SupervisedLearning_Project.ipynb

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   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Domain:\n",
    "  #Banking\n",
    "#Context:\n",
    "#This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability \n",
    "#customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department \n",
    "#to devise campaigns with better target marketing to increase the success ratio with minimal budget.\n",
    "\n",
    "#Attribute Information:\n",
    "# ID : Customer ID\n",
    "# Age : Customer's age in completed years\n",
    "# Experience : #years of professional experience\n",
    "# Income : Annual income of the customer ($000)\n",
    "# ZIP Code : Home Address ZIP code.\n",
    "# Family : Family size of the customer\n",
    "# CCAvg : Avg. spending on credit cards per month ($000)\n",
    "# Education : Education Level. 1: Undergrad; 2: Graduate;\n",
    " #3: Advanced/Professional\n",
    "# Mortgage : Value of house mortgage if any. ($000)\n",
    "# Personal Loan : Did this customer accept the personal loan offered in the\n",
    "# last campaign?\n",
    "# Securities Account : Does the customer have a securities account with the bank?\n",
    "# CD Account : Does the customer have a certificate of deposit (CD)\n",
    " #account with the bank?\n",
    "# Online : Does the customer use internet banking facilities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Age', 'Experience', 'Income', 'ZIP Code', 'Family', 'CCAvg',\n",
      "       'Education', 'Mortgage', 'Personal Loan', 'Securities Account',\n",
      "       'CD Account', 'Online', 'CreditCard'],\n",
      "      dtype='object')\n",
      "(5000, 14)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>ID</th>\n",
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       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
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       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
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      "text/plain": [
       "   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n",
       "0   1   25           1      49     91107       4    1.6          1         0   \n",
       "1   2   45          19      34     90089       3    1.5          1         0   \n",
       "2   3   39          15      11     94720       1    1.0          1         0   \n",
       "3   4   35           9     100     94112       1    2.7          2         0   \n",
       "4   5   35           8      45     91330       4    1.0          2         0   \n",
       "5   6   37          13      29     92121       4    0.4          2       155   \n",
       "6   7   53          27      72     91711       2    1.5          2         0   \n",
       "7   8   50          24      22     93943       1    0.3          3         0   \n",
       "8   9   35          10      81     90089       3    0.6          2       104   \n",
       "9  10   34           9     180     93023       1    8.9          3         0   \n",
       "\n",
       "   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n",
       "0              0                   1           0       0           0  \n",
       "1              0                   1           0       0           0  \n",
       "2              0                   0           0       0           0  \n",
       "3              0                   0           0       0           0  \n",
       "4              0                   0           0       0           1  \n",
       "5              0                   0           0       1           0  \n",
       "6              0                   0           0       1           0  \n",
       "7              0                   0           0       0           1  \n",
       "8              0                   0           0       1           0  \n",
       "9              1                   0           0       0           0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data into a data-frame. The data-frame should have data and column description.\n",
    "BankRaw = pd.read_csv('Bank_Personal_Loan_Modelling.csv')\n",
    "print(BankRaw.columns)\n",
    "print(BankRaw.shape)\n",
    "BankRaw.head(10)\n",
    "#Inference: The data set has 5000 data records spread across 14 column types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 14 columns):\n",
      "ID                    5000 non-null int64\n",
      "Age                   5000 non-null int64\n",
      "Experience            5000 non-null int64\n",
      "Income                5000 non-null int64\n",
      "ZIP Code              5000 non-null int64\n",
      "Family                5000 non-null int64\n",
      "CCAvg                 5000 non-null float64\n",
      "Education             5000 non-null int64\n",
      "Mortgage              5000 non-null int64\n",
      "Personal Loan         5000 non-null int64\n",
      "Securities Account    5000 non-null int64\n",
      "CD Account            5000 non-null int64\n",
      "Online                5000 non-null int64\n",
      "CreditCard            5000 non-null int64\n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 547.0 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, ID                      int64\n",
       " Age                     int64\n",
       " Experience              int64\n",
       " Income                  int64\n",
       " ZIP Code                int64\n",
       " Family                  int64\n",
       " CCAvg                 float64\n",
       " Education               int64\n",
       " Mortgage                int64\n",
       " Personal Loan           int64\n",
       " Securities Account      int64\n",
       " CD Account              int64\n",
       " Online                  int64\n",
       " CreditCard              int64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ensure the attribute types are correct. If not, take appropriate actions.\n",
    "BankRaw.info(), BankRaw.dtypes\n",
    "#Inference: This data set has nominal data such as ID, ZIP code  of type Int which can be execluded during analysis\n",
    "# Categorical data-Personal Loan, Online, CD Account, Securities Account, Credit Card are already represented in binary format.\n",
    "#Hence no further action is needed for these categorical variables\n",
    "#Age, Income, Family are continuous variable holding intiger values and Education data has ranking and same is represented\n",
    "#where as Experience,CCAvg,Mortgage are continuous variables which holds float (decimal) value\n",
    "#No more further actions needed, all set to go further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22d21790>"
      ]
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     "execution_count": 84,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis\n",
    "#Analyzing the distribution of independent variables one by one below\n",
    "sns.distplot(BankRaw['Age'], rug=True)\n",
    "#Inference: Age has multiple modes and it is symmerically distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22b19910>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['Experience'], rug=True)\n",
    "#Inference: Experience has multiple modes and it is symmerically distributed. Could see few negetive values as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2343d2d0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1wNo+x+7t9bgNuGmAc+8H7u/n+PeA751JsJHgw0N1qMK0NOv/7yshJoobFuXwwodH+bfru0gI4C5jY8zAbC2gELP1UB1eT2it/x9Krl+UQ2tnN3/YXel2KMaEPUsAIWbroTrOyk4iJsrrdigh6bypE8hJjePFbXbbiDEjZW3oENLTo+woq+dz50x2O5RhG+19iz0eYcU5k/mv9w5yrKmdjMSYUX0/Y8YzawGEkJLjzTS2d7Ew1zZaH8z1i3Lo7lFe+chaAcaMhCWAEPJxmX8d/bOnpLgcSWiblZXEnElJvLjdEoAxI2FdQC7q213y8sfl+LzClpI6uwdgCNcvyuFHr+2lrK6F3Ak2ZdaY4bAWQAg5WtdKTmqcffkH4DNnZQGwrigy1ocyZjRYAggR3T1K+YlW+zUboBmZCeROiGNdUbXboRgTtiwBhIiqhja6epScCTb/PxAiwqdnT2TDgeO0d3W7HY4xYckSQIg4WtcKQG6qJYBAXTo7k5aObraU1LkdijFhyRJAiCg70UKcz0taQrTboYSNZTPSifZ6rBvImGGyBBAiyupayZ0QF/Y7gI2l+Ogolk5P4x1LAMYMiyWAENDZ3UNVQ5v1/w/DpbMncqCmmSO1LW6HYkzYsQQQAirr2+hRmJxiCeBMXTrbv0vcun02HdSYM2UJIARU1LcBMNkGgM/Y9IwEclLj+ODAMbdDMSbsWAIIARX1rcT6PEyI97kdStgREZZOT2PjwVr8m9AZYwJlCSAEVNS3kZ1iA8DDtWx6OrXNHeyranI7FGPCiq0F5LIeVSrqWzk/L83tUMJCf8tN1zV3ALDx4HFmT0oa65CMCVuWAFx2vKmDzm4l2waAh21CQjSp8T5WFx7B5x24UXvr0qljGJUxoc+6gFxWUe+/A3hyaqzLkYS36RkJlBxrpsfGAYwJmCUAl5WfaMMrQmaS7Ww1EtMzEmnp6Ka6od3tUIwJG5YAXFZR38rE5BiiPPZRjER+RgIAB4/ZQLAxgbJvHZednAFkRmZCQjQT4n2UHGt2OxRjwoYNAruooa2TpvausOv/H+2N34drekYiuysa6FHFY1NqjRmStQBcVHHCfwewtQCCIz8zgdbObqoa2twOxZiwYAnARSdnAE1KDq8WQKia7owDWDeQMYGxBOCiyoY2UuN9xEV73Q5lXEiNjyYtIZqDNZYAjAlEQAlARK4WkSIRKRaRe/p5PUZEnnNe3yQieb1e+65zvEhErup1PFVEnheRvSKyR0SWBaNC4aS6oZ2sJPv1H0z5dj+AMQEbMgGIiBd4CLgGmAvcIiJz+xS7A6hT1QLgQeAB59y5wEpgHnA18LBzPYCfAa+r6hxgIbBn5NUJH53dPdQ0tpNl3T9BNT3DxgGMCVQgLYAlQLGqHlTVDuBZYEWfMiuAJ5zHzwOXi39lsxXAs6rarqolQDGwRESSgYuBRwFUtUNVT4y8OuGj5Fgz3apMSrEbwILp1P0A1g1kzJACSQA5wJFez8ucY/2WUdUuoB5IH+Tc6UAN8LiIbBORX4lIQn9vLiJ3ikihiBTW1IyfTT+KKhsBrAUQZKfGAWwg2JghBZIA+ptQ3beDdaAyAx2PAs4FHlHVRUAz8ImxBQBVXaWqi1V1cWZmZgDhhoeiykY8ApmJ1gIItukZCZTaOIAxQwokAZQBU3o9zwXKByojIlFAClA7yLllQJmqbnKOP48/IUSMoqpG0hNjiBpk9UozPPnOOEBlvY0DGDOYQL59tgAzRSRfRKLxD+qu6VNmDXC78/hG4G31b8+0BljpzBLKB2YCm1W1EjgiIrOdcy4Hdo+wLmGlqLLR5v+PkumZiQDWDWTMEIZMAE6f/t3AG/hn6qxW1V0icp+IfM4p9iiQLiLFwLdxunNUdRewGv+X++vAXara7ZzzDeA3IvIxcA7ww+BVK7S1dHRxuLaFrGTr/hkNKXE+0hOiKamxheGMGUxAawGp6lpgbZ9j9/Z63AbcNMC59wP393N8O7D4TIIdL05uXWgDwKMnPyOBneX1ti6QMYOwxeBG0UCLphWW1gK2BMRomp6ZSOGhOirr25icamstGdMfG4F0QVVDGz6vMCEh2u1Qxq0/3w9g3UDGDMQSgAuqGtqZmBRrXROj6OQ4gA0EGzMwSwAuqGpos/7/MTA9M4HS43Y/gDEDsQQwxprbu2hs72KSzQAaddMzEmnr7Dm174Ix5nSWAMbYyUXKrAUw+myfYGMGZwlgjFWeTAAplgBGW3Kcj4xE2x/AmIFYAhhjVQ3txPm8JMXYDNyxUDAxiYPHmujs7nE7FGNCjiWAMXZyAFhsBtCYmJWVSGe3cuh4i9uhGBNyLAGMIVWlqqHN9gAYQ9MzEvF6hH1VjW6HYkzIsQQwhupbO2nv6rEB4DEUHeUhLz2e/dWWAIzpyxLAGDo5AGxLQIytWVlJVDW0U1Hf6nYoxoQUSwBjqKqhHYCJthH8mJo5MQmA9/aNnx3ljAkGSwBjqKqhjZQ4H3HRXrdDiShZyTEkx0bxriUAY05jCWAM+WcA2QDwWBMRZmYl8f7+Y3TZdFBjTrEEMEa6e5Tqxnbr/3fJ7KwkGtq62FxS63YoxoQMSwBj5HhTO909ajOAXDIrK4k4n5e1OyvcDsWYkGEJYIxU2hpAroqO8vDpOZm8vrOK7h5bHdQYsB3BxkxVQzsegcwkGwNwyzXzs1m7o5LC0lqWTk8/o3MH2t3tpFuXTh1JaMa4wloAY6SqoY30hBh8XvtP7pbL5kwkJsrD2h3WDWQMWAIYM5UNbbYCqMsSYqK4dHYmr+2spMe6gYyxBDAWOrp6qGvusCmgIeDaBdlUN7bz4eE6t0MxxnWWAMZAdWMbii0BEQpOdgO9sO2o26EY4zpLAGOgst7WAAoVSbE+rls4mRe3HaWxrdPtcIxxlSWAMVDV0IbPK0xIiHY7FAN88YJptHR086K1AkyEswQwBiob2piYFIvHNoEJCQunpLIgJ4WnNh5C1QaDTeSyBDAGqhpsCYhQ88ULprGvqoktpTYYbCJXQAlARK4WkSIRKRaRe/p5PUZEnnNe3yQieb1e+65zvEhErupznldEtonIKyOtSKhqau+iqb3LpoCGmOsWTiY5NoonPyh1OxRjXDNkAhARL/AQcA0wF7hFROb2KXYHUKeqBcCDwAPOuXOBlcA84GrgYed6J30L2DPSSoSyqlNLQNgU0FASF+1l5ZKprN1RQVGl7RZmIlMgLYAlQLGqHlTVDuBZYEWfMiuAJ5zHzwOXi3/X8xXAs6rarqolQLFzPUQkF/gL4Fcjr0boshlAoevrl8wgMSaKf39tXP8GMWZAgSSAHOBIr+dlzrF+y6hqF1APpA9x7k+BfwIGXaBdRO4UkUIRKaypCb8NPaoa2oiP9pIYY8suhZoJCdF847KZrCuq4U/7w+/fljEjFUgC6G/qSt+pEwOV6fe4iHwWqFbVrUO9uaquUtXFqro4MzNz6GhDjH8TmFjEZgCFpNsunEbuhDjuf3WPrRJqIk4gCaAMmNLreS5QPlAZEYkCUoDaQc5dDnxORErxdyldJiJPDyP+kNajajOAQlxMlJfvXD2HvZWNPPxOsdvhGDOmAkkAW4CZIpIvItH4B3XX9CmzBrjdeXwj8Lb6J1ivAVY6s4TygZnAZlX9rqrmqmqec723VfWvg1CfkHKipZOO7h5LACHus2dnc8OiHP7zzX28ubvK7XCMGTNDJgCnT/9u4A38M3ZWq+ouEblPRD7nFHsUSBeRYuDbwD3OubuA1cBu4HXgLlXtDn41QtOpGUA2BTSkiQj//vkFnJ2bwt8/t539VTYryESGgO4DUNW1qjpLVWeo6v3OsXtVdY3zuE1Vb1LVAlVdoqoHe517v3PebFV9rZ9rr1PVzwarQqHk1C5gtglMyIv1efmvL55HrM/LTf/1Ae8UVbsdkjGjzu4EHkWV9W1MiPcR4/MOXdi4Ljsljt99bRmTkmP50uNbeOD1vdS32oJxZvyyuYmj6OQMIBM+8jMSePGu5dz70k4eWXeAJzaU8pfn5hId5WHKhHiio+w3kxk/LAGMkvaubo41tTM3O9ntUMwZivV5+fGNC7ltWR6Pry/luS1H6OjuwStCdmos2SmxTEqJIzs5lkkpscRaC8+EKUsAo+RgTTM9irUAwtj8nBT+868Wcu91c/nPPxRReqyFsroWdh5tOG0RuYlJMeyrauSSWZksL8iwVoIJG5YARsnJ9WVsBlD4S4nzMWdSMnMm+VtzqkpDWxcV9a1U1LdReqyZ324+zK83lJIa7+Oa+dncumQqC3JTXI7cmMFZAhglRVWNeEXISLRNYMYbESElzncqMTAbPn9uDhsOHGPN9nJe2n6U324+zJK8NL58UT5XzM3C67E7wU3osQQwSooqG8lIiibKY90BkSDW5+WyOVlcNieLhrZOVm85wq83lPK1p7cyNS2eLy3PY+X5U4mLtvECEzrs22mUFFU2Wv9/hEqO9fGVT01n3T9cyiNfOJfMpBj+9eXdfOrHb/OrPx2ktSNi7oU0Ic5aAKOgoa2ToydamTfZZgBFsiivh2sWZHPNgmwKS2v52Vv7+cGre/i/7x7ga5fM4AtLp51qETyz6fCA17l16dSxCtlEGGsBjILd5Q2A/8YiYwAW56Xx1B1L+d3XljFnUjI/eHWPtQiM6ywBjIJdTgKYnGpdQOZ05+el8fRXPpkIPjh43JajNmPOEsAo2HW0nolJMSTF+twOxYSok4lg9VeXMSMzkZc/Kudnb+1nT0UD/oV0jRl9NgYwCnaVN1j/fwgarJ8d3OlrX5KfxrN3XsC9L+3itZ2VPLXxEPkZCVw7P5ucCdaFaEaXJYAga+vsprimiSvnZbkdigkhQyWfs7KTmZWVxJbSWt7aU8VD64pZkpfGVfMmjVGEJhJZAgiyvZWNdPco8yYnU9tsK0lGiqG+4APh9QgXTE/nnCmpvLWnig0HjrO7ooHM5BiuOzvbthU1QWdjAEG2q7wegHmTbRkAMzyxPi9/cfZk/vbTBaTE+fjmb7dx++NbOHy8xe3QzDhjCSDIdh5tICXOR67135oRykmN4+uXzuB7181la2ktV/70XVa9d4Cu7h63QzPjhHUBBdnu8nrmTU625noYCkY3TrB5RIiJ8nL3ZTNZs/0oP1y7lyc2HOKGRTlMTvX/yLAbxcxwWQsgiDq7e9hT2WgzgEzQpcT5+OsLpnHLkqmcaO3k4XXFvLGrkk5rDZgRsBZAEB2oaaKjq8f6/82oEBEW5KQwIzOBtTsqeXdfDbvK65k9KYkLpqe7HZ4JQ9YCCKKdR/13AM/PsRaAGT3x0VHceF4uX16eT3ePsnLVRr77wg7qmjvcDs2EGWsBBNGOshPER3vJz0h0OxQTAQomJvKty2dRVtfCY+tLePXjcr5x2Uxuu3AaMVG27LQZmrUAgmjbkRMszE21zT/MmImO8vC/PzuX1751MYumTuD+tXu44ifv8erHFbakhBmStQCCpK2zm93lDdx58XS3QzERaPakJJ748hLe21fDD9fu4a5nPmTR1FS+eflMyutaB5yVZjOIIpslgCDZcbSerh5l0dQJbodiItjFzsb0vys8wi/eLuZLj28hOyWWS2dPZN7kZDxjOD05FNdeMqezBBAk2w7XAXDOlFSXIzGRzusRVi6ZyufPzeWl7Ud54PW9/HbzYTISo7l4ZiYLp6Ti81rvrwlwDEBErhaRIhEpFpF7+nk9RkSec17fJCJ5vV77rnO8SESuco5NEZF3RGSPiOwSkW8Fq0Ju2Xb4BFPS4shMinE7FGMA//jATYun8HefmcUtS6bi83p4YdtRfvxGEW/vraKpvcvtEI3LhmwBiIgXeAi4AigDtojIGlXd3avYHUCdqhaIyErgAeBmEZkLrATmAZOBP4rILKAL+J+q+qGIJAFbReTNPtcMK9