D1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "060fd927",
   "metadata": {
    "id": "060fd927"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd87eeb",
   "metadata": {},
   "source": [
    "## Loading and insights on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "AohuaqoC6gay",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "AohuaqoC6gay",
    "outputId": "00227b9e-a554-43f3-97c7-faefe3177394"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA   \n",
       "0           1        337          118                  4  4.5   4.5  9.65  \\\n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm=pd.read_csv('D1.csv')\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "GJPMaMBW7DLI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GJPMaMBW7DLI",
    "outputId": "7bf1f351-95d1-4b6e-b378-0cc1363bd0ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75fd5c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.           0\n",
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       "SOP                  0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c872b2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No.         400 non-null    int64  \n",
      " 1   GRE Score          400 non-null    int64  \n",
      " 2   TOEFL Score        400 non-null    int64  \n",
      " 3   University Rating  400 non-null    int64  \n",
      " 4   SOP                400 non-null    float64\n",
      " 5   LOR                400 non-null    float64\n",
      " 6   CGPA               400 non-null    float64\n",
      " 7   Research           400 non-null    int64  \n",
      " 8   Chance of Admit    400 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 28.3 KB\n"
     ]
    }
   ],
   "source": [
    "dfm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24800485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.             int64\n",
       "GRE Score              int64\n",
       "TOEFL Score            int64\n",
       "University Rating      int64\n",
       "SOP                  float64\n",
       "LOR                  float64\n",
       "CGPA                 float64\n",
       "Research               int64\n",
       "Chance of Admit      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba5df3b",
   "metadata": {},
   "source": [
    "## Understanding on the features and basic renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e109425b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Admission Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA   \n",
       "0           1        337          118                  4  4.5   4.5  9.65  \\\n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Admission Decision  \n",
       "0         1                   1  \n",
       "1         1                   1  \n",
       "2         1                   0  \n",
       "3         1                   1  \n",
       "4         0                   0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.rename(columns={'Chance of Admit ': 'Admission Decision'}, inplace=True)\n",
    "dfm['Admission Decision'] = dfm['Admission Decision'].apply(lambda x: 1 if x > 0.75 else 0)\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4d789bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=dfm.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7a7b3f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(corr,cbar=False, square= True, fmt='.2%', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10908dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['GRE Score'], dfm['Admission Decision'])\n",
    "plt.xlabel('GRE score')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.title('Scatter Plot between GRE score and Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d3f1ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['TOEFL Score'], dfm['Admission Decision'])\n",
    "plt.xlabel('TOEFL Score')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.title('Scatter Plot between TOEFL Score and Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4152e088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/EwBe+dz36NFDABB3794tsp1SWFiYACC+/PJLlelvvfWW8Pb2LrKveXl5omHDhiqvZSGEACBMTU3FxYsXpWnHjx8XAMTChQulaaGhocLIyEj67HyRsu/avjc0UT6XnTp1Uvk+WrRokfRZrqT8Prh161ahy3vRli1bBABx4cIFIYQQOTk5wtzcXMybN0+lXXBwsDA3NxdXr16Vpp05c0YYGxuLl6MEAGFmZqby/lS+D52cnEROTo40fcqUKWrv5bCwMJXvy1u3bgkAIiIiQqs+veiNO1zVsWNHJCQkoHv37jh+/DhmzpyJgIAAuLi4qBxO2LZtGwoKCjB16lQYGak+TS9eLmdhYSH9ff/+fWRlZcHPzw+PHj1CSkpKsetMTk7GhQsX0K9fP9y+fRtZWVnIysrCw4cP0aFDB+zfv19tF+eHH36o0zoGDRoEe3t7VKtWDUFBQdLhnebNm2tsf/PmTSQnJ2PgwIGoUqWKNL1x48bo2LEjdu7cqXtHX2BiYoLhw4dL901NTTF8+HBkZmYiMTERALB582bUr18f9erVk56TrKws6VCbNv+1+vn5ISkpCQ8fPgTw/D+1wMBANG3aFAcOHADw/L8QhUIhHfbbs2cP7t27h759+6qs19jYGD4+PtJ679y5g3379qFPnz7S6yErKwu3b99GQEAALly4gOvXr6vUM2zYMJXXlJ+fH/Lz83H16tXiPpVFKs76evbsCRMTE5X/0k+dOoUzZ86oXI2xefNm+Pn5wdbWVuV58vf3R35+Pvbv36+y3F69eqnt4bOxscG///6LI0eOvG5XAQB9+vSBubm5yi74mJgYZGVlqZxnU5SIiAjY29vDyckJfn5+OHv2LObMmSOd06b04udBdnY2srKy0LZtW1y+fBnZ2dnF7oOlpaVKraampmjZsqXK1ZC7d++Gi4uLtIcSAMzNzTF06FCt1vFi7U+fPsXt27dRu3Zt2NjYICkpSa39q15He/fuRV5eHsaMGaPS7uWLLwqTk5MDADqfAPvy56Cfn5/aVaMv9vXu3bvIzs6WPhde5u/vDw8PD+l+48aNYWVlJS2zoKAA27ZtQ7du3TR+dir7rut740XK53L8+PEq30dDhw6FlZUVduzYUdRTUqS1a9eiefPm0knKysPwL75f8vPzERMTg+DgYNSoUUOaXr9+fQQEBGhcbocOHVQuA1ce+u/Vq5fKNlVO19eVvS974w5XAUCLFi3w66+/Ii8vD8ePH8fWrVsxb9489O7dG8nJyfDy8sKlS5dgZGQELy+vIpd1+vRpfP7559i3b5/0plR6nQ+1CxcuAECRJ39lZ2er7C53d3fXaR1Tp06Fn58fjI2NYWdnh/r168PEpPCXhPLDq27dumqP1a9fHzExMVqd8FyYatWqqc3r6ekJ4Pnhgf/85z+4cOECzp49W+ihL21OHvfz88OzZ8+QkJAAV1dXZGZmws/PD6dPn1YJOV5eXlKYU24PZZh6mZWVFQDg4sWLEELgiy++wBdffFFojS4uLtL9Fz80gP8dArl79+4r+/IqmsavKM767Ozs0KFDB2zatAnTp08H8PxQlYmJibT7GHj+PJ04cULr7aPpNfvpp59i7969aNmyJWrXro1OnTqhX79+aN26daH1FcXGxgbdunXDunXrpNrXrl0LFxeXQrfny4YNG4b33nsPT548wb59+7BgwQK18zwA4NChQ4iIiEBCQgIePXqk8lh2djasra2L1Yfq1aurbUtbW1ucOHFCun/16lV4eHiotdP2CpvHjx8jKioKK1euxPXr11UOeWv6LHvV60j5eVGnTh2Vdvb29mqH+TRRvqfu37+v9Um05ubmaq89W1tbtdf2H3/8ga+++grJyclq5/K97OV+vrzMW7duIScn55WnP+j63nhRYZ+9pqamqFWrVrH/Ibp37x527tyJ0aNH4+LFi9L01q1b45dffsH58+fh6emJW7du4fHjx2rbUlmTpn9wX37elK99V1dXjdP18XmnyRsZcpRMTU3RokULtGjRAp6enggPD8fmzZvVzjsozL1799C2bVtYWVnhyy+/hIeHB8zNzZGUlIRPP/1U65PJNFHOO2vWrEIvLX95jJQX/zvRRqNGjeDv71+s+gyloKAAjRo1wty5czU+/vIbSJPmzZvD3Nwc+/fvR40aNeDg4ABPT0/4+flhyZIlyM3NxYEDB1TOnVFuj9WrV6scp1ZShkNlu48//rjQ/3Be/tIp7Coh8YrzqszNzZGbmwshhNqHsxACT5480XilTnHX9/777yM8PBzJyclo2rQpNm3ahA4dOkjnXAHP+9+xY0dMmjRJ4zKUoVVJ02u2fv36OHfuHP744w/s3r0bv/zyC5YsWYKpU6dqPPlZG6Ghodi8eTMOHz6MRo0aYfv27Rg5cqTaXtrC1KlTR3qvdO3aFcbGxpg8eTLat28v/fd+6dIldOjQAfXq1cPcuXPh6uoKU1NT7Ny5E/PmzXutz4PibjNdjBkzBitXrsT48ePh6+sLa2trKBQKvP/++xprL+ma6tWrB+D5uSDK8xVfRZsr7g4cOIDu3bujTZs2WLJkCZydnVGhQgWsXLlS5eT2Vy1T137q+t4oDZs3b0Zubi7mzJmDOXPmqD2+du3aYr/nCnveSuO1/KI3OuS8SPlBdfPmTQDPT1ItKCjAmTNnCg0Z8fHxuH37Nn799Ve0adNGmq7p6qTCRoQsbLpy96iVlVWZCSI1a9YE8PyKmpelpKTAzs5O2hNTnBEwb9y4obYnSDnYl3K3p4eHB44fP44OHTq8ch2FPa7c1X/gwAHUqFFD+gD18/NDbm4u1q5di4yMDJVtqtweDg4ORW6PWrVqAXh+CWRJb7eaNWvi2bNnuHTpklpwunjxIvLz86Vtpg/BwcEYPny4dMjq/PnzakMNeHh44MGDB6/d90qVKiEkJAQhISHIy8tDz5498fXXX2PKlCmFXmJd1Ouhc+fOsLe3x9q1a+Hj44NHjx7pdELryz777DOsWLECn3/+uXSlze+//47c3Fxs375d5b9YTYdQ9T1CLPD89XDmzBm10Pvif+hF2bJlC8LCwlS+7J48eVLsAT2Vr70LFy5I7wvg+Z4Pbf5r79atG6KiorBmzRqtQ442fvnlF5ibmyMmJkZlnKWVK1cWa3n29vawsrLCqVOnimz3Ou+NFz97X3wu8/LycOXKlWK/39auXYuGDRtq/Md++fLlWLduHSIjI2Fvbw8LCwtpj/aLNH0f6NvrvF/euHNy4uLiNCZG5e425e7A4OBgGBkZ4csvv1T7L0Y5vzKRvri8vLw8jeNmVKpUSeMuX+UX+ssfJN7e3vDw8MDs2bPx4MEDtflK4hLjV3F2dkbTpk2xatUqlXpPnTqFP//8E4GBgdK0wvpVlGfPnqlckpiXl4fly5fD3t4e3t7eAJ6fX3H9+nWsWLFCbf7Hjx9L59koayhs/X5+fvj7778RFxcnfYAqD9nNmDFDaqMUEBAAKysrfPPNN3j69Kna8pTbw8HBAe3atcPy5culwKypnT4or6p58fJtpcWLF6u00QcbGxsEBARg06ZN2LBhA0xNTdUGWuzTpw8SEhIQExOjNv+9e/fw7NmzV67n9u3bKvdNTU3h5eUFIYTG516pqO1tYmIiXbEXHR2NRo0aaX91hgY2NjYYPnw4YmJipNHKNX0eZGdna/zyLKrW4goICMD169dVzi188uSJxveKJsbGxmqfjQsXLtR4WE4b/v7+qFChAhYuXKiy3FddRaTk6+uLzp0744cfftA4cnleXh4+/vhjnesyNjaGQqFQ6VdqamqxR0c3MjJCcHAwfv/9dxw9elTtcWXfX+e94e/vD1NTUyxYsEDlufzxxx+RnZ2tdkWYNq5du4b9+/ejT58+6N27t9otPDwcFy9exN9//w1jY2MEBARg27ZtSEtLk5Zx9uxZjf3RN+WVfcV5z7xxe3LGjBmDR48eoUePHqhXrx7y8vJw+PBhbNy4EW5ubggPDwfw/JDCZ599hunTp8PPzw89e/aEmZkZjhw5gmrVqiEqKgqtWrWCra0twsLCMHbsWCgUCqxevVpjiPL29sbGjRsxceJEtGjRApaWlujWrRs8PDxgY2ODZcuWoXLlyqhUqRJ8fHzg7u6OH374AV26dEGDBg0QHh4OFxcXXL9+HXFxcbCyssLvv/9e2k8fZs2ahS5dusDX1xeDBw+WLiG3trZWGbdAGUo+++wzvP/++6hQoQK6detW5Pk61apVw4wZM5CamgpPT09s3LgRycnJ+P7776XBoQYMGIBNmzbhww8/RFxcHFq3bo38/HykpKRg06ZNiImJkfbKeXt7Y+/evZg7dy6qVasGd3d36SQ3Pz8/fP3117h27ZpKmGnTpg2WL18ONzc3VK9eXZpuZWWFpUuXYsCAAWjWrBnef/992NvbIy0tDTt27EDr1q2lsLF48WK8/fbbaNSoEYYOHYpatWohIyMDCQkJ+Pfff3H8+HG9bIumTZtiyJAh+O6773DhwgV07NgRwPOTpHfu3IkhQ4agSZMmelmXUkhICD744AMsWbIEAQEBaudKfPLJJ9i+fTu6du2KgQMHwtvbGw8fPsTJkyexZcsWpKamqhze