmzcO2bdtw8uRJ6VhycjJycnJgMBgAAAkJCTCZTNixY4dUM27cOPj6+mLjxo3NbvfcuXMICwtDdnY2hg0b1uj7+fzzz/HEE0+goqIC7u626bLQ0FCkpKQgJSWlaR+KEyaTCTqdDkajET4+Pi0+T2f22Or9yDpfit8ODsRvBgU0Wvv1ycv4+qciRPfxxaORvQAA02N6t0c3iYioC2nq72/ZRsbMZjOysrIQFxfncDwuLg779+93+hqDwVCvPj4+HocPH4bFYmm0xn7OlrTbVPYP2x7E7JYvXw4/Pz8MGzYMb775JszmxkdkqqurYTKZHB7UuHJpmrLxNWPAjWlK7jVGRESu4Pa/udpIcXExrFYrAgMDHY4HBgaisLDQ6WsKCwud1tfU1KC4uBhBQUEN1tjP2ZJ2m+Lq1at4/fXX8dxzzzkcnzNnDiIjI+Hr64uDBw9iwYIFyM3Nxccff9zgudLS0rBkyZIW96Uras40pbSAn2vGiIjIBcgWxuwUCsdtCIQQ9Y7drv7W4005Z3PbbYzJZMLDDz+M8PBwLF682OG5uXPnSn8eOnQofH198fjjj0ujZc4sWLAAqampDucPCQlpUd+6CvsoV5PWjHneWDN2J987ERFRa5AtjPn7+0OpVNYbjSoqKqo3amWn1+ud1ru7u0vBpqEa+zlb0m5jysvLMW7cOHTr1g1bt26FStV4GBgxYgQA4MyZMw2GMY1GA41G0+y+dFVVFisqLVYAgK4JV1Pab5dksQpUmq3w1Mj+/yRERNSFybZmTK1WIyoqChkZGQ7HMzIyMHLkSKeviY2NrVe/a9cuREdHSyGooRr7OVvSbkNMJhPi4uKgVquxbds2aLXa274mOzsbABAUFNSstqhhRaZqAIC7mwJa1e3/SquUbvCqC2Dca4yIiOQm65BAamoqEhMTER0djdjYWKxZswZ5eXlITk4GYJuuy8/Px/r16wHYrpxcsWIFUlNTkZSUBIPBgLVr10pXSQK2NVpjxozB8uXLMXnyZHz55ZfYvXs39u3b1+R2Ads+Ynl5ebh06RIA4NSpUwBsI296vR7l5eWIi4vD9evX8emnnzostO/ZsyeUSiUMBgMOHDiABx54ADqdDocOHcLcuXMxadIk9O7Nq/daS6GpCoBtirKpU47dPVSoqK5B2XULgrt7tGX3iIiIGiVrGEtISMDVq1elfbgiIiKwfft29OnTBwBQUFDgsPdXWFgYtm/fjrlz52LlypUIDg7G+++/j8cee0yqGTlyJDZt2oSFCxdi0aJF6NevHzZv3oyYmJgmtwsA27Ztw9NPPy39PG3aNADA4sWL8dprryErK0vau6x///4O7ys3NxehoaHQaDTYvHkzlixZgurqavTp0wdJSUl4+eWXW/FTJCmMNWGK0q67pwr5ZZUwcq8xIiKSmaz7jFHTcJ+xxn289xe88X8nMbSXDtN+1bQRx3/+cAn7z17F6AH+GB8RxH3GiIio1bn8PmNEraXQ2IKRMQ/eEomIiFwDwxh1eDevGWsqnad9rzFOUxIRkbwYxqjDuyytGWv6EkhdXW15VU2b9ImIiKipGMaow7tct7VFc6Ypvetqy6trwGWTREQkJ4Yx6vBKKmxTjd2asXlrt7qRMWutbeNXIiIiuTCMUYdWXWPFtWrbVKNXM8KYSukGD5USAGCq5lQlERHJp0VhLDc3t7X7QdQi9lExNwWatPv+zbyldWO8opKIiOTTojDWv39/PPDAA/j0009RVVXV2n0iarKr12xhzEvt3uwbfntzET8REbmAFoWxnJwcDB8+HC+++CL0ej2ee+45HDx4sLX7RnRb9pGx5kxR2tkX/DOMERGRnFoUxiIiIvDOO+8gPz8fn3zyCQoLC3HffffhnnvuwTvvvIMrV660dj+JnLKHMU+Nstmv5TQlERG5gjtawO/u7o5HHnkE//u//4vly5fj7NmzeOmll9CrVy/MnDkTBQUFrdVPIqeuVtyYpmwub46MERGRC7ijMHb48GE8//zzCAoKwjvvvIOXXnoJZ8+exTfffIP8/HxMnjy5tfpJ5FRJhW2PsZZMU3JkjIiIXEHzf4MBeOedd/DJJ5/g1KlTmDBhAtavX48JEybAzc2W7cLCwvDhhx9i0KBBrdpZolvdWDPWkmlKjowREZH8WhTGVq9ejT/84Q94+umnodfrndb07t0ba9euvaPOEd3OzVdTNhevpiQiIlfQojCWkZGB3r17SyNhdkIIXLhwAb1794ZarcaTTz7ZKp0kasidXE1pD2Nmay2uVdc0awd/IiKi1tKiNWP9+vVDcXFxveMlJSUICwu7404RNdWdTFNq3JVQu9v+FSgycb88IiKSR4vCWEM3Vr527Rq0Wu0ddYioOe7kakoA8K4bDSsqr261PhERETVHs36DpaamAgAUCgX+9Kc/wdPTU3rOarUiMzMTw4YNa9UOEjXEYq2FsdJ2JWRLpikB2yL+qxVmhjEiIpJNs36DZWdnA7CNjB07dgxqtVp6Tq1W495778VLL73Uuj0kakDpdduomEIBeKqbP00J3Fg3xmlKIiKSS7PC2LfffgsAePrpp/Hee+/Bx8enTTpF1BT29WK+nmq4NfO+lHY+dWHsCkfGiIhIJi2a2/nkk09aux9EzVZSt61FDy/1bSobZt9rjNOUREQklyaHsUcffRTr1q2Dj48PHn300UZrt2zZcscdI7od++L9Owtj9gX8nKYkIiJ5NDmM6XQ6KOqmgnQ6XZt1iKip7NOUfq0xMmbiyBgREcmjyWHs5qlJTlOSK2jdkTGGMSIikkeL9hmrrKzE9evXpZ/Pnz+Pd999F7t27Wq1jhHdjv0m4Xc2MmYLY8ZKC6os1lbpFxERUXO0KIxNnjwZ69evBwCUlZXh17/+Nf7yl79g8uTJWL16dat2kKghJa0wMuahUsLdzTb9XnyNo2NERNT+WhTGjhw5gtGjRwMAvvjiC+j1epw/fx7r16/H+++/36odJGqI/SbhPbppWnwOhUKBbpyqJCIiGbUojF2/fh3e3t4AgF27duHRRx+Fm5sbRowYgfPnz7dqB4ka0hoL+IEbt0TiXmNERCSHFoWx/v374//7//4/XLhwAf/6178QFxcHACgqKuJGsNRuWmOaEgC61V1RyTBGRERyaFEY+9Of/oSXXnoJoaGhiImJQWxsLADbKNnw4cNbtYNEztTWCul2SBwZIyKijqxFO/A//vjjuO+++1BQUIB7771XOv7ggw/ikUceabXOETWkrNKCWmH7s+8dj4zVhTEu4CciIhm0KIwBgF6vh16vdzj261//+o47RNQU9ilKb607VMoWDfBKvHl/SiIiklGLwlhFRQWWLVuGr7/+GkVFRaitrXV4/pdffmmVzhE1pOz6jZuE3ylOUxIRkZxaNKTwxz/+EWvXrsXo0aMxa9YszJkzx+HRHKtWrUJYWBi0Wi2ioqKwd+/eRuv37NmDqKgoaLVa9O3bFx988EG9mvT0dISHh0Oj0SA8PBxbt25tdrtbtmxBfHw8/P39oVAocPTo0XrnqK6uxgsvvAB/f394eXlh0qRJuHjxokNNaWkpEhMTodPpoNPpkJiYiLKystt/MNSo0usWAICvp+qOz8UF/EREJKcWjYzt2LED//d//4dRo0bdUeObN29GSkoKVq1ahVGjRuHDDz/E+PHjceLECfTu3btefW5uLiZMmICkpCR8+umn+P777/H888+jZ8+eeOyxxwAABoMBCQkJeP311/HII49g69atmDp1Kvbt24eYmJgmt1tRUYFRo0bhd7/7HZKSkpz2PyUlBV999RU2bdoEPz8/vPjii5g4cSKysrKgVCoBANOnT8fFixexc+dOAMCzzz6LxMREfPXVV3f02XV19pGx7q05MnatGkII6R6sRERE7UEhhBDNfVFYWBi2b9+OwYMH31HjMTExiIyMdNi1f/DgwZgyZQrS0tLq1c+bNw/btm3DyZMnpWPJycnIycmBwWAAACQkJMBkMmHHjh1Szbhx4+Dr64uNGzc2u91z584hLCwM2dnZGDZsmHTcaDSiZ8+e+Mc//oGEhAQAwKVLlxASEoLt27cjPj4eJ0+eRHh4OA4cOCAFwQMHDiA2NhY//fQTBg4c2KTPyWQyQafTwWg0cuuQOh/9+xe8uf0kJg8LxnvThuOzzLwWn8tircXibccBADmL46DzuPPRNiIioqb+/m7RNOXrr7+OP/3pTw73p2wus9mMrKwsaY8yu7i4OOzfv9/pawwGQ736+Ph4HD58GBaLpdEa+zlb0q4zWVlZsFgsDucJDg5GRESEdB6DwQCdTicFMQAYMWIEdDpdo21VV1fDZDI5PMhRaSuuGVMp3biIn4iIZNOiacq//OUvOHv2LAIDAxEaGgqVynEk4ciRI7c9R3FxMaxWKwIDAx2OBwYGorCw0OlrCgsLndbX1NSguLgYQUFBDdbYz9mSdhvqi1qthq+vb4PnKSwsREBAQL3XBgQENNpWWloalixZ0uS+dEVllbbw3b0V1owBQE9vDcqranClvBr9A7q1yjmJiIiaokVhbMqUKa3WgVvX59xuzY6z+luPN+WczW23qW49j7Nz3q6tBQsWIDU1VfrZZDIhJCTkjvvWmUhrxlppSrFnNw1+uVLBvcaIiKjdtSiMLV68+I4b9vf3h1KprDdCVFRUVG/Uyk6v1zutd3d3h5+fX6M19nO2pN2G+mI2m1FaWuowOlZUVISRI0dKNZcvX6732itXrjTalkajgUbT8ptfdwWlFXVXU97hhq92Pb1tnzenKYmIqL21eLfMsrIyfPzxx1iwYAFKSkoA2KYn8/Pzm/R6tVqNqKgoZGRkOBzPyMiQwsytYmNj69Xv2rUL0dHR0lRpQzX2c7akXWeioqKgUqkczlNQUIAff/xROk9sbCyMRiMOHjwo1WRmZsJoNDarLarvxjQlwxgREXVsLRoZ++GHH/Db3/4WOp0O586dQ1JSEnr06IGtW7fi/PnzWL9+fZPOk5qaisTERERHRyM2NhZr1qxBXl4ekpOTAdim6/Lz86XzJScnY8WKFUhNTUVSUhIMBgPWrl0rXSUJAHPmzMGYMWOwfPlyTJ48GV9++SV2796Nffv2NbldACgpKUFeXh4uXboEADh16hSAG3ce0Ol0eOaZZ/Diiy/Cz88PPXr0wEsvvYQhQ4bgt7/9LQDbFZrjxo1DUlISPvzwQwC2rS0mTpzY5CspybnWnqYM8NYCYBgjIqL216Iwlpqaiqeeegpvv/02vL29pePjx4/H9OnTm3yehIQEXL16FUuXLkVBQQEiIiKwfft29OnTB4BtpCkv78aWBfYtNebOnYuVK1ciODgY77//vrTHGACMHDkSmzZtwsKFC7Fo0SL069cPmzdvdrii8XbtAsC2bdvw9NNPSz9PmzYNgG2K9rXXXgMA/PWvf4W7uzumTp2KyspKPPjgg1i3bp20xxgAbNiwAbNnz5auupw0aRJWrFjR5M+InGvNqymBm0bGuGaMiIjaWYv2GdPpdDhy5Aj69esHb29v5OTkoG/fvjh//jwGDhyIqqqqtuhrl8V9xhxVWawYtMi2ie4Pr8XBR6u6o33GAOAuXw88+beDGKT3xs6UMa3RTSIi6uLadJ8xrVbrdO+rU6dOoWfPni05JVGTldXdCknpppB2z79TPbvZRsaKOTJGRETtrEVhbPLkyVi6dKm00apCoUBeXh7mz5/vMGVI1BZKb1ov1lq3LrJPU16tMKPGWnubaiIiotbTojD25z//GVeuXEFAQAAqKysxduxY9O/fH97e3njzzTdbu49EDqQw1kobvgJADy813BSAEEBJhbnVzktERHQ7LZrj8fHxwb59+/Dtt98iKysLtbW1iIyMlK4iJGpLxrppytZavA/Ypjz9umlwpbwaReXVCPDRttq5iYiIGtPsMFZbW4t169Zhy5YtOHfuHBQKBcLCwqDX61ttF3uixpReb91bIdn1rAtjvKKSiIjaU7OmKYUQmDRpEv74xz8iPz8fQ4YMwT333IPz58/jqaeewiOPPNJW/SSS3JimbL2RMYAbvxIRkTyaNTK2bt06/Pvf/8bXX3+NBx54wOG5b775BlOmTMH69esxc+bMVu0k0c2MlfZpylYeGWMYIyIiGTRrZGzjxo145ZVX6gUxAPjNb36D+fPnY8OGDa3WOSJnSis4MkZERJ1Hs8LYDz/8gHHjxjX4/Pjx45GTk3PHnSJqTFuuGQO4Cz8REbWvZoWxkpISBAYGNvh8YGAgSktL77hTRI0xVrburZDsODJGRERyaFYYs1qtcHdveJmZUqlETU3NHXeKqDHSyFgr3STczh7GihnGiIioHTVrAb8QAk899RQ0Go3T56ur+UuM2l4Zr6YkIqJOpFlh7Mknn7xtDa+kpLYkhJDuTenr1TYjY+XVNag0W+GhVrbq+YmIiJxpVhj75JNP2qofRE1yrboGNbUCANDdo3VHxrw17tC4u6G6phbF16oR0sOzVc9PRETkTIvuTUkkF/uomMbdrdVHrhQKBQJ8bKNjRZyqJCKidsIwRh1KWRvcl/Jm0vYW5VVtcn4iIqJbMYxRh3LjVkitu17Mjov4iYiovTGMUYfCMEZERJ0Nwxh1KG0/TakFwF34iYio/TCMUYdSJt0KqY3CGEfGiIionTGMUYfCaUoiIupsGMaoQ7Hvvu/LMEZERJ0Ewxh1KGWV7TRNea0aQog2aYOIiOhmDGPUobTVTcLt/LvZQp7FKmCsC35ERERtiWGMOhRpmtKrbUbGNO5K6OqCHqcqiYioPTCMUYdyY2uLthkZA7hujIiI2hfDGHUY1loBU5UtjOla+SbhN5NuicS9xoiIqB0wjFGHYay0wL6mvq22tgA4MkZERO2LYYw6DPt6MW+NO1TKtvuryzBGRETtiWGMOgz7lZS6NhwVA4CAujB22VTVpu0QEREBDGPUgdzY8LXt1osBQKCP7f6UhQxjRETUDhjGqMO4cV/Kth0Zs4exyyZOUxIRUdtjGKMOo7SdRsb0OnsYq+Iu/ERE1OYYxqjDaK+RMX3dyNh1sxXl1TVt2hYREZHsYWzVqlUICwuDVqtFVFQU9u7d22j9nj17EBUVBa1Wi759++KDDz6oV5Oeno7w8HBoNBqEh4dj69atzW5XCIHXXnsNwcHB8PDwwP3334/jx49Lz587dw4KhcLp4/PPP5fqQkND6z0/f/785n5MhBsjY211X0o7D7USPlp3AMBlI9eNERFR25I1jG3evBkpKSl49dVXkZ2djdGjR2P8+PHIy8tzWp+bm4sJEyZg9OjRyM7OxiuvvILZs2cjPT1dqjEYDEhISEBiYiJycnKQmJiIqVOnIjMzs1ntvv3223jnnXewYsUKHDp0CHq9Hg899BDKy8sBACEhISgoKHB4LFmyBF5eXhg/frxDv5cuXepQt3Dhwtb8GLsM+03C23L3fTv7VCUX8RMRUVtTCBkXxcTExCAyMhKrV6+Wjg0ePBhTpkxBWlpavfp58+Zh27ZtOHnypHQsOTkZOTk5MBgMAICEhASYTCbs2LFDqhk3bhx8fX2xcePGJrUrhEBwcDBSUlIwb948AEB1dTUCAwOxfPlyPPfcc07fz/DhwxEZGYm1a9dKx0JDQ5GSkoKUlJQmfy7V1dWorr6xeNxkMiEkJARGoxE+Pj5NPk9n8llmHtbu+wVnr1Tgd1G9MLy3b6uef3pMb4efE9dmYu/pYvzP40Pxu+iQVm2LiIi6BpPJBJ1Od9vf37KNjJnNZmRlZSEuLs7heFxcHPbv3+/0NQaDoV59fHw8Dh8+DIvF0miN/ZxNaTc3NxeFhYUONRqNBmPHjm2wb1lZWTh69CieeeaZes8tX74cfn5+GDZsGN58802YzWan57BLS0uDTqeTHiEhDAOAbQ0XAHiq3du8Lb3PjUX8REREbUm2MFZcXAyr1YrAwECH44GBgSgsLHT6msLCQqf1NTU1KC4ubrTGfs6mtGv/Z3P6tnbtWgwePBgjR450OD5nzhxs2rQJ3377LWbNmoV3330Xzz//vNNz2C1YsABGo1F6XLhwodH6rqJSCmPKNm+Le40REVF7afshhttQKBQOPwsh6h27Xf2tx5tyztaqAYDKykp89tlnWLRoUb3