sOn2BJfprbYRjzCR7n/oH5k5M5UNPM+uJj/HFPNeuKaig51swdF+VRMDHJ7TCNCwLpAloCFKvqQQAReRZYAfT+sl4BfN95/DzwS/GPOq0AnlXVdqBERIqBJar6AVABoKqNIrIHyOlzzbBRUd9KZUMbi6Za948JXSJCwcRECiYmUt3QxvoDx/ld4RF+u/kws7OSWF6QwX2ri4QAAA+hSURBVIzMhNMGjK2ffnwLJAHkAEd6PS8Dlg5URlW7RKQeSHeOb+xzbk7vE53uokXApjOIO6RsO3wCwAaAjSuGs4bRxORYbliUwxVzs9hUcpyNB2t5bH0J2SmxLC/I4OycFKJsnGDcCyQB9DdtoO8E44HKDHquiCQCvwf+TlUb+n1zkTuBOwGmTg3NXyPbDtcRHeVhbrbdAWzCS2JMFJfPyeLimZl8dOQE7xcf4/mtZazdUcG5Uydwft4EZmZZ99B4FUgCKAOm9HqeC5QPUKZMRKKAFKB2sHNFxIf/y/83qvrCQG+uqquAVQCLFy8OyTtbth0+wfzJyURH2S8mE558Xg+L89I4b9oEimua2FJSy4YDx7jiwfeYnZXEtQuy+dSsT7YMQnEFVRO4QBLAFmCmiOQDR/EP6t7ap8wa4HbgA+BG4G1VVRFZAzwjIj/BPwg8E9jsjA88CuxR1Z8EpyruaO3o5uOyev5meZ7boRgzYiLCzIlJzJyYRGNbJ3HRXl75qIKfvrWPB/+4j4RoL/NyUpidlUR+RgJFlY3Ex3hJiI4iISaK+GivTTENI0MmAKdP/27gDcALPKaqu0TkPqBQVdfg/zJ/yhnkrcWfJHDKrcY/uNsF3KWq3SJyEfBFYIeIbHfe6n+p6tpgV3C0FR6qpaO7h2UzbDVGM74kxfq4delUbluWR21zBxsPHmfjwePsKm/gxW1HaRxgGmmUR4iP9pIaH01qvI9JybFMSYsnd0KcrVEUYgK6Ecz5Yl7b59i9vR63ATcNcO79wP19jr1P/+MDYWfDgeNEeYQleTYF1IxfaQnRXLsgm2sXZAOgqtS1dPLkhlKaO7ppbu+iuaOL1o5uWju6ae7o5kRrB0dqW/i4zL9NapRHmJWVxMIpqZyVbeMKocDuBB6hDQeOc86UVBJi7D+liRwiQlpCNBOTY4cs29LRxZHaVvZVN7KzrJ7dFQ2kxvkA+KvFU4j1WavALdZZNwINbZ3sKDvBhdb9Y8yA4qOjmD0pievOnsx3rpnDbcumkRzn496XdvGZn7zLO3ur3Q4xYlkCGIFNB2vpUVg2I8PtUIwJCx4R5kxK5qsXT+c3X1lKrM/Ll369hbt+86FtaOMCSwAjsOHAMWKiPJw7ze4ANuZMiAjLCzJY+81P8Q9XzuLN3VVc+/M/UVha63ZoEcUSwAh8cOA45+el2cwGY4YpOsrD3ZfN5Pdfv5DoKA83r9rIo++X2GY2Y8RGLofpWFM7eysb+cerJrsdijFhb0FuCi9/4yL+8Xcf8W+v7KbkWBOzs5IH3V3P1ikaOWsBDNN7+2oAuKjA+v+NCYbkWB+PfOE8vnrJdJ7eeJinNpbS0dXjdljjmrUAhun1nZVMSo5lQU6K26EYE5YGWkZiWloC15+Tw0vbj/LEB6Xctsw2uR8t1gIYhpaOLt7dV8PV8yfhsQ3gjQm6Jflp3LR4CoeON/P4+lLaOrvdDmlcsgQwDOuKamjv6uGqeZPcDsWYceucKamsPH8qZXUtPPp+CS0dtoNZsFkX0DC8vrOStIRozs+z9f/N+Ob2ap/zc1L4gmcaz2w+zKPvl/Cl5fkk2l33QWMtgDPU3tXN23uruXJulm2YYcwYOCs7mS9eMI2axnYeff8gzbaXcdDYN9gZ2lB8nKb2Lq6ab90/xoyVWVlJ3LYsj+NNHTy+voTWDhsTCAZLAGfolY8rSIqJYrkt/2DMmCqYmMhfXzCNqsZ2Ht9QQkNbp9shhT1LAGfgREsHr3xczmcXTrbdv4xxwaysJG5dMpXyE6186fEtNFl30IjYt9gZWF14hPauHm6/cJrboRgTsc7KTmbl+VPZfuQEX/71FpsdNAKWAALU3aM8vfEwS/LSmDPJNn83xk3zc1J48OZzKCyt5X88WWj3CQyTJYAAvbuvmsO1Ldxmv/6NCQmfWziZ/3PjQjYcOM5Xn9pKe5clgTNlCSBAT35wiIlJMXbzlzEh5C/Py+VHn1/Au/tq+NunP7SWwBmyBBCAHWX1rCuq4dalU/HZ3H9jQsrN50/lB9fP56291XzliUIbEzgDdkvdEFSVf315FxmJ0Xz5ovwzOvd//feOTxz74Q0LTjv+wxsWfKJs3zJ9yw507b7n93ftga7fu+xA7x9oHEPVcbDrB1LH3ufbf7/Arh0TJbR36WnlQrWOeenx3HnxjFOvn1xtS/uUBVj+o7dOPfYKvF98jCX3v8X671zGtT9/j9wJ8Tz31WUDxjmYB9/cx99fMSvg4+HIfs4OYc1H5RQequMfr5pNcqzP7XCMGZbeX/6hrvR4y2nPlT9/+ff2zKbDHD3RdupPt1Ooqb2LGx5Zz9ETbWwqGf4OYz97a/8ZHQ9HlgAG0dLRxY9e28v8nGRuPG+K2+EYYwJUa/sLB8QSwABUlR+8uoeK+ja+d928QXcmMsaElv/+2+WnHj+8rpienvBpAY0lSwAD+PWGUp7ZdJivXjKd8/PS3A7HGHMG8jMSTj3+8etF3P74ZsrqWgY5IzJZAujHO3ur+bdXdnPl3Cy+c9Uct8MxxozAD29YwNZDdXzmJ+/y8Lpi22ayF0sAvagqT208xFef2sqcSck8ePM5tuOXMWHu1qVTefPbl3DxzEx+/HoRn/6PdTz1ge0yBgEmABG5WkSKRKRYRO7p5/UYEXnOeX2TiOT1eu27zvEiEbkq0GuOtWNN7Xx79Uf8y4s7WTYjnd98ZSkJtvGEMeNCTmocq25bzJNfXkJWcgz/8tIulv/obe57eTe7yutRjcwxgiG/4UTECzwEXAGUAVtEZI2q7u5V7A6gTlULRGQl8ABws4jMBVYC84DJwB9F5OQE2qGuOepUlX1VTTyz6RDPbjlCR3cP375iFnd/usB++RsTxnrvZNZ3V7O/PDeX/3nlbJ764BBPbSzlsfUlZKfEsrwgg/PzJjA3O4WZWYljHbIrAvmJuwQoVtWDACLyLLAC6P1lvQL4vvP4eeCXIiLO8WdVtR0oEZFi53oEcM2gqW3uoLa5nbqWTqoa2ig91kxxdRMfHDxOVUM7UR7h8+fm8NVLZjAjMzI+eGMilYiwvCCD5QUZ1DV38PquSv60v4Y3d1fx/Nay08pe+7M/MSkllqzkWDISo4mL9gKwessR4qK9JMR4ifNFERftJcojRHmFKI/n1GOf14PXI/g8HrxeIcrjP+YRfxyqysnGx8k2yMnWyJ+f+//2eQX/12rwBJIAcoAjvZ6XAUsHKqOqXSJSD6Q7xzf2OTfHeTzUNYPmxv+7gYM1zacdm5Qcy+K8NC6emcGlsyeSlRw7Wm9vjAlRExKiuWXJVG5ZMpWeHuVQbQu7yxt44cMy3tpbTXePsqeigU0Hj9PS0X3qS/mffv/xmMe699+uJtbnDeo1Zai+LxG5CbhKVb/iPP8isERVv9GrzC6nTJnz/AD+X/r3AR+o6tPO8UeBtfjHHga9Zq9r3wnc6TydDRQNv7pBlQEcczuIUWZ1HB+sjuPDSOo4TVUz+x4MpAVQBvS+DTYXKB+gTJmIRAEpQO0Q5w51TQBUdRWwKoA4x5SIFKrqYrfjGE1Wx/HB6jg+jEYdA5kFtAWYKSL5IhKNf1B3TZ8ya4Dbncc3Am+rv2mxBljpzBLKB2YCmwO8pjHGmFE0ZAvA6dO/G3gD8AKPqeouEbkPKFTVNcCjwFPOIG8t/i90nHKr8Q/udgF3qWo3QH/XDH71jDHGDGTIMQDTPxG50+meGresjuOD1XF8GI06WgIwxpgIZUtBGGNMhLIEcIZCbQmLYBGRUhHZISLbRaTQOZYmIm+KyH7n7wlux3mmROQxEakWkZ29jvVbL/H7ufPZfiwi57oXeeAGqOP3ReSo83luF5Fre73W7/IsoUpEpojIOyKyR0R2ici3nOPj5nMcpI6j+zn670SzP4H8wT9gfQCYDkQDHwFz3Y4rSHUrBTL6HPsxcI/z+B7gAbfjHEa9LgbOBXYOVS/gWuA1/LsQXgBscjv+EdTx+8A/9FN2rvPvNgbId/49e92uwxD1ywbOdR4nAfuceoybz3GQOo7q52gtgDNzalkMVe0ATi5hMV6tAJ5wHj8BXO9iLMOiqu/hn5nW20D1WgE8qX4bgVQRyR6bSIdvgDoO5NTyLKpaAvReniUkqWqFqn7oPG4E9uBfUWDcfI6D1HEgQfkcLQGcmf6WxRjsQwonCvxBRLY6d18DZKlqBfj/gQITXYsuuAaq13j7fO92ukAe69V9F9Z1FP9Kw4uATYzTz7FPHWEUP0dLAGemv5WYxss0quWqei5wDXCXiFzsdkAuGE+f7yPADOAcoAL4T+d42NZRRBKB3wN/p6oNgxXt51i41nFUP0dLAGcmkGUxwpKqljt/VwP/jb85WXWy6ez8Xe1ehEE1UL3GzeerqlWq2q2qPcD/48/dA2FZRxHx4f9i/I2qvuAcHlefY391HO3P0RLAmRmXS1iISIKIJJ18DFwJ7OT0JT5uB15yJ8KgG6hea4DbnFkkFwD1J7sYwk2fPu8b8H+eMPDyLCFLRAT/agN7VPUnvV4aN5/jQHUc9c/R7dHvcPuDf4bBPvyj7v/sdjxBqtN0/DMKPgJ2nawX/iW93wL2O3+nuR3rMOr2W/xN5078v5ruGKhe+JvVDzmf7Q5gsdvxj6COTzl1+Nj5ssjuVf6fnToWAde4HX8A9bsIf/fGx8B258+14+lzHKSOo/o52p3AxhgToawLyBhjIpQlAGOMiVCWAIwxJkJZAjDGmAhlCcAYYyKUJQAT8USkye0YjHGDJQBjjIlQlgCMcYjIpSKyTkSeF5G9IvIb5w5NROR8EdkgIh+JyGYRSRKRWBF5XPz7KGwTkU87Zf9GRF4UkZdFpERE7haRbztlNopImlNuhoi87izA9ycRmeNm/U3kGXJTeGMizCJgHv51VdYDy0VkM/AccLOqbhGRZKAV+BaAqi5wvrz/ICKznOvMd64Vi3+p3u+o6iIReRC4DfgpsAr4mqruF5GlwMPAZWNVUWMsARhzus2qWgYgItuBPKAeqFDVLQDqrEQpIhcBv3CO7RWRQ8DJBPCO+td1bxSReuBl5/gO4Gxn1ccLgd85jQzwb+5hzJixBGDM6dp7Pe7G//+I0P9Su/0tydvfdXp6Pe9xrukBTqjqOcMP1ZiRsTEAY4a2F5gsIucDOP3/UcB7wBecY7OAqfgX5hqS04ooEZGbnPNFRBaORvDGDMQSgDFDUP/2nzcDvxCRj4A38fftPwx4RWQH/jGCv1HV9oGv9AlfAO5wrrmL8b29qAlBthqoMcZEKGsBGGNMhLIEYIwxEcoSgDHGRChLAMYYE6EsARhjTISyBGCMMRHKEoAxxkQoSwDGGBOh/j9W3w0x8b9ZmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['Income'], rug=True)\n",
    "#Inference: Income is right skewed, indicates the presence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a234d3510>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Income'])\n",
    "#From below analysis, it is evident that Income has outliers.Hence this needs to be replaced with median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23927050>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['Family'])\n",
    "# Inference: Data is some what equally distibuted across different family groups(3 memeber family count is less in the entire data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23b8b290>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['CCAvg'], rug=True)\n",
    "#Inference: CCAvg is right skewed, indicates presence of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23faec90>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CCAvg has outliers which needs to be replaced with median\n",
    "sns.boxplot(BankRaw['CCAvg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23f61550>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['Education'])\n",
    "#Inference: Data is spread some what equally between education level 2 and 3 \n",
    "#where as customers which education level 1 has more presence in given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2406b050>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Mortgage'])\n",
    "#Mortgage has outliers which needs to be analyzed further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a242f7f10>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+k2Rrkk8mObTVH5HkP9r/3L+c5OFJnpLkY9MHTfL2JKe15euTvCbJ54BTkpyX5OQkfwo8BLgkySVDY1e05RckuazNdt6dZFl7nJfk60m+luTPZnldfwhcAHwKeNZQX3frvdVf3o731SRnt9qRSb7QZlwfTnJwq1+aZG1bXtECjySnJflQkk8kuTbJ37f62cB92+t47y/496XFrqp8+Fh0D+D+wOXAN4B3Ar/d6vsCnwem2vofABva8heB32/L+wP3A54CfGzouG8HTmvL1wMvH9p2HnDy0LYVQ9uuB1YAjwI+Cuzb6u8EXgQ8Adg8NP6gWV7XN4CHAccBFw3VZ+r9Ge213q/VD2k/rxj68zgTeHNbvpTBbIjW6/Vt+TTgOuDAduxvAYe1bbcv9N+1j8l4LB81VKRJUlW3J3kC8GTgqcD72zf5bQF+DdicBGAZcFOSBwArq+rDbf8fA7Qxc3n/HrZ2LINg+FI79n2BmxkEyC8neRvwbwxmDneR5DeAHVX1rSTbgQ1tVrBrlt6fDrynqn7U6juTHMggiD7TDrsR+JcR+r64qm5rx72KQWDdMPcuWkoMCy1aNbiGcClwaZKvAeuArcCVVfXE4bFJDpjlMLu46+nY/XfbvqfXDAJsrKpX3W1D8ljgeOAM4LnAS3Yb8jzgV6dPDwEHAM9hcA1jtufak4uOw69199d5x9Dynfhvg3bjNQstSkkemWTNUOlIBqdPrgGm2gVwkuyb5NFV9X1ge5KTWv0+7W6mbwFHtPUDGcwMRvED4AEz1C8GTk7yoPY8hyR5WLuesU9VfRD4a+Dxu72efYBTgMdU1eqqWs3gWwmfN0fvnwJeMn1XVpJD2uzg1iRPbod+ITA9y7iewawH4OQRX+dPk+w74ljdi/m/By1W9wfe1m5X3QVsA9ZX1U+SnAy8tf3jvxx4M3Alg384353kTOCnwClVdV2STQzO818LfGXE5z8H+PckN1XVU6eLVXVVkr8CPtUC4KcMZhL/C7yn1QB2n3n8FvCdqvrOUO2zDILs0Fl6/0SSI4EtSX4CfBx4NYMZ1j+1ELkOeHE73huATUleCHx6D17nFUm+XFXPH3Ef3Qt566wkqcvTUJKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqev/AXDdQqbqEeNPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['Securities Account'])\n",
    "#Inference: From below data it is evident that mejority of customers does not have security account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24289350>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['CD Account'])\n",
    "#Inference: From below data we can conclucde that majority of customers does not have CA Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a243c0210>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['Online'])\n",
    "#Inference: Overall more customers are having online access "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2448a090>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['CreditCard'])\n",
    "#Inference: customers having credit card is less in given data set as per below graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a245d8050>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yOs31UuCOp/h72d12lwEXJzkaOBD43+0e8+Wq2gJsSbKp2+7Gp7hdNcrwa6F6sNurf0y3t72mqt63i/V/WVVbYXTeoiQnA2cwOlndO4DTd/GY7c9nsmX7TXU/3wqMAS+qqoeT3AIctDe/md1sdzXw0aq6PMnLgQ/sZp6t+HdZT4GHerQ/WQucleQogCRHJDl2x5WSHAo8s6quAN4FbHsD+RajNwIYRf2be9jeM4FNXfRPA3ba1iztbrvPBGa622fv5XNLO3EvQfuNqroxyZ8BX0tyAPAw8Hbg1h1WfQZwWZKDGO29v7tb/k7gM0n+BNgMvG0Pm7wI+NckU8D1wPpZjHlwku0PyXz0Sbb7AeALSWaAbwPHzeL5pT3y7JyS1BgP9UhSYwy/JDXG8EtSYwy/JDXG8EtSYwy/JDXG8EtSY/4PR7+YGjwHY1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multivariate analysis\n",
    "#Data Distribution of 'Personal Loan' (ie., target variable) across all variables is analyzed through byvariate analysis in below section. \n",
    "#Nominal data sets ie., ID, ZIP code are skipped from this analysis\n",
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Age'])\n",
    "#Inference: Target variable has equal presence across age groups ranging between 35-55 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a246bbc90>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Experience'])\n",
    "#Inference: Target variable has equal presence across different experiance groups ranging between 10-30 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a248cb7d0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Income'])\n",
    "#Inference: Target variable has more presence with customers with high income. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a249f63d0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Family'])\n",
    "#Inference: As family size increased (range=2-4 members), more customers opted for Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24b2bbd0>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['CCAvg'])\n",
    "#Inference: Between 3-5 CCAvg, more customer opted for Personal Loan. Shows the presence of outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24c55ad0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.countplot(BankRaw['Education'])\n",
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Education'])\n",
    "#Inference: Customers opted for Personal Loan have education level between 2-3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24d81d90>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(BankRaw['Personal Loan'], BankRaw['Mortgage'])\n",
    "#Inference: As mortgage increases, presence of personal loan (target variable) descreases \n",
    "#and outliers are present "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24ec4390>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(BankRaw['Personal Loan'], BankRaw['Securities Account'])\n",
    "#sns.countplot(BankRaw['Securities Account'])\n",
    "#Inference: Presence of target variable is some what equally spread across Securities Account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24f0e790>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(BankRaw['Personal Loan'], BankRaw['CD Account'])\n",
    "#Inference: More customers with CD account opted for Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24ff0d50>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(BankRaw['Personal Loan'], BankRaw['Online'])\n",
    "#Inference: Target variable has equal presence w.r.t Online variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25229f50>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(BankRaw['Personal Loan'], BankRaw['CreditCard'])\n",
    "#Inference: Target variable has equal presence w.r.t CreditCard variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2512a190>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Target Column Distribution - In our case 'Personal Loan' is Target Variable\n",
    "sns.countplot(BankRaw['Personal Loan'])\n",
    "#Inference: Very less customers have opted for personal loan so far in the given dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False, <matplotlib.axes._subplots.AxesSubplot at 0x1a252e7950>)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Strategies to address the different data challenges such as data pollution, outliers and missing values.\n",
