Lab5-1(lineer_regrassion).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ed316d1-b725-4f12-a7d9-706eb4f5a1ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df5451aa-aa5a-4e04-aac7-487a9b7c6d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/linear_regression_dataset.csv\")      # Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58bf4bd8-8c60-483c-bc10-a5d07e567b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deneyim;maas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5;2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0;2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1;2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5;8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8;9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4;6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15;20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7;8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3;6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2;3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12;15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10;13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14;18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6;7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deneyim;maas\n",
       "0      0.5;2500\n",
       "1        0;2250\n",
       "2        1;2750\n",
       "3        5;8000\n",
       "4        8;9000\n",
       "5        4;6900\n",
       "6      15;20000\n",
       "7        7;8500\n",
       "8        3;6000\n",
       "9        2;3500\n",
       "10     12;15000\n",
       "11     10;13000\n",
       "12     14;18000\n",
       "13       6;7500"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d07e4f3-87a9-4bc9-b43e-52f087161cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/linear_regression_dataset.csv\",sep=\";\")     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd8d4764-a980-4570-bdc7-2ce2e41c5c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deneyim</th>\n",
       "      <th>maas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.0</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.0</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.0</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    deneyim   maas\n",
       "0       0.5   2500\n",
       "1       0.0   2250\n",
       "2       1.0   2750\n",
       "3       5.0   8000\n",
       "4       8.0   9000\n",
       "5       4.0   6900\n",
       "6      15.0  20000\n",
       "7       7.0   8500\n",
       "8       3.0   6000\n",
       "9       2.0   3500\n",
       "10     12.0  15000\n",
       "11     10.0  13000\n",
       "12     14.0  18000\n",
       "13      6.0   7500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f6cfcdb-c6b1-49f4-b657-a341861af794",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.deneyim,df.maas)\n",
    "plt.xlabel(\"experience\")\n",
    "plt.ylabel(\"salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d723e75-ad1f-4637-8b4d-450aa0c5ee1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.deneyim.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8574b8a-3b03-46b9-b0a5-3b922f873055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "741e0df8-de81-4dc4-a83b-a82d9bbb219f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.deneyim.values.reshape(-1,1)\n",
