TDA PROJECT smote.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3627674e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix,classification_report\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b08ac611",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Bhaskar Konda\\Desktop\\3rd year\\sem6\\da\\mini project\\data\\Training_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16f97781",
   "metadata": {},
   "outputs": [
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       "      <td>-4.353147</td>\n",
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       "      <td>-0.138636</td>\n",
       "      <td>-17.836632</td>\n",
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       "      <td>-0.061114</td>\n",
       "      <td>-0.030057</td>\n",
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       "      <td>0.072322</td>\n",
       "      <td>-4.937924</td>\n",
       "      <td>-34.183774</td>\n",
       "      <td>27.004663</td>\n",
       "      <td>3.537487</td>\n",
       "      <td>0.033629</td>\n",
       "      <td>1.840540</td>\n",
       "      <td>0.006090</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>-4.758934</td>\n",
       "      <td>-4.023612</td>\n",
       "      <td>13.161566</td>\n",
       "      <td>-0.148142</td>\n",
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       "      <td>-0.061114</td>\n",
       "      <td>-0.030057</td>\n",
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       "\n",
       "[5 rows x 62 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1da5af0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y      0\n",
       "x1     0\n",
       "x2     0\n",
       "x3     0\n",
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       "      ..\n",
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   "source": [
    "df.isnull().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4e865a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>12946.000000</td>\n",
       "      <td>12946.000000</td>\n",
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       "      <td>12946.000000</td>\n",
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       "      <td>12946.000000</td>\n",
       "      <td>12946.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>-0.470455</td>\n",
       "      <td>1.278904</td>\n",
       "      <td>-39.528380</td>\n",
       "      <td>-0.152940</td>\n",
       "      <td>0.746041</td>\n",
       "      <td>0.020763</td>\n",
       "      <td>-0.016455</td>\n",
       "      <td>-0.004272</td>\n",
       "      <td>...</td>\n",
       "      <td>1.427684</td>\n",
       "      <td>0.309014</td>\n",
       "      <td>-0.094413</td>\n",
       "      <td>-5.847789</td>\n",
       "      <td>27.762041</td>\n",
       "      <td>0.789931</td>\n",
       "      <td>-0.004297</td>\n",
       "      <td>0.692014</td>\n",
       "      <td>0.002432</td>\n",
       "      <td>0.001004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.080293</td>\n",
       "      <td>0.687200</td>\n",
       "      <td>4.956759</td>\n",
       "      <td>6.125960</td>\n",
       "      <td>136.803500</td>\n",
       "      <td>0.571198</td>\n",
       "      <td>36.168932</td>\n",
       "      <td>0.109847</td>\n",
       "      <td>0.063025</td>\n",
       "      <td>0.160045</td>\n",
       "      <td>...</td>\n",
       "      <td>6.679059</td>\n",
       "      <td>4.912749</td>\n",
       "      <td>3.195014</td>\n",
       "      <td>73.145344</td>\n",
       "      <td>83.268383</td>\n",
       "      <td>2.457149</td>\n",
       "      <td>0.057447</td>\n",
       "      <td>6.599906</td>\n",
       "      <td>0.004427</td>\n",
       "      <td>0.031674</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.787279</td>\n",
       "      <td>-17.316550</td>\n",
       "      <td>-18.198509</td>\n",
       "      <td>-322.781610</td>\n",
       "      <td>-1.623988</td>\n",
       "      <td>-279.408440</td>\n",
       "      <td>-0.429273</td>\n",
       "      <td>-0.451141</td>\n",
       "      <td>-0.120087</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.555008</td>\n",
       "      <td>-14.211369</td>\n",
       "      <td>-5.370937</td>\n",
       "      <td>-230.574030</td>\n",
       "      <td>-269.039500</td>\n",
       "      <td>-12.640370</td>\n",
       "      <td>-0.149790</td>\n",
       "      <td>-100.810500</td>\n",
       "      <td>-0.006588</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.502545</td>\n",
       "      <td>-3.380133</td>\n",
       "      <td>-3.325962</td>\n",
       "      <td>-140.882767</td>\n",
       "      <td>-0.525385</td>\n",
       "      <td>-29.431690</td>\n",
       "      <td>-0.049319</td>\n",
       "      <td>-0.054634</td>\n",
       "      <td>-0.059966</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.634475</td>\n",
       "      <td>-3.335087</td>\n",
       "      <td>-4.889306</td>\n",
       "      <td>-48.173703</td>\n",
       "      <td>-35.689654</td>\n",
       "      <td>-1.470630</td>\n",
       "      <td>0.006213</td>\n",
       "      <td>0.291218</td>\n",
       "      <td>-0.001279</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143820</td>\n",
       "      <td>-0.755299</td>\n",
       "      <td>0.424011</td>\n",
       "      <td>-67.259882</td>\n",
       "      <td>-0.246004</td>\n",
       "      <td>9.339607</td>\n",
       "      <td>0.010803</td>\n",
       "      <td>-0.021135</td>\n",
       "      <td>-0.030057</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060959</td>\n",
       "      <td>1.079891</td>\n",
       "      <td>0.662045</td>\n",
       "      <td>4.556034</td>\n",
       "      <td>26.803293</td>\n",
       "      <td>1.337108</td>\n",
       "      <td>0.017437</td>\n",
       "      <td>0.804430</td>\n",
       "      <td>0.002398</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468526</td>\n",
       "      <td>1.400402</td>\n",
       "      <td>4.437452</td>\n",
       "      <td>46.157211</td>\n",
       "      <td>0.081388</td>\n",
       "      <td>28.936615</td>\n",
       "      <td>0.080690</td>\n",
       "      <td>0.018844</td>\n",
       "      <td>-0.000149</td>\n",
       "      <td>...</td>\n",
       "      <td>7.965622</td>\n",
       "      <td>3.341304</td>\n",
       "      <td>2.648017</td>\n",
       "      <td>38.680789</td>\n",
       "      <td>80.596674</td>\n",
       "      <td>2.863048</td>\n",
       "      <td>0.023250</td>\n",
       "      <td>1.365876</td>\n",
       "      <td>0.005702</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.517970</td>\n",
       "      <td>16.734899</td>\n",
       "      <td>15.900116</td>\n",
       "      <td>296.772589</td>\n",
       "      <td>4.239385</td>\n",
       "      <td>68.886910</td>\n",
       "      <td>1.705590</td>\n",
       "      <td>0.788826</td>\n",
       "      <td>3.206675</td>\n",
       "      <td>...</td>\n",
       "      <td>14.180588</td>\n",