    "BankRaw.isna().sum()\n",
    "BankRaw.isnull().values.any(),BankRaw.isna().values.any()\n",
    "BankRaw.isnull().values.any(),BankRaw.isna().values.any(),sns.heatmap(BankRaw.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "#Based on below outcome, we can conclude that there is no null or NAN Values in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2500.500000</td>\n",
       "      <td>1443.520003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1250.75</td>\n",
       "      <td>2500.5</td>\n",
       "      <td>3750.25</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.104600</td>\n",
       "      <td>11.467954</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>91911.00</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.00</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>56.498800</td>\n",
       "      <td>101.713802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.00</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std     min       25%  \\\n",
       "ID                  5000.0   2500.500000  1443.520003     1.0   1250.75   \n",
       "Age                 5000.0     45.338400    11.463166    23.0     35.00   \n",
       "Experience          5000.0     20.104600    11.467954    -3.0     10.00   \n",
       "Income              5000.0     73.774200    46.033729     8.0     39.00   \n",
       "ZIP Code            5000.0  93152.503000  2121.852197  9307.0  91911.00   \n",
       "Family              5000.0      2.396400     1.147663     1.0      1.00   \n",
       "CCAvg               5000.0      1.937938     1.747659     0.0      0.70   \n",
       "Education           5000.0      1.881000     0.839869     1.0      1.00   \n",
       "Mortgage            5000.0     56.498800   101.713802     0.0      0.00   \n",
       "Personal Loan       5000.0      0.096000     0.294621     0.0      0.00   \n",
       "Securities Account  5000.0      0.104400     0.305809     0.0      0.00   \n",
       "CD Account          5000.0      0.060400     0.238250     0.0      0.00   \n",
       "Online              5000.0      0.596800     0.490589     0.0      0.00   \n",
       "CreditCard          5000.0      0.294000     0.455637     0.0      0.00   \n",
       "\n",
       "                        50%       75%      max  \n",
       "ID                   2500.5   3750.25   5000.0  \n",
       "Age                    45.0     55.00     67.0  \n",
       "Experience             20.0     30.00     43.0  \n",
       "Income                 64.0     98.00    224.0  \n",
       "ZIP Code            93437.0  94608.00  96651.0  \n",
       "Family                  2.0      3.00      4.0  \n",
       "CCAvg                   1.5      2.50     10.0  \n",
       "Education               2.0      3.00      3.0  \n",
       "Mortgage                0.0    101.00    635.0  \n",
       "Personal Loan           0.0      0.00      1.0  \n",
       "Securities Account      0.0      0.00      1.0  \n",
       "CD Account              0.0      0.00      1.0  \n",
       "Online                  1.0      1.00      1.0  \n",
       "CreditCard              0.0      1.00      1.0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.describe().transpose()\n",
    "#Analyzing below data:\n",
    "#Min experiance is mentioned as -3, a negetive number which is incorrect. Hence this should be anlyzed further\n",
    "#Mortgage is showing variation in standard deviation, we will analyze it further.\n",
    "#From bivariate analysis we could see that Mortgage, CCAvg and Income has outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>299.0</td>\n",
       "      <td>2469.889632</td>\n",
       "      <td>1385.488077</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1298.5</td>\n",
       "      <td>2499.0</td>\n",
       "      <td>3617.0</td>\n",
       "      <td>4964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>299.0</td>\n",
       "      <td>44.695652</td>\n",
       "      <td>11.256513</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>299.0</td>\n",
       "      <td>19.551839</td>\n",
       "      <td>11.150578</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>299.0</td>\n",
       "      <td>127.979933</td>\n",
       "      <td>38.659496</td>\n",
       "      <td>61.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>161.5</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>299.0</td>\n",
       "      <td>93195.257525</td>\n",
       "      <td>1785.249345</td>\n",
       "      <td>90007.0</td>\n",
       "      <td>91946.0</td>\n",
       "      <td>93555.0</td>\n",
       "      <td>94608.0</td>\n",
       "      <td>96150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>299.0</td>\n",
       "      <td>2.137124</td>\n",
       "      <td>1.067170</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>299.0</td>\n",
       "      <td>3.040569</td>\n",
       "      <td>2.223273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>299.0</td>\n",
       "      <td>1.709030</td>\n",
       "      <td>0.834824</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>299.0</td>\n",
       "      <td>352.869565</td>\n",
       "      <td>88.140140</td>\n",
       "      <td>251.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.317726</td>\n",
       "      <td>0.466373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.100334</td>\n",
       "      <td>0.300949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.160535</td>\n",
       "      <td>0.367717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.602007</td>\n",
       "      <td>0.490305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.287625</td>\n",
       "      <td>0.453414</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count          mean          std      min      25%  \\\n",
       "ID                  299.0   2469.889632  1385.488077     23.0   1298.5   \n",
       "Age                 299.0     44.695652    11.256513     23.0     35.0   \n",
       "Experience          299.0     19.551839    11.150578     -1.0     10.0   \n",
       "Income              299.0    127.979933    38.659496     61.0     94.0   \n",
       "ZIP Code            299.0  93195.257525  1785.249345  90007.0  91946.0   \n",
       "Family              299.0      2.137124     1.067170      1.0      1.0   \n",
       "CCAvg               299.0      3.040569     2.223273      0.0      1.3   \n",
       "Education           299.0      1.709030     0.834824      1.0      1.0   \n",
       "Mortgage            299.0    352.869565    88.140140    251.0    287.0   \n",
       "Personal Loan       299.0      0.317726     0.466373      0.0      0.0   \n",
       "Securities Account  299.0      0.100334     0.300949      0.0      0.0   \n",
       "CD Account          299.0      0.160535     0.367717      0.0      0.0   \n",
       "Online              299.0      0.602007     0.490305      0.0      0.0   \n",
       "CreditCard          299.0      0.287625     0.453414      0.0      0.0   \n",
       "\n",
       "                        50%      75%      max  \n",
       "ID                   2499.0   3617.0   4964.0  \n",
       "Age                    44.0     54.0     65.0  \n",
       "Experience             20.0     28.0     41.0  \n",
       "Income                122.0    161.5    205.0  \n",
       "ZIP Code            93555.0  94608.0  96150.0  \n",
       "Family                  2.0      3.0      4.0  \n",
       "CCAvg                   2.6      4.3      9.0  \n",
       "Education               1.0      2.0      3.0  \n",
       "Mortgage              326.0    400.0    635.0  \n",
       "Personal Loan           0.0      1.0      1.0  \n",
       "Securities Account      0.0      0.0      1.0  \n",
       "CD Account              0.0      0.0      1.0  \n",
       "Online                  1.0      1.0      1.0  \n",
       "CreditCard              0.0      1.0      1.0  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for Mortgage Value which is > 250. These values will be outliers as per boxplot analysis\n",
    "BankRaw[BankRaw['Mortgage']>250]. describe().transpose()\n",
    "#From this analysis we can conclude that there are 299 reacords having 'Mortgage' value as outliers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can replace these values with median of mortgage\n",
    "BankRaw.Mortgage.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the outliers with median value ie., 0.0 in this case\n",
    "BankRaw[\"Mortgage\"]=np.where(BankRaw[\"Mortgage\"]>250, 0.0,BankRaw['Mortgage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2500.500000</td>\n",
       "      <td>1443.520003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1250.75</td>\n",
       "      <td>2500.5</td>\n",
       "      <td>3750.25</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.104600</td>\n",
       "      <td>11.467954</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>91911.00</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.00</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>35.397200</td>\n",
       "      <td>66.140229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std     min       25%  \\\n",
       "ID                  5000.0   2500.500000  1443.520003     1.0   1250.75   \n",
       "Age                 5000.0     45.338400    11.463166    23.0     35.00   \n",
       "Experience          5000.0     20.104600    11.467954    -3.0     10.00   \n",
       "Income              5000.0     73.774200    46.033729     8.0     39.00   \n",
       "ZIP Code            5000.0  93152.503000  2121.852197  9307.0  91911.00   \n",
       "Family              5000.0      2.396400     1.147663     1.0      1.00   \n",
       "CCAvg               5000.0      1.937938     1.747659     0.0      0.70   \n",
       "Education           5000.0      1.881000     0.839869     1.0      1.00   \n",
       "Mortgage            5000.0     35.397200    66.140229     0.0      0.00   \n",
       "Personal Loan       5000.0      0.096000     0.294621     0.0      0.00   \n",
       "Securities Account  5000.0      0.104400     0.305809     0.0      0.00   \n",
       "CD Account          5000.0      0.060400     0.238250     0.0      0.00   \n",
       "Online              5000.0      0.596800     0.490589     0.0      0.00   \n",
       "CreditCard          5000.0      0.294000     0.455637     0.0      0.00   \n",
       "\n",
       "                        50%       75%      max  \n",
       "ID                   2500.5   3750.25   5000.0  \n",
       "Age                    45.0     55.00     67.0  \n",
       "Experience             20.0     30.00     43.0  \n",
       "Income                 64.0     98.00    224.0  \n",
       "ZIP Code            93437.0  94608.00  96651.0  \n",
       "Family                  2.0      3.00      4.0  \n",
       "CCAvg                   1.5      2.50     10.0  \n",
       "Education               2.0      3.00      3.0  \n",
       "Mortgage                0.0      0.00    250.0  \n",
       "Personal Loan           0.0      0.00      1.0  \n",
       "Securities Account      0.0      0.00      1.0  \n",
       "CD Account              0.0      0.00      1.0  \n",
       "Online                  1.0      1.00      1.0  \n",
       "CreditCard              0.0      1.00      1.0  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the outliers is replaced correctly\n",
    "BankRaw[BankRaw['Mortgage']<=250]. describe().transpose()\n",
    "# We can conclude that  after replacing the outliers, all 5000 records have Mortgage <=250 and\n",
    "#std has reduced significantly and it is in range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
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       "      <th>CreditCard</th>\n",
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       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>113</td>\n",
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       "      <td>226</td>\n",
       "      <td>227</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>39</td>\n",
       "      <td>94085</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>315</td>\n",
       "      <td>316</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>51</td>\n",
       "      <td>90630</td>\n",
       "      <td>3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>451</td>\n",
       "      <td>452</td>\n",
       "      <td>28</td>\n",
       "      <td>-2</td>\n",
       "      <td>48</td>\n",
       "      <td>94132</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>524</td>\n",
       "      <td>525</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>75</td>\n",
       "      <td>93014</td>\n",
       "      <td>4</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>536</td>\n",
       "      <td>537</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>43</td>\n",
       "      <td>92173</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>540</td>\n",
       "      <td>541</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>109</td>\n",
       "      <td>94010</td>\n",
       "      <td>4</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>576</td>\n",
       "      <td>577</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>48</td>\n",
       "      <td>92870</td>\n",
       "      <td>3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>583</td>\n",
       "      <td>584</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>38</td>\n",
       "      <td>95045</td>\n",
       "      <td>2</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>597</td>\n",
       "      <td>598</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>125</td>\n",
       "      <td>92835</td>\n",
       "      <td>2</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>649</td>\n",
       "      <td>650</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>82</td>\n",
       "      <td>92677</td>\n",
       "      <td>4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>670</td>\n",
       "      <td>671</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>92374</td>\n",
       "      <td>4</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>686</td>\n",
       "      <td>687</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>38</td>\n",
       "      <td>92612</td>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>793</td>\n",
       "      <td>794</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>150</td>\n",
       "      <td>94720</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>82</td>\n",
       "      <td>91103</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>909</td>\n",
       "      <td>910</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>149</td>\n",
       "      <td>91709</td>\n",
       "      <td>1</td>\n",
       "      <td>6.33</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1173</td>\n",
       "      <td>1174</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>35</td>\n",
       "      <td>94305</td>\n",
       "      <td>2</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1428</td>\n",
       "      <td>1429</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>21</td>\n",
       "      <td>94583</td>\n",
       "      <td>4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1522</td>\n",
       "      <td>1523</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>101</td>\n",
       "      <td>94720</td>\n",
       "      <td>4</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>1906</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>112</td>\n",
       "      <td>92507</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2102</td>\n",
       "      <td>2103</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>81</td>\n",
       "      <td>92647</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2430</td>\n",
       "      <td>2431</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>73</td>\n",
       "      <td>92120</td>\n",
       "      <td>4</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2466</td>\n",
       "      <td>2467</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>80</td>\n",
       "      <td>94105</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2545</td>\n",
       "      <td>2546</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>39</td>\n",
       "      <td>94720</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2618</td>\n",
       "      <td>2619</td>\n",
       "      <td>23</td>\n",
       "      <td>-3</td>\n",
       "      <td>55</td>\n",
       "      <td>92704</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2717</td>\n",
       "      <td>2718</td>\n",
       "      <td>23</td>\n",
       "      <td>-2</td>\n",
       "      <td>45</td>\n",
       "      <td>95422</td>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2848</td>\n",
       "      <td>2849</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "      <td>78</td>\n",
       "      <td>94720</td>\n",
       "      <td>2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2876</td>\n",
       "      <td>2877</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>80</td>\n",
       "      <td>91107</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>3</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2962</td>\n",
       "      <td>2963</td>\n",
       "      <td>23</td>\n",
       "      <td>-2</td>\n",
       "      <td>81</td>\n",
       "      <td>91711</td>\n",
       "      <td>2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2980</td>\n",
       "      <td>2981</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>53</td>\n",
       "      <td>94305</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3076</td>\n",
       "      <td>3077</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>62</td>\n",
       "      <td>92672</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3130</td>\n",
       "      <td>3131</td>\n",
       "      <td>23</td>\n",
       "      <td>-2</td>\n",
       "      <td>82</td>\n",
       "      <td>92152</td>\n",
       "      <td>2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3157</td>\n",
       "      <td>3158</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>13</td>\n",
       "      <td>94720</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3279</td>\n",
       "      <td>3280</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>44</td>\n",
       "      <td>94901</td>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3284</td>\n",
       "      <td>3285</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>101</td>\n",
       "      <td>95819</td>\n",
       "      <td>4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3292</td>\n",
       "      <td>3293</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>13</td>\n",
       "      <td>95616</td>\n",
       "      <td>4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3394</td>\n",
       "      <td>3395</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>113</td>\n",
       "      <td>90089</td>\n",
       "      <td>4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3425</td>\n",
       "      <td>3426</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>12</td>\n",
       "      <td>91605</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3626</td>\n",
       "      <td>3627</td>\n",
       "      <td>24</td>\n",
       "      <td>-3</td>\n",
       "      <td>28</td>\n",
       "      <td>90089</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3796</td>\n",
       "      <td>3797</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>50</td>\n",
       "      <td>94920</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3824</td>\n",
       "      <td>3825</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>12</td>\n",
       "      <td>95064</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3887</td>\n",
       "      <td>3888</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>118</td>\n",
       "      <td>92634</td>\n",
       "      <td>2</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3946</td>\n",
       "      <td>3947</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>40</td>\n",
       "      <td>93117</td>\n",
       "      <td>3</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4015</td>\n",
       "      <td>4016</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>139</td>\n",
       "      <td>93106</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4088</td>\n",
       "      <td>4089</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>71</td>\n",
       "      <td>94801</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4116</td>\n",
       "      <td>4117</td>\n",
       "      <td>24</td>\n",
       "      <td>-2</td>\n",
       "      <td>135</td>\n",
       "      <td>90065</td>\n",
       "      <td>2</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4285</td>\n",
       "      <td>4286</td>\n",
       "      <td>23</td>\n",
       "      <td>-3</td>\n",
       "      <td>149</td>\n",
       "      <td>93555</td>\n",
       "      <td>2</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4411</td>\n",
       "      <td>4412</td>\n",
       "      <td>23</td>\n",
       "      <td>-2</td>\n",
       "      <td>75</td>\n",
       "      <td>90291</td>\n",
       "      <td>2</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4481</td>\n",
       "      <td>4482</td>\n",
       "      <td>25</td>\n",
       "      <td>-2</td>\n",
       "      <td>35</td>\n",
       "      <td>95045</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4514</td>\n",
       "      <td>4515</td>\n",
       "      <td>24</td>\n",
       "      <td>-3</td>\n",
       "      <td>41</td>\n",
       "      <td>91768</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4582</td>\n",
       "      <td>4583</td>\n",
       "      <td>25</td>\n",
       "      <td>-1</td>\n",
       "      <td>69</td>\n",
       "      <td>92691</td>\n",
       "      <td>3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4957</td>\n",
       "      <td>4958</td>\n",
       "      <td>29</td>\n",
       "      <td>-1</td>\n",
       "      <td>50</td>\n",
       "      <td>95842</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  \\\n",
       "89      90   25          -1     113     94303       4   2.30          3   \n",
       "226    227   24          -1      39     94085       2   1.70          2   \n",
       "315    316   24          -2      51     90630       3   0.30          3   \n",
       "451    452   28          -2      48     94132       2   1.75          3   \n",
       "524    525   24          -1      75     93014       4   0.20          1   \n",
       "536    537   25          -1      43     92173       3   2.40          2   \n",
       "540    541   25          -1     109     94010       4   2.30          3   \n",
       "576    577   25          -1      48     92870       3   0.30          3   \n",
       "583    584   24          -1      38     95045       2   1.70          2   \n",
       "597    598   24          -2     125     92835       2   7.20          1   \n",
       "649    650   25          -1      82     92677       4   2.10          3   \n",
       "670    671   23          -1      61     92374       4   2.60          1   \n",
       "686    687   24          -1      38     92612       4   0.60          2   \n",
       "793    794   24          -2     150     94720       2   2.00          1   \n",
       "889    890   24          -2      82     91103       2   1.60          3   \n",
       "909    910   23          -1     149     91709       1   6.33          1   \n",
       "1173  1174   24          -1      35     94305       2   1.70          2   \n",
       "1428  1429   25          -1      21     94583       4   0.40          1   \n",
       "1522  1523   25          -1     101     94720       4   2.30          3   \n",
       "1905  1906   25          -1     112     92507       2   2.00          1   \n",
       "2102  2103   25          -1      81     92647       2   1.60          3   \n",
       "2430  2431   23          -1      73     92120       4   2.60          1   \n",
       "2466  2467   24          -2      80     94105       2   1.60          3   \n",
       "2545  2546   25          -1      39     94720       3   2.40          2   \n",
       "2618  2619   23          -3      55     92704       3   2.40          2   \n",
       "2717  2718   23          -2      45     95422       4   0.60          2   \n",
       "2848  2849   24          -1      78     94720       2   1.80          2   \n",
       "2876  2877   24          -2      80     91107       2   1.60          3   \n",
       "2962  2963   23          -2      81     91711       2   1.80          2   \n",
       "2980  2981   25          -1      53     94305       3   2.40          2   \n",
       "3076  3077   29          -1      62     92672       2   1.75          3   \n",
       "3130  3131   23          -2      82     92152       2   1.80          2   \n",
       "3157  3158   23          -1      13     94720       4   1.00          1   \n",
       "3279  3280   26          -1      44     94901       1   2.00          2   \n",
       "3284  3285   25          -1     101     95819       4   2.10          3   \n",
       "3292  3293   25          -1      13     95616       4   0.40          1   \n",
       "3394  3395   25          -1     113     90089       4   2.10          3   \n",
       "3425  3426   23          -1      12     91605       4   1.00          1   \n",
       "3626  3627   24          -3      28     90089       4   1.00          3   \n",
       "3796  3797   24          -2      50     94920       3   2.40          2   \n",
       "3824  3825   23          -1      12     95064       4   1.00          1   \n",
       "3887  3888   24          -2     118     92634       2   7.20          1   \n",
       "3946  3947   25          -1      40     93117       3   2.40          2   \n",
       "4015  4016   25          -1     139     93106       2   2.00          1   \n",
       "4088  4089   29          -1      71     94801       2   1.75          3   \n",
       "4116  4117   24          -2     135     90065       2   7.20          1   \n",
       "4285  4286   23          -3     149     93555       2   7.20          1   \n",
       "4411  4412   23          -2      75     90291       2   1.80          2   \n",
       "4481  4482   25          -2      35     95045       4   1.00          3   \n",
       "4514  4515   24          -3      41     91768       4   1.00          3   \n",