    "y=df.maas.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67b4e779-8479-433f-b880-9ddbdcc57829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_reg=LinearRegression()\n",
    "linear_reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a79b16fb-2765-41e6-bee4-b6eaeba1ce20",
   "metadata": {},
   "outputs": [],
   "source": [
    "b0=linear_reg.predict([[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e688c4e5-470f-47f1-a426-3efa0e993a6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1663.89519747]\n"
     ]
    }
   ],
   "source": [
    "b0=linear_reg.intercept_\n",
    "print(b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0c1d9b9-1135-44dc-9aac-370de2b6cb35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1138.34819698]]\n"
     ]
    }
   ],
   "source": [
    "b1=linear_reg.coef_\n",
    "print(b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "63037fe7-32d3-4cc3-b0e7-07853c04ac44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14181\n"
     ]
    }
   ],
   "source": [
    "new_salary=1663+1138*11\n",
    "print(new_salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8168907-74b6-467f-8c7d-1e65473520dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14185.72536421]]\n"
     ]
    }
   ],
   "source": [
    "b11=linear_reg.predict([[11]])\n",
    "print(b11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01a606a6-79cd-4e81-9c2f-118e43380d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/e3Hu26vos267nozIiKizuCVV+xDztq1DDltxI4OERGRp3K0bLypCejGj++2YkeHiIjI0+zcaR9y/vhHaxeHIadd+KdFRETkSRx1cc6fB0JD3V+LF2BHh4iIyBMcPWofcm66ydrFYcjpMAYdIiIitcXGAjfeqBw7fNj6oGvCU1dERERqkWXguuvsx7miymnY0SEiIlLDnDn2IWf7doYcJ2NHh4iIyJ3MZscrpywWxxci0zVhR4eIiMhd3nrLPuSsXGnt4jDkuAQ7OkRERO7gKMg0NDi+tQM5DTs6RERErvTvf9uHnMmTrV0chhyXY0eHiIjIVRx1cc6cAfR699fSRbGjQ0RE5GwlJfYhJzzc2sVhyHErBh0iIiJnuvtuIDJSOfb110B5uTr1dHE8dUVEROQMdXXAr35lP859cVTFjg4REdG1euYZ+5DzwQcMOR6AHR0iIqKOslgAX1/H49wXxyOwo0NERNQRmzfbh5y//Y2b/3kYdnSIiIjay1GQqasDund3fy10RezoEBERtVV+vn3IGTfO2sVhyPFI7OgQERG1RY8ewPnzyrHSUqBXL3XqoTZh0CEiIrqS8nIgIkI55u9vvU8VeTyeuiIiImrN735nH3JychhyOhF2dIiIiFpqaAACAuzHuS9Op8OODhER0eVefNE+5Lz9NkNOJ8WODhEREWANMj4Ofv83mx2PU6fAvzkiIqJt2+zDzJNPth5+qNNgR4eIiLo2R5v/1dQAwcHur4WcjjGViIi6poMH7UPO8OHWLg5DjtdgR4eIiLqeG24Ajh9Xjh0/DvTtq0495DIMOkRE1HWcPAn07m0/zhVVXounroiIqGsIDbUPObt2MeR4OXZ0iIjIu7Wy+V/OsXMYFqWDrwolkfu0u6Pz5ZdfIjk5GUajERqNBlu3blW8PmPGDGg0GsUjLi5OMaehoQFz5sxBWFgYgoKCMHHiRJw8eVIxp6qqCiaTCZIkQZIkmEwmVFdXK+aUlJQgOTkZQUFBCAsLw9y5c9HY2NjeQyIiIm/10EN2IeeDmHtw/cJteOCNXIxcsQuZRWUqFUfu0O6gU1dXh8GDB2PNmjWtzklMTERZWZntsX37dsXraWlp2LJlC9LT05GdnY0LFy4gKSkJZrPZNmfq1KkoLCxEZmYmMjMzUVhYCJPJZHvdbDZjwoQJqKurQ3Z2NtLT0/Hhhx9i/vz57T0kIiLyRhoN8O67iqH+C7bgqQlptuflcj1mbyxg2PFiGiE6fnJSo9Fgy5YtuPfee21jM2bMQHV1tV2np5ksy+jZsyfeffdd3H///QCA06dPo3fv3ti+fTvGjRuHw4cPIzo6Grm5uRg+fDgAIDc3F/Hx8Thy5AgGDBiAzz77DElJSSgtLYXRaAQApKenY8aMGaioqEBISMhV66+pqYEkSZBluU3ziYioE3j9deDRRxVDVd0l3DZnk8PpGgAGKQDZC++Cr4+DPXXI47Tn89slFyN/8cUX0Ov1uPHGG5GSkoKKigrba/n5+WhqakJCQoJtzGg0IiYmBnv37gUA5OTkQJIkW8gBgLi4OEiSpJgTExNjCzkAMG7cODQ0NCA/P99hXQ0NDaipqVE8iIjIi2g0diEnP/tAqyEHAASAMrkeecXnXVwcqcHpQWf8+PHYtGkTdu3ahVWrVmH//v2466670PDzLe3Ly8vh7++P0NBQxdeFh4ejvLzcNkev19u9t16vV8wJDw9XvB4aGgp/f3/bnJaWL19uu+ZHkiT0drTEkIiIOp/9+x3vcCwETgaF2o87UFFb7+SiyBM4fdVV8+koAIiJicHQoUMRGRmJTz/9FJMmTWr164QQ0Fz2j1Tj4B9sR+ZcbtGiRZg3b57teU1NDcMOEVFn5+hn/hdfAKNHAwD0wfYrrhxp6zzqXFy+j05ERAQiIyNx9OhRAIDBYEBjYyOqqqoU8yoqKmwdGoPBgDNnzti919mzZxVzWnZuqqqq0NTUZNfpaabVahESEqJ4EBFRJ3X2bKtdnOaQAwDDonSIkALQ2tU3GgARUgCGRelcUiapy+VBp7KyEqWlpYiIiAAAxMbGws/PD1lZWbY5ZWVlKCoqwogRIwAA8fHxkGUZeXl5tjn79u2DLMuKOUVFRSgr++VK+R07dkCr1SI2NtbVh0VERGq68Uag5SUOL7/scPM/Xx8NFidHA4Bd2Gl+vjg5mhcie6l2r7q6cOECjh07BgC47bbbsHr1atx5553Q6XTQ6XRYsmQJ7rvvPkRERODHH3/EX/7yF5SUlODw4cMI/vkmabNnz8a2bduwYcMG6HQ6LFiwAJWVlcjPz4evr3XrpvHjx+P06dNYt24dAGDWrFmIjIxERkYGAOvy8ltvvRXh4eFYuXIlzp8/jxkzZuDee+/FK6+80qZj4aorIqJO5tIlwM/PftxicdzduUxmURmWZhxCmfzLtTgRUgAWJ0cjMSbC2ZWSC7Xr81u00+7duwWsF6krHtOnTxc//fSTSEhIED179hR+fn6iT58+Yvr06aKkpETxHhcvXhSpqalCp9OJwMBAkZSUZDensrJSTJs2TQQHB4vg4GAxbdo0UVVVpZhz4sQJMWHCBBEYGCh0Op1ITU0V9fX1bT4WWZYFACHLcnv/GIiIyN3mzhXC2rP55fHb37brLS6ZLWLvsXNi69cnxd5j58Qls8U1tZJLtefz+5r20ens2NEhIuokHHVr6uqA7t3dXwupTvV9dIiIiJxi8+bWLzhmyKE24E09iYjIMzkKOMeOAf36ub8W6rTY0SEiIs9SVNR6F4chh9qJQYeIiDyHRgMMGqQc277d4bJxorbgqSsiIlKfLAPXXWc/zoBD14gdHSIiUteIEfYhZ/FihhxyCnZ0iIhIHRYL8PMmsQpmM+DD38PJOfgviYiI3G/JEvuQc8cd1i4OQw45ETs6RETkXo5WVFVXA5Lk9lLI+zE2ExGRe2zf3vqycYYcchF2dIiIyPUcBZyiIuDmm91fC3UpDDpEROQ6x48DN9xgP84VVeQmPHVFRESuodHYh5zNmxlyyK3Y0SEiIueqqwN+9Sv7cQYcUgE7OkRE5DzJyfYh54knGHJINezoEBHRtWtt/5umJqAbP2pIPezoEBHRtXn5ZfuQc+ON1vDDkEMq479AIiLqOEfLxisqgJ493V8LkQPs6BARUfvt2dP65n8MOeRB2NEhIqL2cRRw8vKA2293fy1EV8GgQ0REbXPqFNCrl/04V1SRB+OpKyIiurqwMPuQ88YbDDnk8djRISKi1jU0AAEB9uMMONRJsKNDRESOTZ9uH3KmT2fIoU6FHR0iIrLn6ILj+npAq3V/LUTXgB0dIqIuzGwRyDleiY8LTyHneCUsb/w/+5ATGmrt4jDkUCfEjg4RUReVWVSGpRmHUCbXAwB+XJFkP6m01PFKK6JOgkGHiKgLyiwqw+yNBRAABpUdRcY7T9pP4rU45AUYdIiIuhizRWBpxiEIAFvfmYdby75XvD7lgWU4MWgYsi0Cvj4OrtUh6kQYdIiIupi84vOorTiPH/8x2e616xdus/6HXI+84vOI79fDzdUROReDDhFRF6P/20IUvfemYmzmfX/DrhuGKcYqauvdWRaRSzDoEBF1FWYz0K0b+rUYvv7PGQ6Xk+uDHWwUSNTJcHk5EVFX8PbbQDfl77Yvjp5hPVXVIuRoAERIARgWpXNjgUSuwY4OEZG3c9Ct+Vd+Mdb98yA0AC5fW9U8c3FyNC9EJq/Ajg4RkbfKybEPOZMmAUJg3JDrsfbBITBIytNTBikAax8cgsSYCDcWSuQ67Q46X375JZKTk2E0GqHRaLB161bba01NTVi4cCEGDRqEoKAgGI1GPPTQQzh9+rTiPcaMGQONRqN4TJkyRTGnqqoKJpMJkiRBkiSYTCZUV1cr5pSUlCA5ORlBQUEICwvD3Llz0djY2N5DIiLyPt26ASNGKMfKyoAPP7Q9TYyJQPbCu/B+Shz+a8qteD8lDtkL72LIIa/S7lNXdXV1GDx4MB5++GHcd999itd++uknFBQU4Nlnn8XgwYNRVVWFtLQ0TJw4Ef/3f/+nmJuSkoLnnnvO9jwwMFDx+tSpU3Hy5ElkZmYCAGbNmgWTyYSMjAwAgNlsxoQJE9CzZ09kZ2ejsrIS06dPhxACr7zySnsPi4jIO5SWAn36KMfCwoCzZx1O9/XRcAk5eTdxDQCILVu2XHFOXl6eACBOnDhhGxs9erR44oknWv2aQ4cOCQAiNzfXNpaTkyMAiCNHjgghhNi+fbvw8fERp06dss15//33hVarFbIst6l+WZYFgDbPJyLyaGPHCmHdz/iXR36+2lUROV17Pr9dfo2OLMvQaDS47rrrFOObNm1CWFgYbr75ZixYsAC1tbW213JyciBJEoYPH24bi4uLgyRJ2Lt3r21OTEwMjEajbc64cePQ0NCA/Px8h7U0NDSgpqZG8SAi6vR++sl6LU5WlnJcCGDIEHVqIvIQLg069fX1ePrppzF16lSEhITYxqdNm4b3338fX3zxBZ599ll8+OGHmDRpku318vJy6PV6u/fT6/UoLy+3zQkPD1e8HhoaCn9/f9uclpYvX2675keSJPTu3dsZh0lEpJ6//Q0IClKOpafzPlVEP3PZ8vKmpiZMmTIFFosFr776quK1lJQU23/HxMSgf//+GDp0KAoKCjDk598+NA6WQwohFONtmXO5RYsWYd68ebbnNTU1DDtE1DkJAfg4+F3VYnG4nJyoq3JJR6epqQmTJ09GcXExsrKyFN0cR4YMGQI/Pz8cPXoUAGAwGHDmzBm7eWfPnrV1