SVM - Bank Customer retirement predictions - Hassan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BANK CUSTOMERS RETIREMENT PREDICTIONS USING SUPPORT VECTOR MACHINES\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #1: PROBLEM STATEMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You work as a data scientist at a major bank in NYC and you have been tasked to develop a model that can predict whether a customer is able to retire or not based on his/her features. Features are his/her age and net 401K savings (retirement savings in the U.S.). You thought that Support Vector Machines can be a great candidate to solve the problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #2: IMPORTING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np # Import Numpy for data statistical analysis \n",
    "import seaborn as sns # Statistical data visualization\n",
    "import matplotlib.pyplot as plt # Import matplotlib for data visualisation\n",
    "import pandas as pd # Import Pandas for data manipulation using dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read the data from the original file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "retirement_data = pd.read_csv('Bank_Customer_retirement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Customer ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>401K Savings</th>\n",
       "      <th>Retire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>39.180417</td>\n",
       "      <td>322349.8740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>56.101686</td>\n",
       "      <td>768671.5740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>57.023043</td>\n",
       "      <td>821505.4718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>43.711358</td>\n",
       "      <td>494187.4850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>54.728823</td>\n",
       "      <td>691435.7723</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>495</td>\n",
       "      <td>46.678128</td>\n",
       "      <td>704243.0776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>496</td>\n",
       "      <td>43.545007</td>\n",
       "      <td>483175.9998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>497</td>\n",
       "      <td>46.476724</td>\n",
       "      <td>354919.1987</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>498</td>\n",
       "      <td>50.903631</td>\n",
       "      <td>639789.9219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>499</td>\n",
       "      <td>38.590586</td>\n",
       "      <td>491310.0096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID        Age  401K Savings  Retire\n",
       "0              0  39.180417   322349.8740       0\n",
       "1              1  56.101686   768671.5740       1\n",
       "2              2  57.023043   821505.4718       1\n",
       "3              3  43.711358   494187.4850       0\n",
       "4              4  54.728823   691435.7723       1\n",
       "..           ...        ...           ...     ...\n",
       "495          495  46.678128   704243.0776       1\n",
       "496          496  43.545007   483175.9998       0\n",
       "497          497  46.476724   354919.1987       0\n",
       "498          498  50.903631   639789.9219       1\n",
       "499          499  38.590586   491310.0096       0\n",
       "\n",
       "[500 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retirement_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Age', '401K Savings', 'Retire'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retirement_data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #3: VISUALIZING THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x235ab3e25e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(retirement_data, hue = 'Retire', vars=['Age', '401K Savings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235abcdda90>"
      ]
     },
     "execution_count": 7,
     "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(x = 'Retire', data = retirement_data )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Possible to use other plots for data visualization as well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #4: MODEL TRAINING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "retirement_data = retirement_data.drop(['Customer ID'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Age</th>\n",
       "      <th>401K Savings</th>\n",
       "      <th>Retire</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.180417</td>\n",
       "      <td>322349.8740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.101686</td>\n",
       "      <td>768671.5740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.023043</td>\n",
       "      <td>821505.4718</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.711358</td>\n",
       "      <td>494187.4850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.728823</td>\n",
       "      <td>691435.7723</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>46.678128</td>\n",
       "      <td>704243.0776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>43.545007</td>\n",
       "      <td>483175.9998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>46.476724</td>\n",