       "4582  4583   25          -1      69     92691       3   0.30          3   \n",
       "4957  4958   29          -1      50     95842       2   1.75          3   \n",
       "\n",
       "      Mortgage  Personal Loan  Securities Account  CD Account  Online  \\\n",
       "89         0.0              0                   0           0       0   \n",
       "226        0.0              0                   0           0       0   \n",
       "315        0.0              0                   0           0       1   \n",
       "451       89.0              0                   0           0       1   \n",
       "524        0.0              0                   0           0       1   \n",
       "536      176.0              0                   0           0       1   \n",
       "540        0.0              0                   0           0       1   \n",
       "576        0.0              0                   0           0       0   \n",
       "583        0.0              0                   0           0       1   \n",
       "597        0.0              0                   1           0       0   \n",
       "649        0.0              0                   0           0       1   \n",
       "670      239.0              0                   0           0       1   \n",
       "686        0.0              0                   0           0       1   \n",
       "793        0.0              0                   0           0       1   \n",
       "889        0.0              0                   0           0       1   \n",
       "909        0.0              0                   0           0       0   \n",
       "1173       0.0              0                   0           0       0   \n",
       "1428      90.0              0                   0           0       1   \n",
       "1522       0.0              0                   0           0       0   \n",
       "1905     241.0              0                   0           0       1   \n",
       "2102       0.0              0                   0           0       1   \n",
       "2430       0.0              0                   0           0       1   \n",
       "2466       0.0              0                   0           0       1   \n",
       "2545       0.0              0                   0           0       1   \n",
       "2618     145.0              0                   0           0       1   \n",
       "2717       0.0              0                   0           0       1   \n",
       "2848       0.0              0                   0           0       0   \n",
       "2876     238.0              0                   0           0       0   \n",
       "2962       0.0              0                   0           0       0   \n",
       "2980       0.0              0                   0           0       0   \n",
       "3076       0.0              0                   0           0       0   \n",
       "3130       0.0              0                   1           0       0   \n",
       "3157      84.0              0                   0           0       1   \n",
       "3279       0.0              0                   0           0       0   \n",
       "3284       0.0              0                   0           0       0   \n",
       "3292       0.0              0                   1           0       0   \n",
       "3394       0.0              0                   0           0       1   \n",
       "3425      90.0              0                   0           0       1   \n",
       "3626       0.0              0                   0           0       0   \n",
       "3796       0.0              0                   1           0       0   \n",
       "3824       0.0              0                   1           0       0   \n",
       "3887       0.0              0                   1           0       1   \n",
       "3946       0.0              0                   0           0       1   \n",
       "4015       0.0              0                   0           0       0   \n",
       "4088       0.0              0                   0           0       0   \n",
       "4116       0.0              0                   0           0       1   \n",
       "4285       0.0              0                   0           0       1   \n",
       "4411       0.0              0                   0           0       1   \n",
       "4481       0.0              0                   0           0       1   \n",
       "4514       0.0              0                   0           0       1   \n",
       "4582       0.0              0                   0           0       1   \n",
       "4957       0.0              0                   0           0       0   \n",
       "\n",
       "      CreditCard  \n",
       "89             1  \n",
       "226            0  \n",
       "315            0  \n",
       "451            0  \n",
       "524            0  \n",
       "536            0  \n",
       "540            0  \n",
       "576            1  \n",
       "583            0  \n",
       "597            1  \n",
       "649            0  \n",
       "670            0  \n",
       "686            0  \n",
       "793            0  \n",
       "889            1  \n",
       "909            1  \n",
       "1173           0  \n",
       "1428           0  \n",
       "1522           1  \n",
       "1905           0  \n",
       "2102           1  \n",
       "2430           0  \n",
       "2466           0  \n",
       "2545           0  \n",
       "2618           0  \n",
       "2717           1  \n",
       "2848           0  \n",
       "2876           0  \n",
       "2962           0  \n",
       "2980           0  \n",
       "3076           1  \n",
       "3130           1  \n",
       "3157           0  \n",
       "3279           0  \n",
       "3284           1  \n",
       "3292           0  \n",
       "3394           0  \n",
       "3425           0  \n",
       "3626           0  \n",
       "3796           0  \n",
       "3824           1  \n",
       "3887           0  \n",
       "3946           0  \n",
       "4015           1  \n",
       "4088           0  \n",
       "4116           0  \n",
       "4285           0  \n",
       "4411           1  \n",
       "4481           0  \n",
       "4514           0  \n",
       "4582           0  \n",
       "4957           1  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if experience is less than 0 which is cannot be the real time scenario\n",
    "BankRaw[BankRaw['Experience']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    52\n",
       "Age                   52\n",
       "Experience            52\n",
       "Income                52\n",
       "ZIP Code              52\n",
       "Family                52\n",
       "CCAvg                 52\n",
       "Education             52\n",
       "Mortgage              52\n",
       "Personal Loan         52\n",
       "Securities Account    52\n",
       "CD Account            52\n",
       "Online                52\n",
       "CreditCard            52\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['Experience']<0].count()\n",
    "#There are 52 records having experience as -ve values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2427.346154</td>\n",
       "      <td>1478.834118</td>\n",
       "      <td>90.0</td>\n",
       "      <td>767.25</td>\n",
       "      <td>2783.5</td>\n",
       "      <td>3669.500</td>\n",
       "      <td>4958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.519231</td>\n",
       "      <td>1.475159</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.000</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-1.442308</td>\n",
       "      <td>0.639039</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>52.0</td>\n",
       "      <td>69.942308</td>\n",
       "      <td>37.955295</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.75</td>\n",
       "      <td>65.5</td>\n",
       "      <td>86.750</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>52.0</td>\n",
       "      <td>93240.961538</td>\n",
       "      <td>1611.654806</td>\n",
       "      <td>90065.0</td>\n",
       "      <td>92167.75</td>\n",
       "      <td>93060.0</td>\n",
       "      <td>94720.000</td>\n",
       "      <td>95842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.865385</td>\n",
       "      <td>0.970725</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.129423</td>\n",
       "      <td>1.750562</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.325</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.076923</td>\n",
       "      <td>0.836570</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>52.0</td>\n",
       "      <td>26.769231</td>\n",
       "      <td>65.343921</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.322603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.498867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.457467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count          mean          std      min       25%  \\\n",
       "ID                   52.0   2427.346154  1478.834118     90.0    767.25   \n",
       "Age                  52.0     24.519231     1.475159     23.0     24.00   \n",
       "Experience           52.0     -1.442308     0.639039     -3.0     -2.00   \n",
       "Income               52.0     69.942308    37.955295     12.0     40.75   \n",
       "ZIP Code             52.0  93240.961538  1611.654806  90065.0  92167.75   \n",
       "Family               52.0      2.865385     0.970725      1.0      2.00   \n",
       "CCAvg                52.0      2.129423     1.750562      0.2      1.00   \n",
       "Education            52.0      2.076923     0.836570      1.0      1.00   \n",
       "Mortgage             52.0     26.769231    65.343921      0.0      0.00   \n",
       "Personal Loan        52.0      0.000000     0.000000      0.0      0.00   \n",
       "Securities Account   52.0      0.115385     0.322603      0.0      0.00   \n",
       "CD Account           52.0      0.000000     0.000000      0.0      0.00   \n",
       "Online               52.0      0.576923     0.498867      0.0      0.00   \n",
       "CreditCard           52.0      0.288462     0.457467      0.0      0.00   \n",
       "\n",
       "                        50%        75%      max  \n",
       "ID                   2783.5   3669.500   4958.0  \n",
       "Age                    24.0     25.000     29.0  \n",
       "Experience             -1.0     -1.000     -1.0  \n",
       "Income                 65.5     86.750    150.0  \n",
       "ZIP Code            93060.0  94720.000  95842.0  \n",
       "Family                  3.0      4.000      4.0  \n",
       "CCAvg                   1.8      2.325      7.2  \n",
       "Education               2.0      3.000      3.0  \n",
       "Mortgage                0.0      0.000    241.0  \n",
       "Personal Loan           0.0      0.000      0.0  \n",
       "Securities Account      0.0      0.000      1.0  \n",
       "CD Account              0.0      0.000      0.0  \n",
       "Online                  1.0      1.000      1.0  \n",
       "CreditCard              0.0      1.000      1.0  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['Experience']<0].describe().transpose()\n",
    "#From below data, it is evident that Experience is marked as -ve for age group <30 years.\n",
    "# Hence we will replace these negetive values of Experience with median experience of age group <30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw1=BankRaw[BankRaw['Age']<30]\n",
    "BankRaw1.Experience.median()\n",
    "#So meadian experience of customers with Age<30 is 2.Lets replace this value for all negetive experiences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the -ve experience with median value  mentioned above ie., 2.0 in this case\n",
    "BankRaw[\"Experience\"]=np.where(BankRaw[\"Experience\"]<0, 2.0,BankRaw['Experience'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2500.500000</td>\n",
       "      <td>1443.520003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1250.75</td>\n",
       "      <td>2500.5</td>\n",
       "      <td>3750.25</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.140400</td>\n",
       "      <td>11.405644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>73.774200</td>\n",
       "      <td>46.033729</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>98.00</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>91911.00</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.00</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.937938</td>\n",
       "      <td>1.747659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.50</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>35.397200</td>\n",
       "      <td>66.140229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std     min       25%  \\\n",
       "ID                  5000.0   2500.500000  1443.520003     1.0   1250.75   \n",
       "Age                 5000.0     45.338400    11.463166    23.0     35.00   \n",
       "Experience          5000.0     20.140400    11.405644     0.0     10.00   \n",
       "Income              5000.0     73.774200    46.033729     8.0     39.00   \n",