0qRTp05wcnJC69at4ejoiLNnz2LRokUICgoq8iRUb29vLF26FF999RVq164NBwcHlXNuQkNDsWDBAsTFxUlB9nWMGzcO8+fPx7fffosNGzagU6dOMDU1Rbdu3TB8+HA8ePAAK1asgIODg1rgfVWtxTF8+HAsWrQIffv2xbhx4+Ds7Iy1a9dKe75e9d9w165dsXr1alhbW8PLywsJCQnYu3dvkUNKFEU5Ro3y0uHAwEAcO3YMu3bteuVrQOnnn39Gp06d0LNnT3Tr1g0dOnRApUqVcOHCBWzYsAE3b97UOFZOUYKCgjB37lx07twZ/fr1Q2ZmJhYvXozatWurnOOki2+++QZ//vkn2rZtKw1tcfPmTWzevBkHDx6EjY3Na7037O3tMWXKFERGRqJz587o3r07zp07hyVLlqBFixZan0D/onXr1kEIoXKi+osCAwNhYmIi7f2MjIzE7t274efnh5EjR+LZs2fSeFbFfd60ZWFhAS8vL2zcuBGenp6oUqUKGjZsqN3vHep8PVY5t2vXLjFo0CBRr149YWlpKUxNTUXt2rXFmDFjREZGhlr7n376Sbz11lvCzMxM2NrairZt24o9e/ZIjx86dEj85z//ERYWFqJatWrSJel46ZLNBw8eiH79+gkbGxsBQOXyuN9++014eXkJExMTtUu2jx07Jnr27CmqVq0qzMzMRM2aNUWfPn1EbGys1EbXSwaVl6m+fPnryzRdQi6EEHv37hWtW7cWFhYWwsrKSnTr1k2cOXNGbf7p06cLFxcXYWRk9MrLydu2bSsaNGggjh49Knx9fYW5ubmoWbOmWLRokVrbvLw8MWPGDNGgQQNpu3h7e4vIyEiRnZ0ttUtJSRFt2rQRFhYWapes5uTkCGNjY1G5cmXx7NkzafqaNWsEADFgwACNdcbFxYmAgABhbW0tzM3NhYeHhxg4cKA4evSoSrtLly6J0NBQ4eTkJCpUqCBcXFxE165dxZYtW6Q2yktxX77sVNNlxIXJz88X3333nWjSpIkwNzcX5ubmokmTJmLBggVqQx/osr6XLyFXysnJkZ7PNWvWaKzp/v37YsqUKaJ27drC1NRU2NnZiVatWonZs2dLQwEoX1uzZs1Sm3/58uWiTZs20mvew8NDfPLJJyrbVtMl5Onp6SIoKEhUrlxZANBYf4MGDYSRkZH4999/Ndb+sqLqFEKIgQMHCmNjY+kS4+3bt4vGjRsLc3Nz4ebmJmbMmCF++uknrWstbFtouqT+5ctshRDi8uXLIigoSFhYWAh7e3vx0UcfiV9++UUAEP/973+L7Ovdu3dFeHi4sLOzE5aWliIgIECkpKSImjVrahyKQJvXUX5+voiMjBTOzs7CwsJCtGvXTpw6dUptmUV59OiRmD17tmjRooX0mV2nTh0xZswYlUu7CxtSQfn5+KIff/xR1KlTR5iZmYl69eqJlStXamwHQIwaNUptmZrqv3r1qggNDRX29vbCzMxM1KpVS4waNUrk5uZKbbR5bxRl0aJFol69eqJChQrC0dFRjBgxQu0Se22/Dxo1aiRq1KhRZJt27doJBwcH8fTpUyGEEH/99Zfw9vYWpqamolatWmLZsmVaP2+FvZc0fR9pem0fPnxYWjd0uJxc8f8LIiKSvbfeegtVqlRBbGysoUspNfPnz8eECRPw77//qlzVR/QmeOPOySGiN9PRo0eRnJyM0NBQQ5dSYh4/fqxy/8mTJ1i+fDnq1KnDgENvpDfunBwierOcOnUKiYmJmDNnDpydnVUGL5Sbnj17okaNGmjatCmys7OxZs0apKSkFPpbRERyx5BDRLK2ZcsWfPnll6hbty7Wr18v61/5DggIwA8//IC1a9ciPz8fXl5e2LBhg6yDHVFReE4OERERyRLPySEiIiJZYsghIiIiWXrjzskpKCjAjRs3ULly5RIZWp2IiIj0TwiB+/fvo1q1alr/7twbF3Ju3Lih1Y84EhERUdlz7do1lRHpi/LGhRzlsPDXrl2DlZWVgashIiIibeTk5MDV1bXIn3d52RsXcpSHqKysrBhyiIiIyhldTjXhicdEREQkSww5REREJEsMOURERCRLDDlEREQkSww5REREJEsMOURERCRLDDlEREQkSww5REREJEsMOURERCRLb9yIxyUlv0Dgnyt3kHn/CRwqm6OlexUYG/EHQMuT63ceo8uCv/AwNx+VzIyxa2xbuFSxMHRZpKVFMScxOy5Nuv9x+xoYHdDIgBWRrj7eeABbjuVI93u/ZYXZIX4GrIh0NfXX/+Lnf25L90NbVsWXPf9jsHoUQghhqJXv378fs2bNQmJiIm7evImtW7ciODi4yHni4+MxceJEnD59Gq6urvj8888xcOBArdeZk5MDa2trZGdn6+1nHXafuonI38/gZvYTaZqztTkiunmhc0NnvayDSpbnZzuRl6/+VjA1VuD814EGqIh04TZ5R6GPpX4bVIqVUHFxG5Z/Jb0Ni/P9bdDDVQ8fPkSTJk2wePFirdpfuXIFQUFBaN++PZKTkzF+/HgMGTIEMTExJVxp4XafuokRa5JUAg4ApGc/wYg1Sdh96qaBKiNtFRZwACAvX8Dzs52lXBHpoqgPVm0eJ8PjNiz/yuo2NGjI6dKlC7766iv06NFDq/bLli2Du7s75syZg/r162P06NHo3bs35s2bV8KVapZfIBD5+xlo+npUTov8/QzyCwy2s4xe4fqdx4UGHKW8fIHrdx6XUkWki0UxJ/XajkrfxxsP6LUdlb6pv/5Xr+30qVydeJyQkAB/f3+VaQEBAUhISCh0ntzcXOTk5Kjc9OWfK3fU9uC8SAC4mf0E/1y5o7d1kn51WfCXXttR6XrxHBx9tKPS9+I5OPpoR6XvxXNw9NFOn8pVyElPT4ejo6PKNEdHR+Tk5ODxY83/aUdFRcHa2lq6ubq66q2ezPuFB5zitKPS9zA3X6/tiIio7ChXIac4pkyZguzsbOl27do1vS3bobK5XttR6atkZqzXdkREVHaUq5Dj5OSEjIwMlWkZGRmwsrKChYXmS33NzMxgZWWlctOXlu5V4GxtjsIuFFfg+VVWLd2r6G2dpF+7xrbVazsqXR+3r6HXdlT6er+l3Weytu2o9IW2rKrXdvpUrkKOr68vYmNjVabt2bMHvr6+BqnH2EiBiG5eAKAWdJT3I7p5cbycMsyligVMjYvePqbGCo6XU0ZpOw4Ox8spu7QdB4fj5ZRd2o6DY4jxcgwach48eIDk5GQkJycDeH6JeHJyMtLSnp8kOGXKFISGhkrtP/zwQ1y+fBmTJk1CSkoKlixZgk2bNmHChAmGKB8A0LmhM5Z+0AxO1qqHpJyszbH0g2YcJ6ccOP91YKFBh+PklH2vGn+DY6yUfdyG5V9Z3YYGHQwwPj4e7du3V5seFhaG6OhoDBw4EKmpqYiPj1eZZ8KECThz5gyqV6+OL774wuCDAQIc8VgOOOJx+cYRj8s/jnhc/pXkiMfF+f42aMgxhJIKOURERFRyyt2Ix0REREQlhSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZIkhh4iIiGSJIYeIiIhkiSGHiIiIZMngIWfx4sVwc3ODubk5fHx88M8//xTZfv78+ahbty4sLCzg6uqKCRMm4MmTJ6VULREREZUXBg05GzduxMSJExEREYGkpCQ0adIEAQEByMzM1Nh+3bp1mDx5MiIiInD27Fn8+OOP2LhxI/7v//6vlCsnIiKiss6gIWfu3LkYOnQowsPD4eXlhWXLlqFixYr46aefNLY/fPgwWrdujX79+sHNzQ2dOnVC3759X7n3h4iIiN48Bgs5eXl5SExMhL+///+KMTKCv78/EhISNM7TqlUrJCYmSqHm8uXL2LlzJwIDAwtdT25uLnJyclRuREREJH8mhlpxVlYW8vPz4ejoqDLd0dERKSkpGufp168fsrKy8Pbbb0MIgWfPnuHDDz8s8nBVVFQUIiMj9Vo7ERERlX0GP/FYF/Hx8fjmm2+wZMkSJCUl4ddff8WOHTswffr0QueZMmUKsrOzpdu1a9dKsWIiIiIyFIPtybGzs4OxsTEyMjJUpmdkZMDJyUnjPF988QUGDBiAIUOGAAAaNWqEhw8fYtiwYfjss89gZKSe2czMzGBmZqb/DhAREVGZZrA9OaampvD29kZsbKw0raCgALGxsfD19dU4z6NHj9SCjLGxMQBACFFyxRIREVG5Y7A9OQAwceJEhIWFoXnz5mjZsiXmz5+Phw8fIjw8HAAQGhoKFxcXREVFAQC6deuGuXPn4q233oKPjw8uXryIL774At26dZPCDhERERFg4JATEhKCW7duYerUqUhPT0fTpk2xe/du6WTktLQ0lT03n3/+ORQKBT7//HNcv34d9vb26NatG77++mtDdYGIiIjKKIV4w47z5OTkwNraGtnZ2bCysjJ0OURERKSF4nx/l6urq4iIiIi0xZBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREssSQQ0RERLLEkENERESyxJBDREREsqRzyKlVqxZu376tNv3evXuoVauWXooiIiIiel06h5zU1FTk5+erTc/NzcX169f1UhQRERHR6zLRtuH27dulv2NiYmBtbS3dz8/PR2xsLNzc3PRaHBEREVFxaR1ygoODAQAKhQJhYWE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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['University Rating'], dfm['Admission Decision'])\n",
    "plt.xlabel('University Rating')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.title('Scatter Plot between University Rating and Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10b920f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/cRQeHg4/Pz8cPnwYAHD48GHIZDK88cYbAIADBw4gKysL/fv3V9mvqakpAgIClPt9+PAhDh48iL59+yqfD/fv38eDBw8QFhaGK1eu4Pbt2yr9DB8+XOU5FRwcjKKiIty4caPMcQwZMgR79+5Fu3btcOTIEcyYMQPBwcGoV68ejh07pqzLzc0FAFSrVq3UbSkey8nJUVm+evVq1KhRA05OTggICMDRo0cRExOjdmF8cS/+XuTl5eH+/fsICgqCEAKnTp1Sqy/+3A0ODlb55N6ePXtgZmamcnGqqampxh8AyMnJKXP8JfH19UVwcLDyfo0aNVC/fn2VviwsLJR/H4qKivDgwQPY2Nigfv36SEpKUtvm4MGDVa6PUmxfsc1Tp04hNTUVY8aMUbvWRfEcKc9z7EV79uyBqakpRo0apbJ83LhxEELgt99+0+THo6awsBCbN29GZGSkstf27dvDyckJGzZsUNadOHECmZmZePfdd1V+FoMGDYKdnV2J246KikKVKlWU9wMCAiCEwJAhQ1TqAgICcOvWLRQWFpZrDP9GvKBYQlq2bIlt27ahoKAAp0+fxvbt27FgwQL06dMHycnJ8PX1xbVr12BiYgJfX98yt3X+/Hl8/vnnOHjwoNoLQ3Z2drl7vHLlCoDnF4KWJjs7Gw4ODsr7Xl5eWu1j8uTJCA4OhqmpKRwdHdGwYUOYmZX+VFe82NavX1/tsYYNG2Lfvn0aXchcGldXV7V1fXx8ADyfz+Q///kPrly5gosXL6JGjRolbkOTi8KDg4NRWFiIxMREuLu7IzMzE8HBwTh//rxKuPH19VWGOMXxUISo4mxtbQEAV69ehRACkyZNwqRJk0rt0c3NTXm/du3aKo8rjuk///zz0rGEhYUhLCwMjx8/xsmTJ7F582asWLECXbt2RUpKCpycnJQv6rm5uaVeHFpaAOrevTtGjhwJmUyGatWqoVGjRhod35s3b2Ly5MnYtWuX2jiK/15YWlqqHU8HBweV9W7cuIGaNWvCxsZGpa6k52JJbG1tlWPUVPHjUlJfcrkcixYtwrJly5CamoqioiLlY6+99tpLt1n8WF+7dg0AypzzqTzPsRfduHEDrq6uase6YcOGysfLY//+/bh37x5atWqFq1evKpeHhITgxx9/xOzZs2FiYqLcfr169VTWV0xJUZLiPzdFCHJ3d1dbLpfLkZ2dXeLPn9Qx3EiQubk5WrZsiZYtW8LHxweDBw/G1q1bMWXKFI3Wz8rKQtu2bWFra4vp06fD29sblpaWSEpKwieffKJ2ZkUbinXnzp1b6kfEi/+hf/F/y5po3LgxQkNDy9WfvsjlcjRu3Bjz588v8fHif+xK0qJFC1haWuLQoUOoXbs2nJyc4OPjg+DgYCxbtgz5+fk4fPgwevbsqbJfAFi/fj1cXFzUtqkIhYq6jz76CGFhYSXuv27duir3i3/8WkGUMPdMaaytrREcHIzg4GA4Ojpi2rRp+O233xAdHY2GDRtix44dOHPmDNq0aVPi+mfOnAEAtTBfq1YtrZ8jRUVF6NixIx4+fIhPPvkEDRo0QNWqVXH79m0MGjRI7feitPHrUoMGDZCcnIyCggK1T5aVRpPj8sUXX2DSpEkYMmQIZsyYgerVq8PExARjxowp8fdfF8e6PM+xyqA4O9O3b98SH//jjz8QEhJSrm2X9nPTxc/z347hRuIUb8XcvXsXAODt7Q25XI4LFy6UGi4SEhLw4MEDbNu2TeVFIzU1Va32xbcdNFnu7e0N4Pn/OA0lgHh4eAB4Pm9EcSkpKXB0dFT+r760cZXlzp07amd+Ll++DADKj5J6e3vj9OnT6NChw0v3Udrj5ubmaNWqFQ4fPozatWsr3xoIDg5Gfn4+NmzYgIyMDJVjqjgeTk5OZR4Pxf88q1SporfjVvy53LVrV8TGxuL7778vMdwUFRVh48aNcHBwQOvWrV95/2fPnsXly5exbt06REVFKZcfOHCg3Nv08PBAXFwcHj16pBLqS3ouliQiIgKJiYn4+eef0b9//3L3UdxPP/2EkJAQrF69WmV5VlYWHB0dtd6e4nl27ty5Up8/r/oc8/DwwO+//47c3FyVszeKt9EVv+fayMvLw86dOxEZGamcXuJFo0aNwoYNGxASEqLc/pUrV1TOhD579gypqalo2rSp1vvXRnn+NkkZr7mRiPj4+BJTveJ6EcVp7h49esDExATTp09X+x+YYn3F/xpe3F5BQQGWLVumtv2qVauW+DaV4oW8+Oyo/v7+8Pb2xldffYVHjx6prXfv3r1Sx1hRatasCT8/P6xbt06l33PnzmH//v0IDw9XLittXGUpLCzEypUrlfcLCgqwcuVK1KhRQznhWt++fXH79u0S5yR58uSJ8joaRQ+l7T84OBh//fUX4uPjleFG8dbc7NmzlTUKYWFhsLW1xRdffIFnz56pbU9xPJycnNCuXTusXLlSGS5KqtOFuLi4EpcXfy4HBQUhNDQUa9aswa+//qpW/9lnn+Hy5csYP3681mf/SlLS74UQAosWLSr3NsPDw1FYWKgy421RUZHGs4q/++67qFmzJsaNG6cMzC/KzMzEzJkzte7L1NRU7e/J1q1by7zmpSzNmzeHl5cXFi5cqPbcVeznVZ9j4eHhKCoqwpIlS1SWL1iwADKZrFzzF23fvh15eXn44IMP0KdPH7Vb165d8fPPPyM/Px8tWrRAjRo1sGLFChQUFCi3sXbt2kqZJbo8f5ukjGduJOLDDz/E48eP0bNnTzRo0AAFBQU4duwYNm/eDE9PTwwePBjA89O6n332mfIizV69esHCwgLHjx+Hq6srYmNjERQUBAcHB0RHR2PUqFGQyWRYv359ieHJ398fmzdvRkxMDFq2bAkbGxtERETA29sb9vb2WLFiBapVq4aqVasiICAAXl5e+Pbbb9G5c2c0atQIgwcPhpubG27fvo34+HjY2tril19+qewfH+bOnYvOnTsjMDAQQ4cOxZMnT/D111/Dzs4OU6dOVRkv8PyFs1+/fqhSpQoiIiLKvF7D1dUVs2fPRlpaGnx8fLB582YkJyfjm2++UV5MOHDgQGzZsgXvvvsu4uPj0bp1axQVFSElJQVbtmzBvn37lGcu/P398fvvv2P+/PlwdXWFl5cXAgICADwPLrNmzcKtW7dUQkybNm2wcuVKeHp6olatWsrltra2WL58OQYOHIjmzZujX79+qFGjBm7evIndu3ejdevWyheLpUuX4o033kDjxo0xbNgw1KlTBxkZGUhMTMR///tfnD59WifHonv37vDy8lI+j/Ly8vD777/jl19+QcuWLREREaGs/f7779GhQwd0794dAwYMUJ6l2rZtGxISEhAZGYmPP/5YJ301aNAA3t7e+Oijj3D79m3Y2tri559/1ugaotJERESgdevWmDBhAtLS0uDr64tt27ZpfF2bg4MDtm/frrxw/MUZipOSkvDjjz8iMDBQ6766du2K6dOnY/DgwQgKCsLZs2exYcOGUq8deRkTExMsX74cERER8PPzw+DBg1GzZk2kpKTg/Pnz2LdvH4BXe45FREQgJCQEn332GdLS0tC0aVPs378fO3fuxJgxY5Rnj7SxYcMGvPbaawgKCirx8W7dumHVqlXYvXs3evXqhZkzZ2LEiBFo3749IiMjkZqaijVr1pT756YNPz8/mJqaYvbs2cjOzoaFhYXywud/pUr/fBZViN9++00MGTJENGjQQNjY2Ahzc3NRt25d8eGHH4qMjAy1+u+++040a9ZMWFhYCAcHB9G2bVuVuUCOHj0q/vOf/wgrKyvh6uqq/Gg5in3U8NGjR2LAgAHC3t5eOYmfws6dO4Wvr68wMzNT+1jrqVOnRK9evcRrr70mLCwshIeHh+jbt6+Ii4tT1ig+Cn7v3j2NfgalzXNTXGmT+P3++++idevWwsrKStja2oqIiAiVSfwUZsyYIdzc3ISJiUm5JvHz8PAQS5YsUastKCgQs2fPFo0aNVIeF39/fzFt2jTlPDVCCJGSkiLatGkjrKyslJP4KeTk5AhTU1NRrVo1lcnifvjhBwFADBw4sMQ+4+PjRVhYmLCzsxOWlpbC29tbDBo0SJw4cUKl7tq1ayIqKkq4uLiIKlWqCDc3N9G1a1fx008/KWsUHwVXfPT+xX0Uf/6U5McffxT9+vUT3t7ewsrKSlhaWgpfX1/x2WefiZycHLX63NxcMXXqVNGoUSNhZWUlqlWrJlq3bi3Wrl2r8jFjBZQyiZ8mLly4IEJDQ4WNjY1wdHQUw4YNU06g+OLzSTGJX3GK5/SLHjx4IAYOHKicxG/gwIEaT+KncOfOHTF27Fjh4+MjLC0thbW1tfD39xezZs1See4oJvErrvgkc0+fPhXjxo0TNWvWFFZWVqJ169YiMTGx1Mnoiv/OlfY7duTIEdGxY0dRrVo1UbVqVdGkSRPx9ddfq9Ro8hwrTW5urhg7dqxwdXUVVapUEfXq1VOZxO/F8b7so+AZGRnCzMys1N8ZIZ5PDWBtbS169uypXLZs2TLh5eUlLCwsRIsWLcqcxK/4z620352S/hYW/yi4EEKsWrVK1KlTR5iamv7rPxYuE4JXKBEREZF08JobIiIikhSGGyIiIpIUhhsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSlH/dJH5yuRx37txBtWrVOF01ERGRkRBCIDc3F66urspvrS/Nvy7c3LlzR6MvISQiIiLDc+vWLZWZ1kvyrws3ii9Uu3XrFmxtbfXcDREREWkiJycH7u7uKl+MWpp/XbhRvBVla2vLcENERGRkNLmkhBcUExERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpPzrZigmKk1BoRzrE9Nw4+FjeFS3xsBAT5ibSSf/P3paiLGbT+HmP09Q28EKCyKbwcZSOn8CLvw3B12XHIYcz//X9uvIYPjWktYs5PtP3MHwn04p73/Tpxk6tXDVY0e6tXjvGcxPuKW8H9POHaPebKLHjnRryb6z+Cr+pvL+RyG1MTKssR470r1Nh69hwu4U5f0vuzRAv2DvSu9DJoQQlb7X/+/QoUOYO3cuTp48ibt372L79u3o0aNHmeskJCQgJiYG58+fh7u7Oz7//HMMGjRI433m5OTAzs4O2dnZ/PoFUordcwGrDqdC/sJvg4kMGBbshYnhvvprTEe6LTmMM//NUVvepJYtdo0M1kNHuuU5YXepj6V92aUSO6k4Uh8jx2f8KnqM2rx+6/W/pXl5eWjatCmWLl2qUX1qaiq6dOmCkJAQJCcnY8yYMXjnnXewb9++Cu6UpCx2zwWsPKQabABALoCVh1IRu+eCfhrTkdKCDQCc+W8Oui05XMkd6VZZf1A1edwYSH2MHJ9xjw8wvDHqNdx07twZM2fORM+ePTWqX7FiBby8vDBv3jw0bNgQI0eORJ8+fbBgwYIK7pSkqqBQjlWHU8usWXU4FQWF8krqSLcePS0sNdgonPlvDh49LaykjnTrwkvGpm2dIdp/4o5O6wzN4r1ndFpnaJbsO6vTOkO06fA1ndbpglFdUJCYmIjQ0FCVZWFhYUhMTCx1nfz8fOTk5KjciBTWJ6apnbEpTi6e1xmjsZtPvbxIizpD01XDs06a1hmiF6+x0UWdoXnxGhtd1BmaF6+x0UWdIXrxGhtd1OmCUYWb9PR0ODs7qyxzdnZGTk4Onjx5UuI6sbGxsLOzU97c3d0ro1UyEjcePtZpnaG5+U/JvxflrTM0mp5PM87zbkRUXkYVbspj4sSJyM7OVt5u3TLO9E8Vw6O6tU7rDE1tByud1hkaTf+ASf4PHRGpMKrfeRcXF2RkZKgsy8jIgK2tLaysSv7jbGFhAVtbW5UbkcLAQE+YyMquMZE9rzNGCyKb6bTO0Pyq4Se9NK0zRN/00ezYaFpnaGLaaXY2XdM6Q/NRSG2d1hmiL7s00GmdLhhVuAkMDERcXJzKsgMHDiAwMFBPHZGxMzczwbBgrzJrhgV7Ge18NzaWZmjykrlemtSyNdr5bjSdx8aY57vRdB4bY53vRtN5bIx1vhtN57Ex5vluNJ3HpjLnu9HrX+xHjx4hOTkZycnJAJ5/1Ds5ORk3bz6/sGrixImIiopS1r/77ru4fv06xo8fj5SUFCxbtgxbtmzB2LFj9dE+ScTEcF+MaOOldgbHRAaMaGP889zsGhlcasCRwjw3L5s/QwpziEh9jByfcY8PMLwx6nUSv4SEBISEhKgtj46Oxtq1azFo0CCkpaUhISFBZZ2xY8fiwoULqFWrFiZNmsRJ/EgnOEOxceMMxcaPMxQbv4qcoVib12+9hht9YLghIiIyPkYzQzERERGRrjHcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaQw3BAREZGkMNwQERGRpDDcEBERkaToPdwsXboUnp6esLS0REBAAP7+++8y6xcuXIj69evDysoK7u7uGDt2LJ4+fVpJ3RIREZGh02u42bx5M2JiYjBlyhQkJSWhadOmCAsLQ2ZmZon1GzduxIQJEzBlyhRcvHgRq1evxubNm/Hpp59WcudERERkqPQabubPn49hw4Zh8ODB8PX1xYoVK2BtbY3vvvuuxPpjx46hdevWGDBgADw9PdGpUyf079//pWd7iIiI6N9Db+GmoKAAJ0+eRGho6P+aMTFBaGgoEhMTS1wnKCgIJ0+eVIaZ69evY8+ePQgPDy91P/n5+cjJyVG5ERERkXSZ6WvH9+/fR1FREZydnVWWOzs7IyUlpcR1BgwYgPv37+ONN96AEAKFhYV49913y3xbKjY2FtOmTdNp70RERGS49H5BsTYSEhLwxRdfYNmyZUhKSsK2bduwe/duzJgxo9R1Jk6ciOzsbOXt1q1bldgxERERVTa9nblxdHSEqakpMjIyVJZnZGTAxcWlxHUmTZqEgQMH4p133gEANG7cGHl5eRg+fDg+++wzmJioZzULCwtYWFjofgBERERkkPR25sbc3Bz+/v6Ii4tTLpPL5YiLi0NgYGCJ6zx+/FgtwJiamgIAhBAV1ywREREZDb2duQGAmJgYREdHo0WLFmjVqhUWLlyIvLw8DB48GAAQFRUFNzc3xMbGAgAiIiIwf/58NGvWDAEBAbh69SomTZqEiIgIZcghIiKifze9hpvIyEjcu3cPkydPRnp6Ovz8/LB3717lRcY3b95UOVPz+eefQyaT4fPPP8ft27dRo0YNREREYNasWfoaAhERERkYmfiXvZ+Tk5MDOzs7ZGdnw9bWVt/tEBERkQa0ef02qk9LEREREb0Mww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJCsMNERERSQrDDREREUkKww0RERFJitbhpk6dOnjw4IHa8qysLNSpU0cnTRERERGVl9bhJi0tDUVFRWrL8/Pzcfv2bZ00RURERFReZpoW7tq1S/nvffv2wc7OTnm/qKgIcXFx8PT01GlzRERERNrSONz06NEDACCTyRAdHa3yWJUqVeDp6Yl58+bptDkiIiIibWkcbuRyOQDAy8sLx48fh6OjY4U1RURERFReGocbhdTU1Irog4iIiEgnNAo3ixcvxvDhw2FpaYnFixeXWTtq1CidNEZERERUHjIhhHhZkZeXF06cOIHXXnsNXl5epW9MJsP169d12qCu5eTkwM7ODtnZ2bC1tdV3O0RERKQBbV6/NTpz8+JbUXxbioiIiAwZZygmIiIiSdH6gmIhBH766SfEx8cjMzNT+SkqhW3btumsOSIiIiJtaR1uxowZg5UrVyIkJATOzs6QyWQV0RcRERFRuWgdbtavX49t27YhPDy8IvohIiIieiVaX3NjZ2fHL8gkIiIig6V1uJk6dSqmTZuGJ0+eVEQ/RERERK9E67el+vbtix9//BFOTk7w9PRElSpVVB5PSkrSWXNERERE2tI63ERHR+PkyZN4++23eUExERERGRytw83u3buxb98+vPHGGxXRDxEREdEr0fqaG3d3d51+bcHSpUvh6ekJS0tLBAQE4O+//y6zPisrCx988AFq1qwJCwsL+Pj4YM+ePTrrh4iIiIyb1uFm3rx5GD9+PNLS0l5555s3b0ZMTAymTJmCpKQkNG3aFGFhYcjMzCyxvqCgAB07dkRaWhp++uknXLp0CatWrYKbm9sr90JERETSoNEXZ77IwcEBjx8/RmFhIaytrdUuKH748KHG2woICEDLli2xZMkSAIBcLoe7uzs+/PBDTJgwQa1+xYoVmDt3LlJSUtT2qyl+cSYREZHx0fkXZ75o4cKF5e1LRUFBAU6ePImJEycql5mYmCA0NBSJiYklrrNr1y4EBgbigw8+wM6dO1GjRg0MGDAAn3zyCUxNTUtcJz8/H/n5+cr7OTk5OumfiIiIDFO5Pi2lC/fv30dRURGcnZ1Vljs7OyMlJaXEda5fv46DBw/irbfewp49e3D16lW8//77ePbsGaZMmVLiOrGxsZg2bZpOeiYiIiLDp1G40eZsR0W+1SOXy+Hk5IRvvvkGpqam8Pf3x+3btzF37txSw83EiRMRExOjvJ+TkwN3d/cK65GIiIj0S6NwY29vr/F8NkVFRRrVOTo6wtTUFBkZGSrLMzIy4OLiUuI6NWvWRJUqVVTegmrYsCHS09NRUFAAc3NztXUsLCxgYWGhUU9ERERk/DQKN/Hx8cp/p6WlYcKECRg0aBACAwMBAImJiVi3bh1iY2M13rG5uTn8/f0RFxeHHj16AHh+ZiYuLg4jR44scZ3WrVtj48aNkMvlMDF5/kGvy5cvo2bNmiUGGyIiIvoXElpq37692Lhxo9ryDRs2iLZt22q1rU2bNgkLCwuxdu1aceHCBTF8+HBhb28v0tPThRBCDBw4UEyYMEFZf/PmTVGtWjUxcuRIcenSJfHrr78KJycnMXPmTI33mZ2dLQCI7OxsrXolIiIi/dHm9VvrC4oTExOxYsUKteUtWrTAO++8o9W2IiMjce/ePUyePBnp6enw8/PD3r17lRcZ37x5U3mGBng+geC+ffswduxYNGnSBG5ubhg9ejQ++eQTbYdBREREEqX1PDf169dH9+7dMWfOHJXl48ePx86dO3Hp0iWdNqhrnOeGiIjI+FToPDcLFixA79698dtvvyEgIAAA8Pfff+PKlSv4+eefy9cxERERkY5o/fUL4eHhuHLlCrp164aHDx/i4cOHiIiIwOXLlxEeHl4RPRIRERFpTOu3pUqTlZWFH374odRPOhkKvi1FRERkfLR5/db6zE1xcXFxGDBgAGrWrFnqRHpERERElaVc4ebWrVuYPn06vLy80KlTJwDA9u3bkZ6ertPmiIiIiLSlcbh59uwZtm7dirCwMNSvXx/JycmYO3cuTExM8Pnnn+PNN98s9zd1ExEREemKxp+WcnNzQ4MGDfD2229j06ZNcHBwAAD079+/wpojIiIi0pbGZ24KCwshk8kgk8lUvtuJiIiIyJBoHG7u3LmD4cOH48cff4SLiwt69+6N7du3a/yFmkRERESVQeNwY2lpibfeegsHDx7E2bNn0bBhQ4waNQqFhYWYNWsWDhw4oPE3ghMRERFVlHJ9Wsrb2xszZ87EjRs3sHv3buTn56Nr167K74QiIiIi0hetv37hRSYmJujcuTM6d+6Me/fuYf369brqi4iIiKhcdDZDsbHgDMVERETGp1JnKCYiIiIyJAw3REREJCkahZucnJyK7oOIiIhIJzQKNw4ODsjMzAQAtG/fHllZWRXZExEREVG5aRRubGxs8ODBAwBAQkICnj17VqFNEREREZWXRh8FDw0NRUhICBo2bAgA6NmzJ8zNzUusPXjwoO66IyIiItKSRuHmhx9+wLp163Dt2jX88ccfaNSoEaytrSu6NyIiIiKtaT3PTUhICLZv3w57e/sKaqlicZ4bIiIi46PN67fWMxTHx8cr/63IRfzyTCIiIjIU5Zrn5vvvv0fjxo1hZWUFKysrNGnShF+9QERERAZB6zM38+fPx6RJkzBy5Ei0bt0aAHDkyBG8++67uH//PsaOHavzJomIiIg0pfU1N15eXpg2bRqioqJUlq9btw5Tp05FamqqThvUNV5zQ0REZHwq9Lul7t69i6CgILXlQUFBuHv3rrabIyIiItIprcNN3bp1sWXLFrXlmzdvRr169XTSFBEREVF5aX3NzbRp0xAZGYlDhw4pr7k5evQo4uLiSgw9RERERJVJ6zM3vXv3xl9//QVHR0fs2LEDO3bsgKOjI/7++2/07NmzInokIiIi0pjWFxQbO15QTEREZHwq9IJiIiIiIkPGcENERESSwnBDREREksJwQ0RERJJS7nBz9epV7Nu3D0+ePAHwvy/RJCIiItInrcPNgwcPEBoaCh8fH4SHhytnJR46dCjGjRun8waJiIiItKF1uBk7dizMzMxw8+ZNWFtbK5dHRkZi7969Om2OiIiISFtaz1C8f/9+7Nu3D7Vq1VJZXq9ePdy4cUNnjRERERGVh9ZnbvLy8lTO2Cg8fPgQFhYWOmmKiIiIqLy0DjfBwcH4/vvvlfdlMhnkcjnmzJmDkJAQnTZHREREpC2t35aaM2cOOnTogBMnTqCgoADjx4/H+fPn8fDhQxw9erQieiQiIiLSmNZnbl5//XVcvnwZb7zxBrp37468vDz06tULp06dgre3d0X0SERERKQxfnEmERERGbwK/eLMNWvWYOvWrWrLt27dinXr1mm7OSIiIiKd0jrcxMbGwtHRUW25k5MTvvjiC500RURERFReWoebmzdvwsvLS225h4cHbt68qZOmiIiIiMpL63Dj5OSEM2fOqC0/ffo0XnvtNZ00RURERFReWoeb/v37Y9SoUYiPj0dRURGKiopw8OBBjB49Gv369auIHomIiIg0pvU8NzNmzEBaWho6dOgAM7Pnq8vlckRFRfGaGyIiItK7cn8U/PLlyzh9+jSsrKzQuHFjeHh46Lq3CsGPghMRERkfbV6/tT5zo+Dj4wMfH5/yrk5ERERUIbQON0VFRVi7di3i4uKQmZkJuVyu8vjBgwd11hwRERGRtrQON6NHj8batWvRpUsXvP7665DJZBXRFxEREVG5aB1uNm3ahC1btiA8PLwi+iEiIiJ6JVp/FNzc3Bx169atiF6IiIiIXpnW4WbcuHFYtGgR/mXft0lERERGQutwc+TIEWzYsAHe3t6IiIhAr169VG7lsXTpUnh6esLS0hIBAQH4+++/NVpv06ZNkMlk6NGjR7n2S0RERNKj9TU39vb26Nmzp84a2Lx5M2JiYrBixQoEBARg4cKFCAsLw6VLl+Dk5FTqemlpafjoo48QHByss16IiIjI+JV7Ej9dCQgIQMuWLbFkyRIAz2c7dnd3x4cffogJEyaUuE5RURHatGmDIUOG4PDhw8jKysKOHTs02h8n8SMiIjI+2rx+a/22lC4VFBTg5MmTCA0NVS4zMTFBaGgoEhMTS11v+vTpcHJywtChQyujTSIiIjIi5Zqh+KeffsKWLVtw8+ZNFBQUqDyWlJSk8Xbu37+PoqIiODs7qyx3dnZGSkpKiescOXIEq1evRnJyskb7yM/PR35+vvJ+Tk6Oxv0RERGR8dH6zM3ixYsxePBgODs749SpU2jVqhVee+01XL9+HZ07d66IHpVyc3MxcOBArFq1Co6OjhqtExsbCzs7O+XN3d29QnskIiIi/dL6zM2yZcvwzTffoH///li7di3Gjx+POnXqYPLkyXj48KFW23J0dISpqSkyMjJUlmdkZMDFxUWt/tq1a0hLS0NERIRymeLrH8zMzHDp0iV4e3urrDNx4kTExMQo7+fk5DDgEBERSZjWZ25u3ryJoKAgAICVlRVyc3MBAAMHDsSPP/6o1bbMzc3h7++PuLg45TK5XI64uDgEBgaq1Tdo0ABnz55FcnKy8tatWzeEhIQgOTm5xNBiYWEBW1tblRsRERFJl9ZnblxcXPDw4UN4eHigdu3a+PPPP9G0aVOkpqaWa2K/mJgYREdHo0WLFmjVqhUWLlyIvLw8DB48GAAQFRUFNzc3xMbGwtLSEq+//rrK+vb29gCgtpyIiIj+nbQON+3bt8euXbvQrFkzDB48GGPHjsVPP/2EEydOlGsSv8jISNy7dw+TJ09Geno6/Pz8sHfvXuVFxjdv3oSJiV4/1EVERERGROt5buRyOeRyOczMnueiTZs24dixY6hXrx5GjBgBc3PzCmlUVzjPDRERkfHR5vVb75P4VTaGGyIiIuOjzet3uea5ycrKwt9//