       "      <td>354919.1987</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>50.903631</td>\n",
       "      <td>639789.9219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>38.590586</td>\n",
       "      <td>491310.0096</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age  401K Savings  Retire\n",
       "0    39.180417   322349.8740       0\n",
       "1    56.101686   768671.5740       1\n",
       "2    57.023043   821505.4718       1\n",
       "3    43.711358   494187.4850       0\n",
       "4    54.728823   691435.7723       1\n",
       "..         ...           ...     ...\n",
       "495  46.678128   704243.0776       1\n",
       "496  43.545007   483175.9998       0\n",
       "497  46.476724   354919.1987       0\n",
       "498  50.903631   639789.9219       1\n",
       "499  38.590586   491310.0096       0\n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retirement_data\n",
    "# Let's drop the target label coloumns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = retirement_data.drop(['Retire'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Age</th>\n",
       "      <th>401K Savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.180417</td>\n",
       "      <td>322349.8740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.101686</td>\n",
       "      <td>768671.5740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.023043</td>\n",
       "      <td>821505.4718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43.711358</td>\n",
       "      <td>494187.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.728823</td>\n",
       "      <td>691435.7723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>46.678128</td>\n",
       "      <td>704243.0776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>43.545007</td>\n",
       "      <td>483175.9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>46.476724</td>\n",
       "      <td>354919.1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>50.903631</td>\n",
       "      <td>639789.9219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>38.590586</td>\n",
       "      <td>491310.0096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age  401K Savings\n",
       "0    39.180417   322349.8740\n",
       "1    56.101686   768671.5740\n",
       "2    57.023043   821505.4718\n",
       "3    43.711358   494187.4850\n",
       "4    54.728823   691435.7723\n",
       "..         ...           ...\n",
       "495  46.678128   704243.0776\n",
       "496  43.545007   483175.9998\n",
       "497  46.476724   354919.1987\n",
       "498  50.903631   639789.9219\n",
       "499  38.590586   491310.0096\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = retirement_data['Retire']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "495    1\n",
       "496    0\n",
       "497    0\n",
       "498    1\n",
       "499    0\n",
       "Name: Retire, Length: 500, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dividing the data into training and testing data \n",
    "## changing the test_size will change the accuracy of the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #5: EVALUATING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svc_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac0e52b0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.91      0.89        43\n",
      "           1       0.93      0.89      0.91        57\n",
      "\n",
      "    accuracy                           0.90       100\n",
      "   macro avg       0.90      0.90      0.90       100\n",
      "weighted avg       0.90      0.90      0.90       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #6: IMPROVING THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_min = X_train.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             25.0\n",
       "401K Savings    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_train = X_train.max() - x_train_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 42.318208\n",
       "401K Savings    999990.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = (X_train - x_train_min) / range_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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>Age</th>\n",
       "      <th>401K Savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.488640</td>\n",
       "      <td>0.684860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0.731713</td>\n",
       "      <td>0.651642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.636117</td>\n",
       "      <td>0.669911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>0.544773</td>\n",
       "      <td>0.718011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.455655</td>\n",
       "      <td>0.363796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.315494</td>\n",
       "      <td>0.033828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.281290</td>\n",
       "      <td>0.374391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.512265</td>\n",
       "      <td>0.704240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.510404</td>\n",
       "      <td>0.626791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0.375989</td>\n",
       "      <td>0.375724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age  401K Savings\n",