       "ZIP Code            5000.0  93152.503000  2121.852197  9307.0  91911.00   \n",
       "Family              5000.0      2.396400     1.147663     1.0      1.00   \n",
       "CCAvg               5000.0      1.937938     1.747659     0.0      0.70   \n",
       "Education           5000.0      1.881000     0.839869     1.0      1.00   \n",
       "Mortgage            5000.0     35.397200    66.140229     0.0      0.00   \n",
       "Personal Loan       5000.0      0.096000     0.294621     0.0      0.00   \n",
       "Securities Account  5000.0      0.104400     0.305809     0.0      0.00   \n",
       "CD Account          5000.0      0.060400     0.238250     0.0      0.00   \n",
       "Online              5000.0      0.596800     0.490589     0.0      0.00   \n",
       "CreditCard          5000.0      0.294000     0.455637     0.0      0.00   \n",
       "\n",
       "                        50%       75%      max  \n",
       "ID                   2500.5   3750.25   5000.0  \n",
       "Age                    45.0     55.00     67.0  \n",
       "Experience             20.0     30.00     43.0  \n",
       "Income                 64.0     98.00    224.0  \n",
       "ZIP Code            93437.0  94608.00  96651.0  \n",
       "Family                  2.0      3.00      4.0  \n",
       "CCAvg                   1.5      2.50     10.0  \n",
       "Education               2.0      3.00      3.0  \n",
       "Mortgage                0.0      0.00    250.0  \n",
       "Personal Loan           0.0      0.00      1.0  \n",
       "Securities Account      0.0      0.00      1.0  \n",
       "CD Account              0.0      0.00      1.0  \n",
       "Online                  1.0      1.00      1.0  \n",
       "CreditCard              0.0      1.00      1.0  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the outliers is replaced correctly\n",
    "BankRaw[BankRaw['Experience']>=0]. describe().transpose()\n",
    "# We can conclude that  after replacing the -ve, all 5000 records have Experience >=0 and\n",
    "#No negeitive values are found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5, 64.0)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similarly replacing the outliers for columns CCAvg > 5 and Income>180 with respective median values.\n",
    "BankRaw.CCAvg.median(), BankRaw.Income.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(ID                    346\n",
       " Age                   346\n",
       " Experience            346\n",
       " Income                346\n",
       " ZIP Code              346\n",
       " Family                346\n",
       " CCAvg                 346\n",
       " Education             346\n",
       " Mortgage              346\n",
       " Personal Loan         346\n",
       " Securities Account    346\n",
       " CD Account            346\n",
       " Online                346\n",
       " CreditCard            346\n",
       " dtype: int64, ID                    150\n",
       " Age                   150\n",
       " Experience            150\n",
       " Income                150\n",
       " ZIP Code              150\n",
       " Family                150\n",
       " CCAvg                 150\n",
       " Education             150\n",
       " Mortgage              150\n",
       " Personal Loan         150\n",
       " Securities Account    150\n",
       " CD Account            150\n",
       " Online                150\n",
       " CreditCard            150\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['CCAvg']>5].count(),BankRaw[BankRaw['Income']>180].count()\n",
    "#346 records of CCAvg has outliers and 150 records of Income has outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "BankRaw[\"CCAvg\"]=np.where(BankRaw[\"CCAvg\"]>5, 1.5,BankRaw['CCAvg'])\n",
    "BankRaw[\"Income\"]=np.where(BankRaw[\"Income\"]>180, 64.0,BankRaw['Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2500.500000</td>\n",
       "      <td>1443.520003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1250.75</td>\n",
       "      <td>2500.5</td>\n",
       "      <td>3750.25</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>45.338400</td>\n",
       "      <td>11.463166</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>20.140400</td>\n",
       "      <td>11.405644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>69.980000</td>\n",
       "      <td>41.197998</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>64.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>93152.503000</td>\n",
       "      <td>2121.852197</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>91911.00</td>\n",
       "      <td>93437.0</td>\n",
       "      <td>94608.00</td>\n",
       "      <td>96651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2.396400</td>\n",
       "      <td>1.147663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.575418</td>\n",
       "      <td>1.125338</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1.881000</td>\n",
       "      <td>0.839869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>35.397200</td>\n",
       "      <td>66.140229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.294621</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.104400</td>\n",
       "      <td>0.305809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.238250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.596800</td>\n",
       "      <td>0.490589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.455637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean          std     min       25%  \\\n",
       "ID                  5000.0   2500.500000  1443.520003     1.0   1250.75   \n",
       "Age                 5000.0     45.338400    11.463166    23.0     35.00   \n",
       "Experience          5000.0     20.140400    11.405644     0.0     10.00   \n",
       "Income              5000.0     69.980000    41.197998     8.0     39.00   \n",
       "ZIP Code            5000.0  93152.503000  2121.852197  9307.0  91911.00   \n",
       "Family              5000.0      2.396400     1.147663     1.0      1.00   \n",
       "CCAvg               5000.0      1.575418     1.125338     0.0      0.70   \n",
       "Education           5000.0      1.881000     0.839869     1.0      1.00   \n",
       "Mortgage            5000.0     35.397200    66.140229     0.0      0.00   \n",
       "Personal Loan       5000.0      0.096000     0.294621     0.0      0.00   \n",
       "Securities Account  5000.0      0.104400     0.305809     0.0      0.00   \n",
       "CD Account          5000.0      0.060400     0.238250     0.0      0.00   \n",
       "Online              5000.0      0.596800     0.490589     0.0      0.00   \n",
       "CreditCard          5000.0      0.294000     0.455637     0.0      0.00   \n",
       "\n",
       "                        50%       75%      max  \n",
       "ID                   2500.5   3750.25   5000.0  \n",
       "Age                    45.0     55.00     67.0  \n",
       "Experience             20.0     30.00     43.0  \n",
       "Income                 64.0     91.00    180.0  \n",
       "ZIP Code            93437.0  94608.00  96651.0  \n",
       "Family                  2.0      3.00      4.0  \n",
       "CCAvg                   1.5      2.20      5.0  \n",
       "Education               2.0      3.00      3.0  \n",
       "Mortgage                0.0      0.00    250.0  \n",
       "Personal Loan           0.0      0.00      1.0  \n",
       "Securities Account      0.0      0.00      1.0  \n",
       "CD Account              0.0      0.00      1.0  \n",
       "Online                  1.0      1.00      1.0  \n",
       "CreditCard              0.0      1.00      1.0  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.describe().transpose()\n",
    "#From below data, we can conclude that all outliers are replaced with respective median values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Income</th>\n",
       "      <th>ZIP Code</th>\n",
       "      <th>Family</th>\n",
       "      <th>CCAvg</th>\n",
       "      <th>Education</th>\n",
       "      <th>Mortgage</th>\n",
       "      <th>Personal Loan</th>\n",
       "      <th>Securities Account</th>\n",
       "      <th>CD Account</th>\n",
       "      <th>Online</th>\n",
       "      <th>CreditCard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ID</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008473</td>\n",
       "      <td>-0.008383</td>\n",
       "      <td>-0.018094</td>\n",
       "      <td>0.013432</td>\n",
       "      <td>-0.016797</td>\n",
       "      <td>0.004169</td>\n",
       "      <td>0.021463</td>\n",
       "      <td>-0.017069</td>\n",
       "      <td>-0.024801</td>\n",
       "      <td>-0.016972</td>\n",
       "      <td>-0.006909</td>\n",
       "      <td>-0.002528</td>\n",
       "      <td>0.017028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>-0.008473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993922</td>\n",
       "      <td>-0.042154</td>\n",
       "      <td>-0.029216</td>\n",
       "      <td>-0.046418</td>\n",
       "      <td>-0.028952</td>\n",
       "      <td>0.041334</td>\n",
       "      <td>-0.002302</td>\n",
       "      <td>-0.007726</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>0.008043</td>\n",
       "      <td>0.013702</td>\n",
       "      <td>0.007681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Experience</td>\n",
       "      <td>-0.008383</td>\n",
       "      <td>0.993922</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.033861</td>\n",
       "      <td>-0.028795</td>\n",
       "      <td>-0.051627</td>\n",
       "      <td>-0.031311</td>\n",
       "      <td>0.014003</td>\n",
       "      <td>-0.002681</td>\n",
       "      <td>-0.008477</td>\n",
       "      <td>-0.001049</td>\n",
       "      <td>0.009614</td>\n",
       "      <td>0.013944</td>\n",
       "      <td>0.008915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Income</td>\n",
       "      <td>-0.018094</td>\n",
       "      <td>-0.042154</td>\n",
       "      <td>-0.033861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015924</td>\n",
       "      <td>-0.148178</td>\n",
       "      <td>0.428020</td>\n",
       "      <td>-0.189003</td>\n",
       "      <td>-0.094419</td>\n",
       "      <td>0.432136</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>0.142173</td>\n",
       "      <td>0.011567</td>\n",
       "      <td>-0.002479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIP Code</td>\n",
       "      <td>0.013432</td>\n",
       "      <td>-0.029216</td>\n",
       "      <td>-0.028795</td>\n",
       "      <td>-0.015924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011778</td>\n",
       "      <td>-0.005845</td>\n",
       "      <td>-0.017377</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.019972</td>\n",
       "      <td>0.016990</td>\n",
       "      <td>0.007691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Family</td>\n",
       "      <td>-0.016797</td>\n",
       "      <td>-0.046418</td>\n",
       "      <td>-0.051627</td>\n",
       "      <td>-0.148178</td>\n",
       "      <td>0.011778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062663</td>\n",
       "      <td>0.064929</td>\n",
       "      <td>0.037938</td>\n",
       "      <td>0.061367</td>\n",
       "      <td>0.019994</td>\n",
       "      <td>0.014110</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>0.011588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CCAvg</td>\n",
       "      <td>0.004169</td>\n",
       "      <td>-0.028952</td>\n",
       "      <td>-0.031311</td>\n",
       "      <td>0.428020</td>\n",
       "      <td>-0.005845</td>\n",
       "      <td>-0.062663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.065362</td>\n",
       "      <td>-0.031083</td>\n",
       "      <td>0.261052</td>\n",
       "      <td>0.018178</td>\n",
       "      <td>0.077001</td>\n",
       "      <td>-0.015181</td>\n",
       "      <td>-0.005979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Education</td>\n",
       "      <td>0.021463</td>\n",
       "      <td>0.041334</td>\n",
       "      <td>0.014003</td>\n",
       "      <td>-0.189003</td>\n",
       "      <td>-0.017377</td>\n",
       "      <td>0.064929</td>\n",
       "      <td>-0.065362</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>0.136722</td>\n",
       "      <td>-0.010812</td>\n",
       "      <td>0.013934</td>\n",
       "      <td>-0.015004</td>\n",
       "      <td>-0.011014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mortgage</td>\n",
       "      <td>-0.017069</td>\n",
       "      <td>-0.002302</td>\n",
       "      <td>-0.002681</td>\n",
       "      <td>-0.094419</td>\n",
       "      <td>0.006597</td>\n",
       "      <td>0.037938</td>\n",
       "      <td>-0.031083</td>\n",
       "      <td>0.019163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055123</td>\n",
       "      <td>-0.003959</td>\n",
       "      <td>-0.008949</td>\n",
       "      <td>-0.011394</td>\n",
       "      <td>-0.001168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>-0.024801</td>\n",
       "      <td>-0.007726</td>\n",
       "      <td>-0.008477</td>\n",