43MzEzlp5UUoqKiyrNJIiIiIp3QOtz88ssveOutt/Do0SPY2tpCJpMpH5PJZAw3REREpFdaX6k7btw4DBkyBI8ePUJWVhb++ecf5U3beW6IiIiIdE3rcHP79m2MGjUK1tbWFdEPERER0SvROtyEhYXhxIkTFdELERER0SvT6JqbXbt2Kf/dpUsXfPzxx7hw4QIaN26MKlWqqNR269ZNtx0SERERaUGjj4JrOomeTCZDUVHRKzdVkfhRcCIiIuOj84+CF/+4NxEREZGh4vcaEBERkaRoHG4OHjwIX19f5OTkqD2WnZ2NRo0a4dChQzptjoiIiEhbGoebhQsXYtiwYSW+z2VnZ4cRI0ZgwYIFOm2OiIiISFsah5vTp0/jzTffLPXxTp064eTJkzppioiIiKi8NA43GRkZah/7fpGZmRnu3bunk6aIiIiIykvjcOPm5oZz586V+viZM2dQs2ZNnTRFREREVF4ah5vw8HBMmjQJT58+VXvsyZMnmDJlCrp27arT5oiIiIi0pdEkfsDzt6WaN28OU1NTjBw5EvXr1wcApKSkYOnSpSgqKkJSUhKcnZ0rtOFXxUn8iIiIjI/OJ/EDAGdnZxw7dgzvvfceJk6cCEUmkslkCAsLw9KlSw0+2BAREZH0aRxuAMDDwwN79uzBP//8g6tXr0IIgXr16sHBwaGi+iMiIiLSilbhRsHBwQEtW7bUdS9EREREr4xfv0BERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJJiEOFm6dKl8PT0hKWlJQICAvD333+XWrtq1SoEBwfDwcEBDg4OCA0NLbOeiIiI/l30Hm42b96MmJgYTJkyBUlJSWjatCnCwsKQmZlZYn1CQgL69++P+Ph4JCYmwt3dHZ06dcLt27cruXMiIiIyRDIhhNBnAwEBAWjZsiWWLFkCAJDL5XB3d8eHH36ICRMmvHT9oqIiODg4YMmSJYiKinppfU5ODuzs7JCdnQ1bW9tX7p+IiIgqnjav33o9c1NQUICTJ08iNDRUuczExAShoaFITEzUaBuPHz/Gs2fPUL169Ypqk4iIiIyImT53fv/+fRQVFcHZ2VllubOzM1JSUjTaxieffAJXV1eVgPSi/Px85OfnK+/n5OSUv2EiIiIyeHq/5uZVfPnll9i0aRO2b98OS0vLEmtiY2NhZ2envLm7u1dyl0RERFSZ9BpuHB0dYWpqioyMDJXlGRkZcHFxKXPdr776Cl9++SX279+PJk2alFo3ceJEZGdnK2+3bt3SSe9ERERkmPQabszNzeHv74+4uDjlMrlcjri4OAQGBpa63pw5czBjxgzs3bsXLVq0KHMfFhYWsLW1VbkRERGRdOn1mhsAiImJQXR0NFq0aIFWrVph4cKFyMvLw+DBgwEAUVFRcHNzQ2xsLABg9uzZmDx5MjZu3AhPT0+kp6cDAGxsbGBjY6O3cRAREZFh0Hu4iYyMxL179zB58mSkp6fDz88Pe/fuVV5kfPPmTZiY/O8E0/Lly1FQUIA+ffqobGfKlCmYOnVqZbZOREREBkjv89xUNs5zQ0REZHyMZp4bIiIiIl1juCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJMdN3A1Lx8FEB+n1zDJm5BXCqZo5Nw4NQ3cZc323pzLGU+xiw9i/l/Y2DAhDUwFGPHenejsQbGLPznPL+wu6vo0eghx470q0VB87jy7g05f0JHTzxbsdG+mtIx+b8moRlR+4q77//Rk2M79pcjx3p3rL95zDn4A3l/fHtPfB+p9f12JFuHUxOx5BNJ5X3v+vnj/Z+LnrsSLfSs56i69eHkPO0ELaWZvj1wzZwsbfUd1s6VVAox/rENNx4+Bge1a0xMNAT5maVfx5FJoQQlb7XYpYuXYq5c+ciPT0dTZs2xddff41WrVqVWr9161ZMmjQJaWlpqFevHmbPno3w8HCN9pWTkwM7OztkZ2fD1tZWJ/23nHkA9x4VqC2vYWOO45931Mk+9Mlzwu5SH0v7sksldlJxpD5Gjs/4SX2MUh9fw0m/4ckzudpyqyomuDijsx460r3YPRew6nAq5C+kChMZMCzYCxPDfV95+9q8fuv9banNmzcjJiYGU6ZMQVJSEpo2bYqwsDBkZmaWWH/s2DH0798fQ4cOxalTp9CjRw/06NED586dK7G+opUWbADg3qMCtJx5oJI70q2y/uBo8rgxkPoYOT7jHh8g/TFKfXylBRsAePJMjoaTfqvkjnQvds8FrDykGmwAQC6AlYdSEbvnQqX2o/dwM3/+fAwbNgyDBw+Gr68vVqxYAWtra3z33Xcl1i9atAhvvvkmPv74YzRs2BAzZsxA8+bNsWTJkkru/PlbUaUFG4V7jwrw8CU1hupYyn2d1hmiHYk3Xl6kRZ2hWXHgvE7rDM2cX5N0WmeIlu3X7D9umtYZmoPJ6TqtMzTpWU9LDTYKT57JkZ71tJI60r2CQjlWHU4ts2bV4VQUFJb9c9AlvYabgoICnDx5EqGhocplJiYmCA0NRWJiYonrJCYmqtQDQFhYWKn1+fn5yMnJUbnpSr9vjum0ztC8eI2NLuoM0YvX2OiiztC8eI2NLuoMzYvX2OiizhC9eI2NLuoMzYvX2OiiztB0/fqQTusM0frENLUzNsXJxfO6yqLXcHP//n0UFRXB2dlZZbmzszPS00tO6enp6VrVx8bGws7OTnlzd3fXTfMAMnM1OyOjaR0REUlLztNCndYZohsPH+u0Thf0/rZURZs4cSKys7OVt1u3buls207VNPs0lKZ1REQkLbaWmn0oWdM6Q+RR3Vqndbqg13Dj6OgIU1NTZGRkqCzPyMiAi0vJH/9zcXHRqt7CwgK2trYqN13ZNDxIp3WGZuOgAJ3WGaKF3TX7GK2mdYZmQgdPndYZmvffqKnTOkM0vr1m0xFoWmdovuvnr9M6Q/Prh210WmeIBgZ6wkRWdo2J7HldZdFruDE3N4e/vz/i4uKUy+RyOeLi4hAYGFjiOoGBgSr1AHDgwIFS6ytSdRtz1HjJXDY1bMyNdr4bTeexMeb5bjSdx8ZY57vRdB4bY53vRtN5bIx5vhtN57Ex1vluNJ3Hxljnu3Gxt4RVlbJfaq2qmBj1fDfmZiYYFuxVZs2wYK9Kne9G729LxcTEYNWqVVi3bh0uXryI9957D3l5eRg8eDAAICoqChMnTlTWjx49Gnv37sW8efOQkpKCqVOn4sSJExg5cqRe+j/+ecdSA44U5rl52fwSUph/Qupj5PiMe3yA9Mco9fFdnNG51IAjlXluJob7YkQbL7UzOCYyYEQb3cxzow2DmMRvyZIlykn8/Pz8sHjxYgQEPH+ro127dvD09MTatWuV9Vu3bsXnn3+unMRvzpw5ep3ED+AMxVLAGYqNG2coNn6codj4VeQMxdq8fhtEuKlMFRVuiIiIqOIY1QzFRERERLrEcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREksJwQ0RERJLCcENERESSwnBDREREkmK837FeTooJmXNycvTcCREREWlK8bqtyRcr/OvCTW5uLgDA3d1dz50QERGRtnJzc2FnZ1dmzb/uu6Xkcjnu3LmDatWqQSaTvXwFLeTk5MDd3R23bt2S5PdWSX18gPTHyPEZP6mPkeMzfhU1RiEEcnNz4erqChOTsq+q+deduTExMUGtWrUqdB+2traSfdIC0h8fIP0xcnzGT+pj5PiMX0WM8WVnbBR4QTERERFJCsMNERERSQrDjQ5ZWFhgypQpsLCw0HcrFULq4wOkP0aOz/hJfYwcn/EzhDH+6y4oJiIiImnjmRsiIiKSFIYbIiIikhSGGyIiIpIUhhsiIiKSFIYbDR06dAgRERFwdXWFTCbDjh07XrpOQkICmjdvDgsLC9StWxdr166t8D5fhbZjTEhIgEwmU7ulp6dXTsNaio2NRcuWLVGtWjU4OTmhR48euHTp0kvX27p1Kxo0aABLS0s0btwYe/bsqYRutVee8a1du1bt+FlaWlZSx9pZvnw5mjRpopwYLDAwEL/99luZ6xjLsVPQdozGdPxK8uWXX0Imk2HMmDFl1hnbcVTQZHzGdgynTp2q1m+DBg3KXEcfx4/hRkN5eXlo2rQpli5dqlF9amoqunTpgpCQECQnJ2PMmDF45513sG/fvgrutPy0HaPCpUuXcPfuXeXNycmpgjp8NX/88Qc++OAD/Pnnnzhw4ACePXuGTp06IS8vr9R1jh07hv79+2Po0KE4deoUevTogR49euDcuXOV2LlmyjM+4Pksoi8evxs3blRSx9qpVasWvvzyS5w8eRInTpxA+/bt0b17d5w/f77EemM6dgrajhEwnuNX3PHjx7Fy5Uo0adKkzDpjPI6A5uMDjO8YNmrUSKXfI0eOlFqrt+MnSGsAxPbt28usGT9+vGjUqJHKssjISBEWFlaBnemOJmOMj48XAMQ///xTKT3pWmZmpgAg/vjjj1Jr+vbtK7p06aKyLCAgQIwYMaKi23tlmoxvzZo1ws7OrvKa0jEHBwfx7bfflviYMR+7F5U1RmM9frm5uaJevXriwIEDom3btmL06NGl1hrjcdRmfMZ2DKdMmSKaNm2qcb2+jh/P3FSQxMREhIaGqiwLCwtDYmKinjqqOH5+fqhZsyY6duyIo0eP6rsdjWVnZwMAqlevXmqNMR9HTcYHAI8ePYKHhwfc3d1fepbAUBQVFWHTpk3Iy8tDYGBgiTXGfOwAzcYIGOfx++CDD9ClSxe141MSYzyO2owPML5jeOXKFbi6uqJOnTp46623cPPmzVJr9XX8/nVfnFlZ0tPT4ezsrLLM2dkZOTk5ePLkCaysrPTUme7UrFkTK1asQIsWLZCfn49vv/0W7dq1w19//YXmzZvru70yyeVyjBkzBq1bt8brr79eal1px9FQrytS0HR89evXx3fffYcmTZogOzsbX331FYKCgnD+/PkK/4LZ8jh79iwCAwPx9OlT2NjYYPv27fD19S2x1liPnTZjNLbjBwCbNm1CUlISjh8/rlG9sR1HbcdnbMcwICAAa9euRf369XH37l1MmzYNwcHBOHfuHKpVq6ZWr6/jx3BD5Va/fn3Ur19feT8oKAjXrl3DggULsH79ej129nIffPABzp07V+Z7xcZM0/EFBgaqnBUICgpCw4YNsXLlSsyYMaOi29Ra/fr1kZycjOzsbPz000+Ijo7GH3/8UeqLvzHSZozGdvxu3bqF0aNH48CBAwZ90Wx5lWd8xnYMO3furPx3kyZNEBAQAA8PD2zZsgVDhw7VY2eqGG4qiIuLCzIyMlSWZWRkwNbWVhJnbUrTqlUrgw8MI0eOxK+//opDhw699H9GpR1HFxeXimzxlWgzvuKqVKmCZs2a4erVqxXU3asxNzdH3bp1AQD+/v44fvw4Fi1ahJUrV6rVGuOxA7QbY3GGfvxOnjyJzMxMlTO7RUVFOHToEJYsWYL8/HyYmpqqrGNMx7E84yvO0I9hcfb29vDx8Sm1X30dP15zU0ECAwMRFxensuzAgQNlvncuBcnJyahZs6a+2yiREAIjR47E9u3bcfDgQXh5eb10HWM6juUZX3FFRUU4e/aswR7D4uRyOfLz80t8zJiOXVnKGmNxhn78OnTogLNnzyI5OVl5a9GiBd566y0kJyeX+MJvTMexPOMrztCPYXGPHj3CtWvXSu1Xb8evQi9XlpDc3Fxx6tQpcerUKQFAzJ8/X5w6dUrcuHFDCCHEhAkTxMCBA5X1169fF9bW1uLjjz8WFy9eFEuXLhWmpqZi7969+hrCS2k7xgULFogdO3aIK1euiLNnz4rRo0cLExMT8fvvv+trCGV67733hJ2dnUhISBB3795V3h4/fqysGThwoJgwYYLy/tGjR4WZmZn46quvxMWLF8WUKVNElSpVxNmzZ/UxhDKVZ3zTpk0T+/btE9euXRMnT54U/fr1E5aWluL8+fP6GEKZJkyYIP744w+Rmpoqzpw5IyZMmCBkMpnYv3+/EMK4j52CtmM0puNXmuKfJpLCcXzRy8ZnbMdw3LhxIiEhQaSmpoqjR4+K0NBQ4ejoKDIzM4UQhnP8GG40pPjYc/FbdHS0EEKI6Oho0bZtW7V1/Pz8hLm5uahTp45Ys2ZNpfetDW3HOHv2bOHt7S0sLS1F9erVRbt27cTBgwf107wGShobAJXj0rZtW+V4FbZs2SJ8fHyEubm5aNSokdi9e3flNq6h8oxvzJgxonbt2sLc3Fw4OzuL8PBwkZSUVPnNa2DIkCHCw8NDmJubixo1aogOHTooX/SFMO5jp6DtGI3p+JWm+Iu/FI7ji142PmM7hpGRkaJmzZrC3NxcuLm5icjISHH16lXl44Zy/GRCCFGx54aIiIiIKg+vuSEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISIiIklhuCEiIiJJYbghIiIiSWG4ISKDdu/ePbz33nuoXbs2LCws4OLigrCwMBw9elRZc+zYMYSHh8PBwQGWlpZo3Lgx5s+fj6KiIpVtyWQy5c3Ozg6tW7fGwYMHK3tIRFTBGG6IyKD17t0bp06dwrp163D58mXs2rUL7dq1w4MHDwAA27dvR9u2bVGrVi3Ex8cjJSUFo0ePxsyZM9GvXz8Un4R9zZo1uHv3Lo4ePQpHR0d07doV169f18fQiKiC8OsXiMhgZWVlwcHBAQkJCWjbtq3a43l5efDw8EDbtm3x888/qzz2yy+/oFu3bti0aRMiIyMBPD9zs337dvTo0QMAcOfOHbi5uWHFihUYMWJEhY+HiCoHz9wQkcGysbGBjY0NduzYgfz8fLXH9+/fjwcPHuCjjz5SeywiIgI+Pj748ccfS92+lZUVAKCgoEB3TROR3jHcEJHBMjMzw9q1a7Fu3TrY29ujdevW+PTTT3HmzBkAwOXLlwEADRs2LHH9Bg0aKGuKe/z4MT7//HOYmpqWeFaIiIwXww0RGbTevXvjzp072LVrF958800kJCSgefPmWLt2rbJGm3fX+/fvDxsbG1SrVg0///wzVq9ejSZNmlRA50SkL7zmhoiMzjvvvIMDBw5g4cKF6NWrF44ePYqgoCC1Oh8fH/j6+mLHjh0Anl9zs3z5coSGhsLOzg41atSo5M6JqDLwzA0RGR1fX1/k5eWhU6dOqF69OubNm6dWs2vXLly5cgX9+/dXWe7i4oK6desy2BBJGMMNERmsBw8eoH379vjhhx9w5swZpKamYuvWrZgzZw66d++OqlWrYuXKldi5cyeGDx+OM2fOIC0tDatXr8agQYPQp08f9O3bV9/DIKJKZqbvBoiISmNjY4OAgAAsWLAA165dw7Nnz+Du7o5hw4bh008/BQD06dMH8fHxmDVrFoKDg/H06VPUq1cPn332GcaMGQOZTKbnURBRZeM1N0RERCQpfFuKiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgkheGGiIiIJIXhhoiIiCSF4YaIiIgk5f8B/RHYVQSDVXkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['SOP'], dfm['Admission Decision'])\n",
    "plt.xlabel('SOP')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.title('Scatter Plot between SOP and Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67e3d047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['CGPA'], dfm['Admission Decision'])\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Chance of Admit')\n",
    "plt.title('Scatter Plot between CGPA and Chance of Admit')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2693162",
   "metadata": {},
   "source": [
    "## Splitting of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "_QNQGBTZ6m71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_QNQGBTZ6m71",
    "outputId": "6be845dd-2eb7-4ee7-ff13-b24c078e3ea4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "AxjVxtNV8CAB",
   "metadata": {
    "id": "AxjVxtNV8CAB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(dfm.drop(['Admission Decision'], axis = 1), dfm['Admission Decision'], test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "hi9kuinU8RxA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hi9kuinU8RxA",