       "247  0.488640      0.684860\n",
       "390  0.731713      0.651642\n",
       "186  0.636117      0.669911\n",
       "484  0.544773      0.718011\n",
       "139  0.455655      0.363796\n",
       "..        ...           ...\n",
       "486  0.315494      0.033828\n",
       "189  0.281290      0.374391\n",
       "495  0.512265      0.704240\n",
       "206  0.510404      0.626791\n",
       "355  0.375989      0.375724\n",
       "\n",
       "[400 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac19e430>"
      ]
     },
     "execution_count": 33,
     "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.scatterplot(x = X_train['Age'], y = X_train['401K Savings'] , hue = retirement_data['Retire'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now you can see normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac20d280>"
      ]
     },
     "execution_count": 34,
     "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.scatterplot(x = x_train_scaled['Age'], y = x_train_scaled['401K Savings'] , hue = retirement_data['Retire'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, for the test data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_min = X_test.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 31.787663\n",
       "401K Savings    109281.634500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_test = X_test.max() - x_test_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 38.212337\n",
       "401K Savings    778603.066500\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_scaled = (X_test - x_test_min) / range_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Age</th>\n",
       "      <th>401K Savings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.267183</td>\n",
       "      <td>0.094430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0.165409</td>\n",
       "      <td>0.505061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.139272</td>\n",
       "      <td>0.302988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.248950</td>\n",
       "      <td>0.550203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0.323557</td>\n",
       "      <td>0.677682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.633436</td>\n",
       "      <td>0.635622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>0.612141</td>\n",
       "      <td>0.525418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.444550</td>\n",
       "      <td>0.850610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.538401</td>\n",
       "      <td>0.412874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.259855</td>\n",
       "      <td>0.721079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age  401K Savings\n",
       "241  0.267183      0.094430\n",
       "448  0.165409      0.505061\n",
       "75   0.139272      0.302988\n",
       "212  0.248950      0.550203\n",
       "481  0.323557      0.677682\n",
       "..        ...           ...\n",
       "145  0.633436      0.635622\n",
       "234  0.612141      0.525418\n",
       "382  0.444550      0.850610\n",
       "141  0.538401      0.412874\n",
       "140  0.259855      0.721079\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac23d1f0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z74IObtTHaX1g5EX1O00avInP0DuXnLW1ygV0G+j1i+EX15ifYQAYBWPoOJQdhJx1oNyQPUZ/ywzHTcDt1mYoT5SCDS/DqXU39HVIzAqujT8xC379JpTkaKUVm6rzeUpz9WvTuK1hMo+BLe/Wlx/cCG6XMTEGgCl22Q6YP38+WVlZDB3aQnt8R6b0ADxxMiy+QEdfPXGSloWDiAjI8GGaSuvb+msBXcetx0h9U3x8Ijw4ChaOgu0rwFEVnjW1B6LjYOis+vJRlxjlEiBGwbQDLrnkEj744INwL6Nts+kNKPGoXl16AL5/MXzrGT5Xm6NqyBwEg0JaTMI/ZXnw2qVW0AHaWf3GAqgy5W/8ktILLlgMWYO17+rsR6Hr4HCvqt1g1HAw2bAEVtwKxTmQkq1jzI87r8XTTpo0id27d7d8fR2Z0oM+ZGHawYD2p1y6HAp3aodxWp/whrcqNxz6yVtWXa7rrhkaJjYZjp4OPccACuK7BDXTvaNjflLBYsMSePsaKN4LKP389jW+cxsMwWfkr7wjoyQCxl4avvWAVii9x0OvceHPnYiMgT4nesuSukF0iKsTdAREtJkxMcsolyZiflrBYsWt9b8NOiq13BB6knvA5R/DoDPg6Glw2UeQ0jvcq2o7xKfDrMeh70n6htltGPz6DUjoEu6VGTowxkQWLIpzmiY3BJeYBOg+HM5ZpI8byjfpzKRkw3nPgbNa7/aMcjGEGKNggkVKtmUe8yE3tB6dSbG4nDrjvHCXVhbxGY3nzQSznlp1hb7+3tWQ3h9Sj9L1rQwGC2MiCxZTbq6fUxAdp+Ut5IILLmDChAn8+OOPZGdn89RTT7V4TkMHoHA7PDQWnpkOD42BZX/TFZkDofIw2Mtadv3938LCEfDaZbDoZHj/L4FfP9g4q+qXxDGEHbODCRY10WIhiCJ76aWXWjyHIYSUH9I3t/iM1nMCVxbBBzfqrP8avv8PnHit/11MZRHs+hRWPwrxmTDlrzo/JzLa//WcDv0cZZ1Xlg/vX69za2rY+Cqc8teWVx9oCuUFsHOVbgjX+wQYeg7YEnSya2fazbZRjIIJJsedFxSFYmgnVJVCzhpdq8pphxP/oAMMQl3WH3Sb26I99eVl+dDl6IbH7f4Mlvy69njHR7olQHIDbbgdVdr0+8VCfXzCNZDaS4c91+TUeJ3fisUgqyvg8/vgK6u4+ua34Kf3YPxVsPZJ/fvIGhLSasEG/xgTmcHQXEr3w4vn6p4h+VvhjSt0n/PWIC5NN4jyxJYEGf0bHlNZBKsf95ZVl2sfSkOU5sKjv4DvntePR38BJbn6+qMv8T43pVfrBg7YS+GbRd6ynat0T5OfPoSnp9fP/QkWSuncq4Id+udh2mn7xCiYRlCd3K7b2T+/Xza9Wd/uv+4ZvbsINZHRMPo3cMrftFLpN1kndsb7ucFHxvjOx0nwk6Oz7lnvz+Oq1rKoGF39+az7dK7P6N/A/A9av8pwlM37WASvhrZfPaT9M8GmcIf2Oz04Ch44Dn54teU+rQ6IMZH5ITY2loKCAjIyMpBOWDlVKUVBQQGxsbHhXkrbxFcp/cxjQVrp3yohA064FkZdrJVHY10qY+Lh5Jtg27LaVsw9x/g3qUXH+5jH8m3EZ+i6XENmQVRs6xfOjEuFyTfCBzfUyobNgZ+/rD1OyAIJcmuCisPw9rW1pYlc1fDuH2DgacbvUwejYPyQnZ1NTk4O+fn54V5K2IiNjSU7u5OGWjuqdK2uisPaJBSbom/SNRz1C32D3meVc0/vB6N+3brZ3pHRTasSkHoUXPWNXnN8hq6vlZDZ8PkjL9IBAZVWzbK4NBhxYe37ERGt43PyRZRN13zrdbxu1NZzDLjs8Mol+v3YVPjF7xsPYGgqLnt9U6jbBZWFDfuyOiliTCCaMWPGqLVr1zZ+oqFz4HLC7k/hpQu0iSUyBuY8BwOmeJtlyvN1IUmXQ99cam72bjc4K3WnylAoHHspRNq0qSqUuF368/34nj4edLo2g9VtWOas1jdYt0vvZBqKJCs9AD+8oiMtR1+i/TbB/NZflgf71mmfyFEnWOVdgryDsZfC29fpqLkaYhLh6nW6/E4nQ0TWKaXG+HwvVApGRAYBL3uI+gE3A89b8j7AbuA8pdRha8yNwKWAC7hGKfWhJR9Nbcvk94BrlVJKRGzWfKOBAuB8pdRua8w84K/WtW9XSj3nb71GwRi8KD2gS9uX5dXKYlPhqtWN30TKD+nqztuXQ/9Tdcl3f7uEplBRCLs+ge9egC7H6G/o4f7WbC+DbR/Cu3/SO52+k3RFhbo/p7KDsGhKbUKyCMxfpmu1tTdKD8A7f4CfPtA711mPQbfhoVf4bZCwKJg6C4gE9gHHA1cBhUqpu0TkBiBNKXW9iAwGXgLGAT2Aj4CjlVIuEVkDXAt8jVYwC5VS74vIlcBxSqnfishcYJZS6nwRSQfWAlYJVNYBo2sUmS+MgjF4UbwP7vNRlv26HyDVT42zyiJ454+w6bVa2eCz4ZcPNO4jaQy3E9Y8CR9cXytL7Q2XrQhvMc2ivfDAMO+Ah9G/gWn/9DYpbv8I/nOu99j+U2DOM9r8GC7KDsL2lfrLxJCZeocWiD+psljvbkXCX8w0jPhTMK1lLJ4C7FBK/QzMBGp2E88BZ1uvZwKLlVJ2pdQuYDswTkS6A8lKqa+U1obP1xlTM9erwBTR3vhpwHKlVKGlVJYDYWzGYWh3RNmg5yhvWdax2uTlD0cFbH7dW7blreDkh1QUwurHvGXl+TofJOcb+PYF3R6gtaOZCnbUj6bb/SlUl7buOppDWZ4OZ37zt/DRzboiQuGOwMbGpUBS106tXBqjtRTMXPTuBKCrUioXwHqu+e30BDyLeeVYsp7W67pyrzFKKSdQDGT4mcsLEVkgImtFZG1nduQbfJDQBc57AQZOA1sy9DsZLlyiy7b7RbS/xpPIGLxCZ5uLRNRPGpxyC6y6E548FZb+XofN/vxF65ZNyehXvzV1r/EQk+Qt6zZM+1xqEIHJN4R395K7XivlGlwO+PhOE3IcJEKuYEQkBpgBvNLYqT5kyo+8uWNqBUo9oZQao5Qak5kZJBu5oeOQkq19Cb//BuY86980VkNsMpz4R2/ZCdcG5yaa0AWm3q4VTQ1H/QI2LK49VkqH7Za34hemuDSY8aCuaA2QPUbn58TUCXFO7KrbKEz7p862/93XOtM+nPjKkXFW6koFhhbTGmHKpwPfKqVqWg4eFJHuSqlcy/xV40XNATy+3pAN7Lfk2T7knmNyRCQKSAEKLfnkOmNWBesDGToRcSnoP6sAiUmAcQtg4FTY/bmOZErvW/9m21x6joarv4WdH0PGAN++gqoicDu0b0S5dISTrwz78gJ9ni25ZeuzJcHQ2TDgVB19Fx3XcEZ/UjeYcFXzrxVsssfqcG3PsjcT/2zKywSJ1lAwF1BrHgNYCswD7rKe3/KQ/1dE/o128g8E1lhO/lIRGQ+sBi4GHqwz11fAbGClFV32IfBPEakJ0J8K3BiqD2gweBGfrh91fTjBICZBK6z0vvq4LE/nshzaVnvO6Pnw0zJ45zp93HMUXPByra/A5YRDW2HpNdo8dOwv9Y6jJb6E6Djfyq4sXyu5KFv48mX8kZAFV3ymS+iU5sKEK3XrAUNQCGkUmYjEo30h/ZRSxZYsA1gC9Ab2AHOUUoXWezcB8wEncJ1S6n1LPobaMOX3gastRRILvACMRO9c5iqldlpj5gP/ay3lDqXUM/7WaqLIWp/8Ujvl1U5sUREk2qJIig1yQlxnoSQXvnwQDmyAYbN118qHx2p/Qg1TbtGmuohIHWL78DioKq5931fUV0twOSFvM7x+GeT/qEOXZz0e/pDqhnC7tCKs6z8zNErYw5TbA0bBtC45hyu4YNHX7C2sJELgmikD+c0JfUiJ66T/4NUVUF2mzU3NKbnitOsW3bEpWtks/5v3+4NOh3Of1gokd4PO8fEkoQv89ksdFRUMSg/AoxO8+8P0PwVmP9PycG1Dm6IthCkbDEcoszu4872t7C2sBMCt4P6PtnG43NHIyODgdivyS6s4UFzJ4fJWKEzZGCW58N6f4dkzdNOwsoMNn+uo1FV8PXcfYJmgUnVk1oAp9ccNObd2dxKfXj/qK2NA4yVV3G6dxe52Nv6ZqsvqNx/buUorQkOnwSgYQ6tTWe1m64GSevKcw6HvJVLtdPHd3sPMeuRLxt+5ksufX8v+osqQX7dByg/BS3Ph+xe1H+WbRfDmVTphsy5lebD8ZnhyCrx2uW6V7MsCkdwTzn5U+1RiEmHin2DAKbXv25K0z6VGycSlwVn3+28UVn5IV4peMg8++7d3hQNfRCfoApiedBsW/LIthjaNKXZpaHWS46I4dXBXdnxSm38QHSn0zwp9JdrDFQ7mPf0NZXb9LXztz4e58Y0fWDh3JClx0bU37Naqnu2ogNzvvWU7PtI7FU9Tkr1Mm73WWyHJxXvh4Ea4/OP6Zq24VBh2ns6SB202i/a42cemwNjL4bjz9U4oPkN3t2yI6nL4+J+w1mrVvWMF7PgYzn+h4Wix2FSY9YROYHRU6BDlWY+3br8YQ9gxCsbQ6tiiIrl8Yj8Ky6pZun4/3VNiufOcYaTGhd7JX1blPKJcavh6RwF2hxMq9sCaJ/Q377GXQlL34FfirUtEtPa5ODx2UXE+TFjV5br/jCcl+7TJypffJDLKvz8lNlk/UgKolG0v1bXPPNnzpf/qBDFxcPRUHVLtrNQ7GqNcOh1GwRjCQpdEG3+fMYS/TB8ECF0SY1ql506iLQpbVAR2Z20i3dCeKUQ7K+CR8bXNtb55Uhe2DOQG3BJiU3Ty5Lt/0scSYZmrMrzPkwid6OnZoVEi9I08mJQf0tFf+T9ps1qCpaRiEqDSw18lEY33WWkodNnQaTA+GEPYSLBFkZkUS2aSrdUauiXHRbHwgpEk2vR3q17pcdw7Zzhp6xd5d26sLoONrzcwSwupKND5J3lb9XWOmwvXroeL3oBrN+hoq7o7p8RMmPmwt1/jlL/WL8fSEsoL4M3fwXO/hPf+BA+O1jXO4tLh1L97nzt2gWmuZWgUs4MxdBzcbsANEQ3/WcfFRDF5UCYr/nQSdqeLuOgousRH6ez3uviZp9mU58NbV+me8aCjty55F9L66Ic/ug2Ha7+Hw3t0RnxcanAzzisP626XNSg3LLsJfv2mrgjd63jY9Rn0HKlL1IezhpihXWAUjKH943ZDWS5887QO8T3+Ckjr2+A3bFtUJF2T65h3xv8Wvn2u1q8QlwZDzq4/uKUc3FyrXAAKtsOax2HyTdpv4o9oG0R3176hUODLp2IvAdxamcWl6orSBkOAGAVjaP+U58Hjk7T/AOD7/8Cly3WdqUBJ7glXrdFRWlE2nRGfGKSkQ0/yttSXHdyk2/A2pmCag6NSd74MpKtmRJRWzId31cpGX6JDnUNJRaGOiNv6HvSdqHdKzQkIUKr1ov8MAWEUjKH9s2d1rXIBfaP59B449ymd8xEIkdGQ2gtO+p/QrLGGAafqm6Bn/srwC2orEQeL8kO6wdeWt6HPCTB0TuOtBsrz4NwnYcPL2kd0zJmQeSxEBFhdweXU/iWXXfuKEjIbv+E7KmHNIlj1T328+lEdYn3GPYFn/DsqoWS/nic6HsbOh8TuEGlybsKNUTCG9o+vUOKIGO+y9uGgLE9HZLld0HWoDhtO6qr7yiz7q85tOf63unaYJ2639tUc+lEryJReTftGX12uFWxNc7Kt7+jil7Of9p9MmTUYFv8KugyA7sN1O+CBUyEqgFBtl0MHBCy5WK89rY/+nJmD/I+rKoYv7veWbXxFBxUEqmCKc3RZmpraa2ufhCu/brt1zzoRRsEY2j/Zo3UIb9EefRwZDSffEPxdQVMoO6g7JdY0s0ruAZethOTuMOA06D5SO9Hj0+sryJIcWHRy7a6s9wTd/KzRZmcW9lJY+7S3bOfHWvH4UzCJWXDe83oH4rRD/FX+z/ekogAWX6gDBQAO74ZXLoF5S/VOxh91e68ohY/2Tb5xOXTtNc/CnlXFsOUdOH5BYHMYQoYJUza0fxK7wqUf6fyRk2/SvpQ0j5LrZXnw/X9h5W06NLg1uhVufc+7U2LJfvjuP2vXip8AACAASURBVPq1iFYWSV3rKxdHFXz2f94mvz1f6Z1QwEj9Mi0ijfthSvbDW1fCA8Nh8QXaFxNI3THQZqoa5VJD3mZtNvOHLRnGX+ktO3ZG074c+MrHMSVp2gSN7mBEJAGoVEq5ReRo4BjgfaVU61QmNBgCIakrjPlNfXlZHjw/s/YG/dn/wa+XQr9JoV1PSU59WdEebf7yd6N3VetGYYHM1xBxqTD5f+HDG2plo+ZBtB9nfVUxvPtn2LFSHxfsgBdm6W6eSd0av2Z0vDbjeSrG7sMbr4QQEw+/+D30Hg+b34R+k3WJm0B7x0RG6/Hr/1vbnTI+HQadEdh4Q0gJxET2KTDRat61AlgLnA/8KpQLMxiCQnGO97d/pWDlrdBtSeDmn+Zw3FxdFNLT/DPu8sZ3EbHJMGa+rvdVQ2QM9GmCQoyywYi5cNQE2L4Ceh+vnfVxfvJWHJXajOaJvUQrnkAUTHyG9rm8colWpFmDdWn+QHxH8Rlw9DTt72lOFFhKtt61fv9fvfMJVQSgockEomBEKVUhIpcCDyql/iUi34V6YQZDUPBl4nE7Q99zPbmHDpX++A59vZOur+1C2Rh9ToBznoCvHtHJjKfdGrj/pYa4NP3oMSKw8yNjdLXjvas9ZNHahOWPqlKdpFq8F1KPgvnLQTl1aHRT19zcEOMoG6QdBSebprVtjUB8MCIiE9A7lnctWUDBASKSKiKvishWEdkiIhNEJF1ElovINus5zeP8G0Vku4j8KCLTPOSjReQH672FYtUVERGbiLxsyVeLSB+PMfOsa2wTkXmBrNfQAUk7qn6G/KS/hL7wYkwCZI+BOc9qB32fEwMPmY5L06G6F72mne49RtT3qQSb+HSY+YjOBwJdQ2zmI/4rBTgqYctb8MAweOZ0/bz/W717aKpyMXRIGu1oKSInAX8CvlBK3S0i/dDtjK9pdHKR54DPlFJPikgMEI9uY1yolLpLRG4A0pRS14vIYOAlYBzQA/gIOFop5RKRNcC1wNfAe8BCpdT7InIlcJxS6rciMheYpZQ6X0TS0aa8MehwlHXAaKVUHS9kLaajZQem9IA2nxzapv00XQb5Nxd1VtxuqMjX0WZRcdqX469YZWkuLBzlXQEgoQv89ovAzGqGDoG/jpaN7kSUUp8An3gc7wQCUS7JwCTgEmtcNVAtIjOBydZpzwGrgOuBmcBipZQd2CUi24FxIrIbSFZKfWXN+zxwNvC+Nebv1lyvAg9Zu5tpwHKlVKE1ZjkwHa3ADJ2NpG4w8Y86H8VEFzVMRETTfBcuR/3yMuWHQm9+NLQbAokie5v6QenF6B3C40qpqgaG9gPygWdEZDh6F3Et0FUplQuglMoVkSzr/J7oHUoNOZbMYb2uK68Zs9eayykixUCGp9zHGM/PtgBYANC7d+8GPoahw2CUS3CJjtN+mwM/1Mr6nqR3PwYDgflgdgJlwCLrUQIcBI62jhsiChgFPKqUGgmUAzf4Od+Xh0/5kTd3TK1AqSeUUmOUUmMyM43N2GBoEgmZcMHLMOQcneg64iIdnBAfYIixocMTiLN+pFLKM0bybRH5VCk1SUQ2+RmXA+QopWrCUl5FK5iDItLd2r10B/I8zu/lMT4b2G/Js33IPcfkiEgUkAIUWvLJdcasCuCzGgyGppDSE2YshOoKHcQQEx/uFRnaEIHsYDJF5Ij9yHpdE4JT7XsIKKUOAHtFpKYY0RRgM7AUqInqmge8Zb1eCsy1IsP6AgOBNZY5rVRExlv+lYvrjKmZazawUumohQ+BqSKSZkWpTbVkBkPIqHK4OFhSxbaDpRwsqaLK4Qr3kloHW5JOdDXKxVCHQHYwfwI+F5EdaNNTX+BKK8P/uUbGXg28aEWQ7QR+g1ZqS6y8mj3AHACl1CYRWYJWQk7gKqVUzX/o74BngTi0c/99S/4U8IIVEFAIzLXmKhSR24BvrPNurXH4GwyhwOFy8fXOAq54YR12p5vY6AgWXTyGCf0yiIo0FZkMnZNGw5RB55ugS8QIsNWPY7/dYsKU2xllebB3jS4qOXAqJGTphlzBoLpcF4xEfBej9EFeSRVT7/+UooraCkoZCTG8f+1EspJDnMNiMISRFoUpW4wG+ljnHyciKKWeD9L6DIamUZYHz8+obd4VZYMFq3R5kpZSfghW3QnfPq87Yp52Oxx7VqPtgR0u5aVcAArKq3G6A6wKbDB0QBrdu4vIC8C9wInAWOvhU1sZDK1C3hbvzpBOO6y8veVVkt1u2PwWfPOkLjpZUairC5ceaHSoLTqCY7p5Z+oP6ZGMLcqYxwydl0B2MGOAwSoQW5rB0BrYS33IynQiZUuoLtMdIOuy+/NGG2d1SbTx5LwxXP/aBr79uYgxfdK465zjyEgMktnOYGiHBKJgNgLdgNwQr8VgCIzsMbpel2f/kROubXn5l+h43Q++blXh7sMDW1ZaPA9fOIpqpxtbVAQp8QG2GjYYOiiBKJguwGarHpi9RqiUmhGyVbUjKuxOyuxOoiMjSEswN5RWISETrvgMvnhA18Maf6VuSdxSIqN0P/cdK3T7XxEYe3ngVZCBVKNUDIYjBKJg/h7qRbRXDpXa+deHW1m2+SD9uiRy1znD6J+ZQKQJSw0tEZGQ2gum3aHNYsHMv0jsChcs1pFkEVHa0d+Ig99gMPgmoDDlzkBTw5TL7U7+8fYmlqytLZOWGh/NsusmmbBUQ/vH5YTqUohJDChM29B5aVaYsoh8rpQ6UURK8a7jJYBSSjXSiahjU2538uGmg16yogoHhyscRsE0QFFFNbnFVWzeX8LI3ql0SbSRHGduXm2O8nz49gXY/pHuYzP2MkjManxcOHE5oKJA7zxjEiAuHaKMuTLcNKhglFInWs8BdknqXERFRtCvSwLf7S06IosQSIoNNLWoc1Fmd/DkZ7t46OPtR2T3zD6OmSN6EmNCeWspPQg7Vuqb5eAZkNA1eAmkgVBZDO/+D2x+Qx///IXucjn7mdC2mG4Jbhfs+xZeOl8HfsSmwgUvQfY47VczhI1A8mAesDpaGjxIT4jhznOGkRyn/4BF4C/TjzEKpgHKqpw89skOL9nt726hqKLBcnadj7KD8PRUePO3sOwmeGgMFO32fW51OZTsh4ObdJ6O0+77vKbiqNBdKj3Zuap+35e2RPkheGVebVRhVZE+rjgU3nUZAnLyfwv8TUSOBt4AXlZKmZoqwICsRD76w0kUlFeTEhdNUmwUSbHG5OMLp1vVy2ovszvr91DozOz9Bg7vrj122uGTf8HMB3UIdQ2OCtj6Drx5Jbid2iR00es6xLq5fe1rENHzeeYaRcaAtOFdpqtaRxN6UpanzWaGsNLoX41S6jml1BnoVsY/AXeLyLaQr6wdEBUZQVZyLMd2T6ZHapxRLn6Ij45k9FHefUJmDu9BQoxpAnYER6UPWbmuMOBJZTG8fa1WLqB3M29cAeV59cc3ldhUmHKLt2zSX8DWhiPpomyQeYy3LGOAlhvCSlPsOQPQBS/7oCseGwwBk55o49GLRvHsF7v5ZnchU47pypwx2SQapVxLnxP1Db6q1q/HiX/UodKeOKvqK6PDu4PTqjg6FobNgb6TdC5Qj5GQ1EOv6dvn9E5m8Awdzt1WOoQmZmmfy2uXal9Mj5Fw7lNtPzChE9BomLKI3A2cA+wAlgCvK6WK/A5qh5hqyq1DtdNNZbWTBFuUKWNfF7dL+1W+fkQ7+cdfCen9IbZOnE3ZQXjyNCj6uVbW72SY8yzEpQZ/XcU58MgEsJfo47g0+N2XkNwj+NdqCeWH9K4uIgoSujR+viEotLSa8i5gglLKeMwMLSYmKoIYEz7qm5oE0qm3abNYQz+nhCy4+E1Yeg0c2AB9J8Ppd4dGuQCsfaZWuYB2pm9YAideF5rrNRejVNocjSoYpdRjVmfIcUCsh/zTkK7MYOisRET5946KQHo/OP8FHQgQHQ+xIUpLU6oB35APmcFQh0YVjIhcBlyL7mv/PTAe+Ao4JbRLM7QWbrciv8zOii0HUcCUY7qSmWQjMqKFEUmG0BKX1vg5LUUExl4K3yzS0VoAUbEw4sLQX9vQ7gnECH4tugfMz0qpk4GRQH4gk4vIbhH5QUS+F5G1lixdRJaLyDbrOc3j/BtFZLuI/Cgi0zzko615tovIQhEdiykiNhF52ZKvFpE+HmPmWdfYJiLzAllvZyWv1M70+z/lf9/YyE1vbGTa/Z+SV9LhmpYamktyT7jyKxgzH8ZdDld+DUndwr0qQzsgEAVTVdMiWURsSqmtgP/mGN6crJQa4eEEugFYoZQaCKywjhGRwcBcYAgwHXhERGrCVB4FFgADrcd0S34pcFgpNQC4D7jbmisduAU4Hh1efYunIjN488Z3+zjs0Y2xuNLBy2v3hnFFhjZFdKwO+z3jHph+t64ubUKADQEQiILJEZFU4E1guYi8BexvwTVnAs9Zr58DzvaQL1ZK2ZVSu4DtwDgR6Q4kK6W+spqePV9nTM1crwJTrN3NNGC5UqpQKXUYWE6tUjLUocxePyGtrMoZhpUY2jQRUW0nNNnQLggk0XKWUqpIKfV34G/AU9Te4BsdDiwTkXUissCSdVVK5Vpz5wI1weo9Ac+vzTmWrKf1uq7ca4xSygkUAxl+5vJCRBaIyFoRWZufH5DVr0Ny/tjexHiEDEdHCr8af1QYV2QwGDoC/qopxwMOpZTDOh4EjEL7YgItIHWCUmq/iGShdz9b/Zzry6Os/MibO6ZWoNQTwBOg82D8rK1D0zXJxgfXTeTxT3bgVnDFSf3pntJ2K0IfKrWzbPMBNu4r4dzRPRmQlUSKqcpsMLQ5/EWRfYD2cWwTkQHoyLEXgbNEZJxS6obGJldK7bee80TkDbQ/5KCIdFdK5Vrmr5r6FjlAL4/h2WhTXI71uq7cc0yOiEQBKUChJZ9cZ8yqxtbbWbFFR9IvM5HbZw0DILoNJ0AWlNmZ/9w3bMgpBuC/a/Zw75zhzBrZ80jUW7ndSWmVk0qHTujskmAjwkTEGQytjr87SZpSqqbm2DzgJaXU1cDpwJmNTSwiCSKSVPMamApsBJZa89XMW1O6dSkw14oM64t25q+xzGilIjLe8q9cXGdMzVyzgZWWn+ZDYKqVv5NmXfvDxtbc3nG7FfmldvJKqiipanqhv+jIiDatXACKKh1HlEsNC1dso7BcVxMuszt56/t9TPzXSk6+9xPOXPg5uw6Vh2OpBkOnx98OxtNkdApwD4BSqlpEAil61BV4w4oojgL+q5T6QES+AZaIyKXAHmCONe8mEVmCrnPmBK5SSrmsuX4HPAvEAe9bD9D+oBdEZDt65zLXmqtQRG4DvrHOu1UpVRjAmtstldVOvt1TxF9e3UBucSXTBnfj1rOHkpnU8aN9rKB1AMqqHPztrU24rMrN+aV2rn9tA4vmjSEt3lQQMBhaE38KZoOI3AvsQxe6XAZgRZQ1ilJqJzDch7wAmNLAmDuAO3zI1wJDfcirsBSUj/eeBp4OZK0dgeJKB5c8swaHS99Y3990gLSEGP4yfRCpAd5YiyqqySuxs/NQOUN7JpMWH0OCrW31t0mNi2Zk71S+21NbDu+6UweSnqA/Y3Gl84hyqeGng6U4nEEoBGkwGJqEv7vH5egkyz7AVKVUTcehwcC9IV6XoYnsKaw4olxq+HRbPvNP7BuQgimpcvDIqh088elOQHfnfGreWE46OrNN+S8yEm0sungMH2/NY+P+EmaN6EHfzIQj/pe0+GiSbFGU2mvDrCcPyiTeZsJrDYbWpkGDu1KqUil1l1LqWqXUeg/5l0qpF1pneYZA6ZEaV082uHsyW3JLcNbtJ+KDcruTRZ/tPHLsVvC3tzZSUN72Ok52SbQxZ0wv/jFjCCN6p5ESV6tA0xKi+e/l4xnUNYnoSGH6kG789czBJNpMlJnB0Nq0LfuHodmkxEXzv2ccwz0f/ojDpejbJYHfntSfDTlFRAbQ5bDa6aZu54b8Ujs+orvbNNGRkQzLTuHFy4/HrRRx0ZGmEZzBECaMgukgJMVGM2tET47vm4Hd6eZwRTX3f/QT984ZjgSgYBJsUfTPTGBHfm3E1ezR2STEtM8/kS6JHT+4wWBo6zTYcExEhnuaxuq89zul1KMhXVkr01Eajh0qs5NbVInTrchOi/eKIssvtfPeD7lsPVDC+WN60S8zkWSPBMXc4koWrtjGxn0lTB/alblje5NhbtQGg8EP/hqO+VMwO4E5Sql1deT/AH6plBoV9JWGkY6iYBriUJmdCxd9zU8Hy47IHr5wJGcM6+61w6msdlHpcJIcG91mO04qpcgrtfPZtkO4leKkozPpkmjaCxgM4aC5HS3nAK+IyK+UUl9ZSY6PAkfjnSVvaAfkldi9lAvAAyu2cXy/DC9