PDS project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channels:\n",
      " - conda-forge\n",
      " - defaults\n",
      "Platform: osx-arm64\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries for modeling and evaluation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_curve\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('nasa.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Understanding Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id                 name  est_diameter_min  est_diameter_max  \\\n",
      "0  2162635  162635 (2000 SS164)          1.198271          2.679415   \n",
      "1  2277475    277475 (2005 WK4)          0.265800          0.594347   \n",
      "2  2512244   512244 (2015 YE18)          0.722030          1.614507   \n",
      "3  3596030          (2012 BV13)          0.096506          0.215794   \n",
      "4  3667127          (2014 GE35)          0.255009          0.570217   \n",
      "\n",
      "   relative_velocity  miss_distance orbiting_body  sentry_object  \\\n",
      "0        13569.24922    54839744.08         Earth          False   \n",
      "1        73588.72666    61438126.52         Earth          False   \n",
      "2       114258.69210    49798724.94         Earth          False   \n",
      "3        24764.30314    25434972.72         Earth          False   \n",
      "4        42737.73376    46275567.00         Earth          False   \n",
      "\n",
      "   absolute_magnitude  hazardous  \n",
      "0               16.73      False  \n",
      "1               20.00       True  \n",
      "2               17.83      False  \n",
      "3               22.20      False  \n",
      "4               20.09       True  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90836 entries, 0 to 90835\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   id                  90836 non-null  int64  \n",
      " 1   name                90836 non-null  object \n",
      " 2   est_diameter_min    90836 non-null  float64\n",
      " 3   est_diameter_max    90836 non-null  float64\n",
      " 4   relative_velocity   90836 non-null  float64\n",
      " 5   miss_distance       90836 non-null  float64\n",
      " 6   orbiting_body       90836 non-null  object \n",
      " 7   sentry_object       90836 non-null  bool   \n",
      " 8   absolute_magnitude  90836 non-null  float64\n",
      " 9   hazardous           90836 non-null  bool   \n",
      "dtypes: bool(2), float64(5), int64(1), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\n",
      "count  9.083600e+04      90836.000000      90836.000000       90836.000000   \n",
      "mean   1.438288e+07          0.127432          0.284947       48066.918918   \n",
      "std    2.087202e+07          0.298511          0.667491       25293.296961   \n",
      "min    2.000433e+06          0.000609          0.001362         203.346432   \n",
      "25%    3.448110e+06          0.019256          0.043057       28619.020648   \n",
      "50%    3.748362e+06          0.048368          0.108153       44190.117890   \n",
      "75%    3.884023e+06          0.143402          0.320656       62923.604635   \n",
      "max    5.427591e+07         37.892650         84.730541      236990.128100   \n",
      "\n",
      "       miss_distance  absolute_magnitude  \n",
      "count   9.083600e+04        90836.000000  \n",
      "mean    3.706655e+07           23.527103  \n",
      "std     2.235204e+07            2.894086  \n",
      "min     6.745533e+03            9.230000  \n",
      "25%     1.721082e+07           21.340000  \n",
      "50%     3.784658e+07           23.700000  \n",
      "75%     5.654900e+07           25.700000  \n",
      "max     7.479865e+07           33.200000  \n"
     ]
    }
   ],
   "source": [
    "# Summary statistics of numerical columns\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                    0\n",
      "name                  0\n",
      "est_diameter_min      0\n",
      "est_diameter_max      0\n",
      "relative_velocity     0\n",
      "miss_distance         0\n",
      "orbiting_body         0\n",
      "sentry_object         0\n",
      "absolute_magnitude    0\n",
      "hazardous             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum() # no duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sentry_object\n",
       "False    90836\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sentry_object.value_counts() # we can remove this column as it is include only 1 record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orbiting_body\n",
       "Earth    90836\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.orbiting_body.value_counts() # we can remove this column as it is include only 1 record also"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the 'est_diameter_min' column\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=df, x='est_diameter_min', bins=30, kde=True)\n",
    "plt.title('Distribution of Estimated Minimum Diameter')\n",
    "plt.xlabel('Estimated Minimum Diameter (km)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verifying the correlation between our variables\n",
    "\n",
    "##### Verifying the correlation between our variables, here we can see that we have a strong correlation between the variables est_diameter_min and est_diameter_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only numeric columns\n",
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "\n",
    "# Visualize the correlation matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(df_numeric.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relation between object parameters and hazardous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Diamtere VS hazardous')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=df.hazardous,y=df.est_diameter_max)\n",
    "plt.title('Diamtere VS hazardous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Diamtere VS relative_velocity')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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F3H///VRWVvL000932meVlJTE7373O377299yww03cO2111JfX88999yDXq/nrrvuioz961//ysyZMxk3bhx33HEH/fr1o6Kign//+9+Ry2utGT58OK+//jpPPPEEo0ePJiYmJipLbcqUKQwZMoSPP/6Y66+//oSrkkLI3XNCfE9H7to58qXVatX09HR14sSJ6n333afabLYWr2nt7rkNGzaoRUVFqtFoVK1Wq/qTn/xE3bZtmwqozz33XGTc3LlzVZPJ1OKYEydOVIcOHdpie25urjpjxoyobQ8//LCal5enxsbGtjj+unXr1BkzZqgpKSlqfHy82qdPH3XGjBnqq6++2mL+tbW1rX4mzz77rDp27FjVZDKpBoNBLSgoUG+44Qb1iy++aHV8ax555BEVUIcMGdLq/t27d6uLFy9Wx4wZo1qtVjUuLk5NTk5WJ06cqL744osnPP6Ru+eOfl9Ha8/n0NodW7t371anTJmiJiQkqMnJyepVV12lVlRUqIB61113RZ1j6dKlanZ2thoTExN1R+Oxd88doSiKmpOTowLqf//3f7c6b6fTqf7P//yPWlhYqGq1WtVisajDhw9X77jjDrWmpuaEn8uxnn76aRVQDQaDarfbWx3T0c9KVVX1b3/7mzpixIjIXH/0ox+pu3btanGOjRs3qtOnT1ctFouq0+nUgoIC9Y477mjz+A0NDeqVV16pJiUlqRqNptU7Vu+++24VUDdt2nSSn4w4E2lUtZ3BHUIIIUQvM2bMGDQaDZ9//nlXT0X0AHJ5TgghxBnF4XCwc+dO3n33XbZu3cobb7zR1VMSPYQUTUIIcQYJhUInfC5fVz9771Tbtm0bkydPJjU1lbvuuovLLrusq6ckegi5PCeEEGeQefPmRT2PsDXyY0GI1knRJIQQZ5D9+/efMOTy6DvMhBDfkaJJCCGEEKId5DEqQgghhBDt0Lu7/U6zUCjEoUOHSEhIaPFoBiGEEEJ0T6qq0tzcTHZ2dounDBxNiqZOdOjQIXJycrp6GkIIIYTogMrKyjYfDi1FUyc68viDyspKEhMTu3g2QgghhGgPh8NBTk5Om48xAimaOtWRS3KJiYlSNAkhhBA9zIlaa6QRXAghhBCiHaRoEkIIIYRoBymahBBCCCHaQYomIYQQQoh2kKJJCCGEEKIdpGgSQgghhGgHKZqEEEIIIdpBiiYhhBBCiHaQokkIIYQQoh2kaBJCCCGEaAcpmoQQQggh2kGKJiGEEEKIdpCiSQghhBCiHaRoEkIIIUSP4A+GUFW1y84vRZMQQgghujVVVamxe9le2UijO9Bl84jrsjMLIYQQQpyAL6hwoM7NgQY3SiiEEuq6lSYpmoQQQgjRLdndAUpqm6lt9pFq0tHo9nfpfKRoEkIIIUS3EgqpHLJ7KK11EgiqZCYaiI3RdPW0pGgSQgghRPfhDSiU1TqpbHBj0sWTkhjf1VOKkKJJCCGEEN1CvdNHsc1Jk8uPNUGPNq573a8mRZMQQgghulRQCVHV6Ka8zkVIhawkAzGarr8cdywpmoQQQgjRZVy+IKW1Tg41ebAYtJh13bc06b4zE0IIIUSvpaoqtc3hy3HN3gDpCXriY7vX5bhjSdEkhBBCiNPKHwxR0eBif52b+NgYsi0GNN3wctyxundJJ4QQQohexe4JsOuQneLDThL18aSYtO0qmLZXNvHYxyUElNBpmGXrZKVJCCGEEKdcKKRS4/BSYnPiCypkWdqXvRRQQryw8QBvbj8IwMtbKlg8tfBUT7dVUjQJIYQQ4pTyBhT217moaHBjiI8lM9HQrtdVNrhZtnovZXUuACYOsnL1mJxTOdU2dfnluYMHD3L99deTmpqK0Whk5MiRbN26FYBAIMBvfvMbhg8fjslkIjs7mxtuuIFDhw5FHWPSpEloNJqor2uuuSZqTGNjI3PmzMFisWCxWJgzZw5NTU1RYyoqKpg5cyYmk4m0tDQWLFiA39+1ke1CCCFET9bo8vP1QTv7612kmLQkGbUnfI2qqnyws5qF/9xOWZ2LRH0c/zNjMNeN7Yc+PvY0zLp1XbrS1NjYyIQJE5g8eTIffPAB6enplJaWkpSUBIDb7Wbbtm387ne/4+yzz6axsZGFCxcya9Ysvvjii6hjzZ8/nz/84Q+R7w2G6Cp29uzZVFVVsWrVKgBuueUW5syZwzvvvAOAoijMmDEDq9XK+vXrqa+vZ+7cuaiqyqOPPnoKPwUhhBCi91FCKgcb3ZTVulBCKlmW9mUv2T0B/vJRMVv2NwAwKieJhRcNIsWkpdruOdXTblOXFk1/+tOfyMnJ4bnnnots69+/f+TXFouFNWvWRL3m0Ucf5Yc//CEVFRX069cvst1oNJKZmdnqefbs2cOqVavYtGkTY8eOBeCZZ56hqKiIvXv3UlhYyOrVq9m9ezeVlZVkZ2cDsHz5cubNm8cf//hHEhMTO+ttCyGEEL2a2x+krNZFVaOHRH0cCfr2PQplW0UjD3+4j0Z3gLgYDfPG92fm2dndJuiySy/Pvf3224wZM4arrrqK9PR0Ro0axTPPPNPma+x2OxqNJrIadcTKlStJS0tj6NChLFmyhObm5si+jRs3YrFYIgUTwLhx47BYLGzYsCEyZtiwYZGCCWDatGn4fL7I5cJj+Xw+HA5H1JcQQghxJqtt9vFVpZ2qRjfpCbp2FUz+YIhnPi3jrrd30egOkJNi5MGrR/KjkX26TcEEXbzSVFZWxhNPPMGiRYv47W9/y5YtW1iwYAE6nY4bbrihxXiv18udd97J7Nmzo1Z+rrvuOvLy8sjMzGTnzp0sXbqUHTt2RFapampqSE9Pb3G89PR0ampqImMyMjKi9icnJ6PVaiNjjnX//fdzzz33dPj9CyGEEL1FQAlRUe+mvN5FnEbT7uylA/Uulq3ey/56NwCXDs9i3oT+6OK6rnfpeLq0aAqFQowZM4b77rsPgFGjRrFr1y6eeOKJFkVTIBDgmmuuIRQK8fjjj0ftmz9/fuTXw4YNY+DAgYwZM4Zt27ZxzjnnALT6G6eqatT29ow52tKlS1m0aFHke4fDQU5O13X1CyGEEF2h2RugrNZFtd1DslGLUXvi8kJVVd7/uppnP9uPXwlhMcTzywsH8oP+Kadhxh3TpUVTVlYWQ4YMido2ePBgXnvttahtgUCAq6++mvLyctauXXvC/qJzzjmH+Ph4iouLOeecc8jMzOTw4cMtxtXW1kZWlzIzM9m8eXPU/sbGRgKBQIsVqCN0Oh06ne6E71MIIYTojVRV5bDDR4mtGZdfISNBT1w7HoXS5PbzyEfFfHGgEYDRucn88sKBJLfjzrqu1KU9TRMmTGDv3r1R2/bt20dubm7k+yMFU3FxMR9++CGpqaknPO6uXbsIBAJkZWUBUFRUhN1uZ8uWLZExmzdvxm63M378+MiYnTt3Ul1dHRmzevVqdDodo0eP/l7vUwghhOhtfEGF4sNOvj5oJ6RCtsXQroLpiwMN3P7yl3xxoJH4WA23nJfPXZcO6fYFE4BGVVW1q07++eefM378eO655x6uvvpqtmzZwvz583n66ae57rrrCAaDXHHFFWzbto133303asUnJSUFrVZLaWkpK1eu5JJLLiEtLY3du3ezePFiDAYDn3/+ObGx4Wui06dP59ChQzz11FNAOHIgNzc3KnJg5MiRZGRk8MADD9DQ0MC8efO47LLL2h054HA4sFgs2O12udtOCCFEr2V3Byi2NVPn9JFq0rUrO8kfDPHchnLe/Sq8OJGbYuRX0wrJTTW1+7zVdg+j+iVjTejcqzzt/fndpUUTwLvvvsvSpUspLi4mLy+PRYsWRXqU9u/fT15eXquv+/jjj5k0aRKVlZVcf/317Ny5E6fTSU5ODjNmzOCuu+4iJeW766INDQ0sWLCAt99+G4BZs2bx2GOPRd2FV1FRwW233cbatWsxGAzMnj2bZcuWtfsSnBRNQggherNQSOWQ3UOpzUlAUUkz69r1KJT9deFm7wMN4WbvmSOymDc+D23cyV3wOuOLpt5EiiYhhBC9lTegUFbrpLLBjVkXT6LhxFECqqryzlfVrNhQTkBRSTKGm73H5J58s7c/GKLO5WN0bjJp5q4pmuTZc0IIIYRoU73TR7HNSZPLjzVB364VokaXn4c/KmZbRbjZ+wf9k1lwwcB2PUblWE1uP+6AQk6ygcR2BmWeClI0CSGEEKJVQSVEVaM7/MBcVUNWUvsehbKlvIG/rC3G7gmgjY3hpnPzuGRYZrtym44WUELUNvsw6WIZ3sdCZqKemHZcDjxVpGgSQgghRAsuX5DSWifVdg+Jei1m3YlLBl9Q4bnP9vPe1+Fm7/6pRpZMPblm7yOavQEc3iBZFj35VlO7H8VyKknRJIQQQogIVVWpbQ5fjmv2BkhP0BPfjiiB8jonD6zeR+W3zd4/OjubueP7t+u1R1NCKnVOH/GxGoZkJdAn2diuZvPTQYomIYQQQgDhZuuKehf7693Ex8a061EoIVXl7R2HeH7DfoIhlWRjPAsvGsQ5/ZJP+vxuf5BGt5/0hPDqUkf6n04lKZqEEEIIgd0ToKzWyWGHjxSjFoP2xNlLDS4/D3+4jy8rmwAYm5fC7RcMxNKOO+uOFlLDq0saDQxMT6BfqvGkV6hOBymahBBCiDNYKKRS4/BSYnPiCypkJurbdTlsc3k9j3xUTLM3iDYuhp+cm8fFQ0++2dsbUMIhmWYtBVYzqZ0cJ9CZpGgSQgghzlDegEJ5nZPKBg9GbRyZiYZ2vebZz8r5YGcNAPlpJpZMLSQnxXhS51ZVlQaXn2BIpcBqpl+qsV3J4l1JiiYhhBDiDNTo8lNS66Te6SPNrEMXd+KCpbTWybLVe6lq9ADwX6P6MGdc7klfSvMHQ9icXpIM8Qy2mklP0J30ClVXkKJJCCGEOIMoIZWDjW7Kal0oIZUsy4mzl0KqyptfHuTFTQcIhlRSTFruuGgQI3OSTvr8TW4/noBCboqR/mkmjNqeU4r0nJkKIYQQ4ntx+4OU1bqoavRg0cdj1p+4DKh3+njow33sqLIDUJSfyi8mD2jXY1SOFlRC2Jw+jNpYhvaxkNXFQZUdIUWTEEIIcQawNXsps7lo8vjbnb20sbSOR9eW0OwLoouLYf55+UwdknHSl9LCQZUBsiyGbhNU2RFSNAkhhBC9WEAJUVHvprzeRZxG067sJW9A4W+flvHv3YcBGGA1s3jqIPomn1yz95GgyrhYDYOzEumTZCCuG0YJtJcUTUIIIUQv1ewNUFbrotruIdmobVf/UIkt3Ox9sMmDBrj8nL5cN7bfSTd7HwmqtCboKLCau11QZUdI0SSEEEL0Mqqqctjho8TWjMuvkJGgP+EKjxJSeePLg/x98wGUkEqqScuiKYMY0TfppM4dUlXqnX5UjcqAdDP9Ukxo43ru6tLRpGgSQgghehFfUOFAnZsDDS50cbFkW06cvVTn9PHgmn18fTDc7D2hIJWfTx5w0r1H3oBCvctHiklLvtVMWjcOquwIKZqEEEKIXqLJ7afE5gwnbJt07QqL/Kykjsc+LsHpC6KPj+HW8wq4cHD6STV7q6pKozuAXwnRP9VE/zRTtw+q7AgpmoQQQogeLhRSOWT3UGpzElBUMhMNJ3wUisev8MynZazZE272HphuZsnUQrKTTrwydTR/MESt00uiPp6zsiw9JqiyI6RoEkIIIXowb0ChrNZJZYMbsy6eFNOJL6ntO9zMstV7qbZ70QBXju7L7B/2O+k72+yeAC5fgL7JRvKtPSuosiN697sTQggherE6p48Sm5Mmlx9rgv6EDddKSOX1bVWs3FKBElJJM+tYNGUQw/tYTuq8RwdVDuub1CODKjtCiiYhhBCihwkqIaoa3ZTVuUDVkJV04keh2Jq9PLhmH7sOOQA4d0AaP580oF2p4EdzeoM4fH4yEw3kWU0k9tCgyo6QokkIIYToQVy+ICU2JzUODxa9FpPuxD/KPy2u5a+flODyKRjiY7n1/HwuOOvkmr2PDqo8K7PnB1V2hBRNQgghRA+gqiq25vDlOKc3SHo7spfc/iBP/aeMtd/YACjMSGDx1EFktSOG4NjjNLoDWBO05KeZSTb1/KDKjpCiSQghhOjm/MEQB+pdHKh3Ex8bQ5ZFf8JVom9qHCxfvY8ah5cYDVw1JodrxuSc1OpQdFClqVcFVXaEFE1CCCFEN2b3BCirdXLY4SPFqMWgbTv/SAmpvLq1kpe3VBBSIT0h3Ow9NPvkmr2PBFUmGbUUWM1YE3pXUGVHSNEkhBBCdEOhkEq1w0upzYkvqJCZqD9h9tJhR7jZe3d1uNn7/IFWfjapAHM7+p6O+C6oUunVQZUdIUWTEEII0c14AwrldU4qGzwYtXFkJp64B2ndvloe/6QEtz/c7P2zSQVMLkw/qfMGlBC2Zi8J+ngKM5PISOy9QZUdIUWTEEII0Y00uPyU2JppcPlJM+vQxbW9yuP2B3liXSmf7K0F4KzMBBZPLSQzUX9S5z06qDIvzdSuu/LONPKJCCGEEN2AElKpanRTXutCCalkWU6cvbSn2sHyNXs57PARo4FrftCPq8fknPAy3tGCSohapw99fCxD+1jIthjOiKDKjpCiSQghhOhibn+QsloXVY0eLPr4EwZOKiGVf3xewT++qCSkQkaijsVTChmclXhS53X6gji8fjIS9OSnm8+ooMqOkKJJCCGE6EK2Zi9lNhdNHj/pCXriTxAJUOPwsnz1Xr6paQZgcqGVn04sOKnnvkWCKmM0FGYk0DfZeMYFVXaEFE1CCCFEFwgoISrq3ZTXu4jTaMi2GE7YdP3xXhtPfFKKJ6Bg1MZy26QBTBxkPanzHgmqTDOHowTO1KDKjpCiSQghhDjNmr0BSm1Oahxeko3aE64SuXzhZu91+8LN3kOyElk0ZRAZJ9HsHVJVGlx+FFVlgNVEv9QzO6iyI6RoEkIIIU4TVVWp+TZ7yeVXyEw0nLBpe9chOw+u2YetOdzsPfuH/bhy9Mk1ex8bVJlm1kqUQAdI0SSEEEKcBr6gwoE6NwcaXOjiYsk+wfPfgkqIVz6v5NWt4WbvzEQ9S6YWUpiZ0O5zqqpKkzuAT1HITTWSl2aWoMrvQYomIYQQ4hRrcvspsTmpc/pINelOWLhU2z0sX72PvYfDzd4XnJXOrefnn1Szd0AJUev0YtbFM1yCKjuFFE1CCCHEKRIKqRxs8lBW6ySgqCe8HKeqKmu/sfHUf8rwBBRM2lh+PnkA5w08uWZvuyeA0xegT5KRfKsEVXYW+RSFEEKIU8DjP/IoFDdmXTwpprYzkJzeII+vK+HT4joAhmaHm73TE9rf7H10UOWwPhayLCfumRLt1+Vt8wcPHuT6668nNTUVo9HIyJEj2bp1a2S/qqrcfffdZGdnYzAYmDRpErt27Yo6hs/n4/bbbyctLQ2TycSsWbOoqqqKGtPY2MicOXOwWCxYLBbmzJlDU1NT1JiKigpmzpyJyWQiLS2NBQsW4Pf7T9l7F0II0TvVOX3sqGqiosFDmllPoqHtgunrg3Zuf+VLPi2uIzZGww3jcvnjZcNPqmBy+oIcbvaSnqDj7Jwk+iYbpWDqZF1aNDU2NjJhwgTi4+P54IMP2L17N8uXLycpKSky5s9//jMPPvggjz32GJ9//jmZmZlMmTKF5ubmyJiFCxfyxhtv8Morr7B+/XqcTieXXnopiqJExsyePZvt27ezatUqVq1axfbt25kzZ05kv6IozJgxA5fLxfr163nllVd47bXXWLx48Wn5LIQQQvR8QSXE/jonX1U14fYpZFv0bd7WH1RCvLBxP//9xtfUOX1kWfT8+YoRXHUSj0JRQio2hxdfQOGszASG9bFgOUGRJjpGo6qq2lUnv/POO/nss8/49NNPW92vqirZ2dksXLiQ3/zmN0B4VSkjI4M//elP3HrrrdjtdqxWKy+++CI//vGPATh06BA5OTm8//77TJs2jT179jBkyBA2bdrE2LFjAdi0aRNFRUV88803FBYW8sEHH3DppZdSWVlJdnY2AK+88grz5s3DZrORmHjiaHqHw4HFYsFut7drvBBCiN7D6Qt+m73kwaLXnrCP6FCTh2Wr91JscwIwZXAG88/Lx6Bt/91tHr9CvduH1awj32omRYIqO6S9P7+7dKXp7bffZsyYMVx11VWkp6czatQonnnmmcj+8vJyampqmDp1amSbTqdj4sSJbNiwAYCtW7cSCASixmRnZzNs2LDImI0bN2KxWCIFE8C4ceOwWCxRY4YNGxYpmACmTZuGz+eLulwohBBCHE1VVQ47vOyobKLG7iXdrG+zYFJVlTW7a/jlP76k2ObErIvjzovPYsGFA9tdMKmqSr3Th8MbYIDVzPC+FimYToMubQQvKyvjiSeeYNGiRfz2t79ly5YtLFiwAJ1Oxw033EBNTQ0AGRkZUa/LyMjgwIEDANTU1KDVaklOTm4x5sjra2pqSE9Pb3H+9PT0qDHHnic5ORmtVhsZcyyfz4fP54t873A4TubtCyGE6OH8wRAH6l0cqHcTHxtDlkXf5m39zd4Af/24hM9K6wEY0cfCHVMGkWbWtfucvqBCXbOPJJOWfKsJq1miBE6XLi2aQqEQY8aM4b777gNg1KhR7Nq1iyeeeIIbbrghMu7YPwyqqp7wD8ixY1ob35ExR7v//vu555572pyHEEKI3snuCT8KxdbsI8WoPeEq0VdVTTy4Zh/1Lj+xMRrmjMvlspF92t27pKoqTZ4A3oBCbpqR/qnmk7qUJ76/Lr08l5WVxZAhQ6K2DR48mIqKCgAyMzMBWqz02Gy2yKpQZmYmfr+fxsbGNsccPny4xflra2ujxhx7nsbGRgKBQIsVqCOWLl2K3W6PfFVWVrbrfQshhOi5jmQv7ahsot7lIzNR32bxElBCrNiwn/95cyf1Lj99kgwsu/Jsrjinb7sLpoAS4pDdQ0yMhuF9LRRmJErB1AW6tGiaMGECe/fujdq2b98+cnNzAcjLyyMzM5M1a9ZE9vv9ftatW8f48eMBGD16NPHx8VFjqqur2blzZ2RMUVERdrudLVu2RMZs3rwZu90eNWbnzp1UV1dHxqxevRqdTsfo0aNbnb9OpyMxMTHqSwghRO/lDSjsPexg50E7MRrNCcMqqxrd/PpfX/HatipUYNqQDB7+8UgGpJvbfU6HJ0Ct00d2koGROUlkWQzESJRAl+jSy3N33HEH48eP57777uPqq69my5YtPP300zz99NNA+HLZwoULue+++xg4cCADBw7kvvvuw2g0Mnv2bAAsFgs333wzixcvJjU1lZSUFJYsWcLw4cO56KKLgPDq1cUXX8z8+fN56qmnALjlllu49NJLKSwsBGDq1KkMGTKEOXPm8MADD9DQ0MCSJUuYP3++FENCCCFocPkpsTXT4PKTZtahizv+So+qqqzefZhnPi3DFwyRoIvj9gsGUFSQ1u7zKSGVWqcXbVwMQ7ISyU6SoMqu1qVF0w9+8APeeOMNli5dyh/+8Afy8vJ4+OGHue666yJjfv3rX+PxeLjttttobGxk7NixrF69moSE7x5Y+NBDDxEXF8fVV1+Nx+PhwgsvZMWKFcTGfvcHeuXKlSxYsCByl92sWbN47LHHIvtjY2N57733uO2225gwYQIGg4HZs2ezbNmy0/BJCCGE6K6UkEpVo5vyWhdKSA2v9LTRV+vwBHjs4xI2loWbvc/ua+GOiwaRehLN3i5fkCaPn4xEPflWs+QudRNdmtPU20hOkxBC9C5uf5ASm5NDTV4s+njM+rbXGrZXNvHQh/tocPmJi9FwQ1EuPxrZp80i62hKKBwlEBMD/VNN9E0xEh/b5Q/v6PXa+/Nbnj0nhBBCtMLW7KXM5qLJ4yc9Qd9m8RJQQry46QBvfHkQgL7JBpZMLaTA2v7eJY9focHtI9Wso0CCKrslKZqEEEKIowSUEBX1bsrrXcRpNGRbDG3G3FQ2ulm2ei9ltS4Apg/L5KYJeejj2x9U2eDyEwyp5KeZyU0zttkvJbqOFE1CCCHEt5q9gW8fheIl2ajFqG072XvVrhr+tr4cfzBEoj6OBRcOZGxearvP5wsq1Dn9JBniyU+XoMruToomIYQQZzxVValxeCm1OXH7lRNGCdg9AR5dW8zm8gYARuUksfCiQSd1Sa3R7ccbUOiXYiAvTYIqewIpmoQQQpzRfEGF/XVuKhpc6OJiybIY2hy/raKRhz/cR6M7QFyMhnnj+zPz7Ox2N3sHlBC1zT5MuliG97WQkaCX3KUeQoomIYQQZ6wmt58Sm5M6p49Uk67NPqSAEuL5Dft5a8chAHJSjPxq6iDy0k4uqLLZFyQ7KRwlYG7jwb6i+5HfLSGEEGecI49CKat1ElDUE16Oq2hw88C/v2F/vRuAGcOzuHFC/3Y3bCshlTqnj/hYDUOyEuiTbJSgyh5IiiYhhBBnFI9fobzOSWWDG7MunhTT8YMjVVXl/Z01PLu+HL8SwmKIZ8EFA/lhXkq7zxcVVJlmxmKUoMqeSoomIYQQZ4w6p48Sm5MmdwCrWY827vjZS01uP498VMwXB8IPhB+dm8wvLxxIsrF9zd4hNby6pNHAwPQE+qVKUGVPJ0WTEEKIXi+ohKhsCGcvoWrItujbvLV/64FGHv5oH03uAPGxGuaNz2PmiKx2xwF4Awr1rnBQZX6a6aQeoSK6LymahBBC9GpOX/Db7CUPFr0WUxvN1/5giBUbynnnq2oAclOMLJlaSP80U7vOJUGVvZsUTUIIIXolVVWxNYcvxzm9QdIT9MS1cXnsQL2LB/69lwMN4WbvmSOymDu+/c3e/mCIWqcvHFRpNWFNkKDK3kaKJiGEEL2OPxjiQL2LA/Vu4mNjyGrjcpyqqrz7VTXPbSgnoKgkGeP55YUDGZPb/mbvJrcftwRV9npSNAkhhOhV7J4AJbZmapv9pBi1bRYwjW4/D39YzLaKcLP3mG+bvZPa2ewdFVTZx0JmogRV9mZSNAkhhOgVQiGV6m8fheIPhshM1LeZhfT5/gYe+agYuyeANjaGmyb055Lh7W/2bvYGcHiDZFn05FtNJOglSqC3k6JJCCFEj+cNHMle8mDUxpGRqD/uWF9Q4bnP9vPe1+Fm7/6p4Wbv3NT2NXtLUOWZS4omIYQQPVqDy0+JrZkGl580s67Nxu3yOhcPrN5L5bfN3j86O5sbivq3mdd0NLc/SKPbT3pCeHWpvZfxRO8gRZMQQogeSQmpVDW6Ka91oYRUsiyG4z40N6SqvLPjECs27CcYUkk2xrPwwkGck5vcrnNJUKUAKZqEEEL0QG5/kBKbk0NNXiz6eMz64/84a3D5efjDfXxZ2QTA2LwUbr9gIBZD+3qQjgRVppi0FFjNElR5BpOiSQghRI9iaw43e9s9AdIT9G2u+Gwur+cvHxXj8AbRxsXwk3PzuHhoZruavY8NquyXakQfL1ECZzIpmoQQQvQIASWcvbS/3k2cRkO2xXDc4scbUHj2s3I+2FkDQH6aiSVTC8lJMbbrXP5gCJvTS5IhnsFWM+kSVCmQokkIIUQP4PAGKLM5qXF4STZqMWqP/+OrtNbJstV7qWr0APBfo/owZ1xuu3uQmtx+PAGF3BQj/dNMbZ5LnFnkT4IQQohuS1VVahxeSmxOPH6FzETDcW/vD6kqb355kBc3HSAYUkkxabnjokGMzElq17mCSgib04dRG8vQPhayJKhSHEOKJiGEEN2SL6hQXuuistGNLi6WLIvhuGPrnT4e/qiY7d82e4/LT+H2yQNJbGezt9MbxO71k2UxSFClOC4pmoQQQnQ7TW4/JTYndU4fqSZdmw3YG8vqefSjYpp9QXRxMcw/L5+pQzLa1YN0JKgyLlbD4KxE+iQZ2nyorzizSdEkhBCi2wiFVA42uSmrdREMqW1ejvMGFP62vpx/7wo3exdYw83efZPb1+x9JKjSmqCjwGqWoEpxQlI0CSGE6BY8foWyWieVjW4SdPGkmI5/iazEFm72PtjkQQNcfk5frhvbr13N3iFVpd7pR9WoDEg30y/F1O5EcHFmk6JJCCFEl6tz+iixOWlyt529FFJV3vjyIH//ttk71aRl0ZRBjOib1K7zHB1UmW81kyZBleIkSNEkhBCiywSVEJUNbsrrXaBqyLboj9uLVOf08dCafXx10A7A+IJUfjF5QLuatlVVpdEdwK+E6J9qon+aSYIqxUmTokkIIUSXcPqClNqcVNs9JBm0mHTH/5H0WUkdj31cgtMXRB8fwy3n5XPR4PY1e/uDIWqdXhL18ZyVZZGgStFhUjQJIYQ4rVRVxdbso9jmxOUNkpGgP+4dax6/wjPry1iz+zAAA9LN/GpqIdlJx48fOJrdE8DlC9A32Ui+VYIqxfcjf3qEEEKcNv5g+FEoB+rdaGNjyGrjcty+w80sX72XQ3YvGuDK0X2Z/cN+7YoEODqocljfJAmqFJ1CiiYhhBCnhd0ToMTWTG2znxSjFoO29Z4iJaTy+rYqVm6pQAmppJl1LJoyiOF9LO06j9MbxOHzk5loIM9qIlGCKkUnkaJJCCHEKRUKqVQ7vJTYmgkEVTIT9cfNXqpt9vHgmr3sPOQA4NwBafx80gDM+hP/uDo6qPKsTAmqFJ1PiiYhhBCnjDegUFbnpKrBg1EbR0ri8Vd9Pi2u5a+flODyKRjiY7n1/HwuOCu9XU3b4aDKANYELflpZpJNElQpOp8UTUIIIU6JBpefElszDS4/aWYdurjWL8e5/UGe/k8ZH31jA6AwI4FFUwa1q9k7OqjSJEGV4pSSokkIIUSnUkIqVY1uymqdhFTIshiIOc5q0d6aZpav2Uu13UuMBq4ak8M1Y3LadVntSFBlklFLgdWMNUGCKsWpJUWTEEKITuP2BymxOTnU5MWijz9uL5ISUvnX1kpe2lJBSAVrgo7FUwYxNPvEzd6qqtLkDuBTFAmqFKdVl65h3n333Wg0mqivzMzMyP5j9x35euCBByJjJk2a1GL/NddcE3WexsZG5syZg8ViwWKxMGfOHJqamqLGVFRUMHPmTEwmE2lpaSxYsAC/339K378QQvQW4ewlLzsqm6i2e0hP0B23YLI5vPz2ja/5++ZwwXT+QCt/uWZUuwqmgBLikN1DbKyG4X2SKMxMkIJJnDZdvtI0dOhQPvzww8j3sbHf/eGvrq6OGvvBBx9w8803c8UVV0Rtnz9/Pn/4wx8i3xsM0dfBZ8+eTVVVFatWrQLglltuYc6cObzzzjsAKIrCjBkzsFqtrF+/nvr6eubOnYuqqjz66KOd80aFEKKXCijh7KX9dW7iYjRkJRqO27z9n321PP5JCS5/uNn7Z5MKmDTI2q5m76ODKvPSTG0miAtxKnT5n7i4uLio1aWjHbv9rbfeYvLkyeTn50dtNxqNxz3Gnj17WLVqFZs2bWLs2LEAPPPMMxQVFbF3714KCwtZvXo1u3fvprKykuzsbACWL1/OvHnz+OMf/0hiYuL3fZtCCNErObwBymxOahxeko3a4yZuu/1BnlxXysd7awE4KzOBxVMKybToT3iOoBKi1ulDHx/L0D4Wsi0GCaoUXaJDl+dWrFiB2+3ulAkUFxeTnZ1NXl4e11xzDWVlZa2OO3z4MO+99x4333xzi30rV64kLS2NoUOHsmTJEpqbmyP7Nm7ciMViiRRMAOPGjcNisbBhw4bImGHDhkUKJoBp06bh8/nYunVrp7xPIYToTVRVpdruYUdlE7ZmH5mJhuMWTHuqHSx45Us+3ltLjAZm/7Af/3v5iHYVTE5fEJvTS3qCjpH9kuibbJSCSXSZDq00LV26lAULFnDVVVdx8803M378+A6dfOzYsbzwwgsMGjSIw4cPc++99zJ+/Hh27dpFampq1Njnn3+ehIQELr/88qjt1113HXl5eWRmZrJz506WLl3Kjh07WLNmDQA1NTWkp6e3OHd6ejo1NTWRMRkZGVH7k5OT0Wq1kTGt8fl8+Hy+yPcOh+PkPgAhhOiBfEGF8loXlY1u9HGxZFlajwZQQir//KKSVz4P9y6lJ+hYMrWQwVknXr2PBFXGaCjMSKBvslGCKkWX61DRVFVVxXvvvceKFSuYPHkyeXl53HjjjcydO/e4l8laM3369Mivhw8fTlFREQUFBTz//PMsWrQoauyzzz7Lddddh14f/T+T+fPnR349bNgwBg4cyJgxY9i2bRvnnHMOQKvXylVVjdrenjHHuv/++7nnnntO8C6FEKL3aHT5Ka11Uuf0kWrSHbcJu8bh5cHVe9lTE175n1Ro5afnF7SrD+lIUGWaORwlIEGVorvoUNkeGxvLrFmzeP3116msrOSWW25h5cqV9OvXj1mzZvHWW28RCoVO+rgmk4nhw4dTXFwctf3TTz9l7969/OQnPznhMc455xzi4+Mjx8jMzOTw4cMtxtXW1kZWlzIzM1usKDU2NhIIBFqsQB1t6dKl2O32yFdlZeUJ5yeEED2RElKpbHCxo6oJuydAZqLhuAXTJ3tt/PKVL9lT04xRG8viKYNYPKXwhAVTSA2vLjX7ggywmhjRN0kKJtGtfO+1zvT0dCZMmEBRURExMTF8/fXXzJs3j4KCAj755JOTOpbP52PPnj1kZWVFbf+///s/Ro8ezdlnn33CY+zatYtAIBA5RlFREXa7nS1btkTGbN68GbvdHrmsWFRUxM6dO6Pu1lu9ejU6nY7Ro0cf91w6nY7ExMSoLyGE6G08foU91Q52HXKgjY0hPaH1Z8e5fEGWrd7L8jX7cPsVBmcl8pdrRjGpsGWLxLG8AYXqJg8GbSxn902iIN0syd6i2+nwn8jDhw+zbNkyhg4dyqRJk3A4HLz77ruUl5dz6NAhLr/8cubOndvmMZYsWcK6desoLy9n8+bNXHnllTgcjqjXORwOXn311VZXmUpLS/nDH/7AF198wf79+3n//fe56qqrGDVqFBMmTABg8ODBXHzxxcyfP59NmzaxadMm5s+fz6WXXkphYSEAU6dOZciQIcyZM4cvv/ySjz76iCVLljB//nwphIQQZ7Q6p48dVU1UNXpIT9CToG/92XG7DtlZ8MqXrNsXbva+bmw/7v+v4WQktt3sraoqjS4/TR4/uWlGRuYkYU3QtSuCQIjTrUM9TTNnzuTf//43gwYNYv78+dxwww2kpKRE9hsMBhYvXsxDDz3U5nGqqqq49tprqaurw2q1Mm7cODZt2kRubm5kzCuvvIKqqlx77bUtXq/Vavnoo4945JFHcDqd5OTkMGPGDO66666ovKeVK1eyYMECpk6dCsCsWbN47LHHIvtjY2N57733uO2225gwYQIGg4HZs2ezbNmyjnw8QgjR4wWVEBUNbsrrXcSgIduib7WQUUIqL39ewatfVBJSITNRz+Kpgzgr88T/4QwoIWqdXsy6eIZnJpGRKMWS6N40qqqqJ/uim2++mZ/85CcUFRUdd4yqqlRUVEQVQL2dw+HAYrFgt9tlhUoI0WM5fUFKbU6q7R6SDNrj9iJV2z0sX72PvYfDzd4XnJXOrefnHzd64Gh2TwCnL0CfJCP5VgmqFF2rvT+/O/SndOLEiZE7047m9/t55ZVXuOGGG9BoNGdUwSSEED2dqqocdvgoqXXi8gbJSNC3epu/qqp8vNfGk+vK8AQUTNpYfj55AOcNtJ7wHEcHVQ7rYyHLYmi1P0qI7qhDK02xsbFUV1e3yD+qr68nPT0dRVE6bYI9iaw0CSF6Kn/w20eh1LvRxcaQZIxv9VKZ0xvk8XUlfFpcB8DQ7EQWTRlEekL7girtHj+ZiXryrGYshtb7o4Q43U7pStPx8ouqqqqwWE78wEUhhBDdh90doKS2mdpmPylGLQZt61ECOw/aWb5mH3VOH7ExGmb/sB9XnNP3hCtFSkil/tvXnJUpQZWi5zqpomnUqFFoNBo0Gg0XXnghcXHfvVxRFMrLy7n44os7fZJCCCE6XyikUu3wUmJrJhBUyUxsPUogqIR4aUsF/9pahQpkWfQsmVrIoIyEE57D41eod/uwmnXkW82kSO6S6MFOqmi67LLLANi+fTvTpk3DbDZH9mm1Wvr3788VV1zRqRMUQgjR+bwBhbI6J1UNHozaOFISW79UdqjJw7LVeym2OQGYMjiD+eflH3c16ghVVWlw+QmGVAZYzfRLNaKLa/s1QnR3J1U03XXXXQD079+fH//4xy0eaSKEEKL7q3f6KK110uD0k5aga7WYUVWVj/bYeOrTUryBEGZdHL+YPIAJA9JOeHxfUKGu2UeSSUu+1YTVLFEConfoUE/TiUIrhRBCdD9KSKWq0U1ZrZOQCllJBmJaKWaavQH++nEJn5XWAzC8j4U7LhqENUHX5vFVVaXJE8AbUMhNM9I/1XzCFSkhepJ2F00pKSns27ePtLQ0kpOT2/xfQ0NDQ6dMTgghROdw+YKU1jo51OTFoo/HrG/9n/+vq5p48MN91Dn9xMZouH5sLv81qs8Jm70DSghbsxezPp7hfS1kJOiJkSgB0cu0u2h66KGHSEhIiPxallqFEKL7U1WV2ubw5TiHN0B6gp74Vu5cCyghVm6u4PVt4WbvPkkGlkwtZEC6ueVBj+HwBHD6g2QnGci3mjFLUKXopTqU0yRaJzlNQojuJKB8m71U5yYuRkOKSdvqf3gPNoabvUtqw83eU4eEm7318W1fWlNCKrVOL9q4GPLTzGQnSVCl6JlOaU7T+++/T2xsLNOmTYvavnr1ahRFYfr06R05rBBCiE7i8AYotTk57PCSbNS2+mgTVVVZvfswz3xahi8YIkEXxy8uGMD4ghM3e7t8QZo8fjIS9eRLUKU4Q3QoXezOO+9sNfU7FApx5513fu9JCSGE6BhVVTnU5GFHZRO1zT4yEw2tFkwOT4D7P/iGxz4uwRcMcXZfC49eO+qEBVNIVbE5vHgCQQozEhjWxyIFkzhjdGilqbi4mCFDhrTYftZZZ1FSUvK9JyWEEOLkeQMK++tcVDa60cfFkmUxtDpuR2W42bvB5ScuRsOccblcNqpPq3fSHc3jV2hw+0g16yiQoEpxBupQ0WSxWCgrK6N///5R20tKSjCZTJ0xLyGEECeh0eWnpNZJXbOPNLOu1X6kgBLi75sO8MaXB0+q2fvooMr8NDO5aRJUKc5MHSqaZs2axcKFC3njjTcoKCgAwgXT4sWLmTVrVqdOUAghxPEpIZWDjW7K6lwEFZXs42QvVTa6WbZ6L2W1LgAuHprJzefmnbDZ2xdUqHP6STLEk58uQZXizNahoumBBx7g4osv5qyzzqJv375A+GG95513HsuWLevUCQohhGidx69QWuukqtFNoj6eVFPL3iJVVVm1q4a/rS/HHwyRoI9jwQUDGZefesLjN7r9eAMK/VIM5KVJUKUQHb48t2HDBtasWcOOHTswGAyMGDGC888/v7PnJ4QQohVHspca3X4yjpO9ZPcEeHRtMZvLw4HDI3OSuOOiQSfsRQooIWqbfZh0sRJUKcRRJKepE0lOkxDiVAsqISoa3JTXu4hBQ+pxspe+rGjkoQ/30egOEBejYe74/sw6O/uEzd4OT4BmX5DsJL0EVYozxinNaQJYt24dy5YtY8+ePWg0GgYPHsyvfvUrzjvvvI4eUgghRBucviClNifVdg9JBi2mVgqagBLihY37eXP7IQByksPN3vnWtpu9lZBKndNHfKyGIVkJ9Ek2SlClEMfoUE7T3//+dy666CKMRiMLFizgF7/4BQaDgQsvvJCXXnqps+cohBBnNFVVqbF72V7ZRI3dS0aCvtWCqaLBzeJXd0QKphnDs3jw6pEnLJhcviA1Dg+pZi0jc5Lpl2qSgkmIVnTo8tzgwYO55ZZbuOOOO6K2P/jggzzzzDPs2bOn0ybYk8jlOSFEZ/MHQ+yvc3GgwY0uNobkVvqRVFXl/Z01PLu+HL8SwmKIZ8EFA/lhXkqbxw6p4dUljQZyU0z0SzW22hslRG/X3p/fHSqadDodu3btYsCAAVHbS0pKGDZsGF6v9+Rn3AtI0SSE6Ex2d4CS2mZszT7STK1nLzW5/fxlbTGf728E4Jx+ySy8cGCrxdXRvAGFelc4qDI/zUSqWXdK3oMQPcEp7WnKycnho48+alE0ffTRR+Tk5HTkkEIIIb4VCqkcsnsorXUSCKpkJbb+INytBxp5+KN9NLkDxMdqmDc+j0tHZLXZ7C1BlUJ0XIeKpsWLF7NgwQK2b9/O+PHj0Wg0rF+/nhUrVvDII4909hyFEOKM4Q0olNU6qWr0YNTGkZLYMnvJHwzx/Mb9vL0j3LvUL8XIr6YW0j+t7Scy+IMhap2+cFCl1YQ1QYIqhTgZHSqafvazn5GZmcny5cv55z//CYT7nP7xj3/wox/9qFMnKIQQZ4p6Zzh7qcHpx5qgRxvXsr/oQL2LZav3sr/eDcDMEVnMHd//hKtFTW4/HgmqFOJ7kZymTiQ9TUKIjlBCKlWNbspqnYRUSDPrWlxiU1WVd7+q5rkN5QQUlSRDPL+8aCBjcttu9j46qDLfaiYzUYIqhTjWKc9pEkII8f25fEFKa50cavJgMWhbDZNsdPt55KNith4IN3uPyU3mlxcOJMnYdrN3szeAwxsky6In32oiQd/yUp8Qov3aXTQlJye3+9p3Q0NDhyckhBBnAlVVI49CcXgDpB/nUSif72/gkY+KsXsCaGNjuGlCfy4ZntXmv8cSVCnEqdHuounhhx8+hdMQQogzR0AJcaDexf46N3ExGrISDS2KIF9QYcVn+3n362oA+qcaWTK1kNzUtpu93f4gjW4/6Qnh1aUTrUYJIdqv3UXT3LlzT+U8hBDijODwBii1OTns8JJs1GLUtvxnuLwu3Oxd0RBu9v7R2dncUNS/1cbwI44OqhyYniBBlUKcAh3uaSotLeW5556jtLSURx55hPT0dFatWkVOTg5Dhw7tzDkKIUSPp6oq1XYvpbVOPH6FzFayl0Kqyjs7DrFiw36CIZVkYzwLLxzEObnJbR77SFBliklLgdUsQZVCnCId+m/IunXrGD58OJs3b+b111/H6XQC8NVXX3HXXXd16gSFEKKn8wYU9tY0s/OgHVTIsrQsmBpcfu55Zxd/W19OMKTyw/4pPHrtOW0WTKqqUu/0YfcEyE8zM6JvkhRMQpxCHVppuvPOO7n33ntZtGgRCQkJke2TJ0+WcEshhDhKo8tPSa2TeqeP1OM8CmVLeT2PfFSMwxtEGxvDzefmMX1YZpvN3uGgSi8WQzyDrWbSJahSiFOuQ0XT119/zUsvvdRiu9Vqpb6+/ntPSgghejolpHKw0U1ZnYugopJlMbTIXvIGFJ79rJwPdtYAkJdm4ldTC8lJMbZ57O+CKo30TzO12hclhOh8HfqblpSURHV1NXl5eVHbv/zyS/r06dMpExNCiJ7K41corXVysMlDgi6OVFPLfKSyWifLVu+lstEDwGUj+3BDUW6bzdtBJYTN6cOojWVoHwtZElQpxGnVoaJp9uzZ/OY3v+HVV19Fo9EQCoX47LPPWLJkCTfccENnz1EIIXqMI9lLjW4/Ga1kL4VUlbe2H+SFjQcIhlRSjFrumDKIkTlJbR7X6Q1i9/rJshgkqFKILtKhx6gEAgHmzZvHK6+8gqqqxMXFoSgKs2fPZsWKFcTGnpnPNJLHqAhx5goqIQ7Uu9nf4CIGDakmbYseo3qnj4c/KmZ7ZRMAY/NSuP2CgVgMxy+AjgRVxsVqyEsz0SfJQJxECQjRqdr78/t7PXuutLSUL7/8klAoxKhRoxg4cGBHD9UrSNEkxJnJ6QtSanNSbfccN3tpY1k9j64tptkbRBsXw/xz85k2NKPN5m23P0iDy096oo4Cq1mCKoU4RU7ps+fWrVvHxIkTKSgooKCgoMOTFEKInkxVVQ47fJTUOnH5gmQk6FusAnkDCn9bX86/d4WbvQusJhZPLSQn+fjN3iFVpd7pR9WoDMww0y/F1GawpRDi9OjQ38IpU6bQr18/7rzzTnbu3Nnhk999991oNJqor8zMzMj+efPmtdg/bty4qGP4fD5uv/120tLSMJlMzJo1i6qqqqgxjY2NzJkzB4vFgsViYc6cOTQ1NUWNqaioYObMmZhMJtLS0liwYAF+v7/D700I0bv5gyGKDzv5+qCdkKKSbWl52azE5mThP7bz7101aIArzunDA1ee3WbB5A0oVNs9mPWxnN03iQHpCVIwCdFNdOhv4qFDh/j1r3/Np59+yogRIxgxYgR//vOfWxQr7TF06FCqq6sjX19//XXU/osvvjhq//vvvx+1f+HChbzxxhu88sorrF+/HqfTyaWXXoqiKJExs2fPZvv27axatYpVq1axfft25syZE9mvKAozZszA5XKxfv16XnnlFV577TUWL1580u9HCNH72d0Bvj7YRGmdkyRDPMmm6MtmIVXltW1V/OpfOzjY5CHVpOX/XTaMeePzjnt3nKqqNLj8NHkC9E81MaJvEmkSVClEt/K9epoAysvLeemll3j55Zf55ptvOP/881m7dm27Xnv33Xfz5ptvsn379lb3z5s3j6amJt58881W99vtdqxWKy+++CI//vGPgXBBl5OTw/vvv8+0adPYs2cPQ4YMYdOmTYwdOxaATZs2UVRUxDfffENhYSEffPABl156KZWVlWRnZwPwyiuvMG/ePGw2W7v7k6SnSYjeLRRSOWT3UFrrJBBUSTPrWiR71zl9PPThPr6qsgNQlJ/KLyYPILGNZu8jQZWJ+ngK0iWoUojTrb0/v7/3mm9eXh533nkn//u//8vw4cNZt27dSb2+uLiY7Oxs8vLyuOaaaygrK4va/8knn5Cens6gQYOYP38+Npstsm/r1q0EAgGmTp0a2Zadnc2wYcPYsGEDABs3bsRisUQKJoBx48ZhsViixgwbNixSMAFMmzYNn8/H1q1bjzt3n8+Hw+GI+hJC9E7egMI3NQ52HbQTq4khI1HfomDaUFrH7S9/yVdVdnRxMdx+wQCWTj+rzYLJ7glQ5/TSN9nIyH5JZCTqpWASopv6XkXTZ599xm233UZWVhazZ89m6NChvPvuu+1+/dixY3nhhRf497//zTPPPENNTQ3jx4+PpIpPnz6dlStXsnbtWpYvX87nn3/OBRdcgM/nA6CmpgatVktycvSzmTIyMqipqYmMSU9Pb3Hu9PT0qDEZGRlR+5OTk9FqtZExrbn//vsjfVIWi4WcnJx2v3chRM9R7/Sxo6qJino3aWZ9i4gAj1/hL2uLuf+Db3D6ggxIN/PIj0cxdcjxH4USVEJU2z2oqAzrm8SQrERJ9haim+vQ39Df/va3vPzyyxw6dIiLLrqIhx9+mMsuuwyjse3o/2NNnz498uvhw4dTVFREQUEBzz//PIsWLYpccgMYNmwYY8aMITc3l/fee4/LL7/8uMdVVTXqH6rW/tHqyJhjLV26lEWLFkW+dzgcUjgJ0YsElRBVjW7K61yEVMhKavkolH2Hm1m+ei+H7F40wJWj+zL7h/3azFJyeoM4fH4yEw3kWU0kSlClED1Ch4qmTz75hCVLlvDjH/+YtLS0TpuMyWRi+PDhFBcXt7o/KyuL3NzcyP7MzEz8fj+NjY1Rq002m43x48dHxhw+fLjFsWprayOrS5mZmWzevDlqf2NjI4FAoMUK1NF0Oh06nTRqCtEbuXxBSmudHGryYDFoMeui/7lUQiqvb6ti5ZYKlJBKmlnLoimFDO9jOe4xjw6qPCszUYIqhehhOvS3dcOGDfz85z8/YcE0Y8YMqqur231cn8/Hnj17yMrKanV/fX09lZWVkf2jR48mPj6eNWvWRMZUV1ezc+fOSNFUVFSE3W5ny5YtkTGbN2/GbrdHjdm5c2fUXFevXo1Op2P06NHtnr8QoudTVRWbw8uOyiaq7R7SE/QtCqbaZh//8+bXvLDpAEpIZcKANB695pw2Cya3P0iNw0uyKZ6z+yaRm2qSgkmIHuZ73z3XloSEBHbs2EF+fn6r+5csWcLMmTPp168fNpuNe++9l3Xr1vH111+TmprK3XffzRVXXEFWVhb79+/nt7/9LRUVFezZs4eEhAQAfvazn/Huu++yYsUKUlJSWLJkCfX19WzdujXyOJfp06dz6NAhnnrqKQBuueUWcnNzeeedd4Bw5MDIkSPJyMjggQceoKGhgXnz5nHZZZfx6KOPtvv9yt1zQvRsASXEgXoX++vcxMVoSGnlUSjrS+p47ONiXD4FfXwMt55fwIVnpR/3Un7o2ygBRVXpn2qUoEohuqFTmgjeWaqqqrj22mupq6vDarUybtw4Nm3aRG5uLh6Ph6+//poXXniBpqYmsrKymDx5Mv/4xz8iBRPAQw89RFxcHFdffTUej4cLL7ywxfPvVq5cyYIFCyJ32c2aNYvHHnsssj82Npb33nuP2267jQkTJmAwGJg9ezbLli07fR+GEKJLObwBSm1ODju8rT4Kxe0P8synZXy4J3wH76AMM4unFJKdZDjuMb0BhXqXjySjlgKrGWuCXM4Xoifr0pWm3kZWmoToeVRVpdrupbTWicevkJ7QMkpgb00zy9fspdruJUYDV43O4Zof5Bz38pqqqjS5A/gUhZxkI/3TTOjjz8wHmQvRE/SIlSYhhOhK3oDC/joXFQ1uDPGxZFmiV42UkMq/tlXx0uYDhFSwJuhYPGUQQ7OP37sUUELYmr0k6OMZnplERqIEVQrRW0jRJIQ4IzW6/JTUOql3+kg16VqsBNkcXh78cB+7DoVDa88fmMbPJg1o0RR+NLsngMsXoG+ykbw0E6Y2xgoheh75Gy2EOKMoIZWDjW7K6lwEFZUsS8vspf/sq+XxT0pw+RUM8bH8dGIBkwutbQZV1jp96ONjGdrHQrbFQEyMrC4J0duc0qLpt7/9LSkpKafyFEII0W5uf5CyWhcHmzwk6OJINcW32P/UujLW7g03e5+VmcDiKYVkWvTHPabTF8Th9ZORoCc/3SxBlUL0Yh2+7/XFF19kwoQJZGdnc+DAAQAefvhh3nrrrciYpUuXkpSU9L0nKYQQ31dts4+vKu1UNbqxmnUkHFPcfFPtYMErX7J2r40YDVz7gxz+9/IRxy2YlJDKYYcXX0ChMCOBYX0sUjAJ0ct1qGh64oknWLRoEZdccglNTU0oigJAUlISDz/8cGfOTwghvpeAEqLU5uSrg014AgrZFgPxR931poRUXt5SwW9e/4rDDh/pCTruv3wEs8fmtriL7ogjQZVJxnjOzkmif5pZgiqFOAN06G/5o48+yjPPPMN///d/R+UhjRkzhq+//rrTJieEEN9HszfA7kMOim3NmLVxpJmj72Q77PCy9I2veWlLBSEVJg2y8pdrRjEkq/VbjkNq+DEozb4gA6wmRvRNItmkPV1vRwjRxTrU01ReXs6oUaNabNfpdLhcru89KSGE+D5UVeWww0eJrRmXXyEjQd9iJeiTvTaeWFeK269g1Mbys4kFTCpMP+4xfUGFumYfSaZwUGWauWVauBCid+tQ0ZSXl8f27dvJzc2N2v7BBx8wZMiQTpmYEEJ0hC+ocKDOzYEGN7rYGLKPyV5y+YI8sa6UdftqARiclcjiKYPISGy9d+nooMrcNCN5aWYJqhTiDNWhoulXv/oVP//5z/F6vaiqypYtW3j55Ze5//77+dvf/tbZcxRCiHaxuwOU1DZT29x69tLuagfLV+/F1uwLN3v/sB9Xjc45bu9SQAlR6/Ri1klQpRCig0XTjTfeSDAY5Ne//jVut5vZs2fTp08fHnnkEa655prOnqMQQrQpFFI5ZPdQWuskEFTJTDREFUJKSOWVzyv45xeVhFTISNSxZEohZx2ndwnCQZVOX4A+SUbyrRJUKYTohGfP1dXVEQqFSE8/fi/AmUKePSfE6ecNKJTVOqlscGPSxWMxRN/2X2P3smz1XvYebgbggsJ0bp2Y3+KBvEcoIZVapxddXCz5VhNZFsNxV6KEEL3DKX323D333MP1119PQUEBaWlpHZ6kEEJ8H/VOH8U2J00uP9YEPdq475q9VVXl4702nlxXhiegYNLGctukAZw/yHrc4zl9QeweP5mJevKs5hYFmBDizNahyIHXXnuNQYMGMW7cOB577DFqa2s7e15CCHFcQSXE/jonX1U14fIFyUoyRBVMTl+QB1bv5aEPi/EEFIZmJ/KXa0cdt2BSQio2hxdvIMhZmeGgSimYhBDH6lDR9NVXX/HVV19xwQUX8OCDD9KnTx8uueQSXnrpJdxud2fPUQghIly+ILurHXxT04w+Po70BH3Us+N2HrSz4JUv+bS4jhgNzBmXyx8vG056Qut3x3n8CjUODxZjPGfnJEtQpRDiuL53TxPAZ599xksvvcSrr76K1+vF4XB0xtx6HOlpEuLUUVWV2ubw5bhmb4D0BH1UsndQCfHy55X8a2u42TvLomfJ1EIGZSQc93gNLj/BkEpuqpF+qUZ0cRIlIMSZ6JT2NB3LZDJhMBjQarU0Nzd3xiGFECLCHwxR0eBif52buBgN2RZD1K3/h5o8LF+zl32HnQBcNDid+ecdv9n76KDKfKsJq1miBIQQJ9bhoqm8vJyXXnqJlStXsm/fPs4//3zuvvturrrqqs6cnxDiDGf3BCirdVJj95Ji0kYVQqqq8tEeG099Woo3EMKki+UXkwdy7oDWb1BRVZUmTwBvQKFfmpG8VDMGrawuCSHap0NFU1FREVu2bGH48OHceOONkZwmIYToLKGQSo3DS4nNiS+otLj13+kN8tgnJXxWUgfA8D4W7rhoENYEXavHCyghbM1ezPp4hve1kJGgJ0aiBIQQJ6FDRdPkyZP529/+xtChQzt7PkIIgTegUF7npLLBgyE+lszE6EehfF3VxIMf7qPO6Sc2RsN1Y/tx+ai+x81TcngCOP1BspMM5FvNmCWoUgjRAZ3SCC7CpBFciO+v0eWnpNZJvdNHmlkX1ZwdUEK8tLmC17ZVoQLZ3zZ7DzxOs/eRoEptXAz5aWaykySoUgjRUqc3gi9atIj/9//+HyaTiUWLFrU59sEHH2z/TIUQgnCBc7DRTVmtCyWkkmUxREUJHGz0sGzNXkps4WbvqUMy+Mm5+cftSXJ9G1SZnqgnX4IqhRCdoN1F05dffkkgEIj8WgghOovbH6Ss1kVVo4dEfRwJ+u8KHFVVWb37MM98WoYvGCJBF8cvLhjA+ILWm71Dqkpds4+YGBiUkUDfFGNUNIEQQnSUXJ7rRHJ5ToiTV9vso9TmpMnjb5G95PAEeOzjEjaW1QMwoq+FRRcNItXcerO3x6/Q4PaRatZRYDWTYtKelvcghOjZ2vvzu0P//brppptazWNyuVzcdNNNHTmkEOIME1BClNqc7KhqwhtQyLYYogqmHVVNLHjlSzaW1RMXo+HG8f35fz8a1mrBpKoq9U4fDm+A/DQzI/papGASQnS6Dq00xcbGUl1dTXp6etT2uro6MjMzCQaDnTbBnkRWmoRon2ZvgLJaF9V2D8nG6OylgBLi75sO8MaXB1GBPkkGlkwtZEC6udVj+YIKdU4/SYZ48tMlqFIIcfJOSSK4w+FAVVVUVaW5uRm9/rtnOSmKwvvvv9+ikBJCiCNUVeWww0eJrRmXXyEjQR/1nLfKRjfLV++ltNYFwMVDM7n53Dz08a03eze6/eGgyhQDeWkSVCmEOLVOqmhKSkpCo9Gg0WgYNGhQi/0ajYZ77rmn0yYnhOg9fEGFA3VuDjS40cXFkG35LntJVVVW7arhb+vL8QdDJOjjWHDBQMblp7Z6rIASorbZh0kXK0GVQojT5qSKpo8//hhVVbngggt47bXXSElJiezTarXk5uaSnZ3d6ZMUQvRsTW4/JTYndU4fqSZd1MqR3RPg0bXFbC5vAGBkThILLxx43GZvhydAsy9IdpJegiqFEKfVSf1rM3HiRCD83LmcnBxiYuQ2XiHE8YVCKofsHkptTgKKSmZidLjklxWNPPxhMQ1uP3ExGuYW9WfWyOyofKYjlJBKndNHfKyGIVkJ9Ek2SlClEOK06tB/0XJzcwFwu91UVFTg9/uj9o8YMeL7z0wI0aN5AwpltU4qG9yYdPGkmL7LXgooIV7YuJ83tx8CICc53Oydb2292dvlC9Lk8ZORqCc/zYzFKEGVQojTr0NFU21tLTfeeCMffPBBq/sVRflekxJC9Gz1Th/FNidNLj/WBD3auO9WpSsa3CxbvZfyunCz9yXDs7hxfP9Wm71Danh1SaOBgekJ9EuVoEohRNfpUNG0cOFCGhsb2bRpE5MnT+aNN97g8OHD3HvvvSxfvryz5yiE6CGCSoiqRjdldS5QNWQlffcoFFVVeX9nDc+uL8evhEjUx/HLCwfxw7yUVo/lDSjUu8JBlflppuP2OAkhxOnSoaJp7dq1vPXWW/zgBz8gJiaG3NxcpkyZQmJiIvfffz8zZszo7HkKIbo5ly9Iaa2TaruHRL02qkHb7gnwl4+K2bI/3Ox9Tr8kFl44iORWAihVVaXB5ScYUslPM5ObZox6aK8QQnSVDhVNLpcrkseUkpJCbW0tgwYNYvjw4Wzbtq1TJyiE6N5UVaW2OXw5rtkbaPEolG0HGnnoo300uQPhZO8J/bl0ROvN3v5giFqnLxxUaTVhTZCgSiFE99GhoqmwsJC9e/fSv39/Ro4cyVNPPUX//v158sknycrK6uw5CiG6KX8wREW9i/31buJjw9lLR4ocfzDE8xv38/aOcLN3vxQjS6YWkpdmavVYTW4/HgmqFEJ0Yx3uaaqurgbgrrvuYtq0aaxcuRKtVsuKFSs6c35CiG7K7glQVuvksMNHilEbVeQcqHexbPVe9te7Abh0RBbzxvdv9TLb0UGVw/pYyEyUoEohRPfUoWfPHcvtdvPNN9/Qr18/0tLSOmNePZI8e06cCUIhlRqHlxKbE19QwWrWR/KSVFXlva+refazcgKKSpIhnl9eOJAx/Vtv9m72BnB4g2RZ9ORbTSToJUpACHH6tffnd6fcu2s0GjnnnHNOumC6++67I49lOfKVmZkJQCAQ4De/+Q3Dhw/HZDKRnZ3NDTfcwKFDh6KOMWnSpBbHuOaaa6LGNDY2MmfOHCwWCxaLhTlz5tDU1BQ1pqKigpkzZ2IymUhLS2PBggUt8qeEONN5Awp7DzvYedBOjEYTFVbZ6Pbzh3d389R/yggoKmNyk/nLtaNaLZiUkMphh5eAEmJIVgLD+likYBJCdHvtvjy3aNGidh/0wQcfbPfYoUOH8uGHH0a+j40NL9+73W62bdvG7373O84++2waGxtZuHAhs2bN4osvvog6xvz58/nDH/4Q+d5gMETtnz17NlVVVaxatQqAW265hTlz5vDOO+8A4VypGTNmYLVaWb9+PfX19cydOxdVVXn00Ufb/V6E6M0aXH5Ka53UO32kmXVRl9q+2N/AIx8V0+QJEB+r4aYJecwYntVqE7fbH6TR7Sc9Iby6lGRseQedEEJ0R+0umr788st2jTvZO13i4uIiq0tHs1gsrFmzJmrbo48+yg9/+EMqKiro169fZLvRaGz1GAB79uxh1apVbNq0ibFjxwLwzDPPUFRUxN69eyksLGT16tXs3r2bysrKyLPzli9fzrx58/jjH/8ol9rEGU0JqRxsdFNW60IJqWRZvste8gUVVmzYz7tfhXsc+6eGm71zU1s2e0tQpRCip2t30fTxxx+fkgkUFxeTnZ2NTqdj7Nix3HfffeTn57c61m63o9FoSEpKitq+cuVK/v73v5ORkcH06dO56667SEhIAGDjxo1YLJZIwQQwbtw4LBYLGzZsoLCwkI0bNzJs2LCohw1PmzYNn8/H1q1bmTx5cqvz8fl8+Hy+yPcOh6OjH4MQ3ZLbH6Ss1kVVoweLPh6z/rt/Msrrws3eFQ3hZu9ZZ2czt6h/VPr3EUeCKlNMWgqsZgmqFEL0SN/r8eAlJSWUlpZy/vnnYzAYUFX1pFaaxo4dywsvvMCgQYMiieLjx49n165dpKamRo31er3ceeedzJ49O2rl57rrriMvL4/MzEx27tzJ0qVL2bFjR2SVqqamJpIpdbT09HRqamoiYzIyMqL2Jycno9VqI2Nac//993PPPfe0+/0K0ZPYmr2U2Vw0efxR2UshVeXdrw6xYsP+cLO3MZ6FFw5idG5yi2McG1TZL9XY6uNShBCiJ+hQ0VRfX8/VV1/Nxx9/jEajobi4mPz8fH7yk5+QlJTU7kepTJ8+PfLr4cOHU1RUREFBAc8//3xUD1UgEOCaa64hFArx+OOPRx1j/vz5kV8PGzaMgQMHMmbMGLZt28Y555wDtH7J8NgCrz1jjrV06dKoeTocDnJyctp6y0J0ewElREW9m/J6F3EaTVT2UqPLz8Mf7WNbRRMAP+ifzC8vHITF0LKJOxxU6cViiGew1Uy6BFUKIXq4DjUU3HHHHcTHx1NRUYHRaIxs//GPfxxptu4Ik8nE8OHDKS4ujmwLBAJcffXVlJeXs2bNmhP2F51zzjnEx8dHjpGZmcnhw4dbjKutrY2sLmVmZrZYUWpsbCQQCLRYgTqaTqcjMTEx6kuInqzZG2DXQTvFtmYSdHGkmr8rdLaUN/CLl7exraIJbWwMP5tYwO9mDGm1YLJ7AtS7fPRLMXJ2ThIZiXopmIQQPV6HiqbVq1fzpz/9ib59+0ZtHzhwIAcOHOjwZHw+H3v27Imkih8pmIqLi/nwww9bXLJrza5duwgEApFjFBUVYbfb2bJlS2TM5s2bsdvtjB8/PjJm586dkcDOI+9Rp9MxevToDr8fIXoKVVWptnvYUdnE4WYfGQl6jNrwQrQ3oPDEulL+33u7cXiD5KWZeOjHI7mklbvjgkqIQ3YPKipD+1g4KzMxchwhhOjpOvzsuaNXmI6oq6tDp2t/g+eSJUuYOXMm/fr1w2azce+99+JwOJg7dy7BYJArr7ySbdu28e6776IoSmQ1KCUlBa1WS2lpKStXruSSSy4hLS2N3bt3s3jxYkaNGsWECRMAGDx4MBdffDHz58/nqaeeAsKRA5deeimFhYUATJ06lSFDhjBnzhweeOABGhoaWLJkCfPnz5fVI9Hr+YIKB+rcHGhwoYuLJdvyXWRHWa2TZav3UtnoAeCykX24oSi31bvenN4gdq+fLItBgiqFEL1Sh1aazj//fF544YXI9xqNhlAoxAMPPHDcO81aU1VVxbXXXkthYSGXX345Wq2WTZs2kZubS1VVFW+//TZVVVWMHDmSrKysyNeGDRsA0Gq1fPTRR0ybNo3CwkIWLFjA1KlT+fDDDyN5TxC+u2748OFMnTqVqVOnMmLECF588cXI/tjYWN577z30ej0TJkzg6quv5rLLLmPZsmUd+XiE6DGa3H6+rrJTVuckyaAl+dvMpJCq8uaXB1n86g4qGz2kGLX8YdZQbj43r0XBdCSo0qcoDM5KZGh2ohRMQoheqUOPUdmzZw8TJ05k9OjRrF27llmzZrFr1y4aGhr47LPPKCgoOBVz7fbkMSqipwiFVA42eSirdRJQVNLMukiyd73Tx8MfFbO9sgmAsXkp3H7BwFZ7l9z+IA0uP+mJOgqsZgmqFEL0SO39+X3Sl+cCgQC33XYbb7/9Nh988AGxsbG4XC4uv/xyfv7zn0d6iYQQ3ZPHr1Be56SywY1ZF0+K6btiaFNZPX9ZW0yzN4g2Lob55+YzbWhGi96lkKpS7/SjalQGZpjpl2JqNZ9JCCF6k5MumuLj49m5cyepqamSUSRED1Pn9FFic9Lk8mNN0EcKHW9A4f/Wl7NqV7hvMN9qYsnUQnKSW/YuHh1UmW81kyZBlUKIM0SH/mt4ww038H//93+dPRchxCkSVELsr3PyVVUTbp9CVpIhUjCV2Jws/Md2Vu2qQQNccU4fll15douC6UhQZZMnQP9UEyP6JknBJIQ4o3To7jm/38/f/vY31qxZw5gxYzCZop8zdTIP7BVCnFpOX5BSm5Mah4dEvRazLvzX/kiz94ubDhAMqaSYtCyaMoiz+ya1OEZACWFr9pKoj+esLIsEVQohzkgdKpp27twZSdvet29f1D75h1SI7kFVVWzN4ctxTm8Q61GPQql3+njww318VWUHoCg/lV9MHkDicYIqXb4AfZON5FtNkrskhDhjdehfv1P18F4hROfwB0McqHdxoN5NfGwMWZbvErk3lNbx2NoSmn1BdHEx3HJ+PlMGt2z2Diohap0+DNpYhvVNIitRT0yM/KdICHHmkv8yCtHL2D0BSm1ObM0+UoxaDNpwZpnHr/DM+jLW7A4/VmhAupklUwrpk2xocQynN4jD5ycz0UCe1USi5C4JIYQUTUL0FqGQSrXDS6nNiS+okJmoj2QvFR9uZtnqvRyye79t9u7L7LH9Wg2qrHf5iI3RcFZmIn2SDMS1kv4thBBnIimahOgFvIEj2UsejNo4MhPDq0dKSOX1L6tYubkCJaSSZtay6KJBDG+l2dvtD9LoDmBN0JKfZibZJEGVQghxNCmahOjhGlx+SmzNNLj8pJl16OLCl+Nqm3089OE+vj4YbvaeUJDKzycPaPGIk9C3UQKKqjIg3SRBlUIIcRxSNAnRQykhlapGN+W1LpSQSpbFQMy3zdzrS+r468clOH1B9PEx3Hp+AReeld6i2ftIUGWSUUuB1Yw1QXKXhBDieKRoEqIHcvuDlNW6qGr0YNHHY9bHRbY/82kZH+6xATAow8ziKYVkJ0U3e6uqSpM7gE9R6J9qon+aCX18bIvzCCGE+I4UTUL0MLZmL2U2F00eP+lHZS/t+7bZu/rbZu+rxuRw7Q9yWjRyB5QQtU4vZl08wzOTyEiUoEohhGgPKZqE6CECSoiKejfl9S7iNBqyLQY0Gg1KSOVf26p4afMBQipYE3QsumgQw/pYWhzjSFBln2QjeWkmTDr5J0AIIdpL/sUUogdo9ga+fRSKl2SjNpLKbWv28uCafew65ADgvIFp3DZpQORRKUccCarUx8cytI+FbItBgiqFEOIkSdEkRDemqio132YvufwKmYmGSPbSp8W1/PXjElx+BUN8LD+dWMDkQmuLS21OXxCH109Ggp78dLMEVQohRAdJ0SREN+ULKhyoc3OgwYUuLpZsS7iZ2+0P8tS6MtbuDTd7F2YksGRqIZkWfdTrlZBKndNHXIyGwowE+iYbJahSCCG+BymahOiGmtx+SmxO6pw+Uk26yJ1t39Q4WL56HzUOLzEauHpMDtf8oF9k9emII0GVaeZwlIAEVQohxPcnRZMQ3UgopHKwyUNZrZOAokYuxykhlX9+Uckrn1cQUiE9QcfiqYUMyUqMfv3RQZVWE/1SJahSCCE6ixRNQnQTHv+RR6G4MeviSTGFe48OO7wsX7OPPdXhZu9Jg6z8dGJBizvffEGFumYfSabw6lKaWStRAkII0YmkaBKiG6hz+iixOWlyB7Ca9ZHVoU/22nhiXSluv4JRG8vPJhYwqTA96rVHB1XmphnJSzNLUKUQQpwCUjQJ0YWCSojKhnD2EqqGbIsejUaDyxfkyXWlfLKvFoDBmQksmlpIZmJ0s7cEVQohxOkjRZMQXcTpC36bveTBotdGLrftrnawfPVebM0+YjRwzQ/6cfWYnBbN3nZPAKcvQJ8kI/lWCaoUQohTTf6VFeI0U1UVW3P4cpzTGyQ9QU9cbAxKSOUfn1fwjy8qCamQkahjyZRCzjqm2VsJqdQ6vejiYhnWx0KWxdCioBJCCNH5pGgS4jTyB0McqHdxoN5NfGwMWd9ejquxe1m+Zi/f1DQDcEFhOrdOzI8kfx/h9AWxe/xkJurJs5qxGCSoUgghThcpmoQ4Teye8KNQbM0+UoxaDNpYVFVl7Tc2nlxXiiegYNLGctukAZw/yBr1WiWkUu/0ERMDZ2VKUKUQQnQFKZqEOMVCIZXqbx+F4gsqZCbqiY3R4PQFeeKTEv5TXAfA0OxEFl00iPRjmr09foUGt480s458q5kUCaoUQoguIUWTEKeQNxDOXqpo8GDSxpGZGH4Uyq5Ddpav2Uftt83es8fmcuU5faN6k9RvgyqDIZUCq5l+qUZ0cRIlIIQQXUWKJiFOkQaXnxJbMw0uP2lmHbq4WIJKiFc+r+TVreFm7yyLnsVTCinMTIh67dFBlflWE1azRAkIIURXk6JJiE6mhFSqGt2U17pQQipZFgMxGg2HmjwsX7OXfYedAFw0OJ3550U3e6uqSpMngDeg0C/NSF6qGYNWVpeEEKI7kKJJiE7k9gcpsTk51OTFoo/HrI9DVVU+3HOYp/9TFm721sXyi8kDOXdAWtRrA0qI2mYfJn0cw/tayEjQEyNRAkII0W1I0SREJ7E1eymzuWjy+ElP0BMfG4PTG+SxT0r4rCTc7D0sO5FFUwqxJuiiXuvwBHD6g2Ql6cm3mjFLUKUQQnQ78i+zEN9TQAlRUR9+FEqcRkO2xYBGo+Hrg3YeXLOXOqef2BgN143tx+Wjopu9jwRVauNiGJKVSHaSBFUKIUR3JUWTEN9Dszfw7aNQvCQbtRi1cQSVEC9tOcC/tlahAtkWPYunFjIoI7rZ2/VtUGV6Ynh1SYIqhRCie5OiSYgOUFWVmm+zl9x+hczE8ArRoSYPD6zeS4kt3Ow9dUgGPzk3P6qZO6Sq1DWHgyoHZSTQN8VIvARVCiFEtydFkxAnyRdU2F/npqLBhS4uliyLAVVVWb27hqf/U4YvGMKsi+MXkwcw4Zhm7yNBlalmHQUSVCmEED2KFE1CnIQmt58Sm5M6p49Ukw59fCzN3gCPfVzChtJ6AEb0tXDHRYNIM3/X7H10UGV+mpncNAmqFEKInkaKJiHaIRRSOdjkoazWSUBRI5fjdlQ18dCafdS7/MTFaJgzLpfLRvUh5qggSl9Qoc7pJ8kQT366BFUKIURP1aWNFHfffTcajSbqKzMzM7JfVVXuvvtusrOzMRgMTJo0iV27dkUdw+fzcfvtt5OWlobJZGLWrFlUVVVFjWlsbGTOnDlYLBYsFgtz5syhqakpakxFRQUzZ87EZDKRlpbGggUL8Pv9p+y9i57D41f4psbB7kN24mJiyEjUE1JVVmwo53dv7qTe5adPkoEHrjyby8/pG1UwNbr9NLj89EsxcHZOEukJeimYhBCih+ry7tOhQ4dSXV0d+fr6668j+/785z/z4IMP8thjj/H555+TmZnJlClTaG5ujoxZuHAhb7zxBq+88grr16/H6XRy6aWXoihKZMzs2bPZvn07q1atYtWqVWzfvp05c+ZE9iuKwowZM3C5XKxfv55XXnmF1157jcWLF5+eD0F0W3VOHzuqmqho8JBm1pNoiKey0c2v/rWD17YdRAUuHprJwz8eyYB0c+R1ASXEoSYPMRoY3tfCWZmJkuwthBA9nEZVVbWrTn733Xfz5ptvsn379hb7VFUlOzubhQsX8pvf/AYIryplZGTwpz/9iVtvvRW73Y7VauXFF1/kxz/+MQCHDh0iJyeH999/n2nTprFnzx6GDBnCpk2bGDt2LACbNm2iqKiIb775hsLCQj744AMuvfRSKisryc7OBuCVV15h3rx52Gw2EhMT2/V+HA4HFosFu93e7teI7imohKhsCGcvoWpIM4cbtv+96zDPrC/DHwyRoI/j9gsGUpSfGvXaZm8AhzdItgRVCiFEj9Den99dvtJUXFxMdnY2eXl5XHPNNZSVlQFQXl5OTU0NU6dOjYzV6XRMnDiRDRs2ALB161YCgUDUmOzsbIYNGxYZs3HjRiwWS6RgAhg3bhwWiyVqzLBhwyIFE8C0adPw+Xxs3br1uHP3+Xw4HI6oL9HzOX1Bdh1ysM/WjDE+DmuCDoc3yH0f7OGvn5TgD4YYmZPEo9eMiiqYlJDKYYeXgBJiSFYCQ7MtUjAJIUQv0qX/oo8dO5YXXniBQYMGcfjwYe69917Gjx/Prl27qKmpASAjIyPqNRkZGRw4cACAmpoatFotycnJLcYceX1NTQ3p6ektzp2enh415tjzJCcno9VqI2Nac//993PPPfec5LsW3ZWqqtiafZTYnDi9QdIT9MTFxrC9Mtzs3eAON3vfUJTLj0ZGN3u7fEGaPH4yEvXkp5mxGCWoUgghepsuLZqmT58e+fXw4cMpKiqioKCA559/nnHjxgG0aJpVVfWEjbTHjmltfEfGHGvp0qUsWrQo8r3D4SAnJ6fNuYnuyR8McaDexYF6N/GxMWRZ9ARDKv+3vpw3tx8EICfZwJKpheRbv+tdCqkqdU4fGg0MTE+gX6oEVQohRG/Vrf51N5lMDB8+nOLi4shddMeu9NhstsiqUGZmJn6/n8bGxjbHHD58uMW5amtro8Yce57GxkYCgUCLFaij6XQ6EhMTo75Ez2P3BPj6YBOltS4S9fGkmLRUNXpY8uqOSME0fVgmD149Mqpg8gYUqu0eEg3xnN03iYJ0sxRMQgjRi3Wrf+F9Ph979uwhKyuLvLw8MjMzWbNmTWS/3+9n3bp1jB8/HoDRo0cTHx8fNaa6upqdO3dGxhQVFWG329myZUtkzObNm7Hb7VFjdu7cSXV1dWTM6tWr0el0jB49+pS+Z9F1jmQv7ahsosHlJzNRjz4+hve/rmbhP7ZTVuciUR/H72YM5rZJA9DHh+9+U1WVeqcPuydAfpqZEX0tpB4VZCmEEKJ36tLLc0uWLGHmzJn069cPm83Gvffei8PhYO7cuWg0GhYuXMh9993HwIEDGThwIPfddx9Go5HZs2cDYLFYuPnmm1m8eDGpqamkpKSwZMkShg8fzkUXXQTA4MGDufjii5k/fz5PPfUUALfccguXXnophYWFAEydOpUhQ4YwZ84cHnjgARoaGliyZAnz58+X1aNeyhtQKKtzUtXgwaiNIzPRgN0T4C8fFbNlfwMAo3KSWHjRoKhHnfiDIWqdvnBQpdWENUGCKoUQ4kzRpUVTVVUV1157LXV1dVitVsaNG8emTZvIzc0F4Ne//jUej4fbbruNxsZGxo4dy+rVq0lI+O5p8Q899BBxcXFcffXVeDweLrzwQlasWEFs7HeZOCtXrmTBggWRu+xmzZrFY489FtkfGxvLe++9x2233caECRMwGAzMnj2bZcuWnaZPQpxODS4/JbZmGlx+0sw6dHGxbKto5OEP99HoDhAXo2He+P7MPDs7qtm7ye3HE1Dol2IgL80suUtCCHGG6dKcpt5Gcpq6NyWkUtXoprzWhRJSSUvQEVRUnt+4n7d3HAKgX4qRJVMLyUszRV4XUMKrSyZtLPlWM5mJemJiZHVJCCF6i/b+/JYQGXFGcPuDlNicHGryYtHHY9bHcaDexbLVe9lf7wbg0uFZzJvQP+pBukeCKrMsevKtJhL0EiUghBBnKimaRK9na/ZSanNi9wTC2UsxGt796hDPfbYfvxIiyRDPLy8cyJj+KZHXKKFwlEB8rIYhWQn0STYSK6tLQghxRpOiSfRaASWcvbS/3k2cRkO2Jdzs/chHxXxxIBxTMTo3mV9eOJBk43fN3m5/kEa3n/SE8OpS0lH7hBBCnLmkaBK9ksMboMzmpMbhJdmoxaiN44sDDTzyYTFNngDxsRpumpDHjOFZkbvfJKhSCCFEW6RoEr2KqqrUOLyU2Jx4/AqZiQaCoRBP/aeUd78K53D1Tw03e+emftfs7Q0o1Lt8pJi0FFjNkrskhBCiBSmaRK/hCyqU17qobHSji4sly2Jgf1242ftAQ7jZe9bZ2cwt6o82LryCpKoqDS4/wZBKfpqZfqnGSIilEEIIcTQpmkSv0OT2U2JzUuf0kWrSoYuL4e0dB1mxYT8BRSXJGM/CCwcxOve7hzuHgyq9WAzxDLaaSZegSiGEEG2Qokn0aOFHobgpq3URDKlkJhpweALc/8E3bKsIN3v/oH8yCy4YGNXQbfcEcPuD9Esx0j/NhFErfxWEEEK0TX5SiB7L41corXVS1egmQRdPiimeLeUN/GVtMXZPAG1sDDefm8f0YZmRFaSgEsLm9GHUxjK0j4UsCaoUQgjRTlI0iR6pzumjxOakyR3AatYTUlWeWFfK+1+Hm73z0kwsmVpIvxRj5DVObxC710+WxSBBlUIIIU6aFE2iRwkqISob3JTXu0DVkG3Rs7/exQP/3ktloweAy0Zmc0NR/0hcwJGgyrhYDYOzEumTZCBOogSEEEKcJCmaRI/h9AUptTmptntIMmgxaGN5a8chnt+wn2BIJdkYz8KLBnFOv++avd3+IA0uP+mJOgqsZgmqFEII0WFSNIluT1VVbM0+im1OXN4gGQl6HN4gf1r1DV9WNgEwNi+F2y8YiMUQvuQWUlXqnX5UjcrADDP9UkyRmAEhhBCiI6RoEt2aPxh+FMqBejfxsTFkWfRs/rbZu9kbRBsXw0/OzePiod81ex8dVJlvNZMmQZVCCCE6gRRNotuyuwOU1DZT2+wnxahFo4HHPyll1a4aAPKt4WbvnORws7eqqjS6A/iVEP1TTfRPM0lQpRBCiE4jRZPodkIhlWqHlxJbM4GgSmbid83eB5vCzd6Xj+rD9eNyI83eASWErdlLoj6es7IsElQphBCi00nRJLoVb0ChrM5JVYMHozaOpIQ43vzyIC9uOkAwpJJi0rLookGcnZMUeY3dE8DlC9A32Ui+VYIqhRBCnBry00V0G/VOH6W1ThpcftLMOpzeIL9/ayc7quwAFOWn8ovJA0j8ttk7qISodfowaGMZ1jdJgiqFEEKcUlI0iS6nhFSqGt2U1ToJqZBlMbC5rJ5H15bQ7Auii4th/nn5TB2SEbnk5vQGcfj8ZCYayLOaSJSgSiGEEKeYFE2iS7l8QUprnRxq8mLRxxMXq+GvH5ewevdhAAZYzSyZWkifZAMQLrDqXT5iYzSclSlBlUIIIU4fKZpEl1BVlVqnj1KbE4c3QHqCnv11Lpav2cfBJg8a4Ipz+jJ7bL9Is7fbH6TRHcCaoCU/zUyySYIqhRBCnD5SNInTLqCEs5f217mJi9GQbtbz5vZD/H3zAZSQSqpJy6IpgxjRNwkIB1U2uPwoqsqAdJMEVQohhOgSUjSJ08rhDVBmc1Lj8JJs1OLyKfz+7V18fTDc7D2hIJWfTx4QeZjukaDKJKOWAqsZa4IEVQohhOgaUjSJ00JVVWocXkpsTjx+hcxEA5vK6nns4xKcviD6+BhuPa+ACweno9FoUFWVJncAn6JIUKUQQohuQYomccr5ggrltS4qG93o42JJMmh57ONiPtxjA2BgerjZOzsp3OwdUELUOr2YdfEMz0wiI1GCKoUQQnQ9KZrEKdXo8lNa66TO6SPVpKOiwc2y1XuptnvRAFeNyeHaH+RE7oA7ElTZJ9lIXpoJk07+iAohhOge5CeSOCWUkMqhJjeltS6UkIrVrOfN7Qd5aUsFSkglzaxj8ZRBDOtjAb4LqtTHxzK0j4Vsi0GCKoUQQnQrUjSJTufxK5TWOqlqdJOgi0cJKfz+7Z3sOuQA4LyBadw2cQBmffiPn9MXxOH1k5GgJz/dLEGVQgghuiUpmkSnqm0OPwqlyR3OXtpUVs9fPy7B5VcwxMfy04n5TC4MN3srIZU6p4+4GA2FGQn0TTZKUKUQQohuS4om0SmCSoiKBjfl9S5i0JBkiOOxtSWs3Rtu9i7MSGDx1EFkWcLN3keCKtPM4SgBCaoUQgjR3UnRJL43py9Iqc1Jtd1DkkFLZaOb5av3UePwEqMJN3tfMybc7K2qKvVHgiqtJvqlSlClEEKInkGKJtFhqqpy2OGjpNaJyxskzaTjje0HeXlLBSEV0hN0LJoyiKHZ4WZvX1ChrtlHkim8upRm1kqUgBBCiB5DiibRIf5giP11Lg40uNHFxhCjgd+/vYvd1eFm74mDrPxsYgEmXVxUUGVumpG8NLMEVQohhOhxpGgSJ83uDlBS20xts58Uo5bN5fU8sa4U97fN3rdNKmBSYTogQZVCCCF6DymaRLuFQiqH7B5Ka50EgioJujgeX1fCJ3trARicmcCiqYVkJuqBcFCl0xegT5KRfKsEVQohhOjZ5KeYaBdvQKGszklVgwejNo7DLg/LVu/F1uwjRgPX/KAfV4/JITYmHCVQ6/SijYthWB8LWRYDsRJUKYQQooeTokmcUL0znL3U4PSTbNLy5pcH+ccXlYRUyEjUsXhKIYOzEoHwnXR2j5/MRD15VjMWgwRVCiGE6B2kaBLHpYRUqhrdlNU6Camg0Wi46+1dfFPTDMDkQis/nViAURuHElKpd/qIiYGzMiWoUgghRO/TbX6q3X///Wg0GhYuXBjZptFoWv164IEHImMmTZrUYv8111wTdezGxkbmzJmDxWLBYrEwZ84cmpqaosZUVFQwc+ZMTCYTaWlpLFiwAL/ffyrfcrfm8gXZdcjOnupmdLGx7DzoYOE/tvNNTTMmbSxLphayaEohRm0cHr9CjcODxRjP2TnJ9E8zS8EkhBCi1+kWK02ff/45Tz/9NCNGjIjaXl1dHfX9Bx98wM0338wVV1wRtX3+/Pn84Q9/iHxvMBii9s+ePZuqqipWrVoFwC233MKcOXN45513AFAUhRkzZmC1Wlm/fj319fXMnTsXVVV59NFHO+199gSqqkYeheLwBjBp43jyP2X8pzjc7D0kK5HFUwaRnqhHVVUaXH6CIZUCq5l+qUZ0cRIlIIQQonfq8qLJ6XRy3XXX8cwzz3DvvfdG7cvMzIz6/q233mLy5Mnk5+dHbTcajS3GHrFnzx5WrVrFpk2bGDt2LADPPPMMRUVF7N27l8LCQlavXs3u3buprKwkOzsbgOXLlzNv3jz++Mc/kpiY2Flvt1sLKCEO1LvYX+cmLkZDozPA7z7cFWn2nv3Dflw5OtzsfXRQZb7VhNUsUQJCCCF6ty6/hvLzn/+cGTNmcNFFF7U57vDhw7z33nvcfPPNLfatXLmStLQ0hg4dypIlS2hubo7s27hxIxaLJVIwAYwbNw6LxcKGDRsiY/5/e/ceFXWd9wH8PRdmGO7KXSAFExBRVPBKhY8iluXl8Oxq5ZNpq63Hp0d0M9OndtFOu262sqnp1pqR7UOrm6mnp82Ck4qYhQtijwLHCxfB5Q4KM1zn8n3+IGcbwfwNyQyD79c5c47zne9v+MznoL+33/nNd6Kjo82BCQDmzJmDzs5O5Ofn/9SX6BBaOvS4+M9mXK3TQaOS49jFGvz30Quo03Yi0NMZ2/49BosnPQCFXIYbbV1oau3CAz4uiAn2gp+7MwMTERENenZdaTpw4ADOnTuHf/zjH3edu3//fri7uyM5OdlifMmSJQgNDUVAQAAuXryITZs24bvvvkNWVhYAoKamBn5+fj2ez8/PDzU1NeY5/v7+Fo8PGTIEKpXKPKc3nZ2d6OzsNN9vaWm56+sYaIQQqG7uQEm9Du1dRggBvPa/xbhU2x08Z0X64flHwuCiUnZvVKnthKuzEmODPeHv7gw5txIgIqL7hN1CU2VlJVJSUpCZmQlnZ+e7zn///fexZMmSHnNXrlxp/nN0dDRGjRqFuLg4nDt3DhMnTgSAXldBhBAW41Lm3G7r1q3YsmXLXWsfqDr0RpQ3tKKiqQ3OSjkKq1rw51OlaNcb4apW4D9nPIiHR/kCAFra9dB1GRDo5YwwXze4caNKIiK6z9jt7bn8/HzU1dUhNjYWSqUSSqUS2dnZ2LlzJ5RKJYxGo3luTk4OLl26hBUrVtz1eSdOnAgnJydcuXIFQPd1UbW1tT3m1dfXm1eXAgICeqwo3bhxA3q9vscK1A9t2rQJzc3N5ltlZaWk1z4Q3GjtwoV/NqOsoRVOCjn+nFOGHV9dQbveiDHDPLDzyQl4eJQvjCaBmpZ2GIQJUYEeGDPMk4GJiIjuS3Y7+82aNQsXLlywGFu+fDkiIyPx8ssvQ6H416ew9u3bh9jYWMTExNz1eQsLC6HX6xEYGAgAmDZtGpqbm3H27FlMnjwZAJCbm4vm5mZMnz7dPOe3v/0tqqurzcdlZmZCrVYjNjb2jj9LrVZDrVZb98LtzGgS+OeNNpQ2tMJgFGjUdeGtr66gQdcJhVyGJZMfQPLEYCjkMrR+v1Gln0f36hI3qiQiovuZ3UKTu7s7oqOjLcZcXV3h7e1tMd7S0oKPP/4Y27dv7/EcJSUlyMjIwNy5c+Hj44OioiK8+OKLmDBhAuLj4wEAo0ePxqOPPoqVK1fi3XffBdC95cATTzyBiIgIAEBSUhKioqLwzDPP4M0330RTUxPWr1+PlStXDqpPzrV3GVFSr8P1G21wUSnwZWENDuVfhwAwzNMZLyZFINzfHSYhUNfSAbkcCPd3R/BQFzhx3yUiIrrPDfj3WQ4cOAAhBJ566qkej6lUKnz11VfYsWMHdDodQkJC8PjjjyM1NdVipSojIwNr1qxBUlISAGD+/Pl4++23zY8rFAr8/e9/x+rVqxEfHw+NRoOnn34af/jDH/r/BdrIrb2XbrR1wWgUeO2zYlyt0wEAZkf5Y+VDYdCoFGjvMqKprRPebmqM9HXDUFeVnSsnIiIaGGRCCGHvIgaLlpYWeHp6orm5ecCsUBmMJlQ0taGssRUyARRU3MTe06Xo0JvgplbihX97EPEP+lhsVPnAUBcM9+FGlUREdH+Qev4e8CtN1He6TgNK6nSobm6HUibHvjNlOFPSCAAYF+SJdbPD4eOm7t6oUtcFL40Twvy4USUREVFvGJoGISEEals6cbVeh9YOA+paOrHjqytobO2CUi7Df0wdjoXjg6CQy3CzrQvteiMeGKpBqI8bNCquLhEREfWGoWmQ6TKYUN7QimtNbVAAyCyqxeFz3Rd7B3lpsD4pAg/6uUFvNKH2Zgdc1QpuVElERCQBQ9Mg0tymx9V6Leq0nejUm7Dr+BWU1LcCAOaMCcCKh0Lh7KSAtkOPlg4DhnGjSiIiIsl4thwETCaBquZ2lNTr0KU34cL1Zuw7XYZOgwnuaiX+a+aDmDbSB0aTQG1LB5wUMkQFuiNoiAsUXF0iIiKShKHJwXXojSit1+H6jXYYTQIffnMN35R2X+wdE+yJdYnh8HZTo63LgBttXfD3cEaYjxs8XbhRJRERkTUYmhxYo65776UmXReqmjvw9omraPr+Yu+l04ZjwfggAECdtgMyGTDKzx0PeHOjSiIior5gaHJARpNAZVMryhpa0Wkw4YvCGhw9XwUACB6iwUtJEQjzdUOH3ojG1u6NKsN8XOHt5lhf+UJERDSQMDQ5mNZOA0rqdai62Q5dhxF7Tl5FaUP3xd6PRQfgufhQqJVyNOo6YTAJhPm4caNKIiKie4ChyUEIIVCv7cTVOh1aOrpQUNGM9DPl6DKY4OGsxJpZozAl1BtdBhOqmju6N6r0dYWvOzeqJCIiuhcYmhxEZVMbLtfq0NZlwP98W4Gz5U0AgAkhXlibGI6hripuVElERNSPGJocxM12PYqqWpB+pgw32vRQymVYNn0E5sUMg/H7LQdcVQpEB3kiwIMbVRIREd1rDE0OoENvxHs5pfj0u2oAQMhQF7yUFIFQH1fzRpWBns4I83WFuzO3EiAiIuoPDE0DnMFowqJ3v8H/XW8GADwxNhDL4kdAKZdzo0oiIiIbYmga4JQKOeaMCcC1xjYsnz4Cs0b7o63LgAZdO/zcu1eXvFxU9i6TiIho0GNocgCrEkZiXLAn9IbuT9BBJrhRJRERkY0xNDkAhVwGT40TyhtaEeDpjJG+btyokoiIyMYYmhyExkmByAAPPODtAmcnbiVARERkawxNDuJBPzduUklERGRHvCDGQTAwERER2RdDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEESnsXMJgIIQAALS0tdq6EiIiIpLp13r51Hr8ThqZ7SKvVAgBCQkLsXAkRERFZS6vVwtPT846Py8TdYhVJZjKZUFVVBXd3d8hksnv2vC0tLQgJCUFlZSU8PDzu2fNST+y1bbDPtsE+2wb7bBv92WchBLRaLYYNGwa5/M5XLnGl6R6Sy+UIDg7ut+f38PDgX0gbYa9tg322DfbZNthn2+ivPv/YCtMtvBCciIiISAKGJiIiIiIJGJocgFqtRmpqKtRqtb1LGfTYa9tgn22DfbYN9tk2BkKfeSE4ERERkQRcaSIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoGiD27NmD0NBQODs7IzY2Fjk5OT86Pzs7G7GxsXB2dkZYWBjeeecdG1Xq2Kzp8+HDhzF79mz4+vrCw8MD06ZNw5dffmnDah2btb/Tt3z99ddQKpUYP358/xY4SFjb587OTrzyyisYPnw41Go1Ro4ciffff99G1Toua/uckZGBmJgYuLi4IDAwEMuXL0djY6ONqnVMp06dwrx58zBs2DDIZDIcPXr0rsfY/FwoyO4OHDggnJycxN69e0VRUZFISUkRrq6u4tq1a73OLy0tFS4uLiIlJUUUFRWJvXv3CicnJ3Ho0CEbV+5YrO1zSkqKeOONN8TZs2fF5cuXxaZNm4STk5M4d+6cjSt3PNb2+pabN2+KsLAwkZSUJGJiYmxTrAPrS5/nz58vpkyZIrKyskRZWZnIzc0VX3/9tQ2rdjzW9jknJ0fI5XKxY8cOUVpaKnJycsSYMWPEwoULbVy5Y/n888/FK6+8Ij755BMBQBw5cuRH59vjXMjQNABMnjxZrFq1ymIsMjJSbNy4sdf5GzZsEJGRkRZjv/zlL8XUqVP7rcbBwNo+9yYqKkps2bLlXpc26PS114sXLxavvvqqSE1NZWiSwNo+Hzt2THh6eorGxkZblDdoWNvnN998U4SFhVmM7dy5UwQHB/dbjYONlNBkj3Mh356zs66uLuTn5yMpKcliPCkpCWfOnOn1mG+++abH/Dlz5iAvLw96vb7fanVkfenz7UwmE7RaLYYOHdofJQ4afe11eno6SkpKkJqa2t8lDgp96fOnn36KuLg4bNu2DUFBQQgPD8f69evR3t5ui5IdUl/6PH36dFy/fh2ff/45hBCora3FoUOH8Pjjj9ui5PuGPc6F/MJeO2toaIDRaIS/v7/FuL+/P2pqano9pqamptf5BoMBDQ0NCAwM7Ld6HVVf+ny77du3o7W1FYsWLeqPEgeNvvT6ypUr2LhxI3JycqBU8p8lKfrS59LSUpw+fRrOzs44cuQIGhoasHr1ajQ1NfG6pjvoS5+nT5+OjIwMLF68GB0dHTAYDJg/fz527dpli5LvG/Y4F3KlaYCQyWQW94UQPcbuNr+3cbJkbZ9v+etf/4rNmzfj4MGD8PPz66/yBhWpvTYajXj66aexZcsWhIeH26q8QcOa32mTyQSZTIaMjAxMnjwZc+fORVpaGj744AOuNt2FNX0uKirCmjVr8Jvf/Ab5+fn44osvUFZWhlWrVtmi1PuKrc+F/C+dnfn4+EChUPT4H0tdXV2PBH1LQEBAr/OVSiW8vb37rVZH1pc+33Lw4EH84he/wMcff4zExMT+LHNQsLbXWq0WeXl5KCgowAsvvACg++QuhIBSqURmZiZmzpxpk9odSV9+pwMDAxEUFARPT0/z2OjRoyGEwPXr1zFq1Kh+rdkR9aXPW7duRXx8PF566SUAwLhx4+Dq6oqHH34Yr7/+Ot8NuEfscS7kSpOdqVQqxMbGIisry2I8KysL06dP7/WYadOm9ZifmZmJuLg4ODk59VutjqwvfQa6V5iWLVuGjz76iNcjSGRtrz08PHDhwgWcP3/efFu1ahUiIiJw/vx5TJkyxValO5S+/E7Hx8ejqqoKOp3OPHb58mXI5XIEBwf3a72Oqi99bmtrg1xueXpVKBQA/rUSQj+dXc6F/XaJOUl26+Os+/btE0VFRWLt2rXC1dVVlJeXCyGE2Lhxo3jmmWfM8299zHLdunWiqKhI7Nu3j1sOSGBtnz/66COhVCrF7t27RXV1tfl28+ZNe70Eh2Ftr2/HT89JY22ftVqtCA4OFj/72c9EYWGhyM7OFqNGjRIrVqyw10twCNb2OT09XSiVSrFnzx5RUlIiTp8+LeLi4sTkyZPt9RIcglarFQUFBaKgoEAAEGlpaaKgoMC8tcNAOBcyNA0Qu3fvFsOHDxcqlUpMnDhRZGdnmx979tlnRUJCgsX8kydPigkTJgiVSiVGjBgh/vSnP9m4YsdkTZ8TEhIEgB63Z5991vaFOyBrf6d/iKFJOmv7XFxcLBITE4VGoxHBwcHiV7/6lWhra7Nx1Y7H2j7v3LlTREVFCY1GIwIDA8WSJUvE9evXbVy1Yzlx4sSP/ps7EM6FMiG4VkhERER0N7ymiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIqIBbcaMGVi7dq29y5BkxIgReOutt+xdBhH1E4YmIiIiIgkYmoiIrGA0GmEymexdBhHZAUMTEQ14JpMJGzZswNChQxEQEIDNmzebH0tLS8PYsWPh6uqKkJAQrF69Gjqdzvz4jBkzIJPJetzKy8slHf/BBx/Ay8sLn332GaKioqBWq3Ht2jXU1dVh3rx50Gg0CA0NRUZGRo+6KyoqsGDBAri5ucHDwwOLFi1CbW2t+fFly5Zh4cKFFsesXbsWM2bMMN8/dOgQxo4dC41GA29vbyQmJqK1tfWnNZSI+oShiYgGvP3798PV1RW5ubnYtm0bXnvtNWRlZQEA5HI5du7ciYsXL2L//v04fvw4NmzYYD728OHDqK6uNt+Sk5MREREBf39/SccDQFtbG7Zu3Yr33nsPhYWF8PPzw7Jly1BeXo7jx4/j0KFD2LNnD+rq6szHCCGwcOFCNDU1ITs7G1lZWSgpKcHixYslv+7q6mo89dRTeO6551BcXIyTJ08iOTkZ/MpQIjvp168DJiL6iRISEsRDDz1kMTZp0iTx8ssv9zr/b3/7m/D29u71sbS0NOHl5SUuXbp0x593+/Hp6ekCgDh//rx57NKlSwKA+Pbbb81jxcXFAoD44x//KIQQIjMzUygUClFRUWGeU1hYKACIs2fPCiG6v7V9wYIFFj8/JSXF/E3u+fn5AoAoLy+/Y71EZDtcaSKiAW/cuHEW9wMDA82rOidOnMDs2bMRFBQEd3d3LF26FI2NjT3ewjp27Bg2btyIgwcPIjw83Dwu5XiVSmVRQ3FxMZRKJeLi4sxjkZGR8PLyspgTEhKCkJAQ81hUVBS8vLxQXFws6XXHxMRg1qxZGDt2LH7+859j7969uHHjhqRjiejeY2giogHPycnJ4r5MJoPJZMK1a9cwd+5cREdH45NPPkF+fj52794NANDr9eb5RUVFePLJJ/H73/8eSUlJ5nGpx2s0GshkMvN98f3bYz8cu50QotfHfzgul8t7vNX2w5+rUCiQlZWFY8eOISoqCrt27UJERATKysru+HOJqP8wNBGRw8rLy4PBYMD27dsxdepUhIeHo6qqymJOY2Mj5s2bh+TkZKxbt87q43szevRoGAwG5OXlmccuXbqEmzdvmu9HRUWhoqIClZWV5rGioiI0Nzdj9OjRAABfX19UV1dbPPf58+ct7stkMsTHx2PLli0oKCiASqXCkSNH7lojEd17DE1E5LBGjhwJg8GAXbt2obS0FH/5y1/wzjvvWMxJTk6GRqPB5s2bUVNTY74ZjUZJx/cmIiICjz76KFauXInc3Fzk5+djxYoV0Gg05jmJiYkYN24clixZgnPnzuHs2bNYunQpEhISzG/rzZw5E3l5efjwww9x5coVpKam4uLFi+bnyM3Nxe9+9zvk5eWhoqIChw8fRn19vTl0EZFtMTQRkcMaP3480tLS8MYbbyA6OhoZGRnYunWrxZxTp06hsLAQI0aMQGBgoPlWWVkp6fg7SU9PR0hICBISEpCcnIznn38efn5+5sdlMhmOHj2KIUOG4JFHHkFiYiLCwsJw8OBB85w5c+bg17/+NTZs2IBJkyZBq9Vi6dKl5sc9PDxw6tQpzJ07F+Hh4Xj11Vexfft2PPbYYz+xc0TUFzJx+xvqRERERNQDV5qIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJ/h/z015KIVyOCAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=df.hazardous,y=df.relative_velocity)\n",
    "plt.title('Diamtere VS relative_velocity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Absolute_maginitude - Describes intrinsic luminosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Diamtere VS absolute_magnitude')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=df.hazardous,y=df.absolute_magnitude)\n",
    "plt.title('Diamtere VS absolute_magnitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot for 'hazardous' column\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.countplot(data=df, x='hazardous')\n",
    "plt.title('Count of Hazardous and Non-Hazardous Asteroids')\n",
    "plt.xlabel('Hazardous')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts for each category in 'hazardous':\n",
      "hazardous\n",
      "False    81996\n",
      "True      8840\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Get the actual counts\n",
    "counts = df['hazardous'].value_counts()\n",
    "print(\"Counts for each category in 'hazardous':\")\n",
    "print(counts)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to compare 'est_diameter_min' between hazardous and non-hazardous asteroids\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(data=df, x='hazardous', y='est_diameter_min')\n",
    "plt.title('Comparison of Estimated Minimum Diameter between Hazardous and Non-Hazardous Asteroids')\n",
    "plt.xlabel('Hazardous')\n",
    "plt.ylabel('Estimated Minimum Diameter (km)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to visualize the relationship between 'est_diameter_min' and 'miss_distance'\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(data=df, x='est_diameter_min', y='miss_distance', hue='hazardous')\n",
    "plt.title('Relationship between Estimated Minimum Diameter and Miss Distance')\n",
    "plt.xlabel('Estimated Minimum Diameter (km)')\n",
    "plt.ylabel('Miss Distance (km)')\n",
    "plt.legend(title='Hazardous')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate Analysis\n",
    "##### When we compare our variable target with others Continuous variables, we can see some insights: Most dangerous asteroids do not have a large diameter, the Relative Velocity starts at almost 20000 and the most important feeling we can see here is the Absolute Magnitude, most of the dangerous asteroids have the value of the variable Absolute Magnitude between 18-23.When we compare the variable Absolute Magnitude which others Continuous Variables we can see that most part of the Dangerous Asteroids have the variable Absolute Magnitude between 18-23, Miss Distance Between 0-2 and the Relative Velocity between 50000 and 200000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9q/vqgvcv992rb1j75lkp2l_80h0000gn/T/ipykernel_64723/4289459134.py:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
      "  sns.catplot(x = \"hazardous\", y = \"est_diameter_min\", palette = \"Set2\", data = df)\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x308dc1b50>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = \"hazardous\", y = \"est_diameter_min\", palette = \"Set2\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9q/vqgvcv992rb1j75lkp2l_80h0000gn/T/ipykernel_64723/2129380272.py:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
      "  sns.catplot(x = \"hazardous\", y = \"est_diameter_max\", palette = \"Set2\", data = df)\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x308e2ee50>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = \"hazardous\", y = \"est_diameter_max\", palette = \"Set2\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9q/vqgvcv992rb1j75lkp2l_80h0000gn/T/ipykernel_64723/2041479392.py:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
      "  sns.catplot(x = \"hazardous\", y = \"relative_velocity\", palette = \"Set2\", data = df)\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x308efd750>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = \"hazardous\", y = \"relative_velocity\", palette = \"Set2\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9q/vqgvcv992rb1j75lkp2l_80h0000gn/T/ipykernel_64723/871839151.py:1: FutureWarning: Passing `palette` without assigning `hue` is deprecated.\n",
      "  sns.catplot(x = \"hazardous\", y = \"miss_distance\", palette = \"Set2\", data = df)\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/Users/anxiousviking/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
      "  data_subset = grouped_data.get_group(pd_key)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x3100f5f10>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = \"hazardous\", y = \"miss_distance\", palette = \"Set2\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS to test the significance of each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your X and y\n",
    "X = df[['est_diameter_min', 'est_diameter_max', 'relative_velocity', 'miss_distance', 'absolute_magnitude']]\n",
    "y = df['hazardous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              hazardous   R-squared:                       0.144\n",
      "Model:                            OLS   Adj. R-squared:                  0.144\n",
      "Method:                 Least Squares   F-statistic:                     3832.\n",
      "Date:                Mon, 22 Apr 2024   Prob (F-statistic):               0.00\n",
      "Time:                        09:08:12   Log-Likelihood:                -11344.\n",
      "No. Observations:               90836   AIC:                         2.270e+04\n",
      "Df Residuals:                   90831   BIC:                         2.275e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  0.9888      0.011     93.409      0.000       0.968       1.010\n",
      "est_diameter_min      -0.0058      0.001     -9.399      0.000      -0.007      -0.005\n",
      "est_diameter_max      -0.0129      0.001     -9.399      0.000      -0.016      -0.010\n",
      "relative_velocity   1.097e-06   3.98e-08     27.543      0.000    1.02e-06    1.18e-06\n",
      "miss_distance      -1.087e-09   4.37e-11    -24.883      0.000   -1.17e-09      -1e-09\n",
      "absolute_magnitude    -0.0382      0.000    -95.488      0.000      -0.039      -0.037\n",
      "==============================================================================\n",
      "Omnibus:                    34822.236   Durbin-Watson:                   1.971\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           109309.955\n",
      "Skew:                           2.046   Prob(JB):                         0.00\n",
      "Kurtosis:                       6.483   Cond. No.                     2.61e+18\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 2.5e-17. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "# Add a constant to the predictors\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Fit the OLS model\n",
    "model = sm.OLS(y, X)\n",
    "results = model.fit()\n",