    "outputId": "c2ad3a6d-e9b3-4d6a-8645-7c2d7f9d0674"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (320, 8)\n",
      "X_test shape: (80, 8)\n",
      "y_train shape: (320,)\n",
      "y_test shape: (80,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:\",X_train.shape)\n",
    "print(\"X_test shape:\",X_test.shape)\n",
    "print(\"y_train shape:\",y_train.shape)\n",
    "print(\"y_test shape:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "q6MCQpBg8Ued",
   "metadata": {
    "id": "q6MCQpBg8Ued"
   },
   "outputs": [],
   "source": [
    "# To store results of models\n",
    "result_dict_train = {}\n",
    "result_dict_test = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a081fa1c",
   "metadata": {},
   "source": [
    "## Gradient Boost Classfier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "MKLLo0y38WKs",
   "metadata": {
    "id": "MKLLo0y38WKs"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eiUhAbUZ8YWd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eiUhAbUZ8YWd",
    "outputId": "0dd111c0-c4dd-496b-8552-b83e1ae4d4f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.878125\n",
      "Test Score: 0.8875\n"
     ]
    }
   ],
   "source": [
    "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
    "accuracies = cross_val_score(gradient_booster, X_train, y_train, cv=5)\n",
    "gradient_booster.fit(X_train,y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",gradient_booster.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "gRRqpD_o8eDI",
   "metadata": {
    "id": "gRRqpD_o8eDI"
   },
   "outputs": [],
   "source": [
    "result_dict_train[\"Gradient-Boost Default Train Score\"] = np.mean(accuracies)\n",
    "result_dict_test[\"Gradient-Boost Default Test Score\"] = gradient_booster.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "XVplzJnN8ge6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XVplzJnN8ge6",
    "outputId": "9da698f4-adf7-4224-e434-995b114eebef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBC accuracy is 0.89\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"GBC accuracy is %2.2f\" % accuracy_score(\n",
    "     y_test, gradient_booster.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "nZnLatcG8tcy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nZnLatcG8tcy",
    "outputId": "e22a1926-1dce-462a-d326-777be9196be0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.96      0.91        47\n",
      "           1       0.93      0.79      0.85        33\n",
      "\n",
      "    accuracy                           0.89        80\n",
      "   macro avg       0.90      0.87      0.88        80\n",
      "weighted avg       0.89      0.89      0.89        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "pred=gradient_booster.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8ddef00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IjgSh7lsdnDp1Cps3b4afn59O+9y5c7F3717s2rULKSkpuHbtGiIiIgw+PsOMiIgaVElJCV588UV8+OGHaN68uba9qKgIW7ZswYoVK/D888/D398fW7duxYkTJ3Dy5EmD+mCYERHJUT2mGdVqNYqLi3U2tVpda1cxMTEYOnQoQkNDddrT09NRWVmp0965c2d4eXkhLS3NoNNhmBERyVE9phlVKhWUSqXOplKpauzms88+w5kzZ2p8Py8vD5aWlnB0dNRpd3V1RV5enkGnw9WMRERyVI+FHHFxcYiNjdVpUygU1fa7evUqZs+ejUOHDsHKyqrO/emDYUZEJEf1+J6ZQqGoMbwelp6ejoKCAvTq1UvbVlVVhdTUVKxbtw5JSUmoqKhAYWGhTnWWn58PNzc3g8bEMCMikiGhEb40PWDAAJw7d06nbfLkyejcuTMWLlwIT09PNGvWDMnJyYiMjAQAZGZm4sqVKwgMDDSoL4YZERE1CHt7e/j6+uq02drawtnZWds+ZcoUxMbGwsnJCQ4ODpg1axYCAwPxzDPPGNQXw4yISIYaozLTx8qVK2FhYYHIyEio1WqEhYVhw4YNBh9HEEVRbIDxmZR1z5mmHgLJxO1T60w9BJIJKyOXHrZ/21rnz5bummzEkRgHKzMiIhl6UiozY2GYERHJEMOMiIjMntTCjHcAISIis8fKjIhIhqRWmTHMiIjkSFpZxjAjIpIjVmZERGT2GGZERGT2pBZmXM1IRERmj5UZEZEMSa0yY5gREcmRtLKMYUZEJEeszIiIyOwxzIiIyOxJLcy4mpGIiMweKzMiIjmSVmHGMCMikiOpTTMyzIiIZIhhRkREZo9hRkREZk9qYcbVjEREZPZYmRERyZG0CjOGGRGRHHGakYiIzJ4gCHXeDLFx40b4+fnBwcEBDg4OCAwMxLfffqt9PyQkpNrxZ8yYYfD5sDIjIpKhxqrMWrdujWXLlqFjx44QRRHbtm3DiBEj8NNPP+Gpp54CAEybNg3x8fHaz9jY2BjcD8OMiIgazPDhw3Vev/3229i4cSNOnjypDTMbGxu4ubnVqx9OMxIRyZFQ902tVqO4uFhnU6vVj+2yqqoKn332GUpLSxEYGKht37FjB1xcXODr64u4uDiUlZUZfDoMMxmbP3kg7v60Du/Oj9S2JX04G3d/WqezrfnXOBOOkqRgy4ebMWFMJAL79ERI30DMmfUqLudcMvWwZK0+18xUKhWUSqXOplKpau3r3LlzsLOzg0KhwIwZM7B792507doVADBhwgR88sknOHr0KOLi4rB9+3ZMnDjR4PPhNKNM+Xf1wpTIIJz9/c9q72358nss3bhP+7qsvLIxh0YSdPrUjxg7/kU81a0bqu5VYe3qFZgxbQr+8/X+Ol0fofqrzzWzuLg4xMbG6rQpFIpa9/fx8UFGRgaKiorwxRdfICoqCikpKejatSumT5+u3a9bt25wd3fHgAEDkJ2djfbt2+s9JoaZDNlaW2LrO9F4demnWDR1cLX375ZXIP/mHROMjKRq4wdbdF7Hv70M/fsG4tdfLsC/dx8TjUre6hNmCoXikeH1MEtLS3To0AEA4O/vj1OnTmH16tXYvHlztX0DAgIAAFlZWQaFGacZZWhV3Fgc+O48jv6QWeP7Y4f0xtUjy3B61z8RP+sFWFs1a+QRktSV3Ln/P0sOSqWJRyJfjbU0vyYajabWa2wZGRkAAHd3d4OOadLK7MaNG/joo4+QlpaGvLw8AICbmxueffZZREdHo0WLFqYcniT9LcwfPTp74rmJy2t8f+e3p3El9xZyrxehW0cPvDV7BDp5t8S4+f/byCMlqdJoNFj+P++gR89e6Nixk6mHQw0sLi4O4eHh8PLywp07d5CYmIhjx44hKSkJ2dnZSExMxJAhQ+Ds7IyzZ89i7ty5CA4Ohp+fn0H9mCzMTp06hbCwMNjY2CA0NBSdOt3/o87Pz8eaNWuwbNkyJCUloXfv3o88jlqtrpbwoqYKgkWTBhu7uWrt6oh3F0Ri2CvroK64V+M+H/3ne+3PF7KuIfdGMQ588A+0be2CnD9vNNZQScLeeWsJsi9eRML2RFMPRd4a6QYgBQUFmDRpEnJzc6FUKuHn54ekpCQMHDgQV69exeHDh7Fq1SqUlpbC09MTkZGReP311w3uRxBFUWyA8T/WM888g+7du2PTpk3VylZRFDFjxgycPXsWaWlpjzzO4sWLsWTJEp22Jq590Mz9aaOP2dwND/HD5yun4969Km1b06ZNoNFooNGIUAbMgUaj++dgY2WJm2krMPzV9Tic9mtjD/mJd/vUOlMPway881Y8jh1NxkfbPkHr1p6mHo5ZsTJy6dEu9ps6f/bSiiFGHIlxmKwy+/nnn5GQkFDj/KsgCJg7dy569uz52OPUtKqmZd+FRhunlBz9MRP+o9/WaftgyURk5uTj/YRD1YIMALr7tAYA5N0oapQxkjSJogjV20txJPkQtiRsZ5A9AaR2b0aThZmbmxt+/PFHdO7cucb3f/zxR7i6uj72ODWtquEUY81KytT4JTtXp630bgVuFZXil+xctG3tgrHhvZF0/AJuFpaiW6dWWD4vAt+lX8T5i9dMNGqSgneWLsG33+zDqrUbYGtjixvXrwMA7OztYWVlZeLRyZPEssx0YTZ//nxMnz4d6enpGDBggDa48vPzkZycjA8//BDvvfeeqYYnS5WV9/B8gA9mTugPW2tL/Jl/G3uSM7Dsf5NMPTQyc5/v/BQAMCX6JZ32+LdUGDEqwhRDkj2pVWYmu2YGADt37sTKlSuRnp6Oqqr713GaNGkCf39/xMbGYsyYMXU6rnXPmcYcJlGteM2MGouxr5l1XHCgzp+9+G7176eamkmX5o8dOxZjx45FZWUlbty4v1LOxcUFzZrxe01ERA1JYoXZk3EHkGbNmhn8BTkiIqo7qU0zPhFhRkREjUtiWcYwIyKSIwsLaaUZw4yISIakVpnxRsNERGT2WJkREckQF4AQEZHZk1iWMcyIiOSIlRkREZk9hhkREZk9iWUZVzMSEZH5Y2VGRCRDnGYkIiKzJ7EsY5gREckRKzMiIjJ7EssyhhkRkRxJrTLjakYiIjJ7rMyIiGRIYoUZw4yISI6kNs3IMCMikiGJZRmvmRERyZEgCHXeDLFx40b4+fnBwcEBDg4OCAwMxLfffqt9v7y8HDExMXB2doadnR0iIyORn59v8PkwzIiIZEgQ6r4ZonXr1li2bBnS09Nx+vRpPP/88xgxYgQuXLgAAJg7dy727t2LXbt2ISUlBdeuXUNERITh5yOKomjwp55w1j1nmnoIJBO3T60z9RBIJqyMfFHo2eWpdf7sideC69W3k5MT3n33XYwePRotWrRAYmIiRo8eDQD47bff0KVLF6SlpeGZZ57R+5i8ZkZEJEP1WQCiVquhVqt12hQKBRQKxSM/V1VVhV27dqG0tBSBgYFIT09HZWUlQkNDtft07twZXl5eBocZpxmJiGSoPtOMKpUKSqVSZ1OpVLX2de7cOdjZ2UGhUGDGjBnYvXs3unbtiry8PFhaWsLR0VFnf1dXV+Tl5Rl0PqzMiIhkqD6VWVxcHGJjY3XaHlWV+fj4ICMjA0VFRfjiiy8QFRWFlJSUOvdfE4YZEZEM1SfM9JlS/CtLS0t06NABAODv749Tp05h9erVGDt2LCoqKlBYWKhTneXn58PNzc2gMXGakYhIhhprNWNNNBoN1Go1/P390axZMyQnJ2vfy8zMxJUrVxAYGGjQMVmZERFRg4mLi0N4eDi8vLxw584dJCYm4tixY0hKSoJSqcSUKVMQGxsLJycnODg4YNasWQgMDDRo8QfAMCMikqXGup1VQUEBJk2ahNzcXCiVSvj5+SEpKQkDBw4EAKxcuRIWFhaIjIyEWq1GWFgYNmzYYHA//J4ZUT3we2bUWIz9PbP+q0/U+bNHZz9rxJEYByszIiIZ4o2GiYjI7EksyxhmRERyZCGxNOPSfCIiMnuszIiIZEhihRnDjIhIjrgAhIiIzJ6FtLKMYUZEJEeszIiIyOxJLMu4mpGIiMwfKzMiIhkSIK3SjGFGRCRDXABCRERmjwtAiIjI7EksyxhmRERyxHszEhERPWFYmRERyZDECjOGGRGRHHEBCBERmT2JZRnDjIhIjqS2AIRhRkQkQ9KKMq5mJCIiCWBlRkQkQ1wAQkREZo/3ZiQiIrMntcqM18yIiGRIEOq+GUKlUqFPnz6wt7dHy5YtMXLkSGRmZursExISAkEQdLYZM2YY1A/DjIhIhh4OD0M2Q6SkpCAmJgYnT57EoUOHUFlZiUGDBqG0tFRnv2nTpiE3N1e7LV++3KB+6jTN+N1332Hz5s3Izs7GF198gVatWmH79u1o27YtnnvuubockoiIJOjAgQM6rxMSEtCyZUukp6cjODhY225jYwM3N7c692NwZfbll18iLCwM1tbW+Omnn6BWqwEARUVFeOedd+o8ECIiajwWQt03tVqN4uJine1BFjxOUVERAMDJyUmnfceOHXBxcYGvry/i4uJQVlZm2PkYtDeAt956C5s2bcKHH36IZs2aaduDgoJw5swZQw9HREQmUJ9pRpVKBaVSqbOpVKrH9qnRaDBnzhwEBQXB19dX2z5hwgR88sknOHr0KOLi4rB9+3ZMnDjRoPMxeJoxMzNTpzR8QKlUorCw0NDDERGRCdRnLWNcXBxiY2N12hQKxWM/FxMTg/Pnz+P48eM67dOnT9f+3K1bN7i7u2PAgAHIzs5G+/bt9RqTwWHm5uaGrKwstGnTRqf9+PHjaNeunaGHIyIiE6jPvRkVCoVe4fVXM2fOxL59+5CamorWrVs/ct+AgAAAQFZWlt5hZvA047Rp0zB79mz88MMPEAQB165dw44dOzB//ny88sorhh6OiIgkTBRFzJw5E7t378aRI0fQtm3bx34mIyMDAODu7q53PwZXZosWLYJGo8GAAQNQVlaG4OBgKBQKzJ8/H7NmzTL0cEREZAKN9Z3pmJgYJCYm4quvvoK9vT3y8vIA3L80ZW1tjezsbCQmJmLIkCFwdnbG2bNnMXfuXAQHB8PPz0/vfgRRFMW6DLCiogJZWVkoKSlB165dYWdnV5fDNAjrnjNNPQSSidun1pl6CCQTVka+X9P0XRfq/NkP/vaU3vvW9r20rVu3Ijo6GlevXsXEiRNx/vx5lJaWwtPTE6NGjcLrr78OBwcHvfup86/H0tISXbt2revHiYjIhBqrMntcveTp6YmUlJR692NwmPXv3/+R3wA/cuRIvQZEREQNT/YP5+zRo4fO68rKSmRkZOD8+fOIiooy1riIiKgBSSzLDA+zlStX1ti+ePFilJSU1HtAREREhjLajYYnTpyIjz76yFiHIyKiBtRYNxpuLEZbH5OWlgYrKytjHa5efjv8vqmHQDIxOTHD1EMgmfh0Ug+jHk9qj0wxOMwiIiJ0XouiiNzcXJw+fRpvvPGG0QZGREQN50mtsOrK4DBTKpU6ry0sLODj44P4+HgMGjTIaAMjIqKGYyGtLDMszKqqqjB58mR069YNzZs3b6gxERFRA5NamBk0bdqkSRMMGjSId8cnIqInisHXAH19fXHp0qWGGAsRETUSqa1mrNPDOefPn499+/YhNze32tNGiYjoyVefJ00/ifS+ZhYfH4958+ZhyJAhAIAXXnhBJ6FFUYQgCKiqqjL+KImIyKie0AKrzvQOsyVLlmDGjBk4evRoQ46HiIgagWzvzfjgzsf9+vVrsMEQEVHjkNqXpg06nyf1wh8REcmbQd8z