       "      <td>0.432136</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.061367</td>\n",
       "      <td>0.261052</td>\n",
       "      <td>0.136722</td>\n",
       "      <td>-0.055123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021954</td>\n",
       "      <td>0.316355</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.002802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Securities Account</td>\n",
       "      <td>-0.016972</td>\n",
       "      <td>-0.000436</td>\n",
       "      <td>-0.001049</td>\n",
       "      <td>0.006041</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.019994</td>\n",
       "      <td>0.018178</td>\n",
       "      <td>-0.010812</td>\n",
       "      <td>-0.003959</td>\n",
       "      <td>0.021954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317034</td>\n",
       "      <td>0.012627</td>\n",
       "      <td>-0.015028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CD Account</td>\n",
       "      <td>-0.006909</td>\n",
       "      <td>0.008043</td>\n",
       "      <td>0.009614</td>\n",
       "      <td>0.142173</td>\n",
       "      <td>0.019972</td>\n",
       "      <td>0.014110</td>\n",
       "      <td>0.077001</td>\n",
       "      <td>0.013934</td>\n",
       "      <td>-0.008949</td>\n",
       "      <td>0.316355</td>\n",
       "      <td>0.317034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175880</td>\n",
       "      <td>0.278644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Online</td>\n",
       "      <td>-0.002528</td>\n",
       "      <td>0.013702</td>\n",
       "      <td>0.013944</td>\n",
       "      <td>0.011567</td>\n",
       "      <td>0.016990</td>\n",
       "      <td>0.010354</td>\n",
       "      <td>-0.015181</td>\n",
       "      <td>-0.015004</td>\n",
       "      <td>-0.011394</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.012627</td>\n",
       "      <td>0.175880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CreditCard</td>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.007681</td>\n",
       "      <td>0.008915</td>\n",
       "      <td>-0.002479</td>\n",
       "      <td>0.007691</td>\n",
       "      <td>0.011588</td>\n",
       "      <td>-0.005979</td>\n",
       "      <td>-0.011014</td>\n",
       "      <td>-0.001168</td>\n",
       "      <td>0.002802</td>\n",
       "      <td>-0.015028</td>\n",
       "      <td>0.278644</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ID       Age  Experience    Income  ZIP Code  \\\n",
       "ID                  1.000000 -0.008473   -0.008383 -0.018094  0.013432   \n",
       "Age                -0.008473  1.000000    0.993922 -0.042154 -0.029216   \n",
       "Experience         -0.008383  0.993922    1.000000 -0.033861 -0.028795   \n",
       "Income             -0.018094 -0.042154   -0.033861  1.000000 -0.015924   \n",
       "ZIP Code            0.013432 -0.029216   -0.028795 -0.015924  1.000000   \n",
       "Family             -0.016797 -0.046418   -0.051627 -0.148178  0.011778   \n",
       "CCAvg               0.004169 -0.028952   -0.031311  0.428020 -0.005845   \n",
       "Education           0.021463  0.041334    0.014003 -0.189003 -0.017377   \n",
       "Mortgage           -0.017069 -0.002302   -0.002681 -0.094419  0.006597   \n",
       "Personal Loan      -0.024801 -0.007726   -0.008477  0.432136  0.000107   \n",
       "Securities Account -0.016972 -0.000436   -0.001049  0.006041  0.004704   \n",
       "CD Account         -0.006909  0.008043    0.009614  0.142173  0.019972   \n",
       "Online             -0.002528  0.013702    0.013944  0.011567  0.016990   \n",
       "CreditCard          0.017028  0.007681    0.008915 -0.002479  0.007691   \n",
       "\n",
       "                      Family     CCAvg  Education  Mortgage  Personal Loan  \\\n",
       "ID                 -0.016797  0.004169   0.021463 -0.017069      -0.024801   \n",
       "Age                -0.046418 -0.028952   0.041334 -0.002302      -0.007726   \n",
       "Experience         -0.051627 -0.031311   0.014003 -0.002681      -0.008477   \n",
       "Income             -0.148178  0.428020  -0.189003 -0.094419       0.432136   \n",
       "ZIP Code            0.011778 -0.005845  -0.017377  0.006597       0.000107   \n",
       "Family              1.000000 -0.062663   0.064929  0.037938       0.061367   \n",
       "CCAvg              -0.062663  1.000000  -0.065362 -0.031083       0.261052   \n",
       "Education           0.064929 -0.065362   1.000000  0.019163       0.136722   \n",
       "Mortgage            0.037938 -0.031083   0.019163  1.000000      -0.055123   \n",
       "Personal Loan       0.061367  0.261052   0.136722 -0.055123       1.000000   \n",
       "Securities Account  0.019994  0.018178  -0.010812 -0.003959       0.021954   \n",
       "CD Account          0.014110  0.077001   0.013934 -0.008949       0.316355   \n",
       "Online              0.010354 -0.015181  -0.015004 -0.011394       0.006278   \n",
       "CreditCard          0.011588 -0.005979  -0.011014 -0.001168       0.002802   \n",
       "\n",
       "                    Securities Account  CD Account    Online  CreditCard  \n",
       "ID                           -0.016972   -0.006909 -0.002528    0.017028  \n",
       "Age                          -0.000436    0.008043  0.013702    0.007681  \n",
       "Experience                   -0.001049    0.009614  0.013944    0.008915  \n",
       "Income                        0.006041    0.142173  0.011567   -0.002479  \n",
       "ZIP Code                      0.004704    0.019972  0.016990    0.007691  \n",
       "Family                        0.019994    0.014110  0.010354    0.011588  \n",
       "CCAvg                         0.018178    0.077001 -0.015181   -0.005979  \n",
       "Education                    -0.010812    0.013934 -0.015004   -0.011014  \n",
       "Mortgage                     -0.003959   -0.008949 -0.011394   -0.001168  \n",
       "Personal Loan                 0.021954    0.316355  0.006278    0.002802  \n",
       "Securities Account            1.000000    0.317034  0.012627   -0.015028  \n",
       "CD Account                    0.317034    1.000000  0.175880    0.278644  \n",
       "Online                        0.012627    0.175880  1.000000    0.004210  \n",
       "CreditCard                   -0.015028    0.278644  0.004210    1.000000  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.corr()\n",
    "#From below data, we can analyze the Income,CCAvg, CD Account ,Education has considerable + influence on target variable Personal Loan\n",
    "#and Mortgage has -ve influence on target variable Personal Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping ID and ZIP code columns as they are just identifiers (Nominal data)\n",
    "Bank=BankRaw.drop('ZIP Code',axis=1)\n",
    "Bank=Bank.drop('ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(      Age  Experience  Income  Family  CCAvg  Education  Mortgage  \\\n",
       " 2858   36        11.0   158.0       2    1.5          1     114.0   \n",
       " 1559   59        35.0   102.0       4    3.0          2     115.0   \n",
       " 1441   58        33.0    43.0       2    1.6          3       0.0   \n",
       " 2179   49        23.0    68.0       1    1.5          2       0.0   \n",
       " 1390   29         3.0    80.0       4    1.8          2       0.0   \n",
       " ...   ...         ...     ...     ...    ...        ...       ...   \n",
       " 4931   57        27.0    55.0       1    1.4          3       0.0   \n",
       " 3264   67        41.0   114.0       4    2.4          3       0.0   \n",
       " 1653   26         1.0    24.0       2    0.9          3     123.0   \n",
       " 2607   57        33.0    49.0       4    1.5          1     214.0   \n",
       " 2732   33         9.0    38.0       1    2.1          3     133.0   \n",
       " \n",
       "       Securities Account  CD Account  Online  CreditCard  \n",
       " 2858                   0           0       1           1  \n",
       " 1559                   0           0       1           0  \n",
       " 1441                   0           0       1           0  \n",
       " 2179                   0           0       0           0  \n",
       " 1390                   0           0       1           1  \n",
       " ...                  ...         ...     ...         ...  \n",
       " 4931                   0           0       1           0  \n",
       " 3264                   0           0       1           0  \n",
       " 1653                   0           0       0           1  \n",
       " 2607                   1           1       1           1  \n",
       " 2732                   0           0       0           0  \n",
       " \n",
       " [3500 rows x 11 columns],\n",
       "       Age  Experience  Income  Family  CCAvg  Education  Mortgage  \\\n",
       " 398    54        30.0    23.0       2   0.40          1       0.0   \n",
       " 3833   33         9.0    83.0       1   0.10          1       0.0   \n",
       " 4836   54        24.0    72.0       3   1.40          3     218.0   \n",
       " 4572   32         7.0    81.0       4   1.80          3       0.0   \n",
       " 636    40        16.0   120.0       2   1.50          1     196.0   \n",
       " ...   ...         ...     ...     ...    ...        ...       ...   \n",
       " 4554   41        16.0   109.0       3   1.00          1       0.0   \n",
       " 4807   40        14.0    53.0       1   2.00          1       0.0   \n",
       " 1073   33         8.0   122.0       1   0.00          1       0.0   \n",
       " 2906   35         8.0    55.0       2   1.67          2       0.0   \n",
       " 1357   55        29.0    53.0       1   1.40          1       0.0   \n",
       " \n",
       "       Securities Account  CD Account  Online  CreditCard  \n",
       " 398                    0           0       0           0  \n",
       " 3833                   0           0       1           0  \n",
       " 4836                   0           0       0           1  \n",
       " 4572                   0           0       1           0  \n",
       " 636                    0           0       1           0  \n",
       " ...                  ...         ...     ...         ...  \n",
       " 4554                   1           0       1           0  \n",
       " 4807                   0           0       1           1  \n",
       " 1073                   0           0       1           1  \n",
       " 2906                   0           0       1           0  \n",
       " 1357                   0           0       0           0  \n",
       " \n",
       " [1500 rows x 11 columns],\n",
       " 2858    0\n",
       " 1559    1\n",
       " 1441    0\n",
       " 2179    0\n",
       " 1390    0\n",
       "        ..\n",
       " 4931    0\n",
       " 3264    0\n",
       " 1653    0\n",
       " 2607    0\n",
       " 2732    0\n",
       " Name: Personal Loan, Length: 3500, dtype: int64,\n",
       " 398     0\n",
       " 3833    0\n",
       " 4836    0\n",
       " 4572    0\n",
       " 636     0\n",
       "        ..\n",
       " 4554    0\n",
       " 4807    0\n",
       " 1073    0\n",
       " 2906    0\n",
       " 1357    0\n",
       " Name: Personal Loan, Length: 1500, dtype: int64)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the training set and test set in ration of 70:30\n",
    "#Importing required libaries\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "#Drop the target 'Personal Loan' from data\n",
    "x=Bank.drop('Personal Loan',axis=1)\n",
    "y=Bank['Personal Loan']\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.70,test_size=0.30,random_state=0)\n",
    "x_train,x_test,y_train,y_test\n",
    "\n",
    "#From the below data we can conclude that data is split as 3500: 1500 which is 70:30 ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform the data i.e. scale / normalize if required\n",
    "# Feature Scaling as we have variables in different scale (Ex: Age, Income, Mortgae etc.,)\n",
    "#Also all categorical variables as  represented in binary so good to scale it\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chai3/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First create Logistic Regression algorithm. Note the model performance.\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression(random_state = 0)\n",
    "logmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1356,   16],\n",
       "       [  61,   67]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = logmodel.predict(x_test)\n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_LogR = confusion_matrix(y_test, y_pred)\n",
    "cm_LogR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the ROC curve : 0.755888\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0.        , 0.01166181, 1.        ]),\n",
       " array([0.       , 0.5234375, 1.       ]),\n",
       " array([2, 1, 0]))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute ROC curve and area the curve for logistic\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"Area under the ROC curve : %f\" % roc_auc), \n",
    "fpr, tpr, thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve\n",
    "import pylab as pl\n",
    "pl.clf()\n",