cQwGg13npqqqCk1NTXadnmZarRYhISGKBxFRp/PBB/Yh55lnrOGHIYdIwelBpznkHD16FDt37kSPHle/mv/gwYNoampCRIR1SWN8fDxkWUZeXp5tzr59+yDLMkb8vFwyPj4eRUVFKCsrs83ZsWMHtFotYmNjnXxUREQeQqMBJre4GWddHfD88+rUQ+Th2n3q6sKFCzh27JjteXFxMQoLC6HT6WA0GvH73/8eBQUF2LZtG8xms63rotPp4O/vj+PHj2PTpk34zW9+g7CwMBw6dAjz58/HbbfdhjvuuAMAMHDgQCQmJiIlJQXr1q0DYF1enpSUhAEDBgAAEhISEB0dDZPJhJUrV+L8+fNYsGABUlJS2KkhIu/z9df2FxbffTewc6c69RB1Fu1d0rV7924B647hisf06dNFcXGxw9cAiN27dwshhCgpKRGjRo0SOp1O+Pv7i379+om5c+eKyspKxfeprKwU06ZNE8HBwSI4OFhMmzZNVFVVKeacOHFCTJgwQQQGBgqdTidSU1NFfX19m4+Fy8uJqFPQ6+2XjZeUqF0VkWra8/mtEaLrXppfU1MDSZIgyzK7QETkec6cAQwG5ZiPj/Uu5ERdWHs+v3mvKyIiTzR5sn3I+fe/GXKI2ol3Lyci8iQNDUBAgP14122+E10TdnSIiDzFypX2IWf9eoYcomvAjg4Rkdpa2/zv0iXA19f99RB5EXZ0iIjU9Omn9iFnzhxr+GHIIbpm7OgQEanF0S7G1dWAJLm9FCJvxY4OEZG7HT5sH3JiY61dHIYcIqdiR4eIyJ0GDAC+/145dvQocMMN6tRD5OUYdIiI3OH8ecDRvf+4oorIpXjqiojI1f74R/uQs3MnQw6RG7CjQ0TkKpcuAX5+9uMMOERuw44OEZErvPqqfch55RWGHCI3Y0eHiMjZHC0bb2x03N0hIpdiR4eIyFl277YPOTNmWLs4DDlEqmBHh4jIGRx1cc6dc7zSiojchh0dIqJr8cMP9iGnb19rF4chh0h1DDpERB0VFwf066ccKyoCjh9Xpx4issNTV0RE7VVbC4SE2I9zRRWRx2FHh4ioPebPtw85n3zCkEPkodjRISJqC4sF8PV1PO7oQmQi8gjs6BARXc0779iHnGXLrF0chhwij8aODhHRlTgKMvX1gFbr/lqIqN3Y0SEiciQnxz7k/O531i4OQw5Rp8GODhFRS466OGVlgMHg/lqI6Jqwo0NE1OzwYfuQo9NZuzgMOUSdEjs6RESA4y7O//0fEBvr/lqIyGkYdIioa6upASTJfpz74hB5BZ66IqKua/Ro+5Dz178y5BB5EXZ0iKjrEQLwcfB7ntnseJyIOi3+H01EXcvf/24fZuLiWg8/RNSpsaNDRF2HowuOq6qA665zeylE5B789YWIvN+//uU45AjBkEPk5djRISLv5ijgfPstMGiQ+2shIrdj0CEi71RcDPTtaz/OFVVEXQpPXRGR9/H1tQ85773HkEPUBbU76Hz55ZdITk6G0WiERqPB1q1bFa8LIbBkyRIYjUYEBgZizJgxOHjwoGJOQ0MD5syZg7CwMAQFBWHixIk4efKkYk5VVRVMJhMkSYIkSTCZTKiurlbMKSkpQXJyMoKCghAWFoa5c+eisbGxvYdERN7i4kXrqSqLRTkuBPDAA+rURESqanfQqaurw+DBg7FmzRqHr7/00ktYvXo11qxZg/3798NgMGDs2LGora21zUlLS8OWLVuQnp6O7OxsXLhwAUlJSTCbzbY5U6dORWFhITIzM5GZmYnCwkKYTCbb62azGRMmTEBdXR2ys7ORnp6ODz/8EPPnz2/vIRGRN5g0CejeXTmWmsouDlFXJ64BALFlyxbbc4vFIgwGg3jxxRdtY/X19UKSJPHaa68JIYSorq4Wfn5+Ij093Tbn1KlTwsfHR2RmZgohhDh06JAAIHJzc21zcnJyBABx5MgRIYQQ27dvFz4+PuLUqVO2Oe+//77QarVCluU21S/LsgDQ5vlE5IEsFiGscUb5aGpSuzIicpH2fH479Rqd4uJilJeXIyEhwTam1WoxevRo7N27FwCQn5+PpqYmxRyj0YiYmBjbnJycHEiShOHDh9vmxMXFQZIkxZyYmBgYjUbbnHHjxqGhoQH5+fkO62toaEBNTY3iQUSd2Cuv2G/y17evNep041oLInLyqqvy8nIAQHh4uGI8PDwcJ06csM3x9/dHaGio3Zzmry8vL4der7d7f71er5jT8vuEhobC39/fNqel5cuXY+nSpR04MiLyOI6WjZ85Azj42UFEXZdLVl1pWvwAEkLYjbXUco6j+R2Zc7lFixZBlmXbo7S09Io1EZEHys5uffM/hhwiasGpQcdgMACAXUeloqLC1n0xGAxobGxEVVXVFeecOXPG7v3Pnj2rmNPy+1RVVaGpqcmu09NMq9UiJCRE8SCiTkSjAX79a+XYvn284JiIWuXUoBMVFQWDwYCsrCzbWGNjI/bs2YMRI0YAAGJjY+Hn56eYU1ZWhqKiItuc+Ph4yLKMvLw825x9+/ZBlmXFnKKiIpSVldnm7NixA1qtFrGxsc48LCJS2+nTrXdxhg1zfz1E1Gm0+xqdCxcu4NixY7bnxcXFKCwshE6nQ58+fZCWloZly5ahf//+6N+/P5YtW4bu3btj6tSpAABJkvDII49g/vz56NGjB3Q6HRYsWIBBgwbhnnvuAQAMHDgQiYmJSElJwbp16wAAs2bNQlJSEgYMGAAASEhIQHR0NEwmE1auXInz589jwYIFSElJYaeGyJtERAAtr7tbtw6YNUudeoioc2nvkq7du3cLAHaP6dOnCyGsS8wXL14sDAaD0Gq1YtSoUeLAgQOK97h48aJITU0VOp1OBAYGiqSkJFFSUqKYU1lZKaZNmyaCg4NFcHCwmDZtmqiqqlLMOXHihJgwYYIIDAwUOp1OpKamivr6+jYfC5eXE3mwhgbHy8aJqMtrz+e3Roiue3K7pqYGkiRBlmV2gYg8yR//CLz5pnJs6lRg0yZ16iEij9Kez29uNEFEnsXRtTj19YBW6/5aiKjT4009icgzvP22fcj51a+sJ6wYcoiog9jRISL1OerilJQAvXu7vxYi8irs6BCRegoKWl82zpBDRE7AoENE6tBogJZ7Xn3+OTf/IyKn4qkrInKvykogLMx+nAGHiFyAHR0icp9Bg+xDzksveVTIMVsEco5X4uPCU8