       "      <td>11.148006</td>\n",
       "      <td>6.054363</td>\n",
       "      <td>287.252017</td>\n",
       "      <td>252.147455</td>\n",
       "      <td>6.922008</td>\n",
       "      <td>0.060264</td>\n",
       "      <td>5.756678</td>\n",
       "      <td>0.020510</td>\n",
       "      <td>1.000000</td>\n",
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       "<p>8 rows × 62 columns</p>\n",
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      "text/plain": [
       "                  y            x1            x2            x3            x4  \\\n",
       "count  12946.000000  12946.000000  12946.000000  12946.000000  12946.000000   \n",
       "mean       0.006488      0.025401     -0.470455      1.278904    -39.528380   \n",
       "std        0.080293      0.687200      4.956759      6.125960    136.803500   \n",
       "min        0.000000     -3.787279    -17.316550    -18.198509   -322.781610   \n",
       "25%        0.000000     -0.502545     -3.380133     -3.325962   -140.882767   \n",
       "50%        0.000000      0.143820     -0.755299      0.424011    -67.259882   \n",
       "75%        0.000000      0.468526      1.400402      4.437452     46.157211   \n",
       "max        1.000000      2.517970     16.734899     15.900116    296.772589   \n",
       "\n",
       "                 x5            x6            x7            x8            x9  \\\n",
       "count  12946.000000  12946.000000  12946.000000  12946.000000  12946.000000   \n",
       "mean      -0.152940      0.746041      0.020763     -0.016455     -0.004272   \n",
       "std        0.571198     36.168932      0.109847      0.063025      0.160045   \n",
       "min       -1.623988   -279.408440     -0.429273     -0.451141     -0.120087   \n",
       "25%       -0.525385    -29.431690     -0.049319     -0.054634     -0.059966   \n",
       "50%       -0.246004      9.339607      0.010803     -0.021135     -0.030057   \n",
       "75%        0.081388     28.936615      0.080690      0.018844     -0.000149   \n",
       "max        4.239385     68.886910      1.705590      0.788826      3.206675   \n",
       "\n",
       "       ...           x52           x53           x54           x55  \\\n",
       "count  ...  12946.000000  12946.000000  12946.000000  12946.000000   \n",
       "mean   ...      1.427684      0.309014     -0.094413     -5.847789   \n",
       "std    ...      6.679059      4.912749      3.195014     73.145344   \n",
       "min    ...    -16.555008    -14.211369     -5.370937   -230.574030   \n",
       "25%    ...     -4.634475     -3.335087     -4.889306    -48.173703   \n",
       "50%    ...      1.060959      1.079891      0.662045      4.556034   \n",
       "75%    ...      7.965622      3.341304      2.648017     38.680789   \n",
       "max    ...     14.180588     11.148006      6.054363    287.252017   \n",
       "\n",
       "                x56           x57           x58           x59           x60  \\\n",
       "count  12946.000000  12946.000000  12946.000000  12946.000000  12946.000000   \n",
       "mean      27.762041      0.789931     -0.004297      0.692014      0.002432   \n",
       "std       83.268383      2.457149      0.057447      6.599906      0.004427   \n",
       "min     -269.039500    -12.640370     -0.149790   -100.810500     -0.006588   \n",
       "25%      -35.689654     -1.470630      0.006213      0.291218     -0.001279   \n",
       "50%       26.803293      1.337108      0.017437      0.804430      0.002398   \n",
       "75%       80.596674      2.863048      0.023250      1.365876      0.005702   \n",
       "max      252.147455      6.922008      0.060264      5.756678      0.020510   \n",
       "\n",
       "                x61  \n",
       "count  12946.000000  \n",
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       "std        0.031674  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9046df1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y          2\n",
       "x1     10408\n",
       "x2     11531\n",
       "x3     11902\n",
       "x4     12319\n",
       "       ...  \n",
       "x57      932\n",
       "x58     8983\n",
       "x59     9479\n",
       "x60     8276\n",
       "x61        2\n",
       "Length: 62, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "c650a03e",
   "metadata": {},
   "source": [
    "df = df.drop(df.columns[0], axis=1)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f88b5a6e",
   "metadata": {},
   "source": [
    "for i in df.columns:\n",
    "    if type(df[i][0])!=str:\n",
    "        sns.boxplot(df[i])\n",
    "        plt.title(i)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0db7e068",
   "metadata": {},
   "source": [
    "def remove_outliers_iqr(column):\n",
    "    Q1 = column.quantile(0.25)\n",
    "    Q3 = column.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    return np.where(column < lower_bound, lower_bound, np.where(column > upper_bound, upper_bound, column))\n",
    "\n",
    "# Get all column names except the first one\n",
    "columns_to_remove_outliers = df.columns[1:]\n",
    "\n",
    "# Iterate over the columns and remove outliers\n",
    "for col in columns_to_remove_outliers:\n",
    "    df[col] = remove_outliers_iqr(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bd8693b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12946, 62)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9187e9c2",
   "metadata": {},
   "source": [
    "### BALANCING THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b6f8435",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "# Assuming y is your target variable\n",
    "# Assuming X is your feature matrix\n",
    "\n",
    "# Extracting 'y' as the first column\n",
    "y = df.iloc[:, 0]\n",
    "\n",
    "# Extracting the rest of the columns as features\n",
    "X = df.iloc[:, 1:]\n",
    "\n",
    "# Convert y to a binary target variable (if not already binary)\n",
    "# For example, if y has multiple classes, you might want to convert it to binary\n",
    "# For this example, let's assume 1 is the minority class and 0 is the majority class\n",
    "y_binary = (y == 1).astype(int)\n",
    "\n",
    "# Apply SMOTE to the entire dataset\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y_binary)\n",
    "\n",
    "# Now, X_resampled and y_resampled contain the balanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b15ab78c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of zeros and ones:\n",
      "y\n",
      "0    12862\n",
      "1    12862\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count occurrences of zeros and ones in y\n",
    "counts = y_resampled.value_counts()\n",
    "\n",
    "# Display counts\n",
    "print(\"Counts of zeros and ones:\")\n",
    "print(counts)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a724b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25724, 61)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f10b9e2",
   "metadata": {},
   "outputs": [