n5s6dK/156NCh8PX1xeOPPy6Nljmj0Wig0WicPteVXW/PMGZfM2bkXmNERNS2ZBsZ8/f3h1KprDfSVFRUVG9Eyk6v1zutd3d3l4JNQzX2czalXb1eDwBN7tsXX3yB69evY+bMmY2+ZwAYMWIEAODMmTO3raUbaqy1MFtrAbTvNGVROUfGiIiobckWxtRqNaKiopCRkeFwPCMjo95Un11sbGy9+l27diE6OhoqlarRGvs5m9JuWFgY9Hq9Q43ZbMaePXuc9m3t2rWYNGkSevbsedv3nZ2dDQAICgq6bS3dcN1iGxVTANCo2v6vrT2MFXJrCyIiamOyTlOmpqYiMTER0dHRiI2NxZo1a5CXl4fk5GQAtrVT+fn5WL9+PQDblZMrVqxAamoqkpKSYDAYsHbtWukqScC2RmvMmDFYvnw5Jk+ejC+//BK7d+/Gvn37mtyuQqFASkoK3nrrLQwYMAADBgzAW2+9BU9PT0yfPt3hPZw5cwb//ve/sX379nrvz2Aw4MCBA3jggQeg0+lw6NAhzJ07F5MmTULv3r3r1VPD7FOUHmol3BqZxm4tgTrbNHHxtWrUWGvhrpR9Sz4iIuqkZA1jCQkJuHr1qrQPV0REBLZv344+ffoAAAoKChz2/goLC8P27dsxd+5crFy5EsHBwXj//ffx2GOPSTUjR47Epk2bsHDhQixatAj9+vXD5s2bERMT0+R2AeDll19GZWUlnn/+eZSWliImJga7du2Ct7e3w3v429/+hrvuuqve1ZmAbe3X5s2bsWTJElRXV6NPnz5ISkrCyy+/3GqfYVdx3WzbCb891osBgL+XBu5uCtTUCly5Vo0gnUe7tEtERF2PrPuMUdM0dZ+SzuzVrcewITMPvXt4Inlsv1Y//637jAHAyLSvcclYha3Pj2z1fc2IiKjzc/l9xoiaQ5qmVLXPyBhw44pK7jVGRERtiWGMOoT23NbCjov4iYioPTCMUYdQ2c5rxoCbN37lXmNERNR2GMaoQ5BGxjTtd81JIG+JRERE7YBhjDoEOdaM6eu2t2AYIyKitsQwRh1CRd00pZcMI2NcM0ZERG2JYYw6hOvVtpExr3ZcMxZct7fYJWMluAMMERG1FYYx6hAqpAX87TcyFtTdNjJWZalFSYW53dolIqKuhWGMXJ61VqBSWsDffiNjGnclArxt68byyyrbrV0iIupaGMbI5ZkqLbBPErbn1hYAcJevbaryYinDGBERtQ1Z701J1BQl121ThBp3N7i7tc3/P3yWmef0eI3VFgPzGcaIiKiNcGSMXF5p3Xqt9ryS0s7XUw2A05RERNR2GMbI5ZVetwBo/ylKAOjuqQLAaUoiImo7DGPk8qSRsXa8ktLOty6McWSMiIjaCsMYuTz7mjF5RsbqpilLr7d720RE1DUwjJHLk3PNmH2a0lRVg/IqS7u3T0REnR/DGLk8+4arcoyMadyV0v0wOVVJRERtgWGMXF7pdfnWjAE3rRvjIn4iImoDDGPk8qSRsXbcff9m3bm9BRERtSGGMXJ5N7a2kGdkrDtHxoiIqA0xjJHLK5G2tpB3ZOwiR8aIiKgNMIyRS6ux1sJYWTcyJsPVlADQ3YMjY0RE1HYYxsilldUFMQUgXdXY3nhLJCIiaksMY+TS7HuMaVVKKN0UsvTBvmbsSnk1qixWWfpARESdF8MYuTRpvZhMV1ICtv3N7HucXeLoGBERtTKGMXJppdKtkORZLwYACoUCvXt4AgDOXa2QrR9ERNQ5MYyRSyupsK0Zk+tKSrswfy8AQG4x71FJRESti2GMXJorjIwBN8LYuWKOjBERUetiGCOXJvfu+3ah9jDGaUoiImplDGPk0kor5L0vpd2NaUqGMSIial0MY+TSSqRpSplHxvxsYexSWSWqa7i9BRERtR6GMXJp0siYTLvv2/l3U6Obxh21ArhQwkX8RETUehjGyKW5ysiYQqFAqL9tewteUUlERK1J9jC2atUqhIWFQavVIioqCnv37m20fs+ePYiKioJWq0Xfvn3xwQcf1KtJT09HeHg4NBoNwsPDsXXr1ma3K4TAa6+9huDgYHh4eOD+++/H8ePHHWruv/9+KBQKh8e0adMcakpLS5GYmAidTgedTofExESUlZU18dOhkmuusWYMuDFVySsqiYioNckaxjZv3oyUlBS8+uqryM7OxujRozF+/Hjk5eU5rc/NzcWECRMwevRoZGdn45VXXsHs2bORnp4u1RgMBiQkJCAxMRE5OTlITEzE1KlTkZmZ2ax23377bbzzzjtYsWIFDh06BL1ej4ceegjl5eUOfUpKSkJBQYH0+PDDDx2enz59Oo4ePYqdO3di586dOHr0KBITE1vj4+v0Ks1WVJht67O6aeUPY9Iifl5RSURErUghhBByNR4TE4PIyEisXr1aOjZ48GBMmTIFaWlp9ernzZuHbdu24eTJk9Kx5ORk5OTkwGAwAAASEhJgMpmwY8cOqWbcuHHw9fXFxo0bm9SuEALBwcFISUnBvHnzAADV1dUIDAzE8uXL8dxzzwGwjYwNGzYM7777rtP3d/LkSYSHh+PAgQOIiYkBABw4cACxsbH46aefMHDgQKevq66uRnV1tfSzyWRCSEgIjEYjfHx8Gv5AO5kLJdcx+u1voXZ3w+KJ4VAo5Lk3JQBMj+mN9KyLePHzHIzs54fPkkbI1hciIuoYTCYTdDrdbX9/yzYyZjabkZWVhbi4OIfjcXFx2L9/v9PXGAyGevXx8fE4fPgwLBZLozX2czal3dzcXBQWFjrUaDQajB07tl7fNmzYAH9/f9xzzz146aWXHEbODAYDdDqdFMQAYMSIEdDpdA2+RwBIS0uTpjV1Oh1CQkIarO3MrlyzBdKe3TSyBjG7UG5vQUREbUC2MFZcXAyr1YrAwECH44GBgSgsLHT6msLCQqf1NTU1KC4ubrTGfs6mtGv/5+36NmPGDGzcuBHfffcdFi1ahPT0dDz66KMO/Q0ICKj3PgICAhp8jwCwYMECGI1G6XHhwoUGazuz4nJbGPPvppa5JzZ968JYgbEKlWZub0FERK1D9oU4t454CCEaHQVxVn/r8aacszVqkpKSpD9HRERgwIABiI6OxpEjRxAZGen0HA21dTONRgONRtPg811Fcd3iff9urvFZ+HqpofNQwVhpwfmSCgzSd50pYyIiajuyjYz5+/tDqVTWGyEqKiqqNyJlp9frnda7u7vDz8+v0Rr7OZvSrl6vB4Bm9Q0AIiMjoVKpcPr0aek8ly9frld35cqVRs9DNsXX7CNjrhHGgJtui8SpSiIiaiWyhTG1Wo2oqChkZGQ4HM/IyMDIkSOdviY2NrZe/a5duxAdHQ2VStVojf2cTWk3LCwMer3eocZsNmPPnj0N9g0Ajh8/DovFgqCgIKkvRqMRBw8elGoyMzNhNBobPQ/ZSGHM2zWmKQGgX10YO1N0TeaeEBFRZyHrNGVqaioSExMRHR2N2NhYrFmzBnl5eUhOTgZgWzuVn5+P9evXA7BdOblixQqkpqYiKSkJBoMBa9eula6SBIA5c+ZgzJgxWL58OSZPnowvv/wSu3fvxr59+5rcrkKhQEpKCt566y0MGDAAAwYMwFtvvQVPT09Mnz4dAHD27Fls2LABEyZMgL+/P06cOIEXX3wRw4cPx6hRowDYrtAcN24ckpKSpC0vnn32WUycOLHBKynpBlccGbtb7w0A+Kmw/DaVRERETSNrGEtISMDVq1exdOlSFBQUICIiAtu3b0efPn0AAAUFBQ57f4WFhWH79u2YO3cuVq5cieDgYLz//vt47LHHpJqRI0di06ZNWLhwIRYtWoR+/fph8+bNDlc03q5dAHj55ZdRWVmJ559/HqWlpYiJicGuXbvg7W37ZaxWq/H111/jvffew7Vr1xASEoKHH34YixcvhlJ5Y7f4DRs2YPbs2dKVmZMmTcKKFSva5gPtZIrLb6wZK6+qkbk3NgPrwtgphjEiImolsu4zRk3T1H1KOpvf/Pk7/FJcgY1JI2TfTmJ6TG8AQKGxCiPSvobSTYETS+OhcZf3Nk1EROS6XH6fMaLbkfYZc6E1Y4E+Gug8VLDWCq4bIyKiVsEwRi6pymKVpiZ7dtPK3JsbFAoFpyqJiKhVMYyRS7paYVsvpla6wcdD9u3wHAxiGCMiolbEMEYuyb77vl83tUvcCulmA3lFJRERtSKGMXJJV8pdb1sLO46MERFRa2IYI5d0Y48x11m8b3d3oC2MFZqqUHbdLHNviIioo2MYI5fkihu+2nlrVbiruwcATlUSEdGdYxgjlyTdJNzb9cIYwKlKIiJqPQxj5JKuuPDIGMBF/ERE1HoYxsglFZe77poxALgnWAcAOJZfJm9HiIiow2MYI5dkXzPW00VHxu4NsYWxnwrKUWWxytwbIiLqyBjGyCW5+pqxu7p7wL+bBjW1Aj/mG+XuDhERdWAMY+RyzDW1MFZaALjumjGFQoFhId0BAEcvlMnaFyIi6tgYxsjlXK2wTVEq3RTo7qGSuTcNG1Y3VckwRkREd4JhjFyOffd9Py813Nxc61ZINxsW4guAYYyIiO4Mwxi5nEJjFQAgSKeVuSeNGxqig0IBXCytlC44ICIiai6GMXI5BVIY85C5J43z0arQr2c3AMDRvDJ5O0NERB2Wu9wdILqVPYzpXWhk7LPMPKfHfbS2NW1HL5Tht+GB7dklIiLqJDgyRi6nwFgJAAju7jphrCEhPWyjdzkXy+TtCBERdVgMY+RyboyMufY0JQCE+HoCsE1TWmuFzL0hIqKOiGGMXI40MuZC05QNCfTRQqtyQ3l1DY5f4uavRETUfAxj5FJqawUuG21XJrrSmrGGKN0UCPXzAgDsP3tV5t4QEVFHxDBGLuVqhRlmay0UCtuoU0dgv6LSwDBGREQtwDBGLsW+x1jPbhqolB3jr2ffnraRsUPnSmCuqZW5N0RE1NF0jN921GVcqlsvFtTd9Rfv2wX6aOHrqcJ1sxU/8KpKIiJqJoYxcinS7vsdZIoSANwUCsT28wPAqUoiImo+hjFyKTdGxjpOGAOA2H7+ALiIn4iImo9hjFxKR7kv5a1i+9pGxrLySlFlscrcGyIi6kgYxsilFJR1jPtS3qpfTy8E+mhgrqnFwdwSubtDREQdCMMYuZQCU900ZQcbGVMoFLj/7gAAwDc/FcncGyIi6kgYxshl1NaKG9OUHehqSrsHB9vC2O6TlyEEb41ERERN4y53B4jsrlaYYbEKKBRAgLdG7u40y2eZeTDX1MLdTYGLpZX46+7T0N90Rej0mN4y9o6IiFwZR8bIZdjvSRng3XE2fL2Z2t1N2o3/pwKTzL0hIqKOQvbfeKtWrUJYWBi0Wi2ioqKwd+/eRuv37NmDqKgoaLVa9O3bFx988EG9mvT0dISHh0Oj0SA8PBxbt25tdrtCCLz22msIDg6Gh4cH7r//fhw/flx6vqSkBC+88AIGDhwIT09P9O7dG7Nnz4bR6Hiz6NDQUCgUCofH/Pnzm/MRdRkFdVOU+g62eP9mg4K8AQA/FZbL3BMiIuooZA1jmzdvRkpKCl599VVkZ2dj9OjRGD9+PPLy8pzW5+bmYsKECRg9ejSys7PxyiuvYPbs2UhPT5dqDAYDEhISkJiYiJycHCQmJmLq1KnIzMxsVrtvv/023nnnHaxYsQKHDh2CXq/HQw89hPJy2y/ZS5cu4dKlS/jzn/+MY8eOYd26ddi5cyeeeeaZev1eunQpCgoKpMfChQtb6yPsVArKbCNjwR1s8f7NBul9AAAXSq7jWnWNzL0hIqKOQCFkXGkcExODyMhIrF69Wjo2ePBgTJkyBWlpafXq582bh23btuHkyZPSseTkZOTk5MBgMAAAEhISYDKZsGPHDqlm3Lhx8PX1xcaNG5vUrhACwcHBSElJwbx58wAA1dXVCAwMxPLly/Hcc885fT+ff/45nnjiCVRUVMDd3bYcLzQ0FCkpKUhJSWnhpwSYTCbodDoYjUb4+Pi0+DyubulXJ/C373Pxx/vCsHBiuMNzn2U6D+iuaMU3p3HJWIXHIu9CVJ8eALhmjIioK2rq72/ZRsbMZjOysrIQFxfncDwuLg779+93+hqDwVCvPj4+HocPH4bFYmm0xn7OprSbm5uLwsJChxqNRoOxY8c22DcA0odtD2J2y5cvh5+fH4YNG4Y333wTZrO5wXMAtuBnMpkcHl1BbvE1AEBY3Y23O6rBwbZ/4X64aLxNJRERkYxhrLi4GFarFYGBgQ7HAwMDUVhY6PQ1hYWFTutrampQXFzcaI39nE1p1/7P5vTt6tWreP311+uNms2ZMwebNm3Ct99+i1mzZuHdd9/F888/7/QcdmlpadDpdNIjJCSk0frO4tzV6wCAMP+OHcbu7dUdAHD2yjVOVRIR0W3JvrWFQqFw+FkIUe/Y7epvPd6Uc7ZWDWAbhnz44YcRHh6OxYsXOzw3d+5c6c9Dhw6Fr68vHn/8cWm0zJkFCxYgNTXV4fydPZBZrLXIK7GFsb7+3WTuzZ3x76bBXd09kF9WiR/zjRjR1/n3TEREBMg4Mubv7w+lUllvpKmoqKjeiJSdXq93Wu/u7i4Fm4Zq7OdsSrt6vR4AmtS38vJyjBs3Dt26dcPWrVuhUqkafd8jRowAAJw5c6bBGo1GAx8fH4dHZ3eh5DqstQIeKiUCfTrWHmPO3NtLBwDIuVAmb0eIiMjlyRbG1Go1oqKikJGR4XA8IyMDI0eOdPqa2NjYevW7du1CdHS0FIIaqrGfsynthoWFQa/XO9SYzWbs2bPHoW8mkwlxcXFQq9XYtm0btNrbXwWYnZ0NAAgKCrptbVdy7moFACDU36vRkdGOYkiv7lAAOF9yHWXXG18jSEREXZus05SpqalITExEdHQ0YmNjsWbNGuTl5SE5ORmAbbouPz8f69evB2C7cnLFihVITU1FUlISDAYD1q5dK10lCdjWaI0ZMwbLly/H5MmT8eWXX2L37t3Yt29fk9tVKBRISUnBW2+9hQEDBmDAgAF466234OnpienTpwOwjYjFxcXh+vXr+PTTTx0W2vfs2RNKpRIGgwEHDhzAAw88AJ1Oh0OHDmHu3LmYNGkSevfm1XU3++WKLYyF+XvK3JPWofNQIdTfC7nFFVzIT0REjZI1jCUkJODq1avSPlwRERHYvn07+vTpAwAoKChw2PsrLCwM27dvx9y5c7Fy5UoEBwfj/fffx2OPPSbVjBw5Eps2bcLChQuxaNEi9OvXD5s3b0ZMTEyT2wWAl19+GZWVlXj++edRWlqKmJgY7Nq1C97etk09s7KypL3L+vfv7/C+cnNzERoaCo1Gg82bN2PJkiWorq5Gnz59kJSUhJdffrn1P8wOzj4y1tEX79/s3l7dkVtcgSN5pbddC0lERF2XrPuMUdN0hX3GZnx8AN+fuYo//+5ePB7Vq97zHWmfMbsqixVpO07CYhX43+di8euwHnJ3iYiI2pHL7zNGdLNzxfZtLTrHNCUAaFVKaZuLzzLPy9sZIiJyWQxjJLsqixX5dbdCCuvg21rcKibMdpXv9mOFKKngQn4iIqqPYYxkd75us1cfrTt8PRvfGqSjucvXA3d194DZWosvsi7I3R0iInJBDGMkuxu3QerWKRe5x9StFduQmQdrLZdoEhGRI4Yxkt0vxXVXUvp1nvViNxvaqzt0Hiqcv3odO34skLs7RETkYhjGSHbn7GGsk60Xs1O7u+EPo8IAACu+OYNajo4REdFNGMZIdqeL7NOUnWePsVs9NTIU3TTu+KmwHF//VCR3d4iIyIUwjJGsLNZanLhku3PBkLt0Mvem7eg8VUiMtW0qvOKb0+D2fkREZMcwRrI6VViO6ppaeGvdEdpJ14zZPXNfGLQqN+RcNGLXictyd4eIiFwEwxjJ6li+7b6NQ3vpOuWVlDfz76bBM/fZ1o4t/eoEKs1WmXtERESugGGMZPXDxTIAtisOu4L/eqA/7urugfyySqz89ozc3SEiIhfAMEayyrlgGxm7t1fnXS92M0+1OxZNDAcArPn3L/jlyjWZe0RERHJjGCPZVFms+PlyOQBgSBcZGQOA+HsCMfbunjBba/HCxmxUWThdSUTUlTGMkWxOFJhQUyvg302NYJ1W7u60G4VCgbRHh6CHlxrHL5nwytZjvLqSiKgLYxgj2fxwoQyAbb1YZ1+8f6vg7h5YMX043BTAliP5WLsvV+4uERGRTBjGSDY/XLStF+vM+4s1ZmQ/fywYPxgA8Mb/ncTKb89whIyIqAtiGCPZ/FC3rcW9IV0zjAHAH0eHYdYD/QEA//OvU1i87TjXkBERdTHucneAuqYr5dU4W3clYVfZ1sIZhUKBl+IHwq+bGku+OoH1hvPYe7oYbz0yBLH9/KS6zzLzGj3P9Jjebd1VIiJqIxwZI1l889NlCGGbovTvppG7O7J7elQYPp4ZjQBvDXKLK/D7jw7g92sO4JufLvPG4kREnRxHxkgWGSdsN8v+7eBAmXviGuwjX8lj++Ffxwtx6FwJDL9cheGXq/DRuuPeXt1xb0h3BOm0Xe5iByKizo5hjNpdpdmKfWeuAAAeCmcYu5lWpcTkYXdh7N09sf/sVRw+XwJTVQ32ninG3jPF6OmtsQWzXjr4cUSRiKhTYBijdrfvTDGqLLW4q7sHBgd5y90dl9TdU40JQ4LwUHggfr5cjpwLZfipsBxXyqux++Rl7D55GSG+HogJ88OQLnL3AiKizophjNpdxolCALZRMU65NU6ldMM9wTrcE6xDlcWK45dMyLlYhrNF13ChtBIXSi9ix48FKK+y4KlRYeim4b/SREQdDf/LTe3KWivw9UmuF2sJrUqJqD6+iOrji/IqC46cL8WB3BIYKy34866fsXZfLp4b2w8zY/vAU81/tYmIOgr+F5vaVWbuVVytMMNb445fh/WQuzsdlrdWhbEDA3DfgJ44ll+Gr08W4WqFGct2/IT/980Z3H93T8SE9YC70nbBNLe+ICJyXQxj1K4+2PMLAOA/hgVD7c6dVe6U0k2BYSG+GHJXd+RcKMM3p4pQUmHG/x0rwPdni/HQ4EDcG9Jd7m4SEVEjGMao3fxwsQz//vkKlG4KJI/pJ3d32tXtNm29U0o3BSL7+OLekO44kleKr09eRtl1Cz7Puoh9Z4rRu4cnxtzds037QERELcOhCWo3K789AwCYdG8wevt5ytybzknppsCvQnsg9aGBiA8PhMbdDQXGKsz820FMW2OA4exVubtIRES34MgYtYufL5fjX8cvAwCev79rjYrJQe3uhrEDA/Cr0B749lQRDp0rxYFfSnDglwP4VagvnrkvDA+F66F049WsRERyYxijNlddY8V/f/EDAGDcPXoMCOTeYu3FU+OOh4cG439+dy9Wf3cWmw9dwKFzpTh0rhR3dffAY1G98OjwuxDq7yV3V4mIuiyFEII3vnNxJpMJOp0ORqMRPj4+cnen2RZsOYaNB/Og81Dhny/ch5AezZ+ibOs1V52d/WrKy6YqrDecw4bMPJRdt0jPDwz0xv2DeiK2rx/u7dUdvl5qubpKRNRpNPX3N8NYB9CRw9h6wzn86cvjUCiAT576Fe4fGOC0jmGrfZlranGywITsC6U4ffkabv2PQJBOi16+Hujl64levh64q7sH/Ltp4OulRg8vNXp4quGtdYdb3TTn7b4/bq1BRF1RU39/c5qS2kSVxYrXth3HpkMXAACpv727wSBG7U/t7oZ7Q2w3H79urkGAjxbfnSpCdl4ZcosrUGCsQoGxCofOlTZ4DqWbAr6eKvh5aaBQwBbUPG1hza+bGn5eGq5JIyJqAtlHxlatWoX/+Z//QUFBAe655x68++67GD16dIP1e/bsQWpqKo4fP47g4GC8/PLLSE5OdqhJT0/HokWLcPbsWfTr1w9vvvkmHnnkkWa1K4TAkiVLsGbNGpSWliImJgYrV67EPffcI9VUV1fjpZdewsaNG1FZWYkHH3wQq1atQq9evaSa0tJSzJ49G9u2bQMATJo0Cf/v//0/dO/evcmfUUcaGauyWLE1Ox9r/v0LcosroFAAs38zAD29NXDjrY86hOvmGhRfM6Psuhml1y0ovW6Gp1qJ0gozSq6bUVphwbXqmtuex00B9PDSoGc3NcYM7Il+PbuhX89u6N+zG3SeqnZ4J21HCIHrZiuMlRYYKy3YciQfNdZaKBQKKBSAQgG4QQGNyg1alRJPjOgDb82NkUQi6ho6xDTl5s2bkZiYiFWrVmHUqFH48MMP8fHHH+PEiRPo3bv+tEZubi4iIiKQlJSE5557Dt9//z2ef/55bNy4EY899hgAwGAwYPTo0Xj99dfxyCOPYOvWrfjTn/6Effv2ISYmpsntLl++HG+++SbWrVuHu+++G2+88Qb+/e9/49SpU/D2ti1A/8///E989dVXWLduHfz8/PDiiy+ipKQEWVlZUCqVAIDx48fj4sWLWLNmDQDg2WefRWhoKL766qsmf06uHMYqzVacKbqGHy8Z8f2ZYuw9XQxjpW0tkn83Nd6bNhyj+vtzGrKDu3WascpiRdl1C0oqzCgqr8LW7HyUVJhRUmFGaYUZxdfMMFtrGzyffzc1Qnp4ItBbi0AfDQJ8tAjw1iDQR4tAHy18vVTQeaigcVe22XuqsdbCVFUDU12gMlZaYKqq+2dlzS0/W6Q6+2tqapv3n06FAuimdoePhwreWve6R/0/d9O4Q+uuhEblBo27GzTuSts/VW5QK5VwVyqgUirg7uZW92c3uLsp4K50k46rlAre95XIBXSIMBYTE4PIyEisXr1aOjZ48GBMmTIFaWlp9ernzZuHbdu24eTJk9Kx5ORk5OTkwGAwAAASEhJgMpmwY8cOqWbcuHHw9fXFxo0bm9SuEALBwcFISUnBvHnzANhGwQIDA7F8+XI899xzMBqN6NmzJ/7xj38gISEBAHDp0iWEhIRg+/btiI+Px8mTJxEeHo4DBw5IQfDAgQOIjY3FTz/9hIEDBzbpc2qrMHboXAmKTNWoqa2FtVZIj5pagVohUGO1/Wy21uJadQ3Kqyy4VlWD8qoaFF+rRoGxCleuVePWv0F3dffA06NCMfVXIfDR2kZAGMa6FiEETFU1uFJejSvXqtHDU4WzVypw9so1FBirmnwelVIBD5USep0WOg8VfLQqaNVKeKiU0KrcoHVXwl3phptzhxC2sFhlseK62faw/bkG181WKVRVmK13/D6VCoXUH5VSASGAWiEgANTWClTX1KLKYm12cGsNbgrATaGA0k0BT7Xtc1LdFNo0dYFPrXSDRmULfGp3Nyhgu4esxSpgra1FTe2N/xZUW2tRbbHCXFOL6ppaVNdYUW2pRaXF9lnavwcFFHBzgxQYe3ipHdq58VBCfdPP6pvCp3RcpYRa6QZV3R077L+yhAAEhO2fAhB1zwkAuOk5S62o66+tr/Z+V1ls76XS/jDf+DtTWff3p9JshVXY3rv9fSkUgLvSDeq6IKx2d0MvXw94qJTwVLtDq1LCQ+343tQ3vVd1XVBWwPb9KBQ3/olbj9V9lzf/7bH/91bcdPTGMfvP9f++3XzI/lrpdQ7POZ7D4UxNavtGubW2FlUW278Dts+0FlU1ts+2+qbjF0srYbHWwmIVqLHW2v5cK1BbK2yft5ub9PdZ5fCZOvucnX/2js+7SX//7UspFFDg12E90NNbU++zuxMuv2bMbDYjKysL8+fPdzgeFxeH/fv3O32NwWBAXFycw7H4+HisXbsWFosFKpUKBoMBc+fOrVfz7rvvNrnd3NxcFBYWOrSl0WgwduxY7N+/H8899xyysrJgsVgcaoKDgxEREYH9+/cjPj4eBoMBOp1OCmIAMGLECOh0Ouzfv7/BMFZdXY3q6mrpZ6PRCMD2pbamP//zaKtsAtrDU4V+Ad3wq9AeGNG3B4b26m67J6K5EiZzJQDgekX5HbdDHYsKQLAXEOylxtToEOn4teoanC+uwCVjFYqvVeGKyYyDuSUor7agvKoG5dUWVJlrIQBU1z3KjK37d/9mnmo3uLvZphNtv0iV0Lq7wUOlhEbtBg93pfScVmULXhqV7Xn3Jo5ATbo32PY/Mvb3WFVT9z82tinfzNwSW0iwWFFtrYXVKlBTW4saq+1/jrw0SlRZbL+kauzPSYHpRli42c3jkpUVrfiBtUBJw0sPO4W8Qrl70LlV376kVXyYGIVR/f1b9Zz239u3G