    "pl.plot(fpr, tpr, label='ROC curve for logistic (area = %0.2f)' % roc_auc)\n",
    "#pl.plot(fpr2, tpr2, label='ROC curve for SVC (area = %0.2f)' % roc_auc2)\n",
    "pl.plot([0, 1], [0, 1], 'k--')\n",
    "pl.xlim([0.0, 1.0])\n",
    "pl.ylim([0.0, 1.0])\n",
    "pl.xlabel('False Positive Rate')\n",
    "pl.ylabel('True Positive Rate')\n",
    "pl.title('Receiverrating characteristic example')\n",
    "pl.legend(loc=\"lower right\")\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        fpr       tpr     1-fpr        tf  thresholds\n",
      "1  0.011662  0.523438  0.988338 -0.464901           1\n"
     ]
    }
   ],
   "source": [
    "#calculate the fpr and tpr for all thresholds of the classification\n",
    "i = np.arange(len(tpr)) # index for df\n",
    "roc = pd.DataFrame({'fpr' : pd.Series(fpr, index=i),'tpr' : pd.Series(tpr, index = i), '1-fpr' : pd.Series(1-fpr, index = i), 'tf' : pd.Series(tpr - (1-fpr), index = i), 'thresholds' : pd.Series(thresholds, index = i)})\n",
    "print(roc.loc[(roc.tf-0).abs().argsort()[:1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97      1372\n",
      "           1       0.81      0.52      0.64       128\n",
      "\n",
      "    accuracy                           0.95      1500\n",
      "   macro avg       0.88      0.76      0.80      1500\n",
      "weighted avg       0.94      0.95      0.94      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Best accuracy and Best Parameters using Confusion Matrix \n",
    "#Print Classification Report to check precision, recall & F1 Score\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "\n",
    "#Analysis:\n",
    "#Precision: How consistency model identified the value. \n",
    "   #In this case it is 96% consistently identified for loan rejecters and 81% consistent in case of loan accepters.\n",
    "# Recall: How much accurately model is indetifying the value.\n",
    "   # In this case, it is 99% accurate in identifying the loan rejecters and 52% accurate in identifying the loan accepters\n",
    " #FL score is over all measure of consistency and accuracy of given model\n",
    "   #In this case it is overall 97% accurate and consistent in identifying the loan rejecters\n",
    "    #and overall 64% accurate and consistent in identifying the loan accepters\n",
    "#From the below classification report, FPR=1-Specificity, (1-0.99)=0.01,nothing but false postive rate is 1%\n",
    "# From the below classification report TPR=Sensitivity=0.51,nothing but true postive rate is 51%\n",
    "#Overall accuracy of model is 95%\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 99% of customers who may reject the personal loan if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the loan offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 52% as potential customers who may opt for personal loan if offered. \n",
    "    # Precision and recall value for potential loan accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression with Hyper Parameter Tuning using GridSearchCV\n",
    "# This is N/A as Hyper parameter is not applicable for logistic regression but it is applicable for KNN/SVM etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build KNN algorithm and explain why that algorithm in the comment lines.\n",
    "#Explaination:\n",
    "#KNN works assuming the similariy between the different data and also it works based on voting of its neighbours. \n",
    "#Works good in case of continuous data\n",
    "# Fitting K-NN to the Training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNNModel = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "KNNModel.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1362,   10],\n",
       "       [  64,   64]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = KNNModel.predict(x_test)\n",
    "#Print Best accuracy and Best Parameters using Confusion Matrix \n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_KNN = confusion_matrix(y_test, y_pred)\n",
    "cm_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97      1372\n",
      "           1       0.86      0.50      0.63       128\n",
      "\n",
      "    accuracy                           0.95      1500\n",
      "   macro avg       0.91      0.75      0.80      1500\n",
      "weighted avg       0.95      0.95      0.94      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Classification Report to check precision, recall & F1 Score\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#Precision: How consistency model identified the value. \n",
    "   #In this case it is 96% consistently identified for loan rejecters and 86% consistent in case of loan accepters.\n",
    "# Recall: How much accurately model is indetifying the value.\n",
    "   # In this case, it is 99% accurate in identifying the loan rejecters and 50% accurate in identifying the loan accepters\n",
    " #FL score is over all measure of consistency and accuracy of given model\n",
    "   #In this case it is overall 97% accurate and consistent in identifying the loan rejecters\n",
    "    #and overall 63% accurate and consistent in identifying the loan accepters\n",
    "#From the below classification report, FPR=1-Specificity, (1-0.99)=0.01,nothing but false postive rate is 1%\n",
    "# From the below classification report TPR=Sensitivity=0.51,nothing but true postive rate is 51%\n",
    "#Overall Accuracy of model is 95%\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 99% of customers who may reject the personal loan if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the loan offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 50% as potential customers who may opt for personal loan if offered. \n",
    "    # Precision and recall value for potential loan accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#K-Nearest Neighbour with Hyper Parameter Tuning using GridSearchCV. \n",
    "scores=[]\n",
    "for k in range(1,50):\n",
    "    Model = KNeighborsClassifier(n_neighbors = k, metric = 'minkowski', p = 2)\n",
    "    Model.fit(x_train,y_train)\n",
    "    scores.append(Model.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a25ce13d0>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using Grid Search to identify optimum value for K.\n",
    "plt.plot (range(1,50),scores)\n",
    "#From below graph, we can assume that K~7, model performance could be good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rebuild the KNN using the optimum value to achieve best accuracy \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "KNNModel = KNeighborsClassifier(n_neighbors = 7, metric = 'minkowski', p = 2)\n",
    "KNNModel.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1366,    6],\n",
       "       [  61,   67]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = KNNModel.predict(x_test)\n",
    "#Print Best accuracy and Best Parameters using Confusion Matrix \n",
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_KNN = confusion_matrix(y_test, y_pred)\n",
    "cm_KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      1372\n",
      "           1       0.92      0.52      0.67       128\n",
      "\n",
      "    accuracy                           0.96      1500\n",
      "   macro avg       0.94      0.76      0.82      1500\n",
      "weighted avg       0.95      0.96      0.95      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Build classification report and explain recall, precision and F1 Score\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "\n",
    "#Precision: How consistency model identified the value. \n",
    "   #In this case it is 96% consistently identified for loan rejecters and 92% consistent in case of loan accepters.\n",
    "# Recall: How much accurately model is indetifying the value.\n",
    "   # In this case, it is 100% accurate in identifying the loan rejecters and 52% accurate in identifying the loan accepters\n",
    " #FL score is over all measure of consistency and accuracy of given model\n",
    "   #In this case it is overall 98% accurate and consistent in identifying the loan rejecters\n",
    "    #and overall 67% accurate and consistent in identifying the loan accepters\n",
    "#Overall accuracy of model is 96%\n",
    "#Inference: From the below report, with rebuild KNN model with k=7, we could see that performance of the model \n",
    "#has increased slightly in identifying the potential loan accepters. Also model is capable of identifying more accurately\n",
    "#the potential loan rejecters at k=7\n",
    "#So we can conclude that, at k=7 model is optimized\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Build Naive Bayes Classifier on training Dataset and explain why that algorithm in the comment lines\n",
    "\n",
    "#Explaination:\n",
    "#Naive Bayes factors all available evidence in form of predictors into the naïve Bayes rule to obtain more accurate\n",
    "#probability for class prediction.\n",
    "#It estimates conditional probability which is the probability that something will happen, given that something\n",
    "#else has already occurred. \n",
    "\n",
    "# Fitting Naive Bayes to the Training set\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "NBModel = GaussianNB()\n",
    "NBModel.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Predicting the Test set results\n",
    "y_pred = NBModel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1306,   66],\n",
       "       [  80,   48]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_NB = confusion_matrix(y_test, y_pred)\n",
    "cm_NB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.95      1372\n",
      "           1       0.42      0.38      0.40       128\n",
      "\n",
      "    accuracy                           0.90      1500\n",
      "   macro avg       0.68      0.66      0.67      1500\n",
      "weighted avg       0.90      0.90      0.90      1500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print the accuracy of the model & confusion Matrix for Naïve Bayes Model.\n",
    "#Firstly, calculate the probabilities of predictions made & then plot the ROC Curve\n",
    "\n",
    "#Explain Precision, Recall value & F1 Score using the classification report\n",
    "#Calculate the fpr and tpr for all thresholds of the classification.\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "\n",
    "#Analysis:\n",
    "\n",
    "#Precision: How consistency model identified the value. \n",
    "   #In this case it is 94% consistently identified for loan rejecters and 42% consistent in case of loan accepters.\n",
    "# Recall: How much accurately model is indetifying the value.\n",
    "   # In this case, it is 95% accurate in identifying the loan rejecters and 38% accurate in identifying the loan accepters\n",
    " #FL score is over all measure of consistency and accuracy of given model\n",
    "   #In this case it is overall 95% accurate and consistent in identifying the loan rejecters\n",
    "    #and overall 40% accurate and consistent in identifying the loan accepters\n",
    "#From the below classification report, FPR=1-Specificity, (1-0.95)=0.05,nothing but false postive rate is 5%\n",
    "# From the below classification report TPR=Sensitivity=0.38,nothing but true postive rate is 38%\n",
    "#Overall accuracy of model is 90%\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 95% of customers who may reject the personal loan if offerred to them. \n",
    "    #Hence model is good in identifying the customers who may tend to reject the loan offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 38% as potential customers who may opt for personal loan if offered. \n",
    "    # Precision and recall value for potential loan accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Overall conclusion is that KNN model with K=7 is better model compared to KNN with K=5,Logistic Regression and NB model evaluated above.\n",
    "# The hyper parameter selected here is K, which indicates the number of neighbours picked in given data set (randoma state=0) for given customer to analyze the similarity.\n",
    "#As per the concluded best model, i.e., KNN (at k=7), we can suggest the bank to taregt around 52% \n",
    "#who could be potential customers who may accept Personal Loan offered by bank\n",
    "#Also from end user standpoint (ie., Bank customer in this case), it makes sense to go with KNN model as majority of \n",
    "# customers who opt for personal loan will have lot of similarity as loan requirement/need will be driven by factors such Income, Family Size, Work Experience etc., "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Belo topics are yet to covered in the class. (Tried to plot the RoC for logistic regression above as I could find some additional references)\n",
    "#Roc Curve for Model evaluation and explain the difference among all the models used in your analysis\n",
    "#egularization techniques used for the model.\n",
    "#Range estimate at 95% confidence for the model performance in production.\n",
    "#Roc Curve for Model evaluation and explain the difference among all the models used in your analysis\n",
    "#Firstly, calculate the probabilities of predictions made & then plot the ROC Curve"
   ]
  }
 ],
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