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       "\n",
       "         x56       x57       x58       x59       x60  x61  \n",
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       "2  27.004663  3.537487  0.033629  1.840540  0.006090    0  \n",
       "3  21.672449  3.986095  0.033721  2.554880  0.006097    0  \n",
       "4  21.907251  3.601573  0.033777  1.410494  0.006105    0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84fba2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_csv(r\"C:\\Users\\Bhaskar Konda\\Desktop\\3rd year\\sem6\\da\\mini project\\data\\Test_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc96fe08",
   "metadata": {},
   "outputs": [
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       "      <td>0.666094</td>\n",
       "      <td>-0.001138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.302200</td>\n",
       "      <td>-0.300580</td>\n",
       "      <td>-4.235540</td>\n",
       "      <td>-11.811151</td>\n",
       "      <td>1.145284</td>\n",
       "      <td>48.164559</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>0.088731</td>\n",
       "      <td>-0.059966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.587814</td>\n",
       "      <td>7.18243</td>\n",
       "      <td>0.587030</td>\n",
       "      <td>40.097476</td>\n",
       "      <td>-36.114890</td>\n",
       "      <td>-0.219349</td>\n",
       "      <td>-0.002346</td>\n",
       "      <td>0.156939</td>\n",
       "      <td>-0.001134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.269202</td>\n",
       "      <td>-0.360017</td>\n",
       "      <td>-4.215428</td>\n",
       "      <td>-11.663812</td>\n",
       "      <td>1.163778</td>\n",
       "      <td>48.543892</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>0.088731</td>\n",
       "      <td>-0.059966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.583420</td>\n",
       "      <td>7.18243</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>38.846012</td>\n",
       "      <td>-36.072501</td>\n",
       "      <td>0.391002</td>\n",
       "      <td>-0.002032</td>\n",
       "      <td>0.643130</td>\n",
       "      <td>-0.001131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.271205</td>\n",
       "      <td>-0.317740</td>\n",
       "      <td>-4.311570</td>\n",
       "      <td>-11.516473</td>\n",
       "      <td>1.182272</td>\n",
       "      <td>48.282418</td>\n",
       "      <td>0.020874</td>\n",
       "      <td>0.088731</td>\n",
       "      <td>-0.059966</td>\n",
       "      <td>...</td>\n",
       "      <td>0.579025</td>\n",
       "      <td>7.18243</td>\n",
       "      <td>0.586525</td>\n",
       "      <td>36.684390</td>\n",
       "      <td>-36.030112</td>\n",
       "      <td>-0.555042</td>\n",
       "      <td>-0.001719</td>\n",
       "      <td>1.389887</td>\n",
       "      <td>-0.001127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   y        x1        x2        x3         x4        x5         x6        x7  \\\n",
       "0  0  0.324195 -0.299939 -4.247989 -12.023035  1.108297  47.405922  0.020874   \n",
       "1  0  0.268249 -0.227685 -4.563740 -11.925134  1.126791  47.785256  0.020874   \n",
       "2  0  0.302200 -0.300580 -4.235540 -11.811151  1.145284  48.164559  0.020874   \n",
       "3  0  0.269202 -0.360017 -4.215428 -11.663812  1.163778  48.543892  0.020874   \n",
       "4  0  0.271205 -0.317740 -4.311570 -11.516473  1.182272  48.282418  0.020874   \n",
       "\n",
       "         x8        x9  ...       x52      x53       x54        x55        x56  \\\n",
       "0  0.088731 -0.059966  ...  0.596603  7.18243  0.587533  24.477970 -36.199652   \n",
       "1  0.088731 -0.059966  ...  0.592209  7.18243  0.587281  39.362369 -36.157263   \n",
       "2  0.088731 -0.059966  ...  0.587814  7.18243  0.587030  40.097476 -36.114890   \n",
       "3  0.088731 -0.059966  ...  0.583420  7.18243  0.586777  38.846012 -36.072501   \n",
       "4  0.088731 -0.059966  ...  0.579025  7.18243  0.586525  36.684390 -36.030112   \n",
       "\n",
       "        x57       x58       x59       x60  x61  \n",
       "0 -0.860218 -0.002973  1.257273 -0.001142    0  \n",
       "1 -0.219349 -0.002659  0.666094 -0.001138    0  \n",
       "2 -0.219349 -0.002346  0.156939 -0.001134    0  \n",
       "3  0.391002 -0.002032  0.643130 -0.001131    0  \n",
       "4 -0.555042 -0.001719  1.389887 -0.001127    0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd80f34a",
   "metadata": {},
   "source": [
    "### spliting into test and training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "58e76bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming 'target' is your target variable name\n",
    "# Splitting the training dataset into features and target\n",
    "y_train = df['y']\n",
    "X_train = df.drop('y', axis=1)\n",
    "\n",
    "# Splitting the testing dataset into features and target\n",
    "y_test = df_test['y']\n",
    "X_test = df_test.drop('y', axis=1)\n",
    "\n",
    "# Optionally, you can perform further preprocessing or feature engineering on X_train and X_test\n",
    "\n",
    "# Now, you have X_train, y_train, X_test, and y_test ready for model training and evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34da90b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c974980",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "19cb4819",
   "metadata": {},
   "source": [
    "### scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eab579e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c8b907f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the scaler to the features and transform the features\n",
    "X_scaled_train = scaler.fit_transform(X_train)\n",
    "X_scaled_test = scaler.fit_transform(X_test)\n",
    "\n",
    "# Now, X_scaled contains the scaled features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ba491dce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5111726 , -0.83242681, -0.87739182, ...,  0.03957864,\n",
       "         0.82302073, -0.03170458],\n",
       "       [ 0.65532106, -0.82154569, -0.86475706, ...,  0.06038019,\n",
       "         0.82460194, -0.03170458],\n",
       "       [ 0.49252084, -0.8495675 , -0.91940987, ...,  0.17402833,\n",
       "         0.82618314, -0.03170458],\n",
       "       ...,\n",
       "       [ 0.33891334,  0.01708026, -0.94721442, ...,  0.04655295,\n",
       "        -0.80990867, -0.03170458],\n",
       "       [ 0.31121001,  0.00678765, -0.91452833, ..., -0.08615468,\n",
       "        -0.80900512, -0.03170458],\n",
       "       [ 0.34507336,  0.03536731, -0.92657229, ..., -0.03995163,\n",
       "        -0.80810158, -0.03170458]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a49daaf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of: (12946, 61)\n",
      "Shape of : (5452, 61)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of:\", X_scaled_train.shape)\n",
