Simple Linear Regression Placement dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd78725f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52be4c51",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('placement.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3514163",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "      <th>package</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.89</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.12</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.94</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cgpa  package\n",
       "0  6.89     3.26\n",
       "1  5.12     1.98\n",
       "2  7.82     3.25\n",
       "3  7.42     3.67\n",
       "4  6.94     3.57"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62490ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['cgpa'],df['package'])\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "008bae0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06b0a97b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      3.26\n",
       "1      1.98\n",
       "2      3.25\n",
       "3      3.67\n",
       "4      3.57\n",
       "       ... \n",
       "195    2.46\n",
       "196    2.57\n",
       "197    3.24\n",
       "198    3.96\n",
       "199    2.33\n",
       "Name: package, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f1bfe46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bef9fab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adface92",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85f08a3e",
   "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": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b3a3d33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cgpa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>8.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>5.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>6.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>8.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>7.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>5.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>6.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>8.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>7.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>7.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>6.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>6.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>6.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cgpa\n",
       "112  8.58\n",
       "29   7.15\n",
       "182  5.88\n",
       "199  6.22\n",
       "193  4.57\n",
       "85   4.79\n",
       "10   5.32\n",
       "54   6.86\n",
       "115  8.35\n",
       "35   6.87\n",
       "12   8.94\n",
       "92   7.90\n",
       "13   6.93\n",
       "126  5.91\n",
       "174  7.32\n",
       "2    7.82\n",
       "44   5.09\n",
       "3    7.42\n",
       "113  6.94\n",
       "14   7.73\n",
       "23   6.19\n",
       "25   7.28\n",
       "6    6.73\n",
       "134  7.20\n",
       "165  8.21\n",
       "173  6.75\n",
       "45   7.87\n",
       "65   7.60\n",
       "48   8.63\n",
       "122  5.12\n",
       "178  8.15\n",
       "64   7.36\n",
       "9    8.31\n",
       "57   6.60\n",
       "78   6.59\n",
       "71   7.47\n",
       "128  7.93\n",
       "176  6.29\n",
       "131  6.37\n",
       "53   6.47"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "26dfa2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    4.10\n",
       "29     3.49\n",
       "182    2.08\n",
       "199    2.33\n",
       "193    1.94\n",
       "85     1.48\n",
       "10     1.86\n",
       "54     3.09\n",
       "115    4.21\n",
       "35     2.87\n",
       "12     3.65\n",
       "92     4.00\n",
       "13     2.89\n",
       "126    2.60\n",
       "174    2.99\n",
       "2      3.25\n",
       "44     1.86\n",
       "3      3.67\n",
       "113    2.37\n",
       "14     3.42\n",
       "23     2.48\n",
       "25     3.65\n",
       "6      2.60\n",
       "134    2.83\n",
       "165    4.08\n",
       "173    2.56\n",
       "45     3.58\n",
       "65     3.81\n",
       "48     4.09\n",
       "122    2.01\n",
       "178    3.63\n",
       "64     2.92\n",
       "9      3.51\n",
       "57     1.94\n",
       "78     2.21\n",
       "71     3.34\n",
       "128    3.34\n",
       "176    3.23\n",
       "131    2.01\n",
       "53     2.61\n",