    "# Print the summary\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define categorical and numerical features\n",
    "categorical_features = ['name', 'orbiting_body']\n",
    "numerical_features = ['est_diameter_min', 'est_diameter_max', \n",
    "                      'relative_velocity','miss_distance', 'absolute_magnitude']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into features (X) and target variable (y)\n",
    "X = df.drop(columns=['id', 'hazardous'])\n",
    "y = df['hazardous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into training and testing sets (70% train, 30% test)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create pipeline for preprocessing\n",
    "preprocessor = ColumnTransformer(transformers=[('num', StandardScaler(), numerical_features),('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add classifier to pipeline\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor),('classifier', LogisticRegression())])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;est_diameter_min&#x27;,\n",
       "                                                   &#x27;est_diameter_max&#x27;,\n",
       "                                                   &#x27;relative_velocity&#x27;,\n",
       "                                                   &#x27;miss_distance&#x27;,\n",
       "                                                   &#x27;absolute_magnitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;name&#x27;, &#x27;orbiting_body&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                                  [&#x27;est_diameter_min&#x27;,\n",
       "                                                   &#x27;est_diameter_max&#x27;,\n",
       "                                                   &#x27;relative_velocity&#x27;,\n",
       "                                                   &#x27;miss_distance&#x27;,\n",
       "                                                   &#x27;absolute_magnitude&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                                  [&#x27;name&#x27;, &#x27;orbiting_body&#x27;])])),\n",
       "                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;est_diameter_min&#x27;, &#x27;est_diameter_max&#x27;,\n",
       "                                  &#x27;relative_velocity&#x27;, &#x27;miss_distance&#x27;,\n",
       "                                  &#x27;absolute_magnitude&#x27;]),\n",
       "                                (&#x27;cat&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
       "                                 [&#x27;name&#x27;, &#x27;orbiting_body&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;est_diameter_min&#x27;, &#x27;est_diameter_max&#x27;, &#x27;relative_velocity&#x27;, &#x27;miss_distance&#x27;, &#x27;absolute_magnitude&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;name&#x27;, &#x27;orbiting_body&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
       "                                                  ['est_diameter_min',\n",
       "                                                   'est_diameter_max',\n",
       "                                                   'relative_velocity',\n",
       "                                                   'miss_distance',\n",
       "                                                   'absolute_magnitude']),\n",
       "                                                 ('cat',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
       "                                                  ['name', 'orbiting_body'])])),\n",
       "                ('classifier', LogisticRegression())])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on the test set\n",
    "y_pred = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9603317309456534\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      1.00      0.98     24597\n",
      "        True       0.98      0.60      0.75      2654\n",
      "\n",
      "    accuracy                           0.96     27251\n",
      "   macro avg       0.97      0.80      0.86     27251\n",
      "weighted avg       0.96      0.96      0.96     27251\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix:\n",
      "[[24565    32]\n",
      " [ 1049  1605]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"\\nConfusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted probabilities from the pipeline\n",
    "y_pred_proba = pipeline.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "plt.plot(fpr, tpr)\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Variety ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a list of models to compare\n",
    "models = [\n",
    "    ('Logistic Regression', LogisticRegression(random_state=42)),\n",
    "    ('Random Forest', RandomForestClassifier(random_state=42)),\n",
    "    ('Support Vector Machine', SVC(probability=True, random_state=42)),\n",
    "    ('K-Nearest Neighbors', KNeighborsClassifier())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      1.00      0.98     81996\n",
      "        True       0.99      0.65      0.78      8840\n",
      "\n",
      "    accuracy                           0.97     90836\n",
      "   macro avg       0.97      0.83      0.88     90836\n",
      "weighted avg       0.97      0.97      0.96     90836\n",
      "\n",
      "Classification Report for Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.98      1.00      0.99     81996\n",
      "        True       0.97      0.78      0.86      8840\n",
      "\n",
      "    accuracy                           0.98     90836\n",
      "   macro avg       0.98      0.89      0.93     90836\n",
      "weighted avg       0.98      0.98      0.98     90836\n",
      "\n",
      "Classification Report for Support Vector Machine:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.96      1.00      0.98     81996\n",
      "        True       0.98      0.57      0.72      8840\n",
      "\n",
      "    accuracy                           0.96     90836\n",
      "   macro avg       0.97      0.79      0.85     90836\n",
      "weighted avg       0.96      0.96      0.95     90836\n",
      "\n",
      "Classification Report for K-Nearest Neighbors:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.97      0.98      0.97     81996\n",
      "        True       0.81      0.67      0.73      8840\n",
      "\n",
      "    accuracy                           0.95     90836\n",
      "   macro avg       0.89      0.83      0.85     90836\n",
      "weighted avg       0.95      0.95      0.95     90836\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate each model using cross-validation\n",
    "results = {}\n",
    "for name, model in models:\n",
    "    pipeline = Pipeline(steps=[('preprocessor', preprocessor),('classifier', model)])\n",
    "    scores = cross_val_score(pipeline, X, y, cv=5, scoring='accuracy')\n",
    "    results[name] = scores\n",
    "\n",
    "    # Generate detailed output comparison\n",
    "    y_pred = cross_val_predict(pipeline, X, y, cv=5)\n",
    "    print(f\"Classification Report for {name}:\")\n",
    "    print(classification_report(y, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: 0.97 (+/- 0.00)\n",
      "Random Forest: 0.98 (+/- 0.00)\n",
      "Support Vector Machine: 0.96 (+/- 0.00)\n",
      "K-Nearest Neighbors: 0.95 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "# Print the accuracy scores for comparison\n",
    "for name, scores in results.items():\n",
    "    print(f\"{name}: {scores.mean():.2f} (+/- {scores.std() * 2:.2f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.boxplot(results.values(), labels=results.keys())\n",
    "plt.title('Comparison of Model Performance')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean accuracy of each model\n",
    "models = ['Logistic Regression', 'Random Forest', 'Support Vector Machine', 'K-Nearest Neighbors']\n",
    "accuracies = [0.97, 0.98, 0.96, 0.95]  # Mean accuracy for each model\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(models, accuracies, color=['blue', 'green', 'orange', 'red'])\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Mean Accuracy')\n",
    "plt.title('Comparison of Different Models')\n",
    "plt.ylim(0.9, 1.0)  # Setting y-axis limits for better visualization\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}