    "print(\"Shape of :\", X_scaled_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "376631fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cb21ad20",
   "metadata": {},
   "source": [
    "### Model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2da221e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9840425531914894\n",
      "Precision: 0.2235294117647059\n",
      "Recall: 0.475\n",
      "F1 Score: 0.304\n"
     ]
    },
    {
     "data": {
      "image/png": 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UGBjo9pmQkBBlZmZKkjIzM93CyLnr565drE12drZOnTpVrGdjygYAAKt56D0kI0eOVHx8vNs5p9N53rbt27d3/fnGG29Us2bNVL16dS1YsEC+vr4eGY8nUSEBAMBqHpqycTqd8vf3dzsuFEj+LDAwUNdff722b9+u0NBQ5ebm6siRI25tsrKyXGtOQkNDC+26OfezWRt/f/9ihx4CCQAAfwPHjx/Xjh07FBYWpqZNm8rb21urVq1yXd+6dasyMjIUGRkpSYqMjNTmzZu1f/9+V5vk5GT5+/srIiLC1eaPfZxrc66P4iCQAABgNYfDM0cxDBs2TGvXrtXu3bu1fv16de7cWaVKldKDDz6ogIAA9e3bV/Hx8fryyy+Vlpam3r17KzIyUrfeeqskqV27doqIiFDPnj31ww8/aOXKlXruuecUFxfnqsr0799fO3fu1FNPPaVffvlFM2bM0IIFCzRkyJBif0WsIQEAwGo2vKl1z549evDBB3Xo0CFVrlxZt99+u77++mtVrlxZkjRp0iR5eXmpa9euysnJUXR0tGbMmOH6fKlSpbR06VINGDBAkZGR8vPzU2xsrMaOHetqU7NmTS1btkxDhgzRlClTVKVKFb311lvF3vIrSQ7DMIy//thXFt/GA+0eAnBFOrRhmt1DAK44Zb2tf0eIb/QrHunn1MphHunnSsSUDQAAsB1TNgAAWI1frmeKQAIAgNU89B6SkozIBgAAbEeFBAAAqzFlY4pAAgCA1ZiyMUVkAwAAtqNCAgCA1ZiyMUUgAQDAagQSU3xDAADAdlRIAACwGotaTRFIAACwGlM2pggkAABYjQqJKSIbAACwHRUSAACsxpSNKQIJAABWY8rGFJENAADYjgoJAAAWc1AhMUUgAQDAYgQSc0zZAAAA21EhAQDAahRITBFIAACwGFM25piyAQAAtqNCAgCAxaiQmCOQAABgMQKJOQIJAAAWI5CYYw0JAACwHRUSAACsRoHEFIEEAACLMWVjjikbAABgOyokAABYjAqJOQIJAAAWI5CYY8oGAADYjgoJAAAWo0JijkACAIDVyCOmmLIBAAC2o0ICAIDFmLIxRyABAMBiBBJzBBIAACxGIDHHGhIAAGA7KiQAAFiNAokpAgkAABZjysYcUzYAAMB2VEgAALAYFRJzBBIAACxGIDHHlA0AALAdFRIAACxGhcQcgQQAAKuRR0wxZQMAAGxHhQQAAIsxZWOOQAIAgMUIJOYIJAAAWIxAYo41JAAAwHZUSAAAsBoFElMEEgAALMaUjTmmbAAAgO2okOCinn3sbj3X/263c1t3ZapRlxclSdOe7a62zeoorHKAjp/K0dc/7NJzUz7Rf3ZnFeorKMBPG+Y/rWtCKii0xXAdPX7Kdc3Hu7SeebS9Hoy5WSEVyyvzYLYmzPpMb3/ytbUPCFhof1aWprz6ir5al6LTp0+rarVqGj1ugm6o38DVZueOHZoy6RV99+1GncnPV61a1+qVyVMVFhZu48jhaVRIzBFIYGrL9r2K6T/N9fOZ/ALXn7//+Td98NlG/bbvdwUFlNWz/WO0dEac6nZ4QQUFhls/M1/ooc3b9uqakAqF7vFuYh+FBJVX/zHvaUfGAYVVDpAXf4FxFcs+elS9ej6om29ppukz31SFCkHK+HW3/P0DXG1+y8hQn4d7qFOXbhoQN0h+fuW0Y8d2OX2cNo4cViCQmGPKBqbO5Bco69Ax13HoyAnXtX99/JW++m6HMvYdVvovezTmtSWqGhak6uEV3fp45L7bFVC+rCa/vapQ/3feVk8tmtZWp0Gv68tvtipj32F9s2mXUn/YafmzAVaZ86+3FBoapjEvJqh+gxt1TZUqimx+u6pWq+ZqM33qZN3eopUGDx2uuvUiVLVaNbVu01ZBFStepGfg0rz00ktyOBwaPHiw69zp06cVFxenihUrqly5curatauystwr3BkZGYqJiVHZsmUVHBys4cOH68yZM25t1qxZoyZNmsjpdKp27dpKSkoq9vhsDSQHDx5UYmKiOnfurMjISEVGRqpz5856+eWXdeDAATuHhj+oXa2ydn4+Xj8tGa0542NVNbRwhUOSypbx0cP33qpdew5qT+bvrvN1a4Vq5CPt1e/5twtVTSQpplUDffdThuJ7RWnHyhe1afEoJQzprDJOb8ueCbDa2i9XK+KG+hoe/6TatrxN3bt11scLF7iuFxQUaF3KGlWrUUOPP9pXbVvepp4P3q8vV31h46hhFYfD4ZHjUm3cuFFvvPGGbrzxRrfzQ4YM0ZIlS/Thhx9q7dq12rt3r7p06eK6np+fr5iYGOXm5mr9+vWaO3eukpKSNGrUKFebXbt2KSYmRm3atFF6eroGDx6sfv36aeXKlcUao22BZOPGjbr++us1depUBQQEqGXLlmrZsqUCAgI0depU1a1bV99++61dw8P/2/jjbj066l3dG/eanpgwXzWuqagv/jVE5cr+r6T86H0tdOCriTqU+qraNY9QzIDpyjuTL+ns2pC5Cb30zOTF+u0PIeWPal5TSbc1ulYR14brgfg3NfyVheoc1UhTRj5wWZ4RsMJ/9/ymD+e/r2rVqmvGG2/pvge6KzFhvD79ZJEk6fDhQzp58qTmzH5Tt93eQq/Pmq02d0Rp6OBB+nbjBptHD49zeOi4BMePH9dDDz2kN998UxUq/O8flEePHtXs2bP16quvqm3btmratKnmzJmj9evX6+uvz67f+/zzz/XTTz/p3XffVaNGjdS+fXuNGzdOr732mnJzcyVJM2fOVM2aNTVx4kTVq1dPAwcOVLdu3TRp0qRijdO2NSSDBg3Sfffdp5kzZxZKfYZhqH///ho0aJBSU1Mv2k9OTo5ycnLcP1+QL4dXKY+P+e/o869+cv35x217tXHzbm1dPlZd2zXR3MVn/7v54LONWvXNLwqt5K/BD0fp3X/2Udveryon94zGPXGvtu7K0gfLN17wHl5eDhmGod7PJin7+GlJ0oiJH2vey331ZMJ8nc7Js/YhAQsUFBiKuOEGDRocL0mqWy9C27dt08IFH+jejp1VUHB2LVbrNm31j4d7SZLq1K2nH9K/18IFH+imm2+xa+goYeLi4hQTE6OoqCi9+OKLrvNpaWnKy8tTVFSU61zdunVVrVo1paam6tZbb1VqaqoaNGigkJAQV5vo6GgNGDBAW7ZsUePGjZWamurWx7k2f5waKgrbAskPP/ygpKSk85agHA6HhgwZosaNG5v2k5CQoDFjxridKxVys7zD+MtshaPHT2l7xn5dW7Wy61z28dPKPn5aOzIOaMOm3dqXkqiObRtqwYo0tbr5etWvHa7OGxtJ+t/Crj1fvqR/zl6pF2cuV+bBbO3df9QVRiTpl12Z8vLy0jUhgdqRwfQdrj6VKldWrWtru52rWetarfric0lShQoVVLp06UJtatW6Vt9/l3bZxonLw1OLWs/3j3Cn0ymn8/wLoT/44AN999132rix8D8KMzMz5ePjo8DAQLfzISEhyszMdLX5Yxg5d/3ctYu1yc7O1qlTp+Tr61ukZ7NtyiY0NFQbNly4LLlhw4ZCD3g+I0eO1NGjR92O0iFNPTlU/IGfr49qVqmkzINHz3vd4XDIIYd8vM9m3QeHvaVbHkhQs+4vqVn3lzRg7DxJUlTfyXpjfookKTV9p8IqB8jP18fVz3XVg5WfX6D/Zh2x9oEAizRq3Fi/7t7ldi7j192u7bze3j6KuKG+ft3l3ubX3bsVFs6W35LGU2tIEhISFBAQ4HYkJCSc956//fabnnzySb333nsqU6bMZX7i4rOtQjJs2DA9+uijSktL0x133OEKH1lZWVq1apXefPNNvfLKK6b9nC8ZMl3jOQlDOmtZymZl7D2s8OAAPdc/RvkFBVqwIk01rqmobtFNtSr1Zx38/biuCQnU0N7tdConTyvXbZEk7dpz0K2/ioHlJEm/7Mx0vYdk/mcbNfKRuzRrzD80buZyVQz004TBnTX3k1Sma3DV+kfPXurV80HNnjVTd97VXls2b9JHCxfo+RfGutrE9u6rEcPi1eSmm3TTLc20ft2/lbL2S705520bRw4reGrX78iRIxUfH+927kLVkbS0NO3fv19NmjRxncvPz1dKSoqmT5+ulStXKjc3V0eOHHGrkmRlZSk0NFTS+YsH53bh/LHNn3fmZGVlyd/fv8jVEcnGQBIXF6dKlSpp0qRJmjFjhvLzzy6CLFWqlJo2baqkpCTdf//9dg0P/++akEC9ndBbQQFldfD341qfvlOtHp6og78fl3fpUmre+FoN7NFaFfzLav+hY1r33Xa16TVRB34/XuR7nDiVq5gB0/XqiPv01btP6fDRE/oo+TuNfm2phU8GWOuGBg00cfI0TZvyqmbNnKFrrqmi4SNG6u4O97jatI26U8+OGq1/vTVLiQnjVb1GTb08aaoaN6HKi/O72PTMn91xxx3avHmz27nevXurbt26GjFihKpWrSpvb2+tWrVKXbt2lSRt3bpVGRkZioyMlCRFRkZq/Pjx2r9/v4KDgyVJycnJ8vf3V0REhKvN8uXL3e6TnJzs6qOoHIZhFN6HeZnl5eXp4MGz/5KuVKmSvL3/2nZP38YDPTEsoMQ5tGGaeSPgb6ast/UvLbtu+AqP9LPt5bv+0udbt26tRo0aafLkyZKkAQMGaPny5UpKSpK/v78GDRokSVq/fr2ksxWVRo0aKTw8XImJicrMzFTPnj3Vr18/TZgwQdLZbb/169dXXFyc+vTpo9WrV+uJJ57QsmXLFB0dXeSxXRFvavX29lZYWJjdwwAAwBJX6otaJ02aJC8vL3Xt2lU5OTmKjo7WjBkzXNdLlSqlpUuXasCAAYqMjJSfn59iY2M1duz/ph5r1qypZcuWaciQIZoyZYqqVKmit956q1hhRLpCKiSeRoUEOD8qJEBhl6NCcv1TnqmQ/Cfxr1VIrmRXRIUEAICSjN9lY45AAgCAxcgj5vjlegAAwHZUSAAAsJiXFyUSMwQSAAAsxpSNOaZsAACA7aiQAABgMXbZmCOQAABgMfKIOQIJAAAWo0JijjUkAADAdlRIAACwGBUScwQSAAAsRh4xx5QNAACwHRUSAAAsxpSNOQIJAAAWI4+YY8oGAADYjgoJAAAWY8rGHIEEAACLkUfMMWUDAABsR4UEAACLMWVjjkACAIDFyCPmCCQAAFiMCok51pAAAADbUSEBAMBiFEjMEUgAALAYUzbmmLIBAAC2o0ICAIDFKJCYI5AAAGAxpmzMMWUDAABsR4UEAACLUSAxRyABAMBiTNmYY8oGAADYjgoJAAAWo0JijkACAIDFyCPmCCQAAFiMCok51pAAAADbUSEBAMBiFEjMEUgAALAYUzbmmLIBAAC2o0ICAIDFKJCYI5AAAGAxLxKJKaZsAACA7aiQAABgMQok5ggkAABYjF025ggkAABYzIs8Yoo1JAAAwHZUSAAAsBhTNuYIJAAAWIw8Yo4pGwAAYDsqJAAAWMwhSiRmCCQAAFiMXTbmmLIBAAC2o0ICAIDF2GVjjkACAIDFyCPmmLIBAAC2o0ICAIDFvCiRmCKQAABgMfKIOQIJAAAWY1GrOdaQAAAA2xFIAACwmMPhmaM4Xn/9dd14443y9/eXv7+/IiMj9dlnn7munz59WnFxcapYsaLKlSunrl27Kisry62PjIwMxcTEqGzZsgoODtbw4cN15swZtzZr1qxRkyZN5HQ6Vbt2bSUlJV3Sd0QgAQDAYl4Oh0eO4qhSpYpeeuklpaWl6dtvv1Xbtm3VsWNHbdmyRZI0ZMgQLVmyRB9++KHWrl2rvXv3qkuXLq7P5+fnKyYmRrm5uVq/fr3mzp2rpKQkjRo1ytVm165diomJUZs2bZSenq7BgwerX79+WrlyZbG/I4dhGEaxP3WF82080O4hAFekQxum2T0E4IpT1tv69R0PzP3eI/3Mj238lz4fFBSkl19+Wd26dVPlypU1b948devWTZL0yy+/qF69ekpNTdWtt96qzz77TB06dNDevXsVEhIiSZo5c6ZGjBihAwcOyMfHRyNGjNCyZcv0448/uu7RvXt3HTlyRCtWrCjW2KiQAABgMYeHjpycHGVnZ7sdOTk5pvfPz8/XBx98oBMnTigyMlJpaWnKy8tTVFSUq03dunVVrVo1paamSpJSU1PVoEEDVxiRpOjoaGVnZ7uqLKmpqW59nGtzro/iIJAAAGAxh8PhkSMhIUEBAQFuR0JCwgXvu3nzZpUrV05Op1P9+/fXokWLFBERoczMTPn4+CgwMNCtfUhIiDIzMyVJmZmZbmHk3PVz1y7WJjs7W6dOnSrWd8S2XwAArhIjR45UfHy82zmn03nB9nXq1FF6erqOHj2qhQsXKjY2VmvXrrV6mJeEQAIAgMW8PLRMxel0XjSA/JmPj49q164tSWratKk2btyoKVOm6IEHHlBubq6OHDniViXJyspSaGioJCk0NFQbNmxw6+/cLpw/tvnzzpysrCz5+/vL19e3WM9WpEDy6aefFrnDe++9t1gDAACgpLtSXoxWUFCgnJwcNW3aVN7e3lq1apW6du0qSdq6dasyMjIUGRkpSYqMjNT48eO1f/9+BQcHS5KSk5Pl7++viIgIV5vly5e73SM5OdnVR3EUKZB06tSpSJ05HA7l5+cXexAAAMCzRo4cqfbt26tatWo6duyY5s2bpzVr1mjlypUKCAhQ3759FR8fr6CgIPn7+2vQoEGKjIzUrbfeKklq166dIiIi1LNnTyUmJiozM1PPPfec4uLiXFWa/v37a/r06XrqqafUp08frV69WgsWLNCyZcuKPd4iBZKCgoJidwwAAM6yo0Cyf/9+Pfzww9q3b58CAgJ04403auXKlbrzzjslSZMmTZKXl5e6du2qnJwcRUdHa8aMGa7PlypVSkuXLtWAAQMUGRkpPz8/xcbGauzYsa42NWvW1LJlyzRkyBBNmTJFVapU0VtvvaXo6Ohij5f3kAB/I7yHBCjscryH5OF5mzzSz9s9bvRIP1eiS1rUeuLECa1du1YZGRnKzc11u/bEE094ZGAAAJQUnlrUWpIVO5B8//33uvvuu3Xy5EmdOHFCQUFBOnjwoOs99wQSAABQXMV+MdqQIUN0zz336Pfff5evr6++/vpr/frrr2ratKleeeUVK8YIAMBVzVMvRivJih1I0tPTNXToUHl5ealUqVLKyclR1apVlZiYqGeeecaKMQIAcFXz1KvjS7JiBxJvb295eZ39WHBwsDIyMiRJAQEB+u233zw7OgAA8LdQ7DUkjRs31saNG3XdddepVatWGjVqlA4ePKh33nlH9evXt2KMAABc1bxK+HSLJxS7QjJhwgSFhYVJksaPH68KFSpowIABOnDggGbNmuXxAQIAcLVzODxzlGTFrpDcdNNNrj8HBwdrxYoVHh0QAAD4++GX6wEAYLGSvkPGE4odSGrWrHnRL3bnzp1/aUAAAJQ05BFzxQ4kgwcPdvs5Ly9P33//vVasWKHhw4d7alwAAOBvpNiB5Mknnzzv+ddee03ffvvtXx4QAAAlDbtszBV7l82FtG/fXh999JGnugMAoMRgl405jy1qXbhwoYKCgjzVHQAAJQaLWs1d0ovR/vjFGoahzMxMHThwQDNmzPDo4AAAwN9DsQNJx44d3QKJl5eXKleurNatW6tu3boeHdyl+n3jdLuHAACAi8fWR5RgxQ4ko0ePtmAYAACUXEzZmCt2aCtVqpT2799f6PyhQ4dUqlQpjwwKAAD8vRS7QmIYxnnP5+TkyMfH5y8PCACAksaLAompIgeSqVOnSjpbdnrrrbdUrlw517X8/HylpKRcMWtIAAC4khBIzBU5kEyaNEnS2QrJzJkz3aZnfHx8VKNGDc2cOdPzIwQAACVekQPJrl27JElt2rTRxx9/rAoVKlg2KAAAShIWtZor9hqSL7/80opxAABQYjFlY67Yu2y6du2qf/7zn4XOJyYm6r777vPIoAAAwN9LsQNJSkqK7r777kLn27dvr5SUFI8MCgCAkoTfZWOu2FM2x48fP+/2Xm9vb2VnZ3tkUAAAlCT8tl9zxa6QNGjQQPPnzy90/oMPPlBERIRHBgUAQEni5aGjJCt2heT5559Xly5dtGPHDrVt21aStGrVKs2bN08LFy70+AABAEDJV+xAcs8992jx4sWaMGGCFi5cKF9fXzVs2FCrV69WUFCQFWMEAOCqxoyNOYdxoXfBF1F2drbef/99zZ49W2lpacrPz/fU2C7Z6TN2jwAAcLUoU+x/mhff8yu2eaSfcXdd55F+rkSXPCWVkpKi2NhYhYeHa+LEiWrbtq2+/vprT44NAAD8TRQrF2ZmZiopKUmzZ89Wdna27r//fuXk5Gjx4sUsaAUA4AKYsjFX5ArJPffcozp16mjTpk2aPHmy9u7dq2nTplk5NgAASgQvh2eOkqzIFZLPPvtMTzzxhAYMGKDrriu5c1gAAODyK3KFZN26dTp27JiaNm2qZs2aafr06Tp48KCVYwMAoETwcjg8cpRkRQ4kt956q958803t27dPjz32mD744AOFh4eroKBAycnJOnbsmJXjBADgqsWr480Ve5eNn5+f+vTpo3Xr1mnz5s0aOnSoXnrpJQUHB+vee++1YowAAKCE+0tvoq1Tp44SExO1Z88evf/++54aEwAAJQqLWs395RejXYl4MRoAoKgux4vRJqza4ZF+nrnjWo/0cyW6DP81AADw91bSqxueUNJ/eSAAALgKUCEBAMBiVEjMEUgAALCYo6Tv2fUApmwAAIDtqJAAAGAxpmzMEUgAALAYMzbmmLIBAAC2o0ICAIDFSvovxvMEAgkAABZjDYk5pmwAAIDtqJAAAGAxZmzMEUgAALCYl0gkZggkAABYjAqJOdaQAAAA21EhAQDAYuyyMUcgAQDAYryHxBxTNgAAwHZUSAAAsBgFEnNUSAAAsJiXw+GRozgSEhJ08803q3z58goODlanTp20detWtzanT59WXFycKlasqHLlyqlr167Kyspya5ORkaGYmBiVLVtWwcHBGj58uM6cOePWZs2aNWrSpImcTqdq166tpKSk4n9Hxf4EAAC44q1du1ZxcXH6+uuvlZycrLy8PLVr104nTpxwtRkyZIiWLFmiDz/8UGvXrtXevXvVpUsX1/X8/HzFxMQoNzdX69ev19y5c5WUlKRRo0a52uzatUsxMTFq06aN0tPTNXjwYPXr108rV64s1ngdhmEYf/2xryynz5i3AQBAkspchsUL/9qY4ZF++txc7ZI/e+DAAQUHB2vt2rVq2bKljh49qsqVK2vevHnq1q2bJOmXX35RvXr1lJqaqltvvVWfffaZOnTooL179yokJESSNHPmTI0YMUIHDhyQj4+PRowYoWXLlunHH3903at79+46cuSIVqxYUeTxUSEBAMBiXh46cnJylJ2d7Xbk5OQUaQxHjx6VJAUFBUmS0tLSlJeXp6ioKFebunXrqlq1akpNTZUkpaamqkGDBq4wIknR0dHKzs7Wli1bXG3+2Me5Nuf6KCoCCQAAV4mEhAQFBAS4HQkJCaafKygo0ODBg9W8eXPVr19fkpSZmSkfHx8FBga6tQ0JCVFmZqarzR/DyLnr565drE12drZOnTpV5Gdjlw0AABZzeGibzciRIxUfH+92zul0mn4uLi5OP/74o9atW+eRcViBQAIAgMU8tevX6XQWKYD80cCBA7V06VKlpKSoSpUqrvOhoaHKzc3VkSNH3KokWVlZCg0NdbXZsGGDW3/nduH8sc2fd+ZkZWXJ399fvr6+RR4nUzYAAFjMjm2/hmFo4MCBWrRokVavXq2aNWu6XW/atKm8vb21atUq17mtW7cqIyNDkZGRkqTIyEht3rxZ+/fvd7VJTk6Wv7+/IiIiXG3+2Me5Nuf6KCp22QAA/tYuxy6bd9P2eKSffzStYt7o/z3++OOaN2+ePvnkE9WpU8d1PiAgwFW5GDBggJYvX66kpCT5+/tr0KBBkqT169dLOrvtt1GjRgoPD1diYqIyMzPVs2dP9evXTxMmTJB0dttv/fr1FRcXpz59+mj16tV64okntGzZMkVHRxd5vAQSAMDf2uUIJO95KJA8VIxAcqF1K3PmzFGvXr0knX0x2tChQ/X+++8rJydH0dHRmjFjhms6RpJ+/fVXDRgwQGvWrJGfn59iY2P10ksvqXTp/31xa9as0ZAhQ/TTTz+pSpUqev755133KPJ4CSQAgL+zyxFI5n3nmUDSo0nRA8nVhjUkAADAduyyAQDAYp7a9luSEUgAALAY0xHm+I4AAIDtqJAAAGAxpmzMEUgAALAYccQcUzYAAMB2VEgAALAYUzbmCCQAAFiM6QhzBBIAACxGhcQcoQ0AANiOCgkAABajPmKOQAIAgMWYsTHHlA0AALAdFRIAACzmxaSNKQIJAAAWY8rGHFM2AADAdlRIAACwmIMpG1MEEgAALMaUjTmmbAAAgO2okAAAYDF22ZgjkAAAYDGmbMwRSAAAsBiBxBxrSAAAgO2okAAAYDG2/ZojkAAAYDEv8ogppmwAAIDtqJAAAGAxpmzMEUgAALAYu2zMMWUDAABsR4UEAACLMWVjjkACAIDF2GVjjikbAABgOwIJ/rLZb76hHvd3VeTNjdW6RaQGD3pcu3ftdGuzcMF89e3VU7fd0kQNb6ij7Oxsm0YLXD5p327UoMf7K6r17Wp4Qx2tXvWF2/VDBw/q+WeeVlTr29WsaUMNeLSvfv11tz2DhaUcHvpPSUYgwV/27cYNeuDBh/TO+wv0xptzdObMGfV/pK9OnjzpanP69Cnd1ryF+j7S38aRApfXqVMnVadOHY187oVC1wzD0OAn4rRnz2+aPG2G5i9cpLDwa/RY395uf3dQMjgcnjlKMtaQ4C97fdZst5/Hjn9JbVpE6ueftqjpTTdLkv7xcC9J0sYN31zu4QG2ub1FK93eotV5r/36625t+iFdH32yVLVrXydJem7UaLVt1Vwrli9Tl273Xc6hwmIlPEt4BBUSeNzxY8ckSf4BATaPBLhy5eXmSpKcPk7XOS8vL/n4+Oj779LsGhZgmys6kPz222/q06fPRdvk5OQoOzvb7cjJyblMI8SfFRQUKPGfE9SocRNdd931dg8HuGLVqFlLYWHhmjp5orKPHlVebq7+9dYsZWVm6sCBA3YPDx7m5XB45CjJruhAcvjwYc2dO/eibRISEhQQEOB2vPzPhMs0QvzZhBfHaMe2bUp8ZZLdQwGuaN7e3np1yjT9unu3Wtx2i5rd1EgbN3yj21u0lBd7REsch4eOkszWNSSffvrpRa/v3LnzotclaeTIkYqPj3c7Z5RyXqA1rDThxbFKWbtG/5r7rkJCQ+0eDnDFi7ihvhZ8/ImOHTumvLw8BQUF6aHu9+mGG+rbPTTgsrM1kHTq1EkOh0OGYVywjcOkROV0OuV0ugeQ02c8MjwUkWEYShg/TqtXJWt20juqUqWq3UMCrirly5eXdHah609bflTcoCdtHhE8rqSXNzzA1kASFhamGTNmqGPHjue9np6erqZNm17mUaG4Jowbo8+WL9XkaTPkV9ZPB/9//rtc+fIqU6aMJOnggQM6ePCgfsvIkCRt3/YflS3rp7CwMAUEBto1dMBSJ0+cUMb//9+8JP13zx798vPPCggIUFh4uD5f+ZkqVAhSWFi4tm3bqsSECWrTNkq3Nb/dxlHDCiX9HSKe4DAuVp6w2L333qtGjRpp7Nix573+ww8/qHHjxiooKChWv1RILq+GN9Q57/mxLyaoY+cukqTXX5ummTOmX7QNUNJs3PCN+vV+uND5ezt21rgJL+m9d9/W3DmzdejgIVWuXFkd7u2ox/o/Lm8fHxtG+/dV5jL80/ybHUc90k+za0vu7kVbA8m///1vnThxQnfdddd5r584cULffvutWrU6/z7+CyGQAACK6nIEkg07PRNIbqlFILmqEEgAAEV1OQLJRg8FkptLcCC5orf9AgCAvwdeHQ8AgNVY02qKQAIAgMXYZWOOQAIAgMVK+FvfPYI1JAAAwHZUSAAAsBgFEnMEEgAArEYiMcWUDQAAsB0VEgAALMYuG3MEEgAALMYuG3NM2QAAANtRIQEAwGIUSMwRSAAAsBqJxBRTNgAAlFApKSm65557FB4eLofDocWLF7tdNwxDo0aNUlhYmHx9fRUVFaVt27a5tTl8+LAeeugh+fv7KzAwUH379tXx48fd2mzatEktWrRQmTJlVLVqVSUmJhZ7rAQSAAAs5vDQf4rrxIkTatiwoV577bXzXk9MTNTUqVM1c+ZMffPNN/Lz81N0dLROnz7tavPQQw9py5YtSk5O1tKlS5WSkqJHH33UdT07O1vt2rVT9erVlZaWppdfflmjR4/WrFmzivcdGYZhFPsJr3Cnz9g9AgDA1aLMZVi8sHnPcfNGRdCgSrlL/qzD4dCiRYvUqVMnSWerI+Hh4Ro6dKiGDRsmSTp69KhCQkKUlJSk7t276+eff1ZERIQ2btyom266SZK0YsUK3X333dqzZ4/Cw8P1+uuv69lnn1VmZqZ8fHwkSU8//bQWL16sX375pcjjo0ICAIDFHB46PGnXrl3KzMxUVFSU61xAQICaNWum1NRUSVJqaqoCAwNdYUSSoqKi5OXlpW+++cbVpmXLlq4wIknR0dHaunWrfv/99yKPh0WtAABcJXJycpSTk+N2zul0yul0FruvzMxMSVJISIjb+ZCQENe1zMxMBQcHu10vXbq0goKC3NrUrFmzUB/nrlWoUKFI46FCAgCA1TxUIklISFBAQIDbkZCQcNkfxwpUSAAAsJinXh0/cuRIxcfHu527lOqIJIWGhkqSsrKyFBYW5jqflZWlRo0audrs37/f7XNnzpzR4cOHXZ8PDQ1VVlaWW5tzP59rUxRUSAAAuEo4nU75+/u7HZcaSGrWrKnQ0FCtWrXKdS47O1vffPONIiMjJUmRkZE6cuSI0tLSXG1Wr16tgoICNWvWzNUmJSVFeXl5rjbJycmqU6dOkadrJAIJAACWczg8cxTX8ePHlZ6ervT0dElnF7Kmp6crIyNDDodDgwcP1osvvqhPP/1Umzdv1sMPP6zw8HDXTpx69erprrvu0iOPPKINGzboq6++0sCBA9W9e3eFh4dLknr06CEfHx/17dtXW7Zs0fz58zVlypRClRzT74htvwCAv7PLse33570nPNJPvXC/YrVfs2aN2rRpU+h8bGyskpKSZBiGXnjhBc2aNUtHjhzR7bffrhkzZuj66693tT18+LAGDhyoJUuWyMvLS127dtXUqVNVrtz/tiBv2rRJcXFx2rhxoypVqqRBgwZpxIgRxRorgQQA8LdWkgPJ1YRFrQAAWI3fZWOKQAIAgMU8tcumJGNRKwAAsB0VEgAALHYpO2T+bggkAABYjDxijkACAIDVSCSmWEMCAABsR4UEAACLscvGHIEEAACLsajVHFM2AADAdlRIAACwGAUScwQSAACsRiIxxZQNAACwHRUSAAAsxi4bcwQSAAAsxi4bc0zZAAAA21EhAQDAYhRIzBFIAACwGonEFIEEAACLsajVHGtIAACA7aiQAABgMXbZmCOQAABgMfKIOaZsAACA7aiQAABgMaZszBFIAACwHInEDFM2AADAdlRIAACwGFM25ggkAABYjDxijikbAABgOyokAABYjCkbcwQSAAAsxu+yMUcgAQDAauQRU6whAQAAtqNCAgCAxSiQmCOQAABgMRa1mmPKBgAA2I4KCQAAFmOXjTkCCQAAViOPmGLKBgAA2I4KCQAAFqNAYo5AAgCAxdhlY44pGwAAYDsqJAAAWIxdNuYIJAAAWIwpG3NM2QAAANsRSAAAgO2YsgEAwGJM2ZgjkAAAYDEWtZpjygYAANiOCgkAABZjysYcgQQAAIuRR8wxZQMAAGxHhQQAAKtRIjFFIAEAwGLssjHHlA0AALAdFRIAACzGLhtzBBIAACxGHjFHIAEAwGokElOsIQEAALajQgIAgMXYZWOOQAIAgMVY1GqOKRsAAGA7h2EYht2DQMmUk5OjhIQEjRw5Uk6n0+7hAFcM/m4AhRFIYJns7GwFBATo6NGj8vf3t3s4wBWDvxtAYUzZAAAA2xFIAACA7QgkAADAdgQSWMbpdOqFF15g0R7wJ/zdAApjUSsAALAdFRIAAGA7AgkAALAdgQQAANiOQAIAAGxHIIFlXnvtNdWoUUNlypRRs2bNtGHDBruHBNgqJSVF99xzj8LDw+VwOLR48WK7hwRcMQgksMT8+fMVHx+vF154Qd99950aNmyo6Oho7d+/3+6hAbY5ceKEGjZsqNdee83uoQBXHLb9whLNmjXTzTffrOnTp0uSCgoKVLVqVQ0aNEhPP/20zaMD7OdwOLRo0SJ16tTJ7qEAVwQqJPC43NxcpaWlKSoqynXOy8tLUVFRSk1NtXFkAIArFYEEHnfw4EHl5+crJCTE7XxISIgyMzNtGhUA4EpGIAEAALYjkMDjKlWqpFKlSikrK8vtfFZWlkJDQ20aFQDgSkYggcf5+PioadOmWrVqletcQUGBVq1apcjISBtHBgC4UpW2ewAomeLj4xUbG6ubbrpJt9xyiyZPnqwTJ06od+/edg8NsM3x48e1fft218+7du1Senq6goKCVK1aNRtHBtiPbb+wzPTp0/Xyyy8rMzNTjRo10tSpU9WsWTO7hwXYZs2aNWrTpk2h87GxsUpKSrr8AwKuIAQSAABgO9aQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABSqBevXqpU6dOrp9bt26twYMHX/ZxrFmzRg6HQ0eOHLns9wZwdSGQAJdRr1695HA45HA45OPjo9q1a2vs2LE6c+aMpff9+OOPNW7cuCK1JUQAsAO/ywa4zO666y7NmTNHOTk5Wr58ueLi4uTt7a2RI0e6tcvNzZWPj49H7hkUFOSRfgDAKlRIgMvM6XQqNDRU1atX14ABAxQVFaVPP/3UNc0yfvx4hYeHq06dOpKk3377Tffff78CAwMVFBSkjh07avfu3a7+8vPzFR8fr8DAQFWsWFFPPfWU/vwbIf48ZZOTk6MRI0aoatWqcjqdql27tmbPnq3du3e7ftdKhQoV5HA41KtXL0lnf2NzQkKCatasKV9fXzVs2FALFy50u8/y5ct1/fXXy9fXV23atHEbJwBcDIEEsJmvr69yc3MlSatWrdLWrVuVnJyspUuXKi8vT9HR0Spfvrz+/e9/66uvvlK5cuV01113uT4zceJEJSUl6V//+pfWrVunw4cPa9GiRRe958MPP6z3339fU6dO1c8//6w33nhD5cqVU9WqVfXRRx9JkrZu3ap9+/ZpypQpkqSEhAS9/fbbmjlzprZs2aIhQ4boH//4h9auXSvpbHDq0qWL7rnnHqWnp6tfv356+umnrfraAJQ0BoDLJjY21ujYsaNhGIZRUFBgJCcnG06n0xg2bJgRGxtrhISEGDk5Oa7277zzjlGnTh2joKDAdS4nJ8fw9fU1Vq5caRiGYYSFhRmJiYmu63l5eUaVKlVc9zEMw2jVqpXx5JNPGoZhGFu3bjUkGcnJyecd45dffmlIMn7//XfXudOnTxtly5Y11q9f79a2b9++xoMPPmgYhmGMHDnSiIiIcLs+YsSIQn0BwPmwhgS4zJYuXapy5copLy9PBQUF6tGjh0aPHq24uDg1aNDAbd3IDz/8oO3bt6t8+fJufZw+fVo7duzQ0aNHtW/fPjVr1sx1rXTp0rrpppsKTduck56erlKlSqlVq1ZFHvP27dt18uRJ3XnnnW7nc3Nz1bhxY0nSzz//7DYOSYqMjCzyPQD8vRFIgMusTZs2ev311+Xj46Pw8HCVLv2/v4Z+fn5ubY8fP66mTZvqvffeK9RP5cqVL+n+vr6+xf7M8ePHJUnLli3TNddc43bN6XRe0jgA4I8IJMBl5ufnp9q1axepbZMmTTR//nwFBwfL39//vG3CwsL0zTffqGXLlpKkM2fOKC0tTU2aNDlv+wYNGqigoEBr165VVFRUoevnKjT5+fmucxEREXI6ncrIyLhgZaVevXr69NNP3c59/fXX5g8JAGJRK3BFe+ihh1SpUiV17NhR//73v7Vr1y6tWbNGTzzxhPbs2SNJevLJJ/XSSy9p8eLF+uWXX/T4449f9B0iNWrUUGxsrPr06aPFixe7+lywYIEkqXr16nI4HFq6dKkOHDig48ePq3z58ho2bJiGDBmiuXPnaseOHfruu+80bdo0zZ07V5LUv39/bdu2TcOHD9fWrVs1b948JSUlWf0VASghCCTAFaxs2bJKSUlRtWrV1KVLF9WrV099+/bV6dOnXRWToUOHqmfPnoqNjVVkZKTKly+vzp07X7Tf119/Xd26ddPjjz+uunXr6pFHHtGJEyckSddcc43GjBmjp59+WiEhIRo4cKAkady4cXr++eeVkJCgevXq6a677tKyZctUs2ZNSVK1atX00UcfafHixWrYsKFmzpypCRMmWPjtAChJHMaFVr4BAABcJlRIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2I5AAAADbEUgAAIDtCCQAAMB2BBIAAGA7AgkAALDd/wFLjCUoQYGGmAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine:\n",
      "Accuracy: 0.9924798239178283\n",
      "Precision: 0.42857142857142855\n",
      "Recall: 0.075\n",
      "F1 Score: 0.1276595744680851\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+Avesk2bNkpISNBnn32muXPnas+ePbrlllt0/PhxZWZmysfHR4GBgW7PCQkJUWZmpiQpMzPTLYwUni8892djsrOzderUqRK9RbRsAACwmodaNmPHjtWIESPcjjmdzguO7dKli+ufr7vuOrVp00Z169bV0qVL5evr65H5eBIVEgAArOahConT6ZS/v7/b42KB5HyBgYFq2LChdu7cqdDQUOXm5uro0aNuY7KyslxrTkJDQ4vsuin82WyMv79/iUMPgQQAgCvAiRMntGvXLtWqVUutWrVSxYoVtWbNGtf57du3KyMjQ5GRkZKkyMhIbdmyRfv373eNSUpKkr+/vyIiIlxjzr1G4ZjCa5QEgQQAAKs5vDzzKIFRo0Zp3bp1+uWXX7Rhwwb17NlT3t7euu+++xQQEKCBAwdqxIgR+uKLL5SWlqYBAwYoMjJSbdu2lSR17txZEREReuCBB/Tdd99p1apVevbZZxUbG+uqygwePFi7d+/Wk08+qZ9++klz5szR0qVLFRcXV+K3iDUkAABY7RK+Q+Sv+v3333Xffffp0KFDqlmzpm6++WZt3LhRNWvWlCTNmDFDXl5e6t27t3JychQVFaU5c+a4nu/t7a2VK1dqyJAhioyMlJ+fn2JiYjRx4kTXmPDwcCUmJiouLk4zZ85U7dq19dZbb5V4y68kOQzDMP76yy5bfK8favcUgDLpyObZdk8BKHMqlcJfzX27zPDIdU59WvLKw+WCCgkAAFbjd9mYIpAAAGA1G1o2lxsiGwAAsB0VEgAArEbLxhSBBAAAqxFITPEOAQAA21EhAQDAaixqNUUgAQDAarRsTBFIAACwGhUSU0Q2AABgOyokAABYjZaNKQIJAABWo2VjisgGAABsR4UEAACLOaiQmCKQAABgMQKJOVo2AADAdlRIAACwGgUSUwQSAAAsRsvGHC0bAABgOyokAABYjAqJOQIJAAAWI5CYI5AAAGAxAok51pAAAADbUSEBAMBqFEhMEUgAALAYLRtztGwAAIDtqJAAAGAxKiTmCCQAAFiMQGKOlg0AALAdFRIAACxGhcQcgQQAAKuRR0zRsgEAALajQgIAgMVo2ZgjkAAAYDECiTkCCQAAFiOQmGMNCQAAsB0VEgAArEaBxBSBBAAAi9GyMUfLBgAA2I4KCQAAFqNCYo5AAgCAxQgk5mjZAAAA21EhAQDAYlRIzBFIAACwGnnEFC0bAABgOyokAABYjJaNOQIJAAAWI5CYI5AAAGAxAok51pAAAADbUSEBAMBqFEhMEUgAALAYLRtztGwAAIDtCCT4U888epdOfTvb7ZH+4bMXHLt89hCd+na2ut16ndvxq0Or6cNZg3Vowyv6dU28Jg/vIW9v93/1+nZpra+WPKVDG17R7tUvat74fgoK8LPsdQF2m/+PN9X8b400Nf5Fu6eCUuBwODzyKM9o2cDU1p17FT34NdfPZ/ILiowZ1q+jDKPoc728HPpw1hBlHcpWx/7TFVozQG9NekB5Z/I1fvYKSVJk8/p6a9KDenL6B0pc94OuCg7QrGf6as5z96nvqLcse12AXX7Y8r3ef+9dNWzYyO6poJSU9zDhCVRIYOpMfoGyDh13PQ4dPel2/rqGV+mJB27T4An/LvLcTpFN1KR+qB56ZqG+//m/Wv3lNk2ck6hH72mvihW8JUltrgvXr3sPac7idfp17yFtSN+t+R98qdZN65bK6wNK0x8nT2rsmNEa//wL8g8IsHs6QJlhayA5ePCgpk6dqp49eyoyMlKRkZHq2bOnpk2bpgMHDtg5NZyjQZ2a2r36RW1bMUELXozR1aHVXOd8K1VUQnx/DZ+yVFmHjhd5bpvrwvXDzr3af/h/55I2/KiAqr6KuKaWJOmr7/eodmg1Rd0cIUkKDqqqnp1a6LP12yx+ZUDpm/zCRLVv30FtI2+yeyooRbRszNnWstm8ebOioqJUuXJlderUSQ0bNpQkZWVladasWZoyZYpWrVql1q1b2zVFSNr8wy96ZNy/9fOvWQqtEaBnHu2iz/8Zp1Z9XtSJP3I0dWRvbfxuj1Ymb7ng80Oq+2v/eUFl/+Hss+dq+EvbpdTvdmvA0wv19pSHVMmnoipW9NbKdVs0fMoSy18fUJo+/SRRP/64TYuWvG/3VFDayneW8AjbKiTDhg3T3Xffrd9++00JCQl66aWX9NJLLykhIUEZGRnq06ePhg0bZnqdnJwcZWdnuz2MgvxSeAVXhtVfbtOHn3+rH3bs1eepP6rH0LkKqOKr3p1bKrpDM916Y0ONnvbX/nBtXD9ULz/ZR/Fvfqqb+r2kbo+9rrq1gvTaM3099CoA+2Xu26epU15U/EvT5HQ67Z4OrkBTpkyRw+HQ8OHDXcdOnz6t2NhYVa9eXVWqVFHv3r2VlZXl9ryMjAxFR0ercuXKCg4O1ujRo3XmzBm3McnJyWrZsqWcTqcaNGighISEEs/PtgrJd999p4SEhAuWoBwOh+Li4nT99debXic+Pl7PP/+82zHvkBtUsdaNHpsr/ufYiVPambFf11xdU00bhKl+7RrKTJnmNmbxy4P05be7FPXwTGUdyi6yFiQ4yF+SlHXwbKVk9IDOSk3fpRn/WiNJ+mHHXv1xKkdrFozQ86+vVOb/jQMuZ9u2bdXhQ4fU9+5ermP5+flK+3qz3l38jjZ/u0Xe3t42zhBWsrvdsnnzZr3xxhu67jr3XZBxcXFKTEzUe++9p4CAAA0dOlS9evXSl19+Kensv6PR0dEKDQ3Vhg0btG/fPj344IOqWLGiJk+eLEnas2ePoqOjNXjwYL3zzjtas2aNBg0apFq1aikqKqrYc7QtkISGhmrTpk1q3LjxBc9v2rRJISEhptcZO3asRowY4XYs+JYxHpkjivLz9VF47RrKTNykD1Z/owXLNridT3v/GdduGens+pAxA6NUs1oVHThyQpJ0e9vGOnb8lH7cnSlJquzrozNn3Kta+QVnt+zY/SEGPKVN27Z6f/kKt2PjnxmrevXra8DAhwkj5Zydf5adOHFC/fr10z/+8Q+98MILruPHjh3T/PnztWjRIt12222SpAULFqhJkybauHGj2rZtq9WrV2vbtm36/PPPFRISohYtWmjSpEkaM2aMJkyYIB8fH82bN0/h4eGaPn26JKlJkyZav369ZsyYcXkEklGjRumRRx5RWlqabr/9dlf4yMrK0po1a/SPf/xDL7/8sul1nE5nkfKnw4sPtqfEx/VUYsoWZew9rLDgAD07OFr5BQVa+lmaDh45ccGFrL/tO6Jf9x6SJH2e+qN+3J2p+S/E6JmZyxVS3V/jY7vqjaUpys07W/JLXLdFc567Xw/ffbOSNvyoWjUCNG10b23e8ov2HThWqq8XsIqfXxVde21Dt2O+lSsrMCCwyHGUP57KIzk5OcrJyXE7dqH/Dp4rNjZW0dHR6tSpk1sgSUtLU15enjp16uQ61rhxY9WpU0epqalq27atUlNT1axZM7cCQVRUlIYMGaKtW7fq+uuvV2pqqts1Csec2xoqDtsCSWxsrGrUqKEZM2Zozpw5ys8/+zdkb29vtWrVSgkJCbrnnnvsmh7+z1UhgfpX/AAFBVTWwSMntCF9tzo8OF0H/6/aYaagwFDvJ+Zq5tN9lZwwUidP5+idFZs0cW6ia8y/V3ylqn6VNPjeDpoS10vHTpxS8qbtenbmR1a9LAC4LF1omcL48eM1YcKEC45/99139c0332jz5s1FzmVmZsrHx0eBgYFux0NCQpSZmekac363ovBnszHZ2dk6deqUfH19i/