       "Name: package, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0df7882f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.89111601])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X_test.iloc[0].values.reshape(1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "764c14d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Package(in lpa)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SHXgO2CjynlmqelFSn5OA6UC03tu1qnpTWp/AMEImk1zyXqqVXjXVDJ82t6gXJbfTjALdq6sASVRnde3CvMuPhClxyoD774ejOgzBXnj54oe5cJdaHYBiwdNryEUIpNtnHTBKVXcHBgOHiMgwl34zVXVw5MeEgFFw0nH1jOLmBVPdRVizvs2zNnCx1L/1Wryb17Ym1O8955VHWfqHH9Lty4gQ2H9/JzlcRAiAt3dNXZyBOJ4g9ZPT+Z4y+d0ZAYzFkcX7r8COQDegClijqpv6PadOyPLXkcvqyE9phTEbFUmmAVuQaANYu76Nz5MSvsUvSn4713yqN7xOM11EmDhzCd9r+4z5V/808eaXX8Imm3R6Jl2vm1S2lXR3+JUaB5AtQbyGrgWOBe4HhgI/xbETpEREqoBFwPbA31T1BZdu40Xk+8AbwERVfc9lnAnABIC+ffsGebVhZEymSdqSbQADJs9x7be6uSXlzjWf6g23xRtgw4Z2Vlw5LrHzk0/CQQd5jpWuUTzVwp1upG+p1QEoFgIFlKnqW0CVqrar6q2Au19Y5+faVXUwsA2wl4jsktTlEaC/qu4GPAXc7jHOjao6VFWH9u7dO8irDSNjggQ7BVFX+AUn+S2AuVBv+M23fkhdggqoSoRJz96eIAQeHbgfwy9/2lcIxI8XJGc/pA7gSneHX6yBasVOkBPBWhHpBiwRkSuBD0gzIllVm0VkHo4AeTWu/dO4bjcBV6YzrmHkglS72qDqCj81yfQnlnvuXL0WucbmFgZMnpO2qshvvsmf8+Yh3Rh17MEJzw889wHWde2G5EC94lZBrbqLxBbudHf4lRoHkC1BBMFPcBb+M4CJQB9gfKqHRKQ30BoRAjXAQcAVSX2+o6ofRC7HAUvTmLth5Aw/V8+g6opUi1K6QgJIMKjGv8ONqJ3BbayW1nYueeQ1vmndQEtrO13b25g/5cCEPkeccBWL6wbFrnOmXknOYRF3nUmkb7G66RYzKQWBqr4bORH0Bx4Alqvq+gBjfwe4PWIn6ALcp6qPishUYGEk/uBMERkHtAGfASdl9jEMI3+ko67wWpTSFRLJpMqI6ZZOOZmoIfvyx/7CcS8/GWu/93uHccmY00JPs+BmAJ/+xHJa2xN9SKJlNDMJxDMyI2U9AhE5FLgBeBtHVg8AfqGqj+V+ep2xegRGock2R38Q4hdNr/+hAqyYdmhac4xn6PuvMWvG+Qlt2016iA1dqvjjMYNDXXy98vx7CSq/z2ZkRrb1CK4GRkYMxojIdjgpJgoiCAyj0OQjMVn8bthrUfdT1fi5S27Uuo7l1yRqdw865W+82bsf0OHzH+au20udViVCu8tm1Lx88ksQQfBVVAhEeAf4KkfzMYyiJ9/qilSCx03l4mVk/cfs33PQWx1e3NfufzxX7Xuc67jJZBPb4CWY2lU7nQzMyyf/BBEEC0Xkn8B9OLaqo4H/isiRAKr6QA7nZxhFST4MkvELb22Pajbq2oUvWlpjizDA4EuepLmlI2gtakgev2cdsxc1xhbYEW8v5LZZFye+YMMGtlmymroMymemG9vgJZjq4mwFxW4DKOccRkFsBLf63FZVPcXnfuiYjcCoBFLVzk1lDN6sRzWq0N7czCt/Oibx5jvvwIABac0njNrFpVwLuNTnD1naCFT15PCnZBiGH6lcVN3ux/P52lYa7jiHwR+8EWt7+byp7HbFhRnNJ9vUDaXu/VPutYz90lD/FZ/cQKp6Zk5mZBglTFjqg1QLr98CPO71Z/nLI9Nj1x/23Jxhp91O3WY9mJ/2TBzCSN1Qyv795Z7DyO9EYPoXI23KWY/qRvzn7VVTzZr1bTG/+GxyBPklghsweQ5dXLxttljTzKJrT0ho+97pd9LUczMgu0WrUks4Rin3HEaegkBVXfP+GIYXpZgLPhvBlfx54422UYKqD5LnMXJQ7wRjb5To4p8sBOZfdzJ1X3WUkDx77Lk07DwyoU/QRcvvO6kkIR9PuQtCq1lshEY+9ahhnDyyFVyp9PRRUu3E3eZx14JV1FR3YbMe1TSvbXU9AQCc+OIcLnnq+o6GPfag4aaHeeKBVyCDRSvVd1IpC38y5S4ITRAYoZEvPWpYJ49sBVfQz5VqJ+4lUJwykcIfjxnMxJlLEu7VffEx829Ictj7/HOoraU+btx0F61yN4pmQzkLQhMERmjkS48a1mKVreDy+rzxBNmJ+70v+rli71LlremH01Xjagk/8giMHZvwXKaLVrkbRQ13UqaTFpHeIvJbEblRRG6J/uRjckZpka9c8H5pmtMhVS588M/j71WecrMe1QiOj30QP/NUgnJ1cwuTxgxk4oL7WHnlYTEh8Oz236Phxfc7CYFsCPKdGOVHkBPBQ8C/gP8FUitEjYolX3pUr5244CzcQd8XJHVDEH15tp/Xq0JYlO0/WUX9HomL/ahL5nDmYbuH/t2Wu1HUcCdIZPGSSJWxosAii42GxY1MnLnENcgl3QygfkbndKNps/VAuuSR1xJqHHfZ0M470w9P6Hf0j6fx6ra75zSi1c2Dac7LH8TmVltTzcXjdi5bfXm54hdZHEQQXAr8W1X/mYvJpYsJAgOgv0c94DDTFw+YPMdV2Li9I90UBF5Co2FxI+fe9xIXPHUDJy96JNb/vl1Hc94Pz04Yoy4PnisNixuZNOulTjUDqrsI048O/0Ri5I5s01CfBfxWRNYBrTj/D1RVNw1xjoaRFnV5MEz7BXUlq6DSMWD7qpxaG6lPEjLb/6aBtqrO/1XzEafhVjgGoHWDmidRGZHSWKyqm6hqF1WtUdVNI9cmBIyCkg/DtNs7wAnmmjTrJQZf8mTMiOxlqG5sbulU2N5NaLS3fEP9HtvA3nvH2g496c/0P/9RVyEQJdui9qnw8xYyT6LywS/X0CBVXSYie7jdV9UXczctw/AnH4bp6Fjn3vdSp2Cu1naNRRKn8lZK3rUnL6B/efhKxi19Lnb9xom/4uCtgqu3crkg+7nImidR+eCnGjoHmIBToSwZBcKpyWcYGZKPAJ/6IXWcnRTMlS7JKqLo4rrvyiXcPfOCxM7t7Xy3Sxc2m/pkguHYj1wuyJPGDPS0EZgnUfngl2toQuTPkV59DCMopZyMzqucYjrE79rPH9GPwT/Yj77NH8baDjntJn75s0Oo7+Joay86bOeUxefBMdjlckGO/o7iPZrMa6j88FMN7aeqz/vc3xToq6qv5mRmRtlQisno4slWCEDcrn3qVMZddFGs/bIRJ3PzsKM4bu8+Cd9FvOrLT/Wk5P47LOfUCoaDn2povIhcCTwOLAKagO7A9sBIoB9wbs5naJQ8xZC/JpsTiZeHUlAEuGzrNSASa3twt9FMPOQsp02V2YsaGdpv807CoH5Ina8xus709EYI+KmGJorI5sB4nDrF3wFagKXA3/1OCwAi0h14Dtgo8p5ZqnpRUp+NgDuAPYFPgWNUdWXGn8YoSgqdvybbE4lbtG11lbBxt6580dLqXb0J6LluLQtuPJWea7+Kte37m/tYXdUjoV9Lazvn3vdSpzk1LG5kzbo217H99PRhquJKWa1nBMM3jkBVPwP+EflJl3XAKFX9WkSqgedF5DFVXRDX51Tgc1XdXkSOBa4AjnEbzChdCl3UI9sTSSoPJa8d+/88/Q9OWfhQ7PrHx1zKv/sP9nxPu2qCgPKrS+ynpw9TFVfqaj0jGEGSzn1bRG4Wkcci1zuJyKmpnlOHryOX1ZGf5M3T4UC0AM4s4ECRuPOzURbkKxmdF2GcSOqH1DF/8ihWTDuU+ZNHJSyCk8YMpLqq45/tPu++zMorxsaEwM1DD6f/+Y/6CoEo8XEBXump62prWHLRwZ4LsZ/gS5cwxzKKl5SCALgNeALYOnL9BnB2kMFFpEpElgAfA0+p6gtJXeqA9wBUtQ34AtjCZZwJIrJQRBY2NTUl3zaKnPohdVx+5K7U1daklZUzLHKdUbN+SB0bd+tKr5avWHnFWO6597cANHfvyc5n38fvD/x5WuNFBZSXXSCVAAtTFVdotZ6RH4KkmNhSVe8TkSngLNgiEigLqaq2A4NFpBZ4UER2ycTLSFVvBG4EJ9dQus8bhaeQnie5yKiZoDfv1Z0LZ13B+Ffnxu7X/+Rqlmyd2fhb19bQsLjRyeXicT/V82Gp4gqt1jPyQ5ATwRoR2YLIv0kRGYazcw+MqjYD84BDkm41An0i43YFeuEYjQ0jNMI+kUT15o3NLRz45gvM/+3omBD44/Af0//8R2NCoLam2jVNhRdRATX9ieWeRujG5pZOtRHiCVMVV2i1npEfgpwIzgEeBrYTkflAb+CoVA+JSG+gVVWbRaQGOAjHGBzPw8CJwH8iY87VVOlQDaPATH9iOT0//4Slf/tJrO3d2q04+NTrWNe1W6ytprqKi8ftHHsmamhuXrueNes7H6qrRGICKrk0ZTJ+Rtsw02+Ue61ewyFlGmqI7dYH4rhEL1fVlLHvIrIbjiG4CufkcZ+qThWRqcBCVX044mJ6JzAE+Aw4VlXf8RvX0lAb6ZJuimhfNmzgfwfuw+i3/i/WNOaUa1neu39CNwGOH9aXS+t37TREkPTWfrED8VSJcPWPLB20kZqs0lCLyJFJTd8VkS+AV1T1Y6/nVPVlnAU+uf1/4v7+DU6MgmH4ko0ve2gBbXffDccfz+jI5dRRP+eW7x3u2lWBecvcHRuC6N1TVS2LkuxyahiZEEQ1dCqwD46OH2AETqTxABGZqqp35mhuhgFk78uetefLypUwYEDs8vMdd2Pfw35Pi/jr/pPHjwqzxuaWTobgZL17skqmi0++o3xHaRvlRxBB0BXYUVU/AieuACcaeG+cyGETBEZgLmh4hXteeI92VapEOG7vPq7qk3iy3dFn7PnS3g4jRsDzcUH0b77JZttvT/epT9KSIjto/PjJwkwhJgzqIuUgpz+xnIkzlySceKKfzy+4DDoMyKbHNzIhiNdQn6gQiPBxpO0znIplhhGICxpe4a4Fq2I723ZV7lqwigsaXvF9LtsdfVDPl4bFjQyfNpcBk+cwvf5s6Nq1QwjccguowvbbA9CcQggkj+8mzKJCYNKYgcxe1EhjcwtKx4kn3iso6vlU5RFvKZHnvJ43DD+CCIJnRORRETlRRE4EHoq0bQw053R2RllxzwvvpdUeJduAsCDuo9Edd/e332DFFWOZ9NCfAfh42Pedk8HJJwd+t9v4fsIsaPRu/ZA6rv7R7p2Emlu8gUX/GukQRDV0Ok7iueGR6zuA2RE3T6tVYATGS8edKs1zGAFhfgFtDYsbmXL3Qh669Uy+++mqWPuwX91GVd8+zO/Seb/kNScvTyQ/9VQ6Jx43d85MI5ANI0pKQRBZ8GdFfgwjY7wKvMSrO/y8g3Lhy96wuJG3f3MhS+feGmv71eGTeWzQfgCIx2Ka7pz8hJlXzQGvU0eyUPNyNY0+H/+d1vaoRhW+aGk1W4IRI4j76DDgr8COQDecuIA1VsDeSJfj9u7DXQtWubZDau+g0BesF1+kfs89Y5ePDNqfX487L6FugJ8KKJ05pRIc2Zx4/IRM8ncaX/7SMokaUYKohq4FjgXuB4YCPwW+m8tJGaWL344+6h3k5TWUjndQVrvcNWsco++HHaUi9/j1DD7r0SuhW9ipFLwER7YnHr/nh0+b6xuLYK6nBgSILBaRhao6VEReVtXdIm2LVbVTsFg+sMji4iXbCF6viFtwDKLRBQ4676Djqe4i9Ozelea1iYKhYXEjX02cxE+evSfWd+LJ03jwW7t0GqPQEbthFYPx+06jxEc0G+VLVpHFwFoR6QYsiZSu/IBg3kZGhZErf38gwS1yo65dfHe5rRs0pgJpbG5h4swl3HX1DGbNOD/WZ8bgQ7h07FmM37OOmkWNGQuvXFTvCrMYjN93Gt/HqGyCLOg/ifQ7A1iDky00Oe2EYeTE3z+ZltZ2mluCh69ssm4Ny646IiYEvunajV3PnsnvxpxBS2s785Y1ZZyZND4LaZj++2EWg0n1nVomUQOCnQh2UtV3gW+ASwBE5JfA27mcmFF6ZJu7PlnXnW0a2j88cS3HL3k8dn3U8VewcJudE/qsbm7J2BCdyQkoyAkizGIwyd+peQ0ZbgQRBBeKyDpVnQsgIpOAUcANOZ2ZUXKE7e+/3ZR/urqbdhHYqGuVp3rogHcWcfv9F8Wurxt2FFcecJJr32zUIuku2EFVPn4CNRNVVCGLAhmlQRDV0DjgMhHZX0T+AAzDqTVsGAmEXQDGK9Bsg5Lwns16VFNbU80Wa79g5RVjY0Lgw56bs+PEWZ5CIFu1SLoRz0FVPl4pMUYO6