zUlxMJHExbTviKq/EzpkPfsahMh2AkBofzfvXTqR7Sv1AB4PBED78RZGtQxe1/I+ITEdXK84EpiulShoaZ2ib+HLDRATgm2mLvLV+3xHlAlBU4WDxmj1hXJHBYPBFgwrG6qmSgy7F8h/AAVwBJFrvGdoRmUk2hvTwbqv7x9OOJiOh/WW3l/poJVBc6aQhc29DHC6v5nAbDMNuLpXVTg4UV7F+bxG5RZWU203LBUN48WciW0dtjGopunnXGnRNDgX0C+3SDMFAKYWI0CXRxrO/GcfHW/PYeqCEc0Zl0zs9PqAIs7bG7NHZPP7JTqpdOr8nKkL49YSjAv4sZVUO1ucU8+/lP6GU4rpTj2Zk79R2Hc7sdLlZs6uQy59fR7XLTWSE8O/zhjN9SDds0W3b5GnouDTo5O9sdDQnf0GZdoJ/vv0Q04d0Y9RRaUfKqbR3qhwucg5X8tiq7TgVXHlSf3qlxxEXYEj11twSpj/wmZfs3WtOZEiPlFAst1XIK6nizIWfk19mPyJLiIlk5Z8n0zW57bZeMLR/muvk9zfhMVbrZEMbpKiimluWbuKdDbkAvLouhysm9eO6UwcGfBNuy8RGRzIgK5E7Zg0DAVtU076h+2oH/fI3e7l1ZvtVMG6Fl3IBKK92UW1qsBnCSHPjUJcFdRWGoFJR7TqiXGp49svdlHSwNsi26MgmKxeAvhkJ9WVd6svaE7aoCI7v6+0aHdQ1qc1HBBo6Nv46Wi5s6C0goIrKhtbB7nBRXKn7vyTHRSPoqDFP66eI7zaf7ZFyu5MIkWbfPKcP68azX+5mp9Unpk9GPGcO697keaocLsrtuqlZbJj9HGkJMSy8YCS3v7uZr3cUMrxXCv+YOdRUNDCEFX/2kt8AfwLsPt67IDTLMTSVwxXVLF6zh4c/3oFbKRZM6sevju/NOSN78tq3+46ct2BSf5LaeVvh0ioHPx4o5bFPdpBoi+KaKQPJTosjpom7mKykWJZcMYGfC8tRCo7KSGhy35y80ioe/XgHX+0sYFzfdH5/8gCywuzr6Jocy53nDKPC7iIuxtRgM4Qff5n8K4G/KqW+9PHeLqVU31AvrjVpr07+1TsLOP+Jr71kz88fy9AeKXy7p4gvdhxi6uCuHNMtmbR27uT/bs9hZj1S++cYGx3Byj9N9llJOpQUVVRz9Uvf8dm2Q0dk4/qm89hFoztMIIXBECj+nPz+fDCzge99vdEU5SIikSLynYi8Yx2ni8hyEdlmPad5nHujiGwXkR9FZJqHfLSI/GC9t9CqKoDVXvllS75aRPp4jJlnXWObiMyjg/LBxgP1ZG+vzyUtIYZTB3flll8OYUL/LgErl2qnm7zSKvJKqqhoQ3kUVQ4XT3q0E9AyNx9tOdjqa6l0uLyUC8CaXYVUOVwNjDAYOif+MvkLPZqMtYRrgS0exzcAK5RSA4EV1jEiMhjd8ngIMB14RERqbB+PAguAgdZjuiW/FDislBoA3Afcbc2VDtyCzt0ZB9ziqcg6EuP61s95Hd8vo1n5LcUV1bz8zR5O/fcnTPzXx9yz7EcK20giYqSITyUZaLfOYBIhQlKdTp/xMZGmFprBUAd/mfwpInKXiGwVkQLrscWSBeTkF5Fs4EzgSQ/xTOA56/Vz6HI0NfLFSim7UmoXsB0YJyLdgWSl1FdK2/OerzOmZq5XgSnW7mYasNxSkoeB5dQqpQ7FuL7pXg7qKcdkMXlQZrPm2ldUyd/e2kRJpRO7080zX+zm4615wVpqi4iOiuCKSf1Jjq29sR+VEc+Efq1fVCI1PpqbfznYS3bTGceSEtf+Q8ANhmDi7z9iCbASmKyUOgAgIt3QpWNeAU4LYP77gb8ASR6yrkqpXAClVK6IZFnynoCnMyHHkjms13XlNWP2WnM5RaQYyPCU+xhzBBFZgN4Z0bt37wA+TtsjI9HGHbOGctOZx6LQyXXN/VZf1+wD8P7GXM4Y1o24mCgKyuz8sK+YH/YVc+qxXemRGkdKKwYO9EiJZfkfT+LL7YdIjI1mRK8UMpNa37Fui4pk+tBuHN8vg58OljIwK5H0hBhio42CMRg88fcf0UcpdbenwFI0d4vI/MYmFpGzgDyl1DoRmRzAWnzZF5QfeXPH1AqUegJ4ArSTP4A1tklS42NIjW/5PCN61d+YjuubTkxUJIfLq/nfN37gw03a5/F/y37ivvOHM+O4HkS2Uln/yMgIuibHMmtUdqtczx9JsdEkxUbTOz0IP3iDoYPi787ws4j8RUS61ghEpKuIXI/37qAhTgBmiMhuYDFwioj8Bzhomb2wnmtsMDlAL4/x2cB+S57tQ+41RkSigBSg0M9cBj8M7JrEBeN6H6m8PL5fOueMyiYyQiivdh5RLjX864MfKaxwhGGlBoOhPeBPwZyPNjd9IiKFIlIIrALSgfMam1gpdaNSKlsp1QftvF+plLoIWIo2s2E9v2W9XgrMtSLD+qKd+Wssc1qpiIy3/CsX1xlTM9ds6xoK+BCYKiJplnN/qiUz+CE9IYYbTz+GL68/hc+vP5lHfjX6SKKey11/g6ejptrtxi/oVDtd5JVUsetQOQdLqnC4TFSZoXPToInMco5fbz2CyV3AEhG5FNiDbmyGUmqTiCwBNgNO4CqlVM1/6O+AZ4E44H3rAfAU8IKIbEfvXOZacxWKyG3AN9Z5tyqlCoP8ORrF6XJzsNTOK2v34nYrzhvbi67JNqIj2275juS4aK82yjUk2qI4LjuFDTnFR2SXTexHapzJ+wD9u/52TxGXP7eWUruT5Lgonp43lpG900x0maHT0qRqyiLyvFLq4hCuJ2yEItFyf1ElU+/7lDIrnyQuOpLlf5hEdju12+eX2nn92xy+31vE2SN7Mq5PertP3gwWeSVVnP7AZxR4hHV3S47l7atPbHKVAIOhPdGsasoisrSuCDi5JkRZKTUjeEvsmLy6LueIcgGdoPfi6p+5/vRjw7iq5pOZZOPyif1wuN3NKjLZVjhcXo3D7SY5NjpoNcSqXW4v5QJwoKQKh8tUMzZ0XvxFkWWjzVVPUhuZNQb4v1ZYVweh/u6wvXssIiIEW0T7VC4Ol5sdeWX89c2N7D1cwVnDunPlyQPICEJBSFtUJH27JLDLKqAJupqxLap1IuwMhraIv7/+MeiuljcBxUqpVUClUuoTpdQnrbG49s65o3qR4FHxNzY6gl8df1QYV9SxsDtdHCypIre4kqKKxisOHC6vZvZjX7H258McLLHz1Be7eeLTndiDUOIlM8nG05eM5bhs3VNmZK9Unrh4dFCUl8HQXvHn5HcD94nIK9bzQX/nG+qTlWxj2R9O4r9rfsat4MJxvemabG44waC0ysGHmw7wj6WbKbU7OXFABv8+fwRZfhIv9xVVepksAd5ev5/LJvYlMwimsr5dEnj2knE43W6iIoX0BPO7NnRuGlUYSqkcYI6InAmUhH5JHYfoyAh6psXxP9OOafYceSVVvPtDLofK7MwenU23lFjiTMY4RRUO/vzKhiPHn28v4JGPd3DD6cc06FfJ8BGQcFSXBKKDmCiantjJgh7K86FwN1Qcgu4jICETIs3fp0ET8F+CUupd4N0QrsVQh7zSKmY89AUHSqoAePyTnbx99Ykc2z25SfO43Ir8Ujtvfb+PSoeL2aOyyUq2NbmPSlvip4Ol9WRf7Sig3O5sUMGkxEVzxaR+PP7pziPHt84YEpaCmR2C8nxYMg9+/kIf25Lg8lXQZUBYl2VoO5ivGm2Yb38+fES5ADjdigdWbOPf5w0nPibwX92hUjvTH/iUIivr/tFVO1j2h0kc5aN1cHthYFZiPdm4vukk2Br+uaTEx3Dlyf25aPxRFFU46Jps87mrMQRI4a5a5QJgL4WVt8HMh8FW//dj6HyYEJc2jI/kedxuRRNSlwB4f+OBI8oFwO508+Tnu3C622cIbXGlg+jICO44eyjxVhDFuL7pXH3KgEbDjlPiYuiVHs+w7BSykmNbrY5ah6TMRy+esgPgahstHgzhx+xg2jBjjkqjS2IMh8r0P2yEwNWnDPD7Ld0XLlVfkbjdql3GTBeU2bn9vS28+d0+5ozuxeIF40mLjyHBFmmc6q1Nz9EQkwDVtaHZjL0c4lu/hYKhbWK+vrVhMpNsvHP1RP542kB+Pb43H1w3iX6ZTTc9nDGsu1eDrOhI4dIT+xLVDr+9f7O7kDe+3YdSsGTtXmY89AUbcoqNcgkHCZna53LMWZA9Fs5ZBP2nhHtVhjaE2cG0YUSEbimxXDPlaJRSzepSCZCZaOOD6ybx4uqfqXS4mDehD91SWr+PSjD4Ynv9njWfb8/nzOO6+zjbG4fLTX6pnbfX7yc6MoIzhnUnK8lGhKkV1jwioyHzaJj1uDaLxaVBM/9GDR0To2DaCc1VLgBRVrj0X6Yf0yJF1RY4bXA3Xvh6j5ds6uBuAY09WFLFtPs+pbxaJ1Y+uHIb7187qd0q2zaDcegbGqD92UgMLaI9KxeAoT2T+d3k/tiiIrBFRfDbk/ox3EejtLq43Ypnv9h9RLkAHK5w8O4P4WkTVO10kVtcyRvf5vDF9kMcKrOHZR0GQygxOxhD79c5PwAAFWJJREFUPRwuF4fLHRRWVJMSpzs3JjYxsCBUpCfYuOaUAVzyiz4AJMdGERdgyLbdWT/YodrZcKRDQZmdbXllbMktYeLATLom20iKDU6L6N0FFcx46HOqHHpNw3ul8NS8sUf67xgMHYG2cdcwtCm25JZy4aLVlNmdREYIt80cwtkjehLfRpRMXEzgSqWGiAhh/gl9WfzNHhwurVTioiOZMaKHz/MPl1dzy1ubeOeH3COyRy8axdTB3Vrc36WsysE9H249olwA1u8tZtehcqNgDB0KYyIzeHGozM6flqw/UrPL5VbcsnQTJVXORka2fbqnxrLsupP49fijmH9CH5b9YRJZSb4TLcurnV7KBeCOd7dQWN7yHA+nW3G4vH6r6eJK037a0LEImYIRkVgRWSMi60Vkk4j8w5Kni8hyEdlmPad5jLlRRLaLyI8iMs1DPlpEfrDeW2i1TsZqr/yyJV8tIn08xsyzrrFNROZhCAi3Ul4l5wEcLkVlCysO55fa2VOgWwlXVodHWcVGR9I3M4G/zxjMX88cTK/0+Aa7izpd9U1n5XYnTWnQ1xCp8TFcNrGvlyw5NophPVNaPLfB0JYI5Q7GDpyilBoOjACmi8h44AZghVJqILDCOkZEBqNbHg8BpgOPiEjNf/+jwAJgoPWYbskvBQ4rpQYA9wF3W3OlA7cAxwPjgFs8FZmhYWKjI5lybJaXLCvJRoKt+XXLfi4o59xHv2TSPauYfM8qlm0+SLk9fDuiyIiIRkOTE2OjOKZbkpds3i/6BK1u2YT+GTw1bwwTB3Zh9qhs3rlmojGPGTocITOqK/1Vr8w6jLYeCpgJTLbkzwGrgOst+WKllB3YJSLbgXEishtIVkp9BbptM3A28L415u/WXK8CD1m7m2nAcqVUoTVmOVopvRSaT9txSI6N5raZQ4mNjuTjH/M4plsyd54zjC7NTGQsqqjmhtd/YE9hBaC7ev5pyXo+v/7kJlckaE26JNp4fv44/rP6Z37IKWHmiB6cdHQmMUFqIJYSF8OUY7syrm860ZERQeusaTC0JUL6H27tQNYBA4CHlVKrRaSrUioXQCmVKyI1X5d7Al97DM+xZA7rdV15zZi91lxOESkGMjzlPsZ4rm8BemdE7969W/BJOxZZybHcMWsoFXYX0ZERpLWgIGS1082mfcVeMqdbUVThoFtKXEuX2iIcLhf5pdW8vX4/tqhITh/WjczE2sTLrORYrpkyELvDHTJlGKyoNIOhLRJSBaOUcgEjRCQVeENEhvo53ZfNQvmRN3fM/7d379F1VmUex79PcnJPm6ShDb3QVioCbUWgkUtBQEDaQaWi1SlrZqhOtS5Wx8vSmRHwDxCWLvCGSx1YVlAKKlhxGFkdFDsUrBcoDVCgLZeWgvReQhKaXnLtM3+8O+1JehIgyZvzJuf3WSsrJ/uc982TvXr6nHfv/e4nPb6lwFKA2traYbgzV3zKiwooLxr4f34lhfnMnnYMf9iw60hbQf6AktZg2fVmK3N+sJoDaTdePvilD1Az+siNl6m8PFJFWgsj0h9D8s5x9yaiobC5wG4zGw8Qvu8JL9sGHJd22CRgR2iflKG92zFmlgIqgIY+ziVDbFRxAddfNoPZ06oBmFRVwrJ/fT+VJdn95N556BB3/OWVw8kF4I39bTy0flcfR4nIOxHbFYyZjQXa3b3JzEqAi4km4R8AFgI3he+/C4c8APzKzL4PTCCazH/C3TvNrDksEFgDXAn8KO2YhcBjwHxglbu7mT0EfCttYv8S4Jq4/lbp27EVxdz6T6fT2nGIPIPqsqHf/6uj8xAH2jopK8wnPz8Pd2jtOHplXKabMUWkf+IcIhsPLAvzMHnAcndfYWaPAcvNbBHwGvBJAHffYGbLgY1AB7AkDLEBXAXcCZQQTe7/PrTfAdwdFgQ0EK1Cw90bzOxGYG143Q1dE/6SHdmsGlm/r5V71rzG37a8wQXvGcv8WZOoLi/is+cez2/qttERCu+UFua/rU0zReTtscFY1z8S1NbWel1dXbbDkEHWdKCNL977NKtfOrIL80dPGc83L38vBfnGjqYW7vjrFkpSKT59TrTLdMEwLGMgki1m9qS712Z6LrnrREUGwYG2zm7JBeB/n9vJ1z88nWMripk2rpwb5s3EiO6PEZHBo3eUjGh5ZhT2uCIpKcjvVrYklZen5CISA72rZEQbVZxiyQendWv79zknUpHlVWwiuUBDZEJ7WGFVHlZYjSRlRSkWzp7Kh6bX8My2Nzl9chXHVhTpznmRIaAEk+Neb25h2WN/p+7VBi4+uYbLT5tI9QjbE6uytJDK0kKmT9BmkiJDSQkmhzXsb+OqXzxF3d8bAXh8SwMv7mrmuo9Op1xbmIjIAI2s8RB5Rw60dRxOLl3uf3p7t7LCIiL9pSuYYailvZO9oTjVqOICSgr7N58QrZ4yOg8duReqrCiVcSM3EZF3Slcww0zj/jZ+unoL53/nUc77ziP8aNWmfldZLC/KZ3GPwldfv/TkrN51LyIjh65ghpkXdjXzvZUvHf751kdfpnZKFReeXPOOz1VeXMDi86fxkfdNYP32vcyaUsW4UUWDVvNERHKbEsww838bdx/V9uD6XXzwpHGYvfPBrarSQqpKC5mhFVYDUt/cysH2TgpTeYwuSVFSMHhvrTcPttHafoiSwnzVj5FhRQlmmDnz+DHc8ddXurWdM626X8lFBseOpoN85udreXF3MyUF+Xxj3gwunTme8uKBv722Nhzg6//zHOu2NnHWu6q5Yd6MrBdqE3m7NBYyzMyaUsW8Uycc/nnuzBrOe8/YLEaU25pb2rlxxUZe3N0MRCWhr/7ts+xtaR/wueubW/n0z9ey+qV69h7s4I8bd/OFe56m8UD/5txEhpquYIaZ6vIibrhsBl+bexIQbTGvSfnsaWnvZN3Wpm5thxx2Nh1kQuXArjQOtnfy8uv7urWtfbWR1nbVrJHhQVcww8y+1g4Ohv9glFyyr6woxex3H9OtLZVnTKwa+DBWYSqP8qLunwFrRheRP8TF2kT6SwlmGGnc38atj2zm3JtXMfumVXxl+TPU72vNdlg5rbQwxdfmnMh574mSzNhRRdy+sJaK0oFPxleWFnDzJ6K6NQBFqTxu+dSpjCnThwoZHlRwLBgOBcee2drEvP/6a7e2ay89iUXnHq9PtVnWdKCNlvZD5OVBdWnhoG0aeqC1g70tHTQeaGNMWSEVJQXaqFMSpa+CY7FdwZjZcWb2iJk9b2YbzOxLoX2Mma00s03he1XaMdeY2WYze9HM5qS1zzKz58JzP7SwZMrMiszs16F9jZlNTTtmYfgdm8xsYVx/51B6sse2LgB/2/wGB9u1tUu2VZYWcmxFMeNGFQ/qjtSlRSmOrSjm5PGjqRldrOQiw0qcQ2QdwFfd/WTgLGCJmU0HrgYedvcTgIfDz4TnFgAzgLnArWbW9W66DVgMnBC+5ob2RUCju78buAW4OZxrDHAdcCZwBnBdeiIbrs48fsxRbReeNI5S/acjIgkUW4Jx953u/lR43Aw8D0wE5gHLwsuWAR8Lj+cB97p7q7u/AmwGzjCz8cBod3/Mo/G8u3oc03Wu+4CLwtXNHGCluze4eyOwkiNJadiaWFnCtZeeTGlhPvl5xsdPm8iHTxlPnobHRCSBhmSZchi6Og1YA9S4+06IkpCZjQsvmwg8nnbYttDWHh73bO86Zms4V4eZvQlUp7dnOCY9rsVEV0ZMnjy533/fUKksLeTKs6dE98E4lBbpzm4RSa7YE4yZlQO/Bb7s7nv7uOM80xPeR3t/jznS4L4UWArRJH9vgSVJcUH+iBuHr9/XyvbGg5jBhMoSjhlhBc9EclWsCcbMCoiSyy/d/b9D824zGx+uXsYDe0L7NuC4tMMnATtC+6QM7enHbDOzFFABNIT2C3oc8+gg/VkyiF5vbmHB0sd5+fX9AJwwrpxffe5Mxo4qznJkIjJQca4iM+AO4Hl3/37aUw8AXau6FgK/S2tfEFaGvYtoMv+JMJzWbGZnhXNe2eOYrnPNB1aFeZqHgEvMrCpM7l8S2iRhVjy783ByAdi0Zx8rM2zoKSLDT5xXMOcA/wI8Z2brQtu1wE3AcjNbBLwGfBLA3TeY2XJgI9EKtCXu3rX+9irgTqAE+H34giiB3W1mm4muXBaEczWY2Y3A2vC6G9y9Ia4/VPpv0+59R7X13B5FRIYn3WgZDIcbLUeiZ7c1cdmPu988+uAXP8D0CaOzFJGIvBNZudFSBKJaJnv2thwu8dzT1Ooybr+ylpkTR3PKpAru/Mz7OW6MtqMXGQm0m7LEZnvjQa65/1mefLWR2qlVfOvyU47aBHJ0SQEXT6/htCmVmBtjyrXPlshIoSsYicUb+1r53F11rH6pnv1tnfzppXoW313HG71szlldVqTkIjLCKMFILFo6DrFx595ubRt27KWlQ7VMRHKFEozEIpVnVPXYsn5MWSEF2tZGJGcowUgsxpQVcMs/nkpRKvonVpTK4/ufeh9VqmUikjM0yS+xKMjP56zjq1n9Hx+k6WA7laUFVJQUUDCIW9mLSLIpwUhsigvyKa7Ip6ZC276I5CJ9nBQRkVgowYiISCyUYEREJBZKMCIiEgslGBERiYUSjAya1vZO2nWnvogEWqYsA7a/tYNX6vfz0z9vYVRxis+fN43xFcWkdM+LSE5TgpEB21K/n8t+/Be6Sgvd/9R2Hv7qBRyr+19EclqcJZN/ZmZ7zGx9WtsYM1tpZpvC96q0564xs81m9qKZzUlrn2Vmz4XnfhjKJhNKK/86tK8xs6lpxywMv2OTmXWVVJYYtLR38pM/vUx63br9bZ2sekFlj0VyXZxjGHcCc3u0XQ087O4nAA+HnzGz6UTljmeEY241s/xwzG3AYuCE8NV1zkVAo7u/G7gFuDmcawxwHXAmcAZwXXoik8GVZ0ZZ4dEXwmVFujgWyXWxJRh3Xw009GieBywLj5cBH0trv9fdW939FWAzcIaZjQdGu/tjHtV2vqvHMV3nug+4KFzdzAFWunuDuzcCKzk60ckgKUzlcdUF0ygpyD/cNrGyhLOnVWcxKhFJgqH+mFnj7jsB3H2nmY0L7ROBx9Nety20tYfHPdu7jtkaztVhZm8C1entGY7pxswWE10dMXny5P7/VTluYlUxD3/1fFZu3M2o4hTnnnAM40Zp/kUk1yVlHCNTkRDvo72/x3RvdF8KLAWora3N+Bp5awX5+UyoLGHh7KnZDkVEEmSo15HuDsNehO97Qvs24Li0100CdoT2SRnaux1jZimggmhIrrdziYjIEBrqBPMA0LWqayHwu7T2BWFl2LuIJvOfCMNpzWZ2VphfubLHMV3nmg+sCvM0DwGXmFlVmNy/JLSJiMgQim2IzMzuAS4AjjGzbUQru24ClpvZIuA14JMA7r7BzJYDG4EOYIm7d4ZTXUW0Iq0E+H34ArgDuNvMNhNduSwI52owsxuBteF1N7h7z8UGIiISM3PX1ANEczB1dXXZDkNEZFgxsyfdvTbTc9rLQ0REYqEEIyIisdAQWWBmrwP7gfpsx9KHY1B8A6H4BibJ8SU5NhjZ8U1x97GZnlCCSWNmdb2NJSaB4hsYxTcwSY4vybFB7sanITIREYmFEoyIiMRCCaa7pdkO4C0ovoFRfAOT5PiSHBvkaHyagxERkVjoCkZERGKhBCMiIrHI2QRjZsVm9oSZPWNmG8zsG6G917LOCYnvejPbbmbrwtel2YgvxJJvZk+b2YrwcyL6ro/4ktR3r4ZS4OvMrC60Jab/eokvSf1XaWb3mdkLZva8mZ2dsP7LFF/W+8/MTkz7/evMbK+ZfTmuvsvZOZiwO3OZu+8zswLgL8CXgI8DDe5+k5ldDVS5+9cSFN9cYJ+7f3eoY+rJzL4C1BJVHf2ImX2bBPRdH/FdT3L67lWg1t3r09oS03+9xHc9yem/ZcCf3f12MysESoFrSU7/ZYrvyySk/yD6AAZsJyovv4QY+i5nr2A8si/8WBC+nN7LOg+pPuJLBDObBHwYuD2tORF9B73Gl3SJ6b8kM7PRwHlEO6rj7m3u3kRC+q+P+JLmIuBld/87MfVdziYYODyEso6o8NlKd19Dj7LOwLi+zpGF+AD+zcyeNbOfZXEY4AfAfwKH0toS03dkjg+S0XcQfVj4o5k9aVHpbkhW/2WKD5LRf8cDrwM/D0Ogt5tZGcnpv97ig2T0X5cFwD3hcSx9l9MJxt073f1UoqqXZ5jZzGzHlK6X+G4DpgGnAjuB7w11XGb2EWCPuz851L/77egjvqz3XZpz3P104B+AJWZ2XhZjySRTfEnpvxRwOnCbu59GtIfg1VmKJZPe4ktK/xGG7S4DfhPn78npBNMlXL4+SjS/0VtZ56xJj8/dd4fEcwj4KXBGFkI6B7gsjNPfC1xoZr8gOX2XMb6E9B0A7r4jfN8D3B9iSUr/ZYwvQf23DdiWdkV/H9F/6Enpv4zxJaj/IPrg8JS77w4/x9J3OZtgzGysmVWGxyXAxcAL9F7WORHxdf0jCC4H1g91bO5+jbtPcvepRJfZq9z9n0lI3/UWXxL6DsDMysxsVNdjorLe60lI//UWX1L6z913AVvN7MTQdBFRNdxE9F9v8SWl/4IrODI8BjH1XWwlk4eB8cCysJIiD1ju7ivM7DEylHVOUHx3m9mpRGPkrwKfz1J8mWQsiZ0g305I39UA90cLBUkBv3L3P5jZWpLRf73Fl6R/e18AfhmGerYAnyG8TxLQf73F98Mk9J+ZlQIf6vH7Y3nv5uwyZRERiVfODpGJiEi8lGBERCQWSjAiIhILJRgREYmFEoyIiMRCCUYkAczscjNzMzsp27GIDBYlGJFkuIJox+wF2Q5EZLAowYhkmZmVE21vs4iQYMwsz8xutagW0Aoze9DM5ofnZpnZn8JGlA/1uENcJDGUYESy72PAH9z9JaDBzE4nqks0FXgv8FngbIBQG+hHwHx3nwX8DPhmNoIWeSu5vFWMSFJcQVReAKLNOa8gqv/zm7Ax4i4zeyQ8fyIwE1gZtnLJJ9qZVyRxlGBEssjMqoELgZlm5kQJw4l2MM54CLDB3c8eohBF+k1DZCLZNR+4y92nuPtUdz8OeAWoBz4R5mJqgAvC618ExprZ4SEzM5uRjcBF3ooSjEh2XcHRVyu/BSYQ1RVZD/wEWAO86e5tREnpZjN7BlgHzB66cEXePu2mLJJQZlbu7vvCMNoTRFUmd2U7LpG3S3MwIsm1IhSdKwRuVHKR4UZXMCIiEgvNwYiISCyUYEREJBZKMCIiEgslGBERiYUSjIiIxOL/ARDkrckWEiONAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = X_test['Age'], y = X_test['401K Savings'], hue = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac2d8d00>"
      ]
     },
     "execution_count": 42,
     "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.scatterplot(x = x_test_scaled['Age'], y = x_test_scaled['401K Savings'], hue = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svc_model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac30cf40>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.98      0.92        43\n",
      "           1       0.98      0.89      0.94        57\n",
      "\n",
      "    accuracy                           0.93       100\n",
      "   macro avg       0.93      0.94      0.93       100\n",
      "weighted avg       0.94      0.93      0.93       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPROVING THE MODEL - PART 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is part of the Generalization intuition\n",
    "## C-parameter and Gamma parameter are used for building generalized / overfitted model\n",
    "## For more information, you can search about these parameters and how to use a grid to find the best value for higher model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.912, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=0.1, gamma=1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.537, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=0.1, gamma=0.1, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=0.1, gamma=0.01, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=0.1, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ............ C=1, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] .......... C=1, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.537, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ......... C=1, gamma=0.01, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ........ C=1, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........... C=10, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ......... C=10, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ........ C=10, gamma=0.01, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.512, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.537, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] ....... C=10, gamma=0.001, kernel=rbf, score=0.525, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=100, gamma=1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.925, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=100, gamma=0.1, kernel=rbf, score=0.950, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] ....... C=100, gamma=0.01, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.938, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.975, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.963, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.900, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=100, gamma=0.001, kernel=rbf, score=0.950, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    0.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=4)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'gamma': 1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predict = grid.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, grid_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x235ac3b02e0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.98      0.92        43\n",
      "           1       0.98      0.89      0.94        57\n",
      "\n",
      "    accuracy                           0.93       100\n",
      "   macro avg       0.93      0.94      0.93       100\n",
      "weighted avg       0.94      0.93      0.93       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GREAT JOB! NOW YOU ARE A SUPPORT VECTOR MACHINES PRO! KEEP UP THE GOOD WORK!"
   ]
  }
 ],
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