XabP1itHvvvVf33nuv8vLydPDgQUlSjRo1VLFiRTunhXM8+NSCEo33vX5okWMZ+46o57C5f/q8ue+u09x315XoXsDlbn7C23ZPAaXEUy2bCy1TuFh15LffftMTTzyhpKQkVapUySP3t1KZ+KbWihUrqlatWnZPAwAAS3iqZWPWnjlXWlqa9u/fr5YtW7qO5efnKyUlRbNnz9aqVauUm5uro0ePulVJsrKyFBoaKul/6z3PVbgL59wx5+/MycrKkr+/f7GrIxLf1AoAQLl0++23a8uWLUpPT3c9WrdurX79+rn+uWLFilqzZo3rOdu3b1dGRoYiIyMlSZGRkdqyZYv279/vGpOUlCR/f39FRES4xpx7jcIxhdcorjJRIQEAoDyzY5dN1apV1bRpU7djfn5+ql69uuv4wIEDNWLECAUFBcnf31/Dhg1TZGSk2rZtK0nq3LmzIiIi9MADD2jq1KnKzMzUs88+q9jYWFelZvDgwZo9e7aefPJJPfTQQ1q7dq2WLl2qxMRElQSBBAAAi5XVbzCYMWOGvLy81Lt3b+Xk5CgqKkpz5sxxnff29tbKlSs1ZMgQRUZGys/PTzExMZo4caJrTHh4uBITExUXF6eZM2eqdu3aeuutt0q05VeSHIZxod/Renm70MJKANKRzbPtngJQ5lQqhb+aN35qlUeu89OUkv1H/nJChQQAAIt5eZXREkkZQiABAMBiZbVlU5awywYAANiOCgkAABbj93KZI5AAAGAx8og5AgkAABajQmKONSQAAMB2VEgAALAYFRJzBBIAACxGHjFHywYAANiOCgkAABajZWOOQAIAgMXII+Zo2QAAANtRIQEAwGK0bMwRSAAAsBh5xBwtGwAAYDsqJAAAWIyWjTkCCQAAFiOPmCOQAABgMSok5lhDAgAAbEeFBAAAi1EgMUcgAQDAYrRszNGyAQAAtqNCAgCAxSiQmCOQAABgMVo25mjZAAAA21EhAQDAYhRIzBFIAACwGC0bc7RsAACA7aiQAABgMSok5ggkAABYjDxijkACAIDFqJCYYw0JAACwHRUSAAAsRoHEHIEEAACL0bIxR8sGAADYjgoJAAAWo0BijkACAIDFvEgkpmjZAAAA21EhAQDAYhRIzBFIAACwGLtszBFIAACwmBd5xBRrSAAAgO2okAAAYDFaNuYIJAAAWIw8Yo6WDQAAsB0VEgAALOYQJRIzBBIAACzGLhtztGwAAIDtqJAAAGAxdtmYI5AAAGAx8og5WjYAAMB2VEgAALCYFyUSUwQSAAAsRh4xRyABAMBiLGo1xxoSAABgOyokAABYjAKJOSokAABYzMvh8MijJObOnavrrrtO/v7+8vf3V2RkpD799FPX+dOnTys2NlbVq1dXlSpV1Lt3b2VlZbldIyMjQ9HR0apcubKCg4M1evRonTlzxm1McnKyWrZsKafTqQYNGighIeHS3qNLehYAACjTateurSlTpigtLU1ff/21brvtNnXv3l1bt26VJMXFxWnFihV67733tG7dOu3du1e9evVyPT8/P1/R0dHKzc3Vhg0btHDhQiUkJGjcuHGuMXv27FF0dLQ6duyo9PR0DR8+XIMGDdKqVatKPF+HYRjGX3/ZZYvv9UPtngJQJh3ZPNvuKQBlTqVSWLzQd+G3HrnOuzHX/6XnBwUFadq0aerTp49q1qypRYsWqU+fPpKkn376SU2aNFFqaqratm2rTz/9VF27dtXevXsVEhIiSZo3b57GjBmjAwcOyMfHR2PGjFFiYqJ++OEH1z369u2ro0eP6rPPPivR3KiQAABgMYfD4ZHHpcrPz9e7776rkydPKjIyUmlpacrLy1OnTp1cYxo3bqw6deooNTVVkpSamqpmzZq5wogkRUVFKTs721VlSU1NdbtG4ZjCa5QEi1oBALhM5OTkKCcnx+2Y0+mU0+m84PgtW7YoMjJSp0+fVpUqVbRs2TJFREQoPT1dPj4+CgwMdBsfEhKizMxMSVJmZqZbGCk8X3juz8ZkZ2fr1KlT8vX1LfZro0ICAIDFvByeecTHxysgIMDtER8ff9H7NmrUSOnp6frqq680ZMgQxcTEaNu2baX4youvWBWSjz/+uNgX/Pvf/37JkwEAoDzy1BejjR07ViNGjHA7drHqiCT5+PioQYMGkqRWrVpp8+bNmjlzpu69917l5ubq6NGjblWSrKwshYaGSpJCQ0O1adMmt+sV7sI5d8z5O3OysrLk7+9fouqIVMxA0qNHj2JdzOFwKD8/v0QTAAAAxfNn7ZniKCgoUE5Ojlq1aqWKFStqzZo16t27tyRp+/btysjIUGRkpCQpMjJSL774ovbv36/g4GBJUlJSkvz9/RUREeEa88knn7jdIykpyXWNkihWICkoKCjxhQEAwFl2fDHa2LFj1aVLF9WpU0fHjx/XokWLlJycrFWrVikgIEADBw7UiBEjFBQUJH9/fw0bNkyRkZFq27atJKlz586KiIjQAw88oKlTpyozM1PPPvusYmNjXaFo8ODBmj17tp588kk99NBDWrt2rZYuXarExMQSz5dFrQAAWMyO32Wzf/9+Pfjgg9q3b58CAgJ03XXXadWqVbrjjjskSTNmzJCXl5d69+6tnJwcRUVFac6cOa7ne3t7a+XKlRoyZIgiIyPl5+enmJgYTZw40TUmPDxciYmJiouL08yZM1W7dm299dZbioqKKvF8L+l7SE6ePKl169YpIyNDubm5bucef/zxEk/C0/geEuDC+B4SoKjS+B6S/ou/98h1Eu67ziPXKYtK/H/Dt99+q7vuukt//PGHTp48qaCgIB08eND1tbJlIZAAAIDLS4m3/cbFxalbt246cuSIfH19tXHjRv36669q1aqVXn75ZSvmCADAZc3uL0a7HJQ4kKSnp2vkyJHy8vKSt7e3cnJydPXVV2vq1Kl6+umnrZgjAACXNYeHHuVZiQNJxYoV5eV19mnBwcHKyMiQJAUEBOi3337z7OwAAMAVocRrSK6//npt3rxZ1157rTp06KBx48bp4MGDevvtt9W0aVMr5ggAwGXNq5y3WzyhxBWSyZMnq1atWpKkF198UdWqVdOQIUN04MABvfnmmx6fIAAAlzuHwzOP8qzEFZLWrVu7/jk4OLjEv14YAADgfHwxGgAAFivvO2Q8ocSBJDw8/E/f2N27d/+lCQEAUN6QR8yVOJAMHz7c7ee8vDx9++23+uyzzzR69GhPzQsAAFxBShxInnjiiQsef/311/X111//5QkBAFDesMvGXIl32VxMly5d9MEHH3jqcgAAlBvssjHnsUWt77//voKCgjx1OQAAyg0WtZq7pC9GO/eNNQxDmZmZOnDggNuvLQYAACiuEgeS7t27uwUSLy8v1axZU7feeqsaN27s0cldqsOb+BXrAICyw2PrI8qxEgeSCRMmWDANAADKL1o25koc2ry9vbV///4ixw8dOiRvb2+PTAoAAFxZSlwhMQzjgsdzcnLk4+PzlycEAEB540WBxFSxA8msWbMknS07vfXWW6pSpYrrXH5+vlJSUsrMGhIAAMoSAom5YgeSGTNmSDpbIZk3b55be8bHx0f16tXTvHnzPD9DAABQ7hU7kOzZs0eS1LFjR3344YeqVq2aZZMCAKA8YVGruRKvIfniiy+smAcAAOUWLRtzJd5l07t3b7300ktFjk+dOlV33323RyYFAACuLCUOJCkpKbrrrruKHO/SpYtSUlI8MikAAMoTfpeNuRK3bE6cOHHB7b0VK1ZUdna2RyYFAEB5wm/7NVfiCkmzZs20ZMmSIsffffddRUREeGRSAACUJ14eepRnJa6QPPfcc+rVq5d27dql2267TZK0Zs0aLVq0SO+//77HJwgAAMq/EgeSbt26afny5Zo8ebLef/99+fr6qnnz5lq7dq2CgoKsmCMAAJc1OjbmHMbFvgu+mLKzs7V48WLNnz9faWlpys/P99TcLtmpPLtnAJRN/KEIFFWpxH81L7nnPtvhketMuvNaj1ynLLrkllRKSopiYmIUFham6dOn67bbbtPGjRs9OTcAAHCFKFEuzMzMVEJCgubPn6/s7Gzdc889ysnJ0fLly1nQCgDARVCdNFfsCkm3bt3UqFEjff/993r11Ve1d+9evfbaa1bODQCAcsHL4ZlHeVbsCsmnn36qxx9/XEOGDNG115bfHhYAACh9xa6QrF+/XsePH1erVq3Upk0bzZ49WwcPHrRybgAAlAteDodHHuVZsQNJ27Zt9Y9//EP79u3To48+qnfffVdhYWEqKChQUlKSjh8/buU8AQC4bPHV8eZKvMvGz89PDz30kNavX68tW7Zo5MiRmjJlioKDg/X3v//dijkCAIBy7i99E22jRo00depU/f7771q8eLGn5gQAQLnColZzf/mL0coivhgNuLDyXvIFLkVpfDHa5DW7PHKdp2+/xiPXKYtK4f8GAACubOW9uuEJ5f2XBwIAgMsAFRIAACxGhcQcgQQAAIs5WMBlipYNAACwHRUSAAAsRsvGHIEEAACL0bExR8sGAADYjgoJAAAWK++/GM8TCCQAAFiMNSTmaNkAAADbUSEBAMBidGzMEUgAALCYl0gkZggkAABYjAqJOdaQAAAA21EhAQDAYuyyMUcgAQDAYnwPiTlaNgAAwHZUSAAAsBgFEnMEEgAALEbLxhwtGwAAyqH4+HjdcMMNqlq1qoKDg9WjRw9t377dbczp06cVGxur6tWrq0qVKurdu7eysrLcxmRkZCg6OlqVK1dWcHCwRo8erTNnzriNSU5OVsuWLeV0OtWgQQMlJCSUeL4EEgAALOZweOZREuvWrVNsbKw2btyopKQk5eXlqXPnzjp58qRrTFxcnFasWKH33ntP69at0969e9WrVy/X+fz8fEVHRys3N1cbNmzQwoULlZCQoHHjxrnG7NmzR9HR0erYsaPS09M1fPhwDRo0SKtWrSrZe2QYhlGyl1j2ncqzewZA2UTVGCiqUiksXkjYnOGR6/S/oc4lP/fAgQMKDg7WunXr1L59ex07dkw1a9bUokWL1KdPH0nSTz/9pCZNmig1NVVt27bVp59+qq5du2rv3r0KCQmRJM2bN09jxozRgQMH5OPjozFjxigxMVE//PCD6159+/bV0aNH9dlnnxV7flRIAAC4TOTk5Cg7O9vtkZOTU6znHjt2TJIUFBQkSUpLS1NeXp46derkGtO4cWPVqVNHqampkqTU1FQ1a9bMFUYkKSoqStnZ2dq6datrzLnXKBxTeI3iIpAAAGAxh8PhkUd8fLwCAgLcHvHx8ab3Lygo0PDhw9WuXTs1bdpUkpSZmSkfHx8FBga6jQ0JCVFmZqZrzLlhpPB84bk/G5Odna1Tp04V+z1ilw0AABbzVLd07NixGjFihNsxp9Np+rzY2Fj98MMPWr9+vYdm4nkEEgAALOapbb9Op7NYAeRcQ4cO1cqVK5WSkqLatWu7joeGhio3N1dHjx51q5JkZWUpNDTUNWbTpk1u1yvchXPumPN35mRlZcnf31++vr7FnictGwAAyiHDMDR06FAtW7ZMa9euVXh4uNv5Vq1aqWLFilqzZo3r2Pbt25WRkaHIyEhJUmRkpLZs2aL9+/e7xiQlJcnf318RERGuMedeo3BM4TWKi102wBWEXTZAUaWxy+adtN89cp1+rWqbD/o/jz32mBYtWqSPPvpIjRo1ch0PCAhwVS6GDBmiTz75RAkJCfL399ewYcMkSRs2bJB0dttvixYtFBYWpqlTpyozM1MPPPCABg0apMmTJ0s6u+23adOmio2N1UMPPaS1a9fq8ccfV2JioqKiooo9XwIJcAUhkABFlUYgWfSNZwLJ/S2LH0gcF/nAL1iwQP3795d09ovRRo4cqcWLFysnJ0dRUVGaM2eOqx0jSb/++quGDBmi5ORk+fn5KSYmRlOmTFGFCv9745KTkxUXF6dt27apdu3aeu6551z3KPZ8CSTAlYNAAhRVXgPJ5YZFrQAAWOxi1Qr8D4EEAACLsYPEHO8RAACwHRUSAAAsRsvGHIEEAACLEUfM0bIBAAC2o0ICAIDFaNmYI5AAAGAx2hHmCCQAAFiMCok5QhsAALAdFRIAACxGfcQcgQQAAIvRsTFHywYAANiOCgkAABbzomljikACAIDFaNmYo2UDAABsR4UEAACLOWjZmCKQAABgMVo25mjZAAAA21EhAQDAYuyyMUcgAQDAYrRszBFIAACwGIHEHGtIAACA7aiQAABgMbb9miOQAABgMS/yiClaNgAAwHZUSAAAsBgtG3MEEgAALMYuG3O0bAAAgO2okAAAYDFaNuYIJAAAWIxdNuZo2QAAANtRIcFftvTdRXpvyWLt3ftfSdI1Da7VI4Mf0823dNB///u7oqNuv+Dzpk5/VZ2jupTmVAFbLX13kZYuWay9//3fZ+XRIWc/KyjfaNmYcxiGYdg9CU87lWf3DK4s65LXysvLW3Xq1pUMQx9/tFwLF8zXu+8vU3h4fR05ctht/AfvLdHCBfP1efJ6Va7sZ9Osr0ys9LdX8hdr5e199rNiGIZWfLRcCf+cryUfLFODBtfaPb0rVqVS+Kv5+h1HPHKdm6+t5pHrlEUEElii/U03Km7kaPXsfXeRc/f26aEmTSI0YdJkG2Z2ZSOQlD23RN6ouFGj1esCnxWUjtIIJF96KJC0K8eBhDUk8Kj8/Hx99kmiTp36Q9e1uL7I+W1bf9D2n35Uj159bJgdUHbk5+fr0//7rDRvXvSzAlxpyvQakt9++03jx4/XP//5z4uOycnJUU5OjtuxAi+nnE6n1dPDOXb8vF0P9uur3Nwc+VaurFdmvq5rrmlQZNyyD99X/frXqMX1LW2YJWC/HT9v1wP3n/2sVK5cWTNmva5rGhT9rKB88aI8aapMV0gOHz6shQsX/umY+Ph4BQQEuD2mvRRfSjNEoXrh4VrywXK9vWip7rnnPo17Zox27drpNub06dP69JOVVEdwRatXL1xLP1iufy9eqrvvvU/PPT1Gu3buNH8iLmsODz3KM1vXkHz88cd/en737t0aOXKk8vPzLzqGCknZ9Oig