/eSLYwVFxfDarUiMDDQ4XhgYCAKC53/zS4sLHRaX1NTg+LiYgQFBTVYYz9nU9q1/9NZzfnz56UatVoNX1/fRs8TEFB/0XpAQECD7xEA0tLSsGTJknrHQ0JCnFTL7wKAHABb5O4IuawkuTsgs67+/ok6ggnvtt25y8vLodPpGnxe9qspb/2/SiFEo/+n6az+1uNNOWdr1dzq1hpn9bc7z4IFC5Camir9XFtbi5KSEvj5+XXadSAmkwkhISG4cOGCy62L6yr4HbgGfg+ugd+Da+jo34MQAuXl5QgODm60TrYw5u/vD6VSWW+EqKioqN6IlJ1er3da7+7uDj8/v0Zr7OdsSrt6vR6AbWQrKCiowRqz2YzS0lKH0bGioiKMHDlSqrl8+XK993HlypUG3yNgmxLVaBznrZtz9WVH5uPj0yH/hetM+B24Bn4ProHfg2voyN9DYyNidrLdKFytViMqKgoZGRkOxzMyMqQwc6vY2Nh69bt27UJ0dDRUKlWjNfZzNqXdsLAw6PV6hxqz2Yw9e/ZINVFRUVCpVA41BQUF+PHHH6Wa2NhYGI1GHDx4UKrJzMyE0Whs8D0SERFRFyNktGnTJqFSqcTatWvFiRMnREpKivDy8hLnzp0TQggxf/58kZiYKNX/8ssvwtPTU8ydO1ecOHFCrF27VqhUKvHFF19INd9//71QKpVi2bJl4uTJk2LZsmXC3d1dHDhwoMntCiHEsmXLhE6nE1u2bBHHjh0Tv//970VQUJAwmUxSTXJysujVq5fYvXu3OHLkiPjNb34j7r33XlFTUyPVjBs3TgwdOlQYDAZhMBjEkCFDxMSJE9vk8+zIjEajACCMRqPcXemy+B24Bn4ProHfg2voKt+DrGFMCCFWrlwp+vTpI9RqtYiMjBR79uyRnnvyySfF2LFjHeq/++47MXz4cKFWq0VoaKhYvXp1vXN+/vnnYuDAgUKlUolBgwaJ9PT0ZrUrhBC1tbVi8eLFQq/XC41GI8aMGSOOHTvmUFNZWSlmzZolevToITw8PMTEiRNFXl6eQ83Vq1fFjBkzhLe3t/D29hYzZswQpaWlzfyUOr+qqiqxePFiUVVVJXdXuix+B66B34Nr4PfgGrrK9yD7DvxEREREXZlsa8aIiIiIiGGMiIiISFYMY0REREQyYhgjIiIikhHDGMlu1apVCAsLg1arRVRUFPbu3St3lzqEtLQ0/OpXv4K3tzcCAgIwZcoUnDp1yqFGCIHXXnsNwcHB8PDwwP3334/jx4871FRXV+OFF16Av78/vLy8MGnSJFy8eNGhprS0FImJidDpdNDpdEhMTERZWZlDTV5eHv7jP/4DXl5e8Pf3x+zZs2E2m9vkvbuytLQ0KBQKpKSkSMf4PbSP/Px8PPHEE/Dz84OnpyeGDRuGrKws6Xl+D22rpqYGCxcuRFhYGDw8PNC3b18sXboUtbU37pTK76ABcl7KSWTf8+2jjz4SJ06cEHPmzBFeXl7i/PnzcnfN5cXHx4tPPvlE/Pjjj+Lo0aPi4YcfFr179xbXrl2TapYtWya8vb1Fenq6OHbsmEhISHC6X95dd90lMjIyxJEjR8QDDzzgdL+8iIgIsX//frF//34RERHhsF9eTU2NiIiIEA888IA4cuSIyMjIEMHBwWLWrFnt82G4iIMHD4rQ0FAxdOhQMWfOHOk4v4e2V1JSIvr06SOeeuopkZmZKXJzc8Xu3bvFmTNnpBp+D23rjTfeEH5+fuKf//ynyM3NFZ9//rno1q2bePfdd6UafgfOMYyRrH7961+L5ORkh2ODBg0S8+fPl6lHHVdRUZEAIO2ZV1tbK/R6vVi2bJlUU1VVJXQ6nfjggw+EEEKUlZUJlUolNm3aJNXk5+cLNzc3sXPnTiGEECdOnBAAHDZONhgMAoD46aefhBBCbN++Xbi5uYn8/HypZuPGjUKj0XT6zRrtysvLxYABA0RGRoYYO3asFMb4PbSPefPmifvuu6/B5/k9tL2HH35Y/OEPf3A49uijj4onnnhCCMHvoDGcpiTZmM1mZGVlIS4uzuF4XFwc9u/fL1OvOi6j0QgA6NGjBwAgNzcXhYWFDp+vRqPB2LFjpc83KysLFovFoSY4OBgRERFSjcFggE6nQ0xMjFQzYsQI6HQ6h5qIiAiHm+HGx8ejurraYZqoM/uv//ovPPzww/jtb3/rcJzfQ/vYtm0boqOj8bvf/Q4BAQEYPnw4PvroI+l5fg9t77777sPXX3+Nn3/+GQCQk5ODffv2YcKECQD4HTRGthuFExUXF8Nqtda7aXpgYGC9G7lT44QQSE1NxX333YeIiAgAkD5DZ5/v+fPnpRq1Wu1ws3t7jf31hYWFCAgIqNdmQECAQ82t7fj6+kKtVneJ73LTpk04cuQIDh06VO85fg/t45dffsHq1auRmpqKV155BQcPHsTs2bOh0Wgwc+ZMfg/tYN68eTAajRg0aBCUSiWsVivefPNN/P73vwfAfxcawzBGslMoFA4/CyHqHaPGzZo1Cz/88AP27dtX77mWfL631jirb0lNZ3ThwgXMmTMHu3btglarbbCO30Pbqq2tRXR0NN566y0AwPDhw3H8+HGsXr0aM2fOlOr4PbSdzZs349NPP8Vnn32Ge+65B0ePHkVKSgqCg4Px5JNPSnX8DurjNCXJxt/fH0qlst7/pRQVFdX7Pxpq2AsvvIBt27bh22+/Ra9evaTjer0eABr9fPV6PcxmM0pLSxutuXz5cr12r1y54lBzazulpaWwWCyd/rvMyspCUVERoqKi4O7uDnd3d+zZswfvv/8+3N3dpffP76FtBQUFITw83OHY4MGDkZeXB4D/PrSH//7v/8b8+fMxbdo0DBkyBImJiZg7dy7S0tIA8DtoDMMYyUatViMqKgoZGRkOxzMyMjBy5EiZetVxCCEwa9YsbNmyBd988w3CwsIcng8LC4Ner3f4fM1mM/bs2SN9vlFRUVCpVA41BQUF+PHHH6Wa2NhYGI1GHDx4UKrJzMyE0Wh0qPnxxx9RUFAg1ezatQsajQZRUVGt/+ZdyIMPPohjx47h6NGj0iM6OhozZszA0aNH0bdvX34P7WDUqFH1tnb5+eef0adPHwD896E9XL9+HW5ujrFCqVRKW1vwO2hEO18wQOTAvrXF2rVrxYkTJ0RKSorw8vIS586dk7trLu8///M/hU6nE999950oKCiQHtevX5dqli1bJnQ6ndiyZYs4duyY+P3vf+/0MvJevXqJ3bt3iyNHjojf/OY3Ti8jHzp0qDAYDMJgMIghQ4Y4vYz8wQcfFEeOHBG7d+8WvXr1ctnLyNvazVdTCsHvoT0cPHhQuLu7izfffFOcPn1abNiwQXh6eopPP/1UquH30LaefPJJcdddd0lbW2zZskX4+/uLl19+Warhd+AcwxjJbuXKlaJPnz5CrVaLyMhIaWsGahwAp49PPvlEqqmtrRWLFy8Wer1eaDQaMWbMGHHs2DGH81RWVopZs2aJHj16CA8PDzFx4kSRl5fnUHP16lUxY8YM4e3tLby9vcWMGTNEaWmpQ8358+fFww8/LDw8PESPHj3ErFmzRFVVVVu9fZd2axjj99A+vvrqKxERESE0Go0YNGiQWLNmjcPz/B7alslkEnPmzBG9e/cWWq1W9O3bV7z66quiurpaquF34JxCCCHkHJkjIiIi6sq4ZoyIiIhIRgxjRERERDJiGCMiIiKSEcMYERERkYwYxoiIiIhkxDBGREREJCOGMSIiIiIZMYwRERERyYhhjIi6lNdeew3Dhg2TuxtERBKGMSLqEJ566ikoFArp4efnh3HjxuGHH36Qu2tt4qmnnsKUKVPk7gYRtQOGMSLqMMaNG4eCggIUFBTg66+/hru7OyZOnCh3t4iI7gjDGBF1GBqNBnq9Hnq9HsOGDcO8efNw4cIFXLlyRaqZN28e7r77bnh6eqJv375YtGgRLBZLg+c8dOgQHnroIfj7+0On02Hs2LE4cuSIQ41CocDHH3+MRx55BJ6enhgwYAC2bdvmUHP8+HE8/PDD8PHxgbe3N0aPHo2zZ89Kz3/yyScYPHgwtFotBg0ahFWrVjXrvd9///2YPXs2Xn75ZfTo0QN6vR6vvfaaQ01ZWRmeffZZBAYGQqvVIiIiAv/85z+l59PT03HPPfdAo9EgNDQUf/nLXxxeHxoaijfeeAMzZ85Et27d0KdPH3z55Ze4cuUKJk+ejG7dumHIkCE4fPiww+v279+PMWPGwMPDAyEhIZg9ezYqKiqa9f6IujKGMSLqkK5du4YNGzagf//+8PPzk457e3tj3bp1OHHiBN577z189NFH+Otf/9rgecrLy/Hkk09i7969OHDgAAYMGIAJEyagvLzcoW7JkiWYOnUqfvjhB0yYMAEzZsxASUkJACA/Px9jxoyBVqvFN998g6ysLPzhD39ATU0NAOCjjz7Cq6++ijfffBMnT57EW2+9hUWLFuHvf/97s97z3//+d3h5eSEzMxNvv/02li5dioyMDABAbW0txo8fj/379+PTTz/FiRMnsGzZMiiVSgBAVlYWpk6dimnTpuHYsWN47bXXsGjRIqxbt86hjb/+9a8YNWoUsrOz8fDDDyMxMREzZ87EE088gSNHjqB///6YOXMmhBAAgGPHjiE+Ph6PPvoofvjhB2zevBn79u3DrFmzmvXeiLo0QUTUATz55JNCqVQKLy8v4eXlJQCIoKAgkZWV1ejr3n77bREVFSX9vHjxYnHvvfc2WF9TUyO8vb3FV199JR0DIBYuXCj9fO3aNaFQKMSOHTuEEEIsWLBAhIWFCbPZ7PScISEh4rPPPnM49vrrr4vY2NhG3+/kyZOln8eOHSvuu+8+h5pf/epXYt68eUIIIf71r38JNzc3cerUKafnmz59unjooYccjv33f/+3CA8Pl37u06ePeOKJJ6SfCwoKBACxaNEi6ZjBYBAAREFBgRBCiMTERPHss886nHfv3r3Czc1NVFZWNvj+iOgGjowRUYfxwAMP4OjRozh69CgyMzMRFxeH8ePH4/z581LNF198gfvuuw96vR7dunXDokWLkJeX1+A5i4qKkJycjLvvvhs6nQ46nQ7Xrl2r95qhQ4dKf/by8oK3tzeKiooAAEePHsXo0aOhUqnqnf/KlSu4cOECnnnmGXTr1k16vPHGGw7TmE1xcx8AICgoyKEPvXr1wt133+30tSdPnsSoUaMcjo0aNQqnT5+G1Wp12kZgYCAAYMiQIfWO2dvNysrCunXrHN5bfHw8amtrkZub26z3R9RVucvdASKipvLy8kL//v2ln6OioqDT6fDRRx/hjTfewIEDBzBt2jQsWbIE8fHx0Ol02LRpU721UTd76qmncOXKFbz77rvo06cPNBoNYmNjYTabHepuDVoKhQK1tbUAAA8PjwbPb6/56KOPEBMT4/CcfQqxqVraBwAQQkChUNQ71lgb9npnx+zt1tbW4rnnnsPs2bPrnat3796N9omIbBjGiKjDUigUcHNzQ2VlJQDg+++/R58+ffDqq69KNTePmjmzd+9erFq1ChMmTAAAXLhwAcXFxc3qx9ChQ/H3v/8dFoulXmAKDAzEXXfdhV9++QUzZsxo1nmb24eLFy/i559/djo6Fh4ejn379jkc279/P+6+++5mh8KbRUZG4vjx4w4hmYiah9OURNRhVFdXo7CwEIWFhTh58iReeOEFXLt2Df/xH/8BAOjfvz/y8vKwadMmnD17Fu+//z62bt3a6Dn79++Pf/zjHzh58iQyMzMxY8aM244y3WrWrFkwmUyYNm0aDh8+jNOnT+Mf//gHTp06BcC20WxaWhree+89/Pzzzzh27Bg++eQTvPPOOy37IJwYO3YsxowZg8ceewwZGRnIzc3Fjh07sHPnTgDAiy++iK+//hqvv/46fv75Z/z973/HihUr8NJLL91Ru/PmzYPBYMB//dd/4ejRozh9+jS2bduGF154oTXeFlGXwDBGRB3Gzp07ERQUhKCgIMTExODQoUP4/PPPcf/99wMAJk+ejLlz52LWrFkYNmwY9u/fj0WLFjV6zr/97W8oLS3F8OHDkZiYiNmzZyMgIKBZ/fLz88M333yDa9euYezYsYiKisJHH30kjZL98Y9/xMcff4x169ZhyJAhGDt2LNatW4ewsLAWfQ4NSU9Px69+9Sv8/ve/R3h4OF5++WVpPVhkZCT+93//F5s2bUJERAT+9Kc/YenSpXjqqafuqM2hQ4diz549OH36NEaPHo3hw4dj0aJFCAoKaoV3RNQ1KISzRQNERERE1C44MkZEREQkI4YxIiIiIhkxjBERERHJiGGMiIiISEYMY0REREQyYhgjIiIikhHDGBEREZGMGMaIiIiIZMQwRkRERCQjhjEiIiIiGTGMEREREcno/weDy+CfEls+dQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw['Balance Income']=raw['Total_Income']-(raw['EMI']*1000) \n",
    "validation['Balance Income']=validation['Total_Income']-(raw['EMI']*1000)\n",
    "sns.distplot(raw['Balance Income'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We also want to remove the unnecessary data from the dataset, so removing correlated features will help in reducing the irrelevant data too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw=raw.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
    " 'Loan_Amount_Term'], axis=1)\n",
    "validation = validation.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
    " 'Loan_Amount_Term'], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Model building: part 2 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After incorporating new features, we proceed with the model-building phase. This section focuses on implementing various models, starting from a decision tree and advancing to more sophisticated ones such as RandomForest and XGBoost."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision tree is a supervised learning algorithm primarily utilized in classification tasks. It partitions the dataset into homogeneous subsets based on the most significant differentiator among input variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n1 of kfold 5\n",
      "accuracy_score 0.7723577235772358\n",
      "n2 of kfold 5\n",
      "accuracy_score 0.7073170731707317\n",
      "n3 of kfold 5\n",
      "accuracy_score 0.6910569105691057\n",
      "n4 of kfold 5\n",
      "accuracy_score 0.6991869918699187\n",
      "n5 of kfold 5\n",
      "accuracy_score 0.680327868852459\n"
     ]