g5XgmzxXNqI6L2Y0eHiFzPbAa6OfhxY7E4vkZHJZlFZViacQhlcr1tLEIKwOLkaCTGRKhYGRF1FDs6RORaTz1lH3ISE61dHA8LObM3FihCDgCUy/WYvbEAmUVlrXwlEXkydnSIyHUcBZkLF4CgIPfXcgVmi8DSjENwdJJKANAAWJpxCGOjDfD18ZxwRkRXx44OETnfhx+2vmzcw0IOAOQVn7fr5FxOACiT65FXfN59RRGRU7CjQ0TO5SjgfPcdcOON7q+ljSpqWw85HZlHRJ6DHR0ico4jR1rv4nhwyAEAfXCAU+cRkedg0CGia6fRAAMHKsc++cSjlo1fybAoHSKkALR29Y0G1tVXw6J07iyLiJyAQYeIOq62tvUuTnKy++vpIF8fDRYnRwOAXdhpfr44OZoXIhN1Qgw6RNQxd94JhIQox555xmldHHdv3JcYE4G1Dw6BQVKenjJIAVj74BDuo0PUSfFiZCJqHyEAHwe/I5nNjsc7QK2N+xJjIjA22oC84vOoqK2HPth6uoqdHKLOix0dImq7F16wDzO33956+OkAtTfu8/XRIL5fD/z21v9AfL8eDDlEnRw7OkTUNo6uxTl/HggNddq34MZ9RORs7OgQ0ZXt2NH6BcdODDkAN+4jIudjR4eIWuco4HzzDXDLLS75dty4j4icjUGHiOydOgX06mU/7uJ9cbhxHxE5G09dEZHS4MH2IWfbNrds/seN+4jI2Rh0iMiqvt56qurbb5XjQgATJrilBG7cR0TOxqBD1Em4dAO9tDQgMFA59ve/q3ILB27cR0TOxGt0iDoBl22g19r+N01NQDf1fjxw4z4ichZ2dIg8nMs20HvvPfuQM3asNfyoGHKaceM+InIG9X+aEVGrXLaBnqNl4+fOAT16dLBSIiLPxI4OkQdz+gZ6X3/d+uZ/DDlE5IXY0SHyYE7dQM/Nm/8REXkCBh0iD+aUDfTOngX0evtxFVZUERG5G09dEXmwa95A78477UPO5s0MOUTUZbCjQ+TBmjfQm72xABpAcVHyFTfQu3QJ8POzf0MGHCLqYtjRIfJw7d5Ab/Fi+5CzYAFDDhF1SU4POtdffz00Go3d4/HHHwcAzJgxw+61uLg4xXs0NDRgzpw5CAsLQ1BQECZOnIiTJ08q5lRVVcFkMkGSJEiSBJPJhOrqamcfDpFHSIyJQPbCu/B+Shz+a8qteD8lDtkL77IPORoN8NxzyrGGBmDlyg5/b5fuyExE5GJOP3W1f/9+mM1m2/OioiKMHTsWf/jDH2xjiYmJWL9+ve25v7+/4j3S0tKQkZGB9PR09OjRA/Pnz0dSUhLy8/Ph6+sLAJg6dSpOnjyJzMxMAMCsWbNgMpmQkZHh7EMi8gjNG+g59PHHwL33KseGDgX277+m7+myHZmJiNxEI4Rr+9lpaWnYtm0bjh49Co1GgxkzZqC6uhpbt251OF+WZfTs2RPvvvsu7r//fgDA6dOn0bt3b2zfvh3jxo3D4cOHER0djdzcXAwfPhwAkJubi/j4eBw5cgQDBgxoU201NTWQJAmyLCMkJMQpx0vkdo6WjZ8+DURcWxBp3pG55Q+I5u/G+04RkVra8/nt0mt0GhsbsXHjRsycOROay34Yf/HFF9Dr9bjxxhuRkpKCiooK22v5+floampCQkKCbcxoNCImJgZ79+4FAOTk5ECSJFvIAYC4uDhIkmSbQ+T1Dh9uffO/aww5V9uRGbDuyMzTWETk6VwadLZu3Yrq6mrMmDHDNjZ+/Hhs2rQJu3btwqpVq7B//37cddddaGhoAACUl5fD398foaGhivcKDw9HeXm5bY7ewb4ger3eNseRhoYG1NTUKB5EnVJQEBAdrRzLyXHaBcdO35GZiEglLl1e/uabb2L8+PEwGo22sebTUQAQExODoUOHIjIyEp9++ikmTZrU6nsJIRRdIY2D32Rbzmlp+fLlWLp0aXsPg8hzyDJw3XX2404+A+3UHZmJiFTkso7OiRMnsHPnTvzxj3+84ryIiAhERkbi6NGjAACDwYDGxkZUVVUp5lVUVCA8PNw258yZM3bvdfbsWdscRxYtWgRZlm2P0tLS9h4WkXomTbIPOW++6ZJl407ZkZmIyAO4LOisX78eer0eEyZMuOK8yspKlJaWIuLnawpiY2Ph5+eHrKws25yysjIUFRVhxIgRAID4+HjIsoy8vDzbnH379kGWZdscR7RaLUJCQhQPIo9nsVivxdmyxX585kyXfMtr3pGZiMhDuCToWCwWrF+/HtOnT0e3br+cHbtw4QIWLFiAnJwc/Pjjj/jiiy+QnJyMsLAw/O53vwMASJKERx55BPPnz8fnn3+Or7/+Gg8++CAGDRqEe+65BwAwcOBAJCYmIiUlBbm5ucjNzUVKSgqSkpLavOKKqFNYtQr4eUsFm5QUaxfnCqdpr1XzjswA7MLOFXdkJiLyMC65Rmfnzp0oKSnBzBa/bfr6+uLAgQN45513UF1djYiICNx5553YvHkzgoODbfNefvlldOvWDZMnT8bFixdx9913Y8OGDbY9dABg06ZNmDt3rm111sSJE7FmzRpXHA6ROhwFmbo6oHt3t3z75h2ZW+6jY+A+OkTUibh8Hx1Pxn10yCPt3AmMHasc69sXOH5clXLMFoG84vOoqK2HPth6uoqdHCJSU3s+v3lTT6J2cukHv6Muzo8/ApGRznn/DrjijsxERB6OQYeoHVx2S4TiYmvXpqWu23AlInIK3r2cqI2ab4nQciO9crkeszcWILOorGNv3KePfcjZtYshh4jICdjRIWqDq90SQQPrLRHGRhvafhqrrg741a8cvCEDDhGRs7CjQ9QGTr8lwsyZ9iHn5ZcZcoiInIwdHaI2cNotEYQAfBz8fmE2Ox4nIqJrwp+sRG3glFsivP66fZj5/e9bDz9ERHTN2NEhaoPmWyKUy/UOr9PRwLqRXqu3RHC0bFyWAe7fRETkUvw1kqgNOnxLhL177UNOcLC1i8OQQ0Tkcgw6RG3UfEsEg6Q8PWWQArD2wSH2++hoNMAddyjHjhwBampcXCkRETXjqSuidkiMicDYaMOVd0Y+dQro1cv+i7miiojI7Rh0iNrpirdEuO02oLBQOZaRASQlubwuIiKyx6BD5AwNDUCAgxVX7OIQEamK1+gQXav58+1DznPPMeQQEXkAdnSIOqq1/W+amoBu/F+LiMgTsKND1BHvvWcfcu65xxp+GHKIiDwGfyJTp2W2iCuvfnKV/v2BY8eUY+fOAT1auUCZiIhUw6BDnVJmURmWZhxS3GgzQgrA4uRo+/1snKWkBIiMtB/ntThERB6Lp66o08ksKsPsjQV2dxMvl+sxe2MBMovKnP9NH37YPuSUlDDkEBF5OAYd6lTMFoGlGYcc3m+qeWxpxiGYLU4KILW11h2ON2z4ZeyWW6wBp3dv53wPIiJyGQYd6lTyis/bdXIuJwCUyfXIKz5/7d9s9Wr7+1Hl5QHffHPt701ERG7Ba3SoU6mobT3kdGSeQ2az45VTPE1FRNTpsKNDnYo+2MHuw9cwz05Ghn3I+eADhhwiok6KHR3qVIZF6RAhBaBcrnd4nY4G1ruJD4vStf/NNQ6WpnPzPyKiTo0dHepUfH00WJwcDcAaai7X/HxxcnT79tP55hv7kPP889z8j4jICzDoUKeTGBOBtQ8OgUFSnp4ySAFY++CQ9u2jM3IkcOutyrHqauCZZ665TiIiUh9/XaVOKTEmAmOjDR3fGbm8HIhoEYgmTwY2b3Z+sUREpBoGHeq0fH00iO/XgdsuzJkDrFmjHDt+HOjb1zmFERGRx2DQoa7j4kWge3fl2PXXA8XFqpRDRESux2t0qGt47TX7kPPVVww5RERejh0d8m4WC+Dr63jc0XJyIiLyKuzokPfKyrIPOe+8Y102zpBDRNQlsKND3ik01LpM/HL19YBWq0o5RESkDnZ0yLscPmzt1lwechYtsnZxGHKIiLocdnTIeyQmAv/6l3Ls3DmgRweWoBMRkVdwekdnyZIl0Gg0iofBYLC9LoTAkiVLYDQaERgYiDFjxuDgwYOK92hoaMCcOXMQFhaGoKAgTJw4ESdPnlTMqaqqgslkgiRJkCQJJpMJ1S1PVVDXcO6ctYtzecgZP97axWHIISLq0lxy6urmm29GWVmZ7XHgwAHbay+99BJWr16NNWvWYP/+/TAYDBg7dixqa2ttc9LS0rBlyxakp6cjOzsbFy5cQFJSEsxms23O1KlTUVhYiMzMTGRmZqKwsBAmk8kVh0OebNEioGdP5djhw8D27erUQ0REnkU42eLFi8XgwYMdvmaxWITBYBAvvviibay+vl5IkiRee+01IYQQ1dXVws/PT6Snp9vmnDp1Svj4+IjMzEwhhBCHDh0SAERubq5tTk5OjgAgjhw50uZaZVkWAIQsy+05RPIE9fVCWHs2vzx0OrWrIiIiN2jP57dLOjpHjx6F0WhEVFQUpkyZgh9++AEAUFxcjPLyciQkJNjmarVajB49Gnv37gUA5Ofno6mpSTHHaDQiJibGNicnJweSJGH48OG2OXFxcZAkyTbHkYaGBtTU1Cge1Am98w4QoLyhJ7KygMpKdeohIiKP5fSLkYcPH4533nkHN954I86cOYPnn38eI0aMwMGDB1FeXg4ACA8PV3xNeHg4Tpw4AQAoLy+Hv78/QkND7eY0f315eTn0er3d99br9bY5jixfvhxLly69puMjFQkB+DjI5maz43EiIurynP7pMH78eNx3330YNGgQ7rnnHnz66acAgLfffts2R9NiszYhhN1YSy3nOJp/tfdZtGgRZFm2PUpLS9t0TOQBsrPtw8xrr7UefoiIiOCGfXSCgoIwaNAgHD161Lb6qmXXpaKiwtblMRgMaGxsRFVV1RXnnDlzxu57nT171q5bdDmtVouQkBDFgzqB668Hfv1r5dhPPwGPPqpKOURE1Hm4POg0NDTg8OHDiIiIQFRUFAwGA7KysmyvNzY2Ys+ePRgxYgQAIDY2Fn5+foo5ZWVlKCoqss2Jj4+HLMvIy8uzzdm3bx9kWbbNIS9w/Lh12fjPpzUBAHPmWLs4gYHq1UVERJ2G06/RWbBgAZKTk9GnTx9UVFTg+eefR01NDaZPnw6NRoO0tDQsW7YM/fv3R//+/bFs2TJ0794dU6dOBQBIkoRHHnkE8+fPR48ePaDT6bBgwQLbqTAAGDhwIBITE5GSkoJ169YBAGbNmoWkpCQMGDDA2YdEapg8GfjgA+VYWRlw2Z5MREREV+P0oHPy5Ek88MADOHfuHHr27Im4uDjk5uYiMjISAPDnP/8ZFy9exGOPPYaqqioMHz4cO3bsQHBwsO09Xn75ZXTr1g2TJ0/GxYsXcffdd2PDhg3wvewGjZs2bcLcuXNtq7MmTpyINWvWOPtwyN2qq633qbrcyJHAV1+pUg4REXVuGiGEULsItdTU1ECSJMiyzOt1PMHzzwPPPqsc++Yb4JZb1KmHiIg8Uns+v3mvK1LfpUuAn59yTKMBLBZ16iEiIq/Bdbmkrg8+sA85n3zCkENERE7Bjg6po7X9by5dAi67FouIiOhasKND7rd/v33IWb3aGn4YcoiIyInY0SH3GjwY+PZb5VhtLfCrX6lTDxEReTV2dMg9SkutFxhfHnJmzrR2cRhyiIjIRdjRIdd75BHgrbeUYyUlQO/e6tRDRERdBoMOuc6FC8BlG0ECsO6J88036tRDRERdDk9dkWu8/LJ9yMnLY8ghIiK3YkeHnMtsBro5+GfVdTfgJiIiFbGjQ86zbZt9yPnnPxlyiIhINezokHNotUBjo3KssdF+12MiIiI3YkeHrk11NTB8uDLk/P3v1i4OQw4REamMHR3quF27gBkzrHvkNKuqAq67Tq2KiIiIFNjRofarrwfmzQPuvtsacvr1A/butXZxGHKIiMiDsKND7fP118CDDwKHDlmfP/oo8J//yd2NiYjIIzHoUNuYzcBLLwGLFwNNTUB4OPDmm8CECTBbBPKOV6Kith764AAMi9LB10ejdsVEREQMOtQGx48DDz1kPT0FAL/7HbBuHdCzJzKLyrA04xDK5Hrb9AgpAIuTo5EYE6FSwURERFa8RodaJwTw//6f9Y7je/dadzresAH48ENbyJm9sUARcgCgXK7H7I0FyCwqU6duIiKinzHokGNnzgC//S2QkgLU1QGjRlnvPD59OqDRwGwRWJpxCI62AmweW5pxCGYLNwskIiL1MOiQva1bgUGDgIwMwN8fWLnSupT8+uttU/KKz9t1ci4nAJTJ9cgrPu/ycomIiFrDa3ToFzU1wJNPAm+9ZX1+yy3Axo3W0NNCRW3rIacj84iIiFyBHR2y+uor67U4b70FaDTAwoXWu407CDkAoA8OaNPbtnUeERGRKzDodHUNDdZQM3o08OOP1tNTe/YAL75ovX9VK4ZF6RAhBaC1ReQaWFdfDYvSuaBoIiKitmHQ6coOHACGDbPujyMEMHMm8M03wK9/fdUv9fXRYHFyNADYhZ3m54uTo7mfDhERqYpBpysym627GQ8dal1J1bOn9QLkN98EQkLa/DaJMRFY++AQGCTl6SmDFIC1Dw7hPjpERKQ6Xozc1Zw4YV0ivmeP9XlyMvDGG9adjjsgMSYCY6MNyCs+z52RiYjI4zDodBVCAO+8A8yZA9TWAkFBwD/+ATzyiPXi42vg66NBfL8ezqmTiIjIiRh0uoJz56w33/zoI+vzESOsoadfP3XrIiIicjFeo+PtPv0UiImxhpxu3YBly4Avv2TIISKiLoEdHS9itgjbtTIGn0u4fc0L8HnjDeuL0dHWzf9uu03dIomIiNyIQcdLXH4X8SGnDmP1ttXwqf75pppPPmnt5ARw8z4iIupaGHS8QPNdxH3NlzDv3+/j8dwP4CssOBXcE09NSMNDMx9GIkMOERF1QQw6nVzzXcT7nivFP7b9JwadOQ4A+OjmO7HknkdRG/ArFGccwthoA5d8ExFRl8Og42Euv86mLXvS5B0/h3Gf/xNP79mAgEuNqAoIxjPjHsf2m0ba5jTfRZxLwImIqKtx+qqr5cuX4/bbb0dwcDD0ej3uvfdefPfdd4o5M2bMgEajUTzi4uIUcxoaGjBnzhyEhYUhKCgIEydOxMmTJxVzqqqqYDKZIEkSJEmCyWRCdXW1sw/JbTKLyjByxS488EYunkgvxANv5GLkil3ILCpz/AUnT+KGBydhyeevI+BSI/ZEDcG4mWsUIacZ7yJORERdkdODzp49e/D4448jNzcXWVlZuHTpEhISElBXV6eYl5iYiLKyMttj+/btitfT0tKwZcsWpKenIzs7GxcuXEBSUhLMZrNtztSpU1FYWIjMzExkZmaisLAQJpPJ2YfkFs3X2ZTJykBSLtdj9sYC+7Dz/vvAoEHomZeNi920+GvCY5j+h6WoCHbcteFdxImIqCvSCCGEK7/B2bNnodfrsWfPHowaNQqAtaNTXV2NrVu3OvwaWZbRs2dPvPvuu7j//vsBAKdPn0bv3r2xfft2jBs3DocPH0Z0dDRyc3MxfPhwAEBubi7i4+Nx5MgRDBgw4Kq11dTUQJIkyLKMkHbc48nZzBaBkSt22YWcZhpY7x+VvfAu+FZXAY89BmzeDAAQw4bh/pGPYb9fGBz9RSq+ltfoEBGRF2jP57fLNwyUZRkAoNPpFONffPEF9Ho9brzxRqSkpKCiosL2Wn5+PpqampCQkGAbMxqNiImJwd69ewEAOTk5kCTJFnIAIC4uDpIk2ea01NDQgJqaGsXDE+QVn2815ACAgPU6m+/e+RAYNMgacnx9gaVLofn3vzHzYeufE+8iTkREpOTSoCOEwLx58zBy5EjExMTYxsePH49NmzZh165dWLVqFfbv34+77roLDQ0NAIDy8nL4+/sjNDRU8X7h4eEoLy+3zdHr9XbfU6/X2+a0tHz5ctv1PJIkoXfv3s46VAWzRSDneCU+LjyFnOOVMFuu3DS72vUzAU31WJL1GqIf/gNw+jQwYACQkwP87W9At268izgREVErXLrqKjU1Fd9++y2ys7MV482nowAgJiYGQ4cORWRkJD799FNMmjSp1fcTQkBz2Q0oNQ5uRtlyzuUWLVqEefPm2Z7X1NQ4PexcvnFfswgpAIuTo1sNHFe6fuaWsu/x8rbV6Hf+5wuxU1OBFSuA7t0V83gXcSIiInsuCzpz5szBJ598gi+//BK9evW64tyIiAhERkbi6NGjAACDwYDGxkZUVVUpujoVFRUYMWKEbc6ZM2fs3uvs2bMIDw93+H20Wi20Wm1HD+mqmi8obtm/ab6guLXuyrAoHSKkAJTL9bav9bWY8XjOPzH33++jm7DgbHAP6DZvhO/4xFa/P+8iTkREpOT0U1dCCKSmpuKjjz7Crl27EBUVddWvqaysRGlpKSIirCEgNjYWfn5+yMrKss0pKytDUVGRLejEx8dDlmXk5eXZ5uzbtw+yLNvmuFPzxn2OTlI1jy3NOOTwNJavjwaLk6MBWK+rGXN8P7ZteALzsjehm7Bg202/xreffXnFkENERET2nL7q6rHHHsN7772Hjz/+WLHySZIkBAYG4sKFC1iyZAnuu+8+RERE4Mcff8Rf/vIXlJSU4PDhwwgODgYAzJ49G9u2bcOGDRug0+mwYMECVFZWIj8/H76+vgCs1/qcPn0a69atAwDMmjULkZGRyMjIaFOtzlx1lXO8Eg+8kXvVee+nxLXadcn89hRuGPdr3FBebK1PG4RVv52L+L+mInGQ8ZrqIyIi8hbt+fx2+qmrtWvXAgDGjBmjGF+/fj1mzJgBX19fHDhwAO+88w6qq6sRERGBO++8E5s3b7aFHAB4+eWX0a1bN0yePBkXL17E3XffjQ0bNthCDgBs2rQJc+fOta3OmjhxItasWePsQ2qTtm7I1+q8/HwkDh2qGDqW+SX+Nuo2XmdDRETUQS7fR8eTeUxH5/77gX/+85fnffsC339vXUJORERECh61j05X0XxBcWu9Fw2sq6+GRV22n9CJE4BGoww5//u/wPHjDDlEREROwKDjJC0vKL6cw437nn0WuP565cQLF4D77nNlmURERF0Kg44TtWnjvupqaxfn+ed/mbBqFSAEEBTk3oKJiIi8nEs3DOyKrrhx31tvAY88ovyC8nKglX1/iIiI6Now6LiA3cZ9jY1AWBhQW/vL2OzZwKuvur84IiKiLoRBx9V27gTGjlWOHToEDByoTj1ERERdCK/RcRUhgLg4Zci5807AYmHIISIichN2dFyhuhpoced17N4NtNhEkYiIiFyLQccVnnrql/8ODwdKSwE/P/XqISIi6qJ46soVVq4Ebr3VuhFgeTlDDhERkUrY0XGF664Dvv5a7SqIiIi6PHZ0iIiIyGsx6BAREZHXYtAhIiIir8WgQ0RERF6LQYeIiIi8FoMOEREReS0GHSIiIvJaDDpERETktRh0iIiIyGsx6BAREZHXYtAhIiIir8WgQ0RERF6LQYeIiIi8Vpe+e7kQAgBQU1OjciVERETUVs2f282f41fSpYNObW0tAKB3794qV0JERETtVVtbC0mSrjhHI9oSh7yUxWLB6dOnERwcDI1G49T3rqmpQe/evVFaWoqQkBCnvrcn4vF6Nx6vd+PxejdvPF4hBGpra2E0GuHjc+WrcLp0R8fHxwe9evVy6fcICQnxmn9YbcHj9W48Xu/G4/Vu3na8V+vkNOPFyEREROS1GHSIiIjIazHouIhWq8XixYuh1WrVLsUteLzejcfr3Xi83q2rHW9LXfpiZCIiIvJu7OgQERGR12LQISIiIq/FoENERERei0GHiIiIvBaDjgu8+uqriIqKQkBAAGJjY/HVV1+pXZJLLF++HLfffjuCg4Oh1+tx77334rvvvlO7LLdZvnw5NBoN0tLS1C7FpU6dOoUHH3wQPXr0QPfu3XHrrbciPz9f7bJc4tKlS/jrX/+KqKgoBAYGom/fvnjuuedgsVjULs0pvvzySyQnJ8NoNEKj0WDr1q2K14UQWLJkCYxGIwIDAzFmzBgcPHhQnWKd4ErH29TUhIULF2LQoEEICgqC0WjEQw89hNOnT6tX8DW62t/v5R599FFoNBr84x//cFt9amHQcbLNmzcjLS0NzzzzDL7++mv8+te/xvjx41FSUqJ2aU63Z88ePP7448jNzUVWVhYuXbqEhIQE1NXVqV2ay+3fvx+vv/46brnlFrVLcamqqirccccd8PPzw2effYZDhw5h1apVuO6669QuzSVWrFiB1157DWvWrMHhw4fx0ksvYeXKlXjllVfULs0p6urqMHjwYKxZs8bh6y+99BJWr16NNWvWYP/+/TAYDBg7dqztvoCdzZWO96effkJBQQGeffZZFBQU4KOPPsL333+PiRMnqlCpc1zt77fZ1q1bsW/fPhiNRjdVpjJBTjVs2DDxpz/9STF20003iaefflqlitynoqJCABB79uxRuxSXqq2tFf379xdZWVli9OjR4oknnlC7JJdZuHChGDlypNpluM2ECRPEzJkzFWOTJk0SDz74oEoVuQ4AsWXLFttzi8UiDAaDePHFF21j9fX1QpIk8dprr6lQoXO1PF5H8vLyBABx4sQJ9xTlQq0d78mTJ8V//Md/iKKiIhEZGSlefvllt9fmbuzoOFFjYyPy8/ORkJCgGE9ISMDevXtVqsp9ZFkGAOh0OpUrca3HH38cEyZMwD333KN2KS73ySefYOjQofjDH/4AvV6P2267DW+88YbaZbnMyJEj8fnnn+P7778HAHzzzTfIzs7Gb37zG5Urc73i4mKUl5crfn5ptVqMHj26S/z8Aqw/wzQajdd2LC0WC0wmE5566incfPPNapfjNl36pp7Odu7cOZjNZoSHhyvGw8PDUV5erlJV7iGEwLx58zBy5EjExMSoXY7LpKeno6CgAPv371e7FLf44YcfsHbtWsybNw9/+ctfkJeXh7lz50Kr1eKhhx5SuzynW7hwIWRZxk033QRfX1+YzWa88MILeOCBB9QuzeWaf0Y5+vl14sQJNUpyq/r6ejz99NOYOnWqV9348nIrVqxAt27dMHfuXLVLcSsGHRfQaDSK50IIuzFvk5qaim+//RbZ2dlql+IypaWleOKJJ7Bjxw4EBASoXY5bWCwWDB06FMuWLQMA3HbbbTh48CDWrl3rlUFn8+bN2LhxI9577z3cfPPNKCwsRFpaGoxGI6ZPn652eW7RFX9+NTU1YcqUKbBYLHj11VfVLscl8vPz8V//9V8oKCjw+r/PlnjqyonCwsLg6+tr172pqKiw+y3Jm8yZMweffPIJdu/ejV69eqldjsvk5+ejoqICsbGx6NatG7p164Y9e/bgv//7v9GtWzeYzWa1S3S6iIgIREdHK8YGDhzolRfXA8BTTz2Fp59+GlOmTMGgQYNgMpnw5JNPYvny5WqX5nIGgwEAutzPr6amJkyePBnFxcXIysry2m7OV199hYqKCvTp08f28+vEiROYP38+rr/+erXLcykGHSfy9/dHbGwssrKyFONZWVkYMWKESlW5jhACqamp+Oijj7Br1y5ERUWpXZJL3X333Thw4AAKCwttj6FDh2LatGkoLCyEr6+v2iU63R133GG3ZcD333+PyMhIlSpyrZ9++gk+Psofi76+vl6zvPxKoqKiYDAYFD+/GhsbsWfPHq/8+QX8EnKOHj2KnTt3okePHmqX5DImkwnffvut4ueX0WjEU089hX/9619ql+dSPHXlZPPmzYPJZMLQoUMRHx+P119/HSUlJfjTn/6kdmlO9/jjj+O9997Dxx9/jODgYNtvgpIkITAwUOXqnC84ONju+qOgoCD06NHDa69LevLJJzFixAgsW7YMkydPRl5eHl5//XW8/vrrapfmEsnJyXjhhRfQp08f3Hzzzfj666+xevVqzJw5U+3SnOLChQs4duyY7XlxcTEKCwuh0+nQp08fpKWlYdmyZejfvz/69++PZcuWoXv37pg6daqKVXfclY7XaDTi97//PQoKCrBt2zaYzWbbzzCdTgd/f3+1yu6wq/39tgxyfn5+MBgMGDBggLtLdS91F315p//5n/8RkZGRwt/fXwwZMsRrl1sDcPhYv3692qW5jbcvLxdCiIyMDBETEyO0Wq246aabxOuvv652SS5TU1MjnnjiCdGnTx8REBAg+vbtK5555hnR0NCgdmlOsXv3bof/z06fPl0IYV1ivnjxYmEwGIRWqxWjRo0SBw4cULfoa3Cl4y0uLm71Z9ju3bvVLr1Drvb321JXWV6uEUIIN2UqIiIiIrfiNTpERETktRh0iIiIyGsx6BAREZHXYtAhIiIir8WgQ0RERF6LQYeIiIi8FoMOEREReS0GHSIiIvJaDDpERETktRh0iIiIyGsx6BAREZHXYtAhIiIir/X/AaLT5XlwnijxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_head=linear_reg.predict(x)\n",
    "plt.plot(x,y_head,color=\"red\")\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a95778cd-8113-4a66-a89f-bd7906bf6d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r square score 0.9775283164949902\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(\"r square score\",r2_score(y,y_head))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}