/qp9dR09N36i69jKj5drwrhntXptioKCgmyc3ZWLv6SVPY8MPPtZGTdhovlgWKI01pBs3HnUI9dp2yDQI9cpi2xt2fTo0UMOh0N/lokcJn+COp1FwweLWu1XUFCg3Nxct2PLPvxAt3a8jTACnKOgoEB5531WUA7xlwFTtrZsatWqpQ8//FAFBQUXfHzzzTd2Tg/FNGvGdKV9vVn//e/v2vHzds2aMV1fb96ku6K7ucZkZPyqb9I2q2dv2jW4cs0877Mys/Cz0rWb+ZNxWXN46H/lma0VklatWiktLU3du3e/4Hmz6gnKhsOHD+nZp8fo4IH9qlK1qho2bKQ5b8xX5E3tXGOWf/iBQkJCFXnTzTbOFLDX4cOH9OzYMTpwzmdl7pvunxXgSmXrGpL//Oc/OnnypO68884Lnj958qS+/vprdehQsm8xpGUDXBhrSICiSmMNyabdxzxynRvrB3jkOmURX4wGXEEIJEBRpRFINnsokNxQjgNJmd72CwAArgxl+ovRAAAoF6hOmiKQAABgsfK+Q8YTCCQAAFiM9VvmWEMCAABsR4UEAACLUSAxRyABAMBqJBJTtGwAAIDtqJAAAGAxdtmYI5AAAGAxdtmYo2UDAABsR4UEAACLUSAxRyABAMBqJBJTtGwAACinUlJS1K1bN4WFhcnhcGj58uVu5w3D0Lhx41SrVi35+vqqU6dO2rFjh9uYw4cPq1+/fvL391dgYKAGDhyoEydOuI35/vvvdcstt6hSpUq6+uqrNXXq1BLPlUACAIDFHB76X0mdPHlSzZs31+uvv37B81OnTtWsWbM0b948ffXVV/Lz81NUVJROnz7tGtOvXz9t3bpVSUlJWrlypVJSUvTII4+4zmdnZ6tz586qW7eu0tLSNG3aNE2YMEFvvvlmyd4jwzCMEr/CMu5Unt0zAMomVvoDRVUqhcULW34/YT6oGJrVrnLJz3U4HFq2bJl69Ogh6Wx1JCwsTCNHjtSoUaMkSceOHVNISIgSEhLUt29f/fjjj4qIiNDmzZvVunVrSdJnn32mu+66S7///rvCwsI0d+5cPfPMM8rMzJSPj48k6amnntLy5cv1008/FXt+VEgAALCYw0MPT9qzZ48yMzPVqVMn17GAgAC1adNGqampkqTU1FQFBga6wogkderUSV5eXvrqq69cY9q3b+8KI5IUFRWl7du368iRI8WeD4taAQC4TOTk5CgnJ8ftmNPplNPpLPG1MjMzJUkhISFux0NCQlznMjMzFRwc7Ha+QoUKCgoKchsTHh5e5BqF56pVq1as+VAhAQDAah4qkcTHxysgIMDtER8fX+ovxwpUSAAAsJinvjp+7NixGjFihNuxS6mOSFJoaKgkKSsrS7Vq1XIdz8rKUosWLVxj9u/f7/a8M2fO6PDhw67nh4aGKisry21M4c+FY4qDCgkAAJcJp9Mpf39/t8elBpLw8HCFhoZqzZo1rmPZ2dn66quvFBkZKUmKjIzU0aNHlZaW5hqzdu1aFRQUqE2bNq4xKSkpysv7346SpKQkNWrUqNjtGolAAgCA5RwOzzxK6sSJE0pPT1d6erqkswtZ09PTlZGRIYfDoeHDh+uFF17Qxx9/rC1btujBBx9UWFiYaydOkyZNdOedd+rhhx/Wpk2b9OWXX2ro0KHq27evwsLCJEn333+/fHx8NHDgQG3dulVLlizRzJkzi1RyTN8jtv0CVw62/QJFlca23x/3nvTIdZqE+ZVofHJysjp27FjkeExMjBISEmQYhsaPH68333xTR48e1c0336w5c+aoYcOGrrGHDx/W0KFDtWLFCnl5eal3796aNWuWqlT53xbk77//XrGxsdq8ebNq1KihYcOGacyYMSWaK4EEuIIQSICiynMguZywqBUAAKvxlwFTBBIAACzmqV025RmLWgEAgO2okAAAYDHWb5kjkAAAYDHyiDkCCQAAViORmGINCQAAsB0VEgAALMYuG3MEEgAALMaiVnO0bAAAgO2okAAAYDEKJOYIJAAAWI1EYoqWDQAAsB0VEgAALMYuG3MEEgAALMYuG3O0bAAAgO2okAAAYDEKJOYIJAAAWI1EYopAAgCAxVjUao41JAAAwHZUSAAAsBi7bMwRSAAAsBh5xBwtGwAAYDsqJAAAWIyWjTkCCQAAliORmKFlAwAAbEeFBAAAi9GyMUcgAQDAYuQRc7RsAACA7aiQAABgMVo25ggkAABYjN9lY45AAgCA1cgjplhDAgAAbEeFBAAAi1EgMUcgAQDAYixqNUfLBgAA2I4KCQAAFmOXjTkCCQAAViOPmKJlAwAAbEeFBAAAi1EgMUcgAQDAYuyyMUfLBgAA2I4KCQAAFmOXjTkCCQAAFqNlY46WDQAAsB2BBAAA2I6WDQAAFqNlY45AAgCAxVjUao6WDQAAsB0VEgAALEbLxhyBBAAAi5FHzNGyAQAAtqNCAgCA1SiRmCKQAABgMXbZmKNlAwAAbEeFBAAAi7HLxhyBBAAAi5FHzBFIAACwGonEFGtIAACA7aiQAABgMXbZmCOQAABgMRa1mqNlAwAAbOcwDMOwexIon3JychQfH6+xY8fK6XTaPR2gzOCzARRFIIFlsrOzFRAQoGPHjsnf39/u6QBlBp8NoChaNgAAwHYEEgAAYDsCCQAAsB2BBJZxOp0aP348i/aA8/DZAIpiUSsAALAdFRIAAGA7AgkAALAdgQQAANiOQAIAAGxHIIFlXn/9ddWrV0+VKlVSmzZttGnTJrunBNgqJSVF3bp1U1hYmBwOh5YvX273lIAyg0ACSyxZskQjRozQ+PHj9c0336h58+aKiorS/v377Z4aYJuTJ0+qefPmev311+2eClDmsO0XlmjTpo1uuOEGzZ49W5JUUFCgq6++WsOGDdNTTz1l8+wA+zkcDi1btkw9evSweypAmUCFBB6Xm5urtLQ0derUyXXMy8tLnTp1Umpqqo0zAwCUVQQSeNzBgweVn5+vkJAQt+MhISHKzMy0aVYAgLKMQAIAAGxHIIHH1ahRQ97e3srKynI7npWVpdDQUJtmBQAoywgk8DgfHx+1atVKa9ascR0rKCjQmjVrFBkZaePMAABlVQW7J4DyacSIEYqJiVHr1q1144036tVXX9XJkyc1YMAAu6cG2ObEiRPauXOn6+c9e/YoPT1dQUFBqlOnjo0zA+zHtl9YZvbs2Zo2bZoyMzPVokULzZo1S23atLF7WoBtkpOT1bFjxyLHY2JilJCQUPoTAsoQAgkAALAda0gAAIDtCCQAAMB2BBIAAGA7AgkAALAdgQQAANiOQAIAAGxHIAEAALYjkADlUP/+/dWjRw/Xz7feequGDx9e6vNITk6Ww+HQ0aNHS/3eAC4vBBKgFPXv318Oh0MOh0M+Pj5q0KCBJk6cqDNnzlh63w8//FCTJk0q1lhCBAA78LtsgFJ25513asGCBcrJydEnn3yi2NhYVaxYUWPHjnUbl5ubKx8fH4/cMygoyCPXAQCrUCEBSpnT6VRoaKjq1q2rIUOGqFOnTvr4449dbZYXX3xRYWFhatSokSTpt99+0z333KPAwEAFBQWpe/fu+uWXX1zXy8/P14gRIxQYGKjq1avrySef1Pm/EeL8lk1OTo7GjBmjq6++Wk6nUw0aNND8+fP1yy+/uH7XSrVq1eRwONS/f39JZ39jc3x8vMLDw+Xr66vmzZvr/fffd7vPJ598ooYNG8rX11cdO3Z0mycA/BkCCWAzX19f5ebmSpLWrFmj7du3KykpSStXrlReXp6ioqJUtWpV/ec//9GXX36pKlWq6M4773Q9Z/r06UpISNA///lPrV+/XocPH9ayZcv+9J4PPvigFi9erFmzZunHH3/UG2+8oSpVqujqq6/WBx98IEnavn279u3bp5kzZ0qS4uPj9a9//Uvz5s3T1q1bFRcXp//3//6f1q1bJ+lscOrVq5e6deum9PR0DRo0SE899ZRVbxuA8sYAUGpiYmKM7t27G4ZhGAUFBUZSUpLhdDqNUaNGGTExMUZISIiRk5PjGv/2228bjRo1MgoKClzHcnJyDF9fX2PVqlWGYRhGrVq1jKlTp7rO5+XlGbVr13bdxzAMo0OHDsYTTzxhGIZhbN++3ZBkJCUlXXCOX3zxhSHJOHLkiOvY6dOnjcqVKxsbNmxwGztw4EDjvvvuMwzDMMaOHWtERES4nR8zZkyRawHAhbCGBChlK1euVJUqVZSXl6eCggLdf//9mjBhgmJjY9WsWTO3dSPfffeddu7cqapVq7pd4/Tp09q1a5eOHTumffv2qU2bNq5zFSpUUOvWrYu0bQqlp6fL29tbHTp0KPacd+7cqT/++EN33HGH2/Hc3Fxdf/31kqQff/zRbR6SFBkZWex7ALiyEUiAUtaxY0fNnTtXPj4+CgsLU4UK//sY+vn5uY09ceKEWrVqpXfeeafIdWrWrHlJ9/f19S3xc06cOCFJSkxM1FVXXeV2zul0XtI8AOBcBBKglPn5+alBgwbFGtuyZUstWbJEwcHB8vf3v+CYWrVq6auvvlL79u0lSWfOnFFaWppatmx5wfHNmjVTQUGB1q1bp06dOhU5X1ihyc/Pdx2LiIiQ0+lURkbGRSsrTZo00ccff+x2bOPGjeYvEgDEolagTOvXr59q1Kih7t276z//+Y/27Nmj5ORkPf744/r9998lSU888YSmTJmi5cuX66efftJjjz32p98hUq9ePcXExOihhx7S8uXLXddcunSpJKlu3bpyOBxauXKlDhw4oBMnTqhq1aoaNWqU4uLitHDhQu3atUvffPONXnvtNS1cuFCSNHjwYO3YsUOjR4/W9u3btWjRIiUkJFj9FgEoJwgkQBlWuXJlpaSkqE6dOurVq5eaNGmigQMH6vTp066KyciRI/XAAw8oJiZGkZGRqlq1qnr27Pmn1507d6769Omjxx57TI0bN9bDDz+skydPSpKuuuoqPf/883rqqacUEhKioUOHSpImTZqk5557TvHx8WrSpInuvPNOJSYmKjw8XJJUp04dffDBB1q+fLmaN2+uefPmafLkyRa+OwDKE4dxsZVvAAAApYQKCQAAsB2BBAAA2I5AAgAAbEcgAQAAtiOQAAAA2xFIAACA7QgkAADAdgQSAABgOwIJAACwHYEEAADYjkACAABsRyABAAC2+/8TiqzKRPCcYAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting:\n",
      "Accuracy: 0.9035216434336023\n",
      "Precision: 0.01593625498007968\n",
      "Recall: 0.2\n",
      "F1 Score: 0.02952029520295203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/venkatasai24/.local/lib/python3.11/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Function to calculate and print evaluation metrics\n",
    "def evaluate_model(model, X_scaled_test, y_test):\n",
    "    # Make predictions\n",
    "    y_pred = model.predict(X_scaled_test)\n",
    "    \n",
    "    # Calculate evaluation metrics\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # Display evaluation metrics\n",
    "    print(\"Accuracy:\", accuracy)\n",
    "    print(\"Precision:\", precision)\n",
    "    print(\"Recall:\", recall)\n",
    "    print(\"F1 Score:\", f1)\n",
    "    \n",
    "    # Display confusion matrix\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(cm, annot=True, cmap='Blues', fmt='g')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Random Forest\n",
    "print(\"Random Forest:\")\n",
    "random_forest = RandomForestClassifier()\n",
    "random_forest.fit(X_scaled_train, y_train)\n",
    "evaluate_model(random_forest, X_scaled_test, y_test)\n",
    "\n",
    "# Support Vector Machine\n",
    "print(\"Support Vector Machine:\")\n",
    "svm = SVC()\n",
    "svm.fit(X_scaled_train, y_train)\n",
    "evaluate_model(svm, X_scaled_test, y_test)\n",
    "\n",
    "# Gradient Boosting\n",
    "print(\"Gradient Boosting:\")\n",
    "gradient_boosting = GradientBoostingClassifier()\n",
    "gradient_boosting.fit(X_train, y_train)\n",
    "evaluate_model(gradient_boosting, X_scaled_test, y_test)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "a3a1d56d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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