    }
   ],
   "source": [
    "validation = pd.get_dummies(validation)\n",
    "\n",
    "validation = validation.reindex(columns=X.columns, fill_value=0)\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# Fit decision tree model with 5-fold cross-validation\n",
    "i=1 \n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True) \n",
    "for train_index, test_index in kf.split(X, y): \n",
    "    print('n{} of kfold {}'.format(i, kf.n_splits)) \n",
    "    xtr, xvl = X.loc[train_index], X.loc[test_index] \n",
    "    ytr, yvl = y[train_index], y[test_index] \n",
    "    model = tree.DecisionTreeClassifier(random_state=1) \n",
    "    model.fit(xtr, ytr) \n",
    "    pred_test = model.predict(xvl) \n",
    "    score = accuracy_score(yvl, pred_test) \n",
    "    print('accuracy_score', score) \n",
    "    i+=1 \n",
    "pred_test = model.predict(validation) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean validation accuracy for this decision tree model is 0.69."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest is an ensemble learning method based on decision trees. It aims to improve predictive accuracy by aggregating the predictions of multiple decision trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n1 of kfold 5\n",
      "accuracy_score 0.8211382113821138\n",
      "n2 of kfold 5\n",
      "accuracy_score 0.8373983739837398\n",
      "n3 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "n4 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "n5 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Fit Random Forest model with 5-fold cross-validation\n",
    "i=1 \n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True) \n",
    "for train_index, test_index in kf.split(X, y): \n",
    "    print('n{} of kfold {}'.format(i, kf.n_splits)) \n",
    "    xtr, xvl = X.loc[train_index], X.loc[test_index] \n",
    "    ytr, yvl = y[train_index], y[test_index] \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=10)     \n",
    "    model.fit(xtr, ytr) \n",
    "    pred_test = model.predict(xvl) \n",
    "    score = accuracy_score(yvl, pred_test) \n",
    "    print('accuracy_score', score) \n",
    "    i+=1 \n",
    "pred_test = model.predict(validation)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The mean validation accuracy for this model is 0.766"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let’s build the model using these optimized values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n1 of kfold 5\n",
      "accuracy_score 0.8130081300813008\n",
      "n2 of kfold 5\n",
      "accuracy_score 0.8373983739837398\n",
      "n3 of kfold 5\n",
      "accuracy_score 0.7967479674796748\n",
      "n4 of kfold 5\n",
      "accuracy_score 0.8048780487804879\n",
      "n5 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n"
     ]
    }
   ],
   "source": [
    "# Build model with optimized values\n",
    "i=1 \n",
    "kf = StratifiedKFold(n_splits=5, random_state=1, shuffle=True) \n",
    "for train_index, test_index in kf.split(X, y): \n",
    "    print('n{} of kfold {}'.format(i, kf.n_splits)) \n",
    "    xtr, xvl = X.loc[train_index], X.loc[test_index] \n",
    "    ytr, yvl = y[train_index], y[test_index] \n",
    "    model = RandomForestClassifier(random_state=1, max_depth=3, n_estimators=41)     \n",
    "    model.fit(xtr, ytr) \n",
    "    pred_test = model.predict(xvl) \n",
    "    score = accuracy_score(yvl, pred_test) \n",
    "    print('accuracy_score', score) \n",
    "    i+=1 \n",
    "\n",
    "pred_test = model.predict(validation) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's determine feature importance using the feature_importances_ attribute of sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "\n",
    "# Sort the importances in ascending order\n",
    "sorted_importances = importances.sort_values()\n",
    "\n",
    "# Plot\n",
    "sorted_importances.plot(kind='barh', figsize=(12,8))\n",
    "plt.title('Feature Importances')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}