69Sp02MD7datW/UaEBERNTyp1SYGhdmSJUuq3QGEiIjMj2yvmQHAuHHj0LJly4YaCxERNRKJZZn+YcbrZURE0vGkfl+srgxezUhEROZPttOMGo2mIcdBRERUZ0Z7OCcREZkPiRVmDDMiIjmS7TUzIiKSDgHSSjOGGRGRDEmtMpPa7bmIiEgPjfUIGJVKhT59+sDe3h4tW7bEyJEjkZmZqbNPeXk5YmJi4OzsDDs7O0RGRiI/P9+w8zFsWERERPpLSUlBTEwMTp48iUOHDqGyshKDBg1CaWmpdp+5c+di79692LVrF1JSUnDt2jVEREQY1A+nGYmIZKixboRx4MABndcJCQlo2bIl0tPTERwcjKKiImzZsgWJiYl4/vnnAQBbt25Fly5dcPLkSTzzzDN69cPKjIhIhuozzahWq1FcXKyzqdVqvfotKioCADg5OQEA0tPTUVlZidDQUO0+nTt3hpeXF9LS0vQ/HwPOnYiIJEIQ6r6pVCoolUqdTaVSPbZPjUaDOXPmICgoCL6+vgCAvLw8WFpawtHRUWdfV1dX5OXl6X0+nGYkIpKh+tzOKi4uDrGxsTptCoXisZ+LiYnB+fPncfz48Tr3XRuGGRGRDNVnab5CodArvP5q5syZ2LdvH1JTU9G6dWttu5ubGyoqKlBYWKhTneXn58PNzU3v43OakYiIGowoipg5cyZ2796NI0eOoG3btjrv+/v7o1mzZkhOTta2ZWZm4sqVKwgMDNS7H1ZmREQy1Fj3ZoyJiUFiYiK++uor2Nvba6+DKZVKWFtbQ6lUYsqUKYiNjYWTkxMcHBwwa9YsBAYG6r2SEWCYERHJkkUj3c5q48aNAICQkBCd9q1btyI6OhoAsHLlSlhYWCAyMhJqtRphYWHYsGGDQf0wzIiIZKixKjN9noVpZWWF9evXY/369XXuh2FGRCRDUrs3I8OMiEiGpPakaa5mJCIis8fKjIhIhiRWmDHMiIjkSGrTjAwzIiIZkliWMcyIiORIagsmGGZERDLUWM8zayxSC2ciIpIhVmZERDIkrbqMYUZEJEtczUhERGZPWlHGMCMikiWJFWYMMyIiOeJqRiIioicMKzMiIhmSWiXDMCMikiGpTTMyzIiIZEhaUcYwIyKSJVZmRERk9qR2zUxq50NERDLEyoyISIY4zUhERGZPWlHGMCMikiWJFWYMMyIiObKQWG3GBSBERDIkCHXfDJGamorhw4fDw8MDgiBgz549Ou9HR0dDEASdbfDgwQafD8OMiIgaTGlpKbp3747169fXus/gwYORm5ur3T799FOD++E0IxGRDAmNNM0YHh6O8PDwR+6jUCjg5uZWr35YmRERyVB9phnVajWKi4t1NrVaXeexHDt2DC1btoSPjw9eeeUV3Lx50+BjMMyIiGTIAkKdN5VKBaVSqbOpVKo6jWPw4MH4+OOPkZycjP/5n/9BSkoKwsPDUVVVZdBxOM1IRCRD9VmaHxcXh9jYWJ02hUJRp2ONGzdO+3O3bt3g5+eH9u3b49ixYxgwYIDex2FlRkQkQ/WZZlQoFHBwcNDZ6hpmD2vXrh1cXFyQlZVl0OcYZkRE9MT4888/cfPmTbi7uxv0OU4zEhHJUGOtZiwpKdGpsnJycpCRkQEnJyc4OTlhyZIliIyMhJubG7Kzs/Haa6+hQ4cOCAsLM6gfhhkRkQxZNNINQE6fPo3+/ftrXz+41hYVFYWNGzfi7Nmz2LZtGwoLC+Hh4YFBgwZh6dKlBk9bMsyIiGSosSqzkJAQiKJY6/tJSUlG6YdhRkQkQ1K70TAXgBARkdljZUZEJEONNc3YWBhmMvdSxGDk512r1j48Yixmzf+XCUZEUjDCtyX6eDnCQ6lAxT0Nfr9ehk/PXENuse4tjzq62GBsT3e0d7GBRgT+uH0XqsPZqKyq/RoLGUdjLQBpLAwzmVu7JREajUb7+vKlLCyaPR3Bzw8y4ajI3HVxtcPBzBu4dKMMFhbAuJ7uiAttjwVf/wb1vft/bx1dbLAotD2+Op+PhB//iypRhHdzazxirQAZESszkhTH5k46r3du3wKPVp7w69nbRCMiKViWfEnn9cbvr+CDsd3Q1skavxWUAgBe6tMKB367jq/PF2j3e7hyo4YjtQUgDDPSqqysRHLSfkSOewmC1P7SyaRsLJsAAEoq7t881sGqKTq2sMX3ObexZHBHuNpb4lqRGjszcpH5f2FHDUtq/4ZzNSNpnUg9gpKSOxg0ZISph0ISIgCY1KcVfisowZ+F5QCAlnaWAIDI7m44cvEmliVfQs6tMvxrYHu42VuacLRkrp7oMLt69SpefvnlR+5j7OfqyNmBvbvR55kgOLdoaeqhkIRMDmgNT0drrE39Q9v2oPBP/v0mUrJv4fKtu9h++v4CkZAOziYaqbxYCEKdtyfREx1mt27dwrZt2x65T03P1dmwankjjVA68nOv4afTJxE+PNLUQyEJiX66FXq1dsDSg1m4VVapbS+8ew8A8N//q9Qe+G9ROZxtmzXqGOVKqMf2JDLpNbOvv/76ke9funTpke8DNT9XJ6+kXsOSpaT9e+DY3AkBz/Y19VBIIqKfboU+XkosTcrC9ZIKnfeul1TgVlkF3JW6999zd1Ag4793GnOY8vWkplIdmTTMRo4cCUEQHnnfrsctRFAoFNVuSHm7ktOMhtBoNDi4/ysMDH8BTZpyTRDV38sBrfFs2+Z4/+gl3K3UQGl1/++qrLJK+x2yfReuY3R3N/xx6y7+uH0Xwe2d4OFghZXHLptw5PLBpflG5O7ujg0bNmDEiJoXHGRkZMDf37+RRyU/Z06dREF+LsKGjTT1UEgiBvq4AAD+HdZRp33j91eQmn0LAPDtr9fRrImASX1awdayCa7cLsc7h7NR8FAVRw3jCb30VWcmDTN/f3+kp6fXGmaPq9rIOHoHPIuDJ86aehgkIeM/ztBrv6/PF+h8z4yorkwaZgsWLEBpae3fKenQoQOOHj3aiCMiIpIHiRVmpg2zvn0fvdjA1tYW/fr1a6TREBHJiMTSjFf7iYhkiAtAiIjI7HEBCBERmT2JZdmTfQcQIiIifbAyIyKSI4mVZgwzIiIZ4gIQIiIye1wAQkREZk9iWcYwIyKSJYmlGVczEhFRg0lNTcXw4cPh4eEBQRCwZ88enfdFUcS///1vuLu7w9raGqGhobh48aLB/TDMiIhkSKjHP4YoLS1F9+7dsX79+hrfX758OdasWYNNmzbhhx9+gK2tLcLCwlBeXl7j/rXhNCMRkQzVZwGIWq2GWq373Miani0JAOHh4QgPD6/xOKIoYtWqVXj99de1T0/5+OOP4erqij179mDcuHF6j4mVGRGRDAn12FQqFZRKpc6mUqkMHkNOTg7y8vIQGhqqbVMqlQgICEBaWppBx2JlRkQkR/WozOLi4hAbG6vTVlNV9jh5eXkAAFdXV512V1dX7Xv6YpgREclQfb40XduUoilxmpGIiEzCzc0NAJCfn6/Tnp+fr31PXwwzIiIZEoS6b8bStm1buLm5ITk5WdtWXFyMH374AYGBgQYdi9OMREQy1FjfmS4pKUFWVpb2dU5ODjIyMuDk5AQvLy/MmTMHb731Fjp27Ii2bdvijTfegIeHB0aOHGlQPwwzIiI5aqQ0O336NPr37699/WDhSFRUFBISEvDaa6+htLQU06dPR2FhIZ577jkcOHAAVlZWBvUjiKIoGnXkT4A/bqofvxORESza/6uph0Ay8emkHkY93m+5ZXX+bGd3GyOOxDhYmRERyZDU7prPBSBERGT2WJkREcmQxAozhhkRkSxJLM0YZkREMlSfO4A8iRhmREQyJLUFIAwzIiIZkliWcTUjERGZP1ZmRERyJLHSjGFGRCRDXABCRERmjwtAiIjI7EksyxhmRESyJLE042pGIiIye6zMiIhkiAtAiIjI7HEBCBERmT2JZRnDjIhIjliZERGRBEgrzbiakYiIzB4rMyIiGeI0IxERmT2JZRnDjIhIjliZERGR2ZPal6a5AISISI6EemwGWLx4MQRB0Nk6d+5srLPQYmVGREQN6qmnnsLhw4e1r5s2NX70MMyIiGSoMScZmzZtCjc3twbtg9OMREQyJAh139RqNYqLi3U2tVpda18XL16Eh4cH2rVrhxdffBFXrlwx+vkwzIiIZEioxz8qlQpKpVJnU6lUNfYTEBCAhIQEHDhwABs3bkROTg769u2LO3fuGPd8RFEUjXrEJ8AfN2v/PwQiY1q0/1dTD4Fk4tNJPYx6vOsl9+r8WYdmVdUqMYVCAYVC8djPFhYWwtvbGytWrMCUKVPqPIaH8ZoZEZEM1eeamb7BVRNHR0d06tQJWVlZ9RhBdZxmJCKiRlNSUoLs7Gy4u7sb9bgMMyIiGarPAhBDzJ8/HykpKbh8+TJOnDiBUaNGoUmTJhg/frxRz4fTjEREMtRYdwD5888/MX78eNy8eRMtWrTAc889h5MnT6JFixZG7YdhRkQkQ411b8bPPvusUfrhNCMREZk9VmZERDIktbvmszIjIiKzx8qMiEiGpPYIGIYZEZEMSW2akWFGRCRDEssyhhkRkSxJLM24AISIiMweKzMiIhniAhAiIjJ7XABCRERmT2JZxjAjIpIliaUZw4yISIakds2MqxmJiMjssTIjIpIhqS0AEURRFE09CDI9tVoNlUqFuLg4KBQKUw+HJIx/a9QQGGYEACguLoZSqURRUREcHBxMPRySMP6tUUPgNTMiIjJ7DDMiIjJ7DDMiIjJ7DDMCACgUCrz55pu8IE8Njn9r1BC4AISIiMweKzMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDPC+vXr0aZNG1hZWSEgIAA//vijqYdEEpSamorhw4fDw8MDgiBgz549ph4SSQjDTOZ27tyJ2NhYvPnmmzhz5gy6d++OsLAwFBQUmHpoJDGlpaXo3r071q9fb+qhkARxab7MBQQEoE+fPli3bh0AQKPRwNPTE7NmzcKiRYtMPDqSKkEQsHv3bowcOdLUQyGJYGUmYxUVFUhPT0doaKi2zcLCAqGhoUhLSzPhyIiIDMMwk7EbN26gqqoKrq6uOu2urq7Iy8sz0aiIiAzHMCMiIrPHMJMxFxcXNGnSBPn5+Trt+fn5cHNzM9GoiIgMxzCTMUtLS/j7+yM5OVnbptFokJycjMDAQBOOjIjIME1NPQAyrdjYWERFRaF37954+umnsWrVKpSWlmLy5MmmHhpJTElJCbKysrSvc3JykJGRAScnJ3h5eZlwZCQFXJpPWLduHd59913k5eWhR48eWLNmDQICAkw9LJKYY8eOoX///tXao6KikJCQ0PgDIklhmBERkdnjNTMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDMiIjJ7DDMiPUVHR+s8TDIkJARz5sxp9HEcO3YMgiCgsLCw0fsmelIxzMjsRUdHQxAECIIAS0tLdOjQAfHx8bh3716D9vuf//wHS5cu1WtfBhBRw+KNhkkSBg8ejK1bt0KtVuObb75BTEwMmjVrhri4OJ39KioqYGlpaZQ+nZycjHIcIqo/VmYkCQqFAm5ubvD29sYrr7yC0NBQfP3119qpwbfffhseHh7w8fEBAFy9ehVjxoyBo6MjnJycMGLECFy+fFl7vKqqKsTGxsLR0RHOzs547bXX8PBtTB+eZlSr1Vi4cCE8PT2hUCjQoUMHbNmyBZcvX9beYLd58+YQBAHR0dEA7j9yR6VSoW3btrC2tkb37t3xxRdf6PTzzTffoFOnTrC2tkb//v11xklE9zHMSJKsra1RUVEBAEhOTkZmZiYOHTqEffv2obKyEmFhYbC3t8d3332H77//HnZ2dhg8eLD2M++//z4SEhLw0Ucf4fjx47h16xZ27979yD4nTZqETz/9FGvWrMGvv/6KzZs3w87ODp6envjyyy8BAJmZmcjNzcXq1asBACqVCh9//DE2bdqECxcuYO7cuZg4cSJSUlIA3A/diIgIDB8+HBkZGZg6dSoWLVrUUL82IvMlEpm5qKgoccSIEaIoiqJGoxEPHTokKhQKcf78+WJUVJTo6uoqqtVq7f7bt28XfXx8RI1Go21Tq9WitbW1mJSUJIqiKLq7u4vLly/Xvl9ZWSm2bt1a248oimK/fv3E2bNni6IoipmZmSIA8dChQzWO8ejRoyIA8fbt29q28vJy0cbGRjxx4oTOvlOmTBHHjx8viqIoxsXFiV27dtV5f+HChdWORSR3vGZGkrBv3z7Y2dmhsrISGo0GEyZMwOLFixETE4Nu3brpXCf7+eefkZWVBXt7e51jlJeXIzs7G0VFRcjNzdV5plvTpk3Ru3fvalOND2RkZKBJkybo16+f3mPOyspCWVkZBg4cqNNeUVGBnj17AgB+/fXXas+W41PAiapjmJEk9O/fHxs3boSlpSU8PDzQtOn//9O2tbXV2bekpAT+/v7YsWNHteO0aNGiTv1bW1sb/JmSkhIAwP79+9GqVSud9xQKRZ3GQSRXDDOSBFtbW3To0EGvfXv16oWdO3eiZcuWcHBwqHEfd3d3/PDDDwgODgYA3Lt3D+np6ejVq1eN+3fr1g0ajQYpKSkIDQ2t9v6DyrCqqkrb1rVrVygUCly5cqXWiq5Lly74+uuvddpOnjz5+JMkkhkuACHZefHFF+Hi4oIRI0bgu+++Q05ODo4dO4Z//OMf+PPPPwEAs2fPxrJly7Bnzx789ttvePXVVx/5HbE2bdogKioKL7/8Mvbs2aM95ueffw4A8Pb2hiAI2LdvH65fv46SkhLY29tj/vz5mDt3LrZt24bs7GycOXMGa9euxbZt2wAAM2bMwMWLF7FgwQJkZmYiMTERCQkJDf0rIjI7DDOSHRsbG6SmpsLLywsRERHo0qULpkyZgvLycm2lNm/ePLz00kuIiopCYGAg7O3tMWrUqEced+PGjRg9ejReffVVdO7cGdOmTUNpaSkAoFWrVliyZAkWLVoEV1dXzJw5EwCwdOlSvPHGG1CpVOjSpQsGDx6M/fv3o23btgAALy8vfPnll9izZw+6d++OTZs24Z133mnA3w6ReRLE2q5oExERmQlWZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPYYZkREZPb+HyNRwU29qhfmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = gradient_booster.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aYPFbr2W8B7p",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aYPFbr2W8B7p",
    "outputId": "2c590387-5600-4a12-88ed-016ee4fee89d"
   },
   "outputs": [],
   "source": [
    "grid = {\n",
    "    'learning_rate':[0.01,0.05,0.1],\n",
    "    'n_estimators':np.arange(100,500,100),\n",
    "}\n",
    "\n",
    "gb = GradientBoostingClassifier()\n",
    "gb_cv = GridSearchCV(gb, grid, cv = 4)\n",
    "gb_cv.fit(X_train,y_train)\n",
    "print(\"Best Parameters:\",gb_cv.best_params_)\n",
    "print(\"Train Score:\",gb_cv.best_score_)\n",
    "print(\"Test Score:\",gb_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "TXAWu3S48B06",
   "metadata": {
    "id": "TXAWu3S48B06"
   },
   "outputs": [],
   "source": [
    "result_dict_train[\"Gradient-Boost GridSearch Train Score\"] = gb_cv.best_score_\n",
    "result_dict_test[\"Gradient-Boost GridSearch Test Score\"] = gb_cv.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7pXqQLNc8Bt_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7pXqQLNc8Bt_",
    "outputId": "7af0e216-1c05-4378-e703-9a14f07011cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 2}\n",
      "Train Score: 0.8718750000000001\n",
      "Test Score: 0.875\n"
     ]
    }
   ],
   "source": [
    "grid = {'max_depth':[2,3,4,5,6,7] }\n",
    "\n",
    "gb = GradientBoostingClassifier(learning_rate=0.1,n_estimators=400)\n",
    "\n",
    "gb_cv = GridSearchCV(gb, grid, cv = 4)\n",
    "\n",
    "gb_cv.fit(X_train,y_train)\n",
    "\n",
    "print(\"Best Parameters:\",gb_cv.best_params_)\n",
    "\n",
    "print(\"Train Score:\",gb_cv.best_score_)\n",
    "\n",
    "print(\"Test Score:\",gb_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22f32995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = gb_cv.predict(X_test)\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OsayFTAQ95H1",
   "metadata": {
    "id": "OsayFTAQ95H1"
   },
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2C8esZf18Bov",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2C8esZf18Bov",
    "outputId": "5f5382fc-140d-4bdc-b55f-9915f3c310dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.590625\n",
      "Test Score: 0.6875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(random_state = 101)\n",
    "accuracies = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "svc.fit(X_train,y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",svc.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "h5opFJ7z8BaT",
   "metadata": {
    "id": "h5opFJ7z8BaT"
   },
   "outputs": [],
   "source": [
    "# grid = {\n",
    "#     'C':[0.01,0.1,1,10],\n",
    "#     'kernel' : [\"linear\",\"poly\",\"rbf\",\"sigmoid\"],\n",
    "#     'degree' : [1,3,5,7],\n",
    "#     'gamma' : [0.01,1]\n",
    "# }\n",
    "\n",
    "# svm  = SVC ()\n",
    "# svm_cv = GridSearchCV(svm, grid, cv = 5)\n",
    "# svm_cv.fit(X_train,y_train)\n",
    "# print(\"Best Parameters:\",svm_cv.best_params_)\n",
    "# print(\"Train Score:\",svm_cv.best_score_)\n",
    "# print(\"Test Score:\",svm_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e45f6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=svc.predict(X_test)\n",
    "plt.plot(y_test, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb1b0f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      1.00      0.80        49\n",
      "           1       1.00      0.23      0.37        31\n",
      "\n",
      "    accuracy                           0.70        80\n",
      "   macro avg       0.84      0.61      0.59        80\n",
      "weighted avg       0.80      0.70      0.63        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred=svc.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c150e8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = svc.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OThr97ak-JRa",
   "metadata": {
    "id": "OThr97ak-JRa"
   },
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "h5jlkw9Z8Aur",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h5jlkw9Z8Aur",
    "outputId": "40eced78-81ce-48d4-8864-50583ae09153"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.875\n",
      "Test Score: 0.8875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "classifier = GaussianNB()\n",
    "accuracies = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",classifier.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73480160",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dict_train[\"Naive Bayes Default Train Score\"] = np.mean(accuracies)\n",
    "result_dict_test[\"Naive Bayes Default Test Score\"] = classifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2935debb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "plt.plot(y_test, y_pred)\n",
    "plt.xlabel(\"Admission decission\")\n",
    "plt.ylabel(\"Predicted Admission decission\")\n",
    "plt.title(\"Actual Admission decission vs Predicted Admission decission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4b43bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.96      0.89        49\n",
      "           1       0.91      0.68      0.78        31\n",
      "\n",
      "    accuracy                           0.85        80\n",
      "   macro avg       0.87      0.82      0.83        80\n",
      "weighted avg       0.86      0.85      0.84        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred=classifier.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "412c0fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = classifier.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775cb036",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d4d6f8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7e5bfa03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8/8 [==============================] - 1s 43ms/step - loss: 0.6678 - accuracy: 0.5781 - val_loss: 0.5943 - val_accuracy: 0.7969\n",
      "Epoch 2/50\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 0.5299 - accuracy: 0.8281 - val_loss: 0.4823 - val_accuracy: 0.8594\n",
      "Epoch 3/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.4425 - accuracy: 0.8672 - val_loss: 0.4070 - val_accuracy: 0.8750\n",
      "Epoch 4/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.3780 - accuracy: 0.8672 - val_loss: 0.3539 - val_accuracy: 0.8906\n",
      "Epoch 5/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.3351 - accuracy: 0.8711 - val_loss: 0.3163 - val_accuracy: 0.8906\n",
      "Epoch 6/50\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.3044 - accuracy: 0.8750 - val_loss: 0.2908 - val_accuracy: 0.8750\n",
      "Epoch 7/50\n",
      "8/8 [==============================] - 0s 16ms/step - loss: 0.2821 - accuracy: 0.8789 - val_loss: 0.2745 - val_accuracy: 0.8750\n",
      "Epoch 8/50\n",
      "8/8 [==============================] - 0s 47ms/step - loss: 0.2683 - accuracy: 0.8828 - val_loss: 0.2653 - val_accuracy: 0.8750\n",
      "Epoch 9/50\n",
      "8/8 [==============================] - 0s 12ms/step - loss: 0.2567 - accuracy: 0.8906 - val_loss: 0.2598 - val_accuracy: 0.8750\n",
      "Epoch 10/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.2489 - accuracy: 0.8945 - val_loss: 0.2588 - val_accuracy: 0.8750\n",
      "Epoch 11/50\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.2412 - accuracy: 0.8945 - val_loss: 0.2588 - val_accuracy: 0.8750\n",
      "Epoch 12/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.2355 - accuracy: 0.8906 - val_loss: 0.2605 - val_accuracy: 0.8750\n",
      "Epoch 13/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.2305 - accuracy: 0.8906 - val_loss: 0.2616 - val_accuracy: 0.8750\n",
      "Epoch 14/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.2255 - accuracy: 0.8906 - val_loss: 0.2661 - val_accuracy: 0.8750\n",
      "Epoch 15/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.2205 - accuracy: 0.8867 - val_loss: 0.2691 - val_accuracy: 0.8750\n",
      "Epoch 16/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.2166 - accuracy: 0.8906 - val_loss: 0.2736 - val_accuracy: 0.8750\n",
      "Epoch 17/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.2128 - accuracy: 0.8945 - val_loss: 0.2769 - val_accuracy: 0.8750\n",
      "Epoch 18/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.2085 - accuracy: 0.8984 - val_loss: 0.2798 - val_accuracy: 0.8750\n",
      "Epoch 19/50\n",
      "8/8 [==============================] - 0s 19ms/step - loss: 0.2051 - accuracy: 0.8945 - val_loss: 0.2842 - val_accuracy: 0.8750\n",
      "Epoch 20/50\n",
      "8/8 [==============================] - 0s 17ms/step - loss: 0.2016 - accuracy: 0.8945 - val_loss: 0.2879 - val_accuracy: 0.8750\n",
      "Epoch 21/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1984 - accuracy: 0.9023 - val_loss: 0.2914 - val_accuracy: 0.8750\n",
      "Epoch 22/50\n",
      "8/8 [==============================] - 0s 11ms/step - loss: 0.1953 - accuracy: 0.9023 - val_loss: 0.2967 - val_accuracy: 0.8750\n",
      "Epoch 23/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1922 - accuracy: 0.9023 - val_loss: 0.3009 - val_accuracy: 0.8750\n",
      "Epoch 24/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.1892 - accuracy: 0.9102 - val_loss: 0.3038 - val_accuracy: 0.8750\n",
      "Epoch 25/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1860 - accuracy: 0.9102 - val_loss: 0.3068 - val_accuracy: 0.8750\n",
      "Epoch 26/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1831 - accuracy: 0.9180 - val_loss: 0.3101 - val_accuracy: 0.8750\n",
      "Epoch 27/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1809 - accuracy: 0.9219 - val_loss: 0.3148 - val_accuracy: 0.8594\n",
      "Epoch 28/50\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 0.1780 - accuracy: 0.9219 - val_loss: 0.3159 - val_accuracy: 0.8750\n",
      "Epoch 29/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1754 - accuracy: 0.9258 - val_loss: 0.3211 - val_accuracy: 0.8594\n",
      "Epoch 30/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1726 - accuracy: 0.9219 - val_loss: 0.3242 - val_accuracy: 0.8594\n",
      "Epoch 31/50\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 0.1698 - accuracy: 0.9219 - val_loss: 0.3277 - val_accuracy: 0.8438\n",
      "Epoch 32/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1679 - accuracy: 0.9219 - val_loss: 0.3324 - val_accuracy: 0.8438\n",
      "Epoch 33/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1660 - accuracy: 0.9219 - val_loss: 0.3313 - val_accuracy: 0.8438\n",
      "Epoch 34/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1636 - accuracy: 0.9258 - val_loss: 0.3389 - val_accuracy: 0.8438\n",
      "Epoch 35/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1611 - accuracy: 0.9258 - val_loss: 0.3388 - val_accuracy: 0.8438\n",
      "Epoch 36/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1585 - accuracy: 0.9258 - val_loss: 0.3415 - val_accuracy: 0.8438\n",
      "Epoch 37/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1568 - accuracy: 0.9297 - val_loss: 0.3446 - val_accuracy: 0.8438\n",
      "Epoch 38/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1541 - accuracy: 0.9336 - val_loss: 0.3497 - val_accuracy: 0.8438\n",
      "Epoch 39/50\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 0.1521 - accuracy: 0.9375 - val_loss: 0.3537 - val_accuracy: 0.8438\n",
      "Epoch 40/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1503 - accuracy: 0.9375 - val_loss: 0.3552 - val_accuracy: 0.8438\n",
      "Epoch 41/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1479 - accuracy: 0.9414 - val_loss: 0.3572 - val_accuracy: 0.8438\n",
      "Epoch 42/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1467 - accuracy: 0.9336 - val_loss: 0.3662 - val_accuracy: 0.8438\n",
      "Epoch 43/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1448 - accuracy: 0.9414 - val_loss: 0.3669 - val_accuracy: 0.8438\n",
      "Epoch 44/50\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 0.1428 - accuracy: 0.9453 - val_loss: 0.3647 - val_accuracy: 0.8438\n",
      "Epoch 45/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1415 - accuracy: 0.9453 - val_loss: 0.3643 - val_accuracy: 0.8438\n",
      "Epoch 46/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1403 - accuracy: 0.9453 - val_loss: 0.3726 - val_accuracy: 0.8438\n",
      "Epoch 47/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1376 - accuracy: 0.9492 - val_loss: 0.3724 - val_accuracy: 0.8438\n",
      "Epoch 48/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1357 - accuracy: 0.9492 - val_loss: 0.3746 - val_accuracy: 0.8438\n",
      "Epoch 49/50\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 0.1342 - accuracy: 0.9492 - val_loss: 0.3791 - val_accuracy: 0.8438\n",
      "Epoch 50/50\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 0.1325 - accuracy: 0.9492 - val_loss: 0.3803 - val_accuracy: 0.8438\n",
      "Training accuracy: 94.92%\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Build the neural network model\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')  # Binary classification, so 'sigmoid' activation\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "\n",
    "history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# # Evaluate the model on the test set\n",
    "# test_loss, test_acc = model.evaluate(X_test_scaled, y_test)\n",
    "# print(f'Test accuracy: {test_acc * 100:.2f}%')\n",
    "\n",
    "# Print training accuracy\n",
    "train_acc = history.history['accuracy'][-1]\n",
    "print(f'Training accuracy: {train_acc * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d73317ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step\n",
      "3/3 [==============================] - 0s 4ms/step - loss: 0.2716 - accuracy: 0.8625\n",
      "Test accuracy: 86.25%\n",
      "Training accuracy: 94.92%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# ... (Previous code)\n",
    "\n",
    "# Train the model\n",
    "#history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Predict probabilities on the test set\n",
    "y_pred_proba = model.predict(X_test_scaled)\n",
    "\n",
    "# Compute ROC curve and area under the curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot ROC curve\n",
    "plt.figure(figsize=(8, 8))\n",
    "test_acc = model.evaluate(X_test_scaled, y_test)[1]\n",
    "print(f'Test accuracy: {test_acc*100:.2f}%')\n",
    "train_acc = history.history['accuracy'][-1]\n",
    "print(f'Training accuracy: {train_acc * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c0f06ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 1ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.96      0.89        49\n",
      "           1       0.91      0.68      0.78        31\n",
      "\n",
      "    accuracy                           0.85        80\n",
      "   macro avg       0.87      0.82      0.83        80\n",
      "weighted avg       0.86      0.85      0.84        80\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Assuming you have trained your model as described in your code\n",
    "# and you have X_test_scaled and y_test\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_probs = model.predict(X_test_scaled)\n",
    "y_pred = (y_probs > 0.5).astype(int)  # Assuming a threshold of 0.5 for binary classification\n",
    "\n",
    "# Convert y_test to 1D array if it's not already\n",
    "y_test = np.squeeze(y_test)\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26a6809c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4331fd83",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a39aec01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # auc roc curve\n",
    "\n",
    "# from sklearn.datasets import make_classification\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# # generate two class dataset\n",
    "# X, y = make_classification(n_samples=1000, n_classes=2, n_features=20, random_state=27)\n",
    "\n",
    "# # split into train-test sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=27)\n",
    "\n",
    "\n",
    "\n",
    "# # train models\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# # logistic regression\n",
    "# model1 = LogisticRegression()\n",
    "# # knn\n",
    "# model2 = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "# # fit model\n",
    "# model1.fit(X_train, y_train)\n",
    "# model2.fit(X_train, y_train)\n",
    "\n",
    "# # predict probabilities\n",
    "# pred_prob1 = model1.predict_proba(X_test)\n",
    "# pred_prob2 = model2.predict_proba(X_test)\n",
    "\n",
    "\n",
    "# from sklearn.metrics import roc_curve\n",
    "\n",
    "# # roc curve for models\n",
    "# fpr1, tpr1, thresh1 = roc_curve(y_test, pred_prob1[:,1], pos_label=1)\n",
    "# fpr2, tpr2, thresh2 = roc_curve(y_test, pred_prob2[:,1], pos_label=1)\n",
    "\n",
    "# # roc curve for tpr = fpr \n",
    "# random_probs = [0 for i in range(len(y_test))]\n",
    "# p_fpr, p_tpr, _ = roc_curve(y_test, random_probs, pos_label=1)\n",
    "\n",
    "\n",
    "# from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# # auc scores\n",
    "# auc_score1 = roc_auc_score(y_test, pred_prob1[:,1])\n",
    "# auc_score2 = roc_auc_score(y_test, pred_prob2[:,1])\n",
    "\n",
    "# print(auc_score1, auc_score2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "KttOqs5D8AqU",
   "metadata": {
    "id": "KttOqs5D8AqU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have trained classifiers named svc, gradient_booster, and classifier\n",
    "classifiers = [svc, gradient_booster, classifier]\n",
    "labels = ['SVC', 'Gradient Boosting', 'Naive Bayes', 'Neaural Network']\n",
    "colors = ['orange', 'blue', 'green', 'yellow']\n",
    "\n",
    "# Initialize a plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Plot ROC curve for each classifier\n",
    "for classifier, label, color in zip(classifiers, labels, colors):\n",
    "    # Assuming X_test and y_test are your test data\n",
    "    if isinstance(classifier, SVC):\n",
    "        y_scores = classifier.decision_function(X_test)\n",
    "    else:\n",
    "        y_scores = classifier.predict_proba(X_test)[:, 1]\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    # Plot ROC curve with different line styles and colors\n",
    "    plt.plot(fpr, tpr, label=f'{label} (AUC = {roc_auc:.2f})', color=color, linestyle='--' if label == 'Naive Bayes' else '-')\n",
    "\n",
    "# Plot ROC curve for the neural network\n",
    "y_probs = model.predict(X_test_scaled)\n",
    "fpr_nn, tpr_nn, thresholds_nn = roc_curve(y_test, y_probs)\n",
    "roc_auc_nn = auc(fpr_nn, tpr_nn)\n",
    "plt.plot(fpr_nn, tpr_nn, label=f'Neural Network (AUC = {roc_auc_nn:.2f})', color='purple', linestyle='-')\n",
    "\n",
    "# Plot the diagonal line for reference\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}