p0TVZRhBAko+0RExgH/i5N19CgrHmN4Eebus85jZ1xXW5P4HlU45hi4//5Yn5PPuJ55G/fxHTtbtUi6J6CgJwgvd9DQ0mkbRhKegkBEviIxSWI3YFvgKBFRCygzgpCOKiO578hBvZnt4tGTsNDOmgVHx5W0mDYNzj+fWyPjTbr/JVo3dOxbqrsI04/ucAv1m1+quafr/9+rptrV0N2rprpTm5tA9apaZqkkjGzxFASqukk+J2KUH0F04l45+hubW5i9qJHxe9Yxb1lT54X2vfegb9/Yu5Zt2Y9fnPV3Jh68C/WRtulPLE8QAuC4lkZ30H7zAwLp89M5AXkkDvVsT8YKyRu5IkiKiSNwagl/EbmuBUaoakNup2aUOhc//JqvKsMtR39y33nLmpg/eVRHY3s7jB4NTz8dazrwZ9fz9hZ94Ou2hMU6lSomlc4+bDWMV+rqVCmto4RhjDcMN4IYiy+KCgEAVW0GLvLubpQi8bn4h0+bm7UBsmFxo6e/v99C7NUXcGoCdO0aEwLT68+m//mPOkIgQvxCnsqY6ycovO4FqSPsRT7SaRtGJgQRBG59gridGiVCLgKj/IKfuojQsLgxkG5769oaeOstR39y6qlO4/77Q1sb1w0c7fpMdNxUBWn8FmavewIZfy9BC+T4UT+kjvmTR7Fi2qHMnzzKhIARCkEEwUIRuUZEtov8XIPjPWSUCWFGskbxW+SjBddre3Q2ksazSRdlzu1nww47dDSuWAHPPQdVVSl32Kl20H4L86QxA3FT3Sv+Qs4P29EbxUqQnf2vgQuBmZHrp3CK1fgiIn1with8G+f/z42q+uekPiNwKp6tiDQ9oKpTg0zcCI8wI1mjpMpx09LazkZdu1BTnRgYFjUYT3z1n5w157qOB2bMgB//OGGMIDpzP2NuKq+fs9Pw0gnqHWXBXUYxEiSOYA0wOYOx24BzVfVFEdkEWCQiT6nq60n9/qWqYzMY3wiJML1RvLyA3PiipZU/HjM4YQH9/YB2Rh17cKzPBwcczNEHT6Lx5XVsvWpuwgIbRsGa5IU5aitZ3dziWUgn+XsJM0mcYRSCIF5DvYHzgJ2B7tF2VR3l+ZBz/wOcTKWo6lcishSoA5IFgVFgwvJGcfMC8hMGW8cHhn3zDey0k6P6ifDYky9yznMf0fLlOsB9gQ1zh508f6/I5pGDeidcW6CXUeoEsRHMAJYBA3CSzq0E/pvOS0SkPzAEeMHl9j4i8pKIPCYiO7vcR0QmiMhCEVnY1NSUzquNAISlu3ZbEBUnBYSfkXTZL86BmpqYEFjwx1tBlUsXfR667SLd+bsxb1niv8FcqNYMI58EsRFsoao3i8hZqvos8KyIBBYEItITmA2crapfJt1+Eeinql+LyA+BBmCHpD6o6o3AjeAUpgn6biM4YeysvRa+z9e2UltTTffqLonFYta/D7INgyL97t9lNJN+eBY1n3blch+vosbmFgZMnhN69sygC3dyv3Qihg2jGAkiCKL/wj8QkUOB1cDmQQYXkWocITBDVR9Ivh8vGFT1nyJynYhsqaqfBBnfKC5qe1Qn1MSNp7mllZrqKv54zGDqt98U+vSBL5zwlA0IQ868my9qnGD26K7fa4GFxEI1EI4uPkgRl2i/eLKNGDaMQhNENXSpiPQCzgV+A9wETEz1kIgIcDOwVFWv8eizVaQfIrJXZD6fBpy7UUQ0LG7k62/afPu0tLaz/owzYdNNY0LguGMvY9vzH4kJgSiNzS2sWe8/XnTMsFRFQQrjuNlOso0YDjuYzzDSxS/pXHfgl8D2OEbem1V1ZBpjD8epbvaKiCyJtP0W6AugqjcARwG/EpE2oAU41jKbliZueX3i2XflEu6eeUFHw8SJcM01rJo2F1x24VUitLYH+6cQli7ezQtp5KDe7rmO4sjG68o8joxiwE81dDuOWuhfwA+AnYCzgg6sqs+Da0xOfJ9rgWuDjmkUL16LcW3Llyz5S4f/f3OPTan9qBF69gS8PZaCGG2jhJl0LRNbidtnEDp7F7lhHkdGMeCnGtpJVU9Q1b/j7Nz3z9OcjBKjYXEjXZIV4qpc/ejVCULgRyf/iWeefz0mBMDbY6nOJ8VDPIVIupasygEYv2ddwtwUmL2oMaWaxzyOjGLA70QQU3CqapuY5ctwIaraiPe5H/3mC9z0wO9j19fsdzyzDz0l7Whbt5OCZ1rqPOGlyule3cU1e2qqnb2lljaKAT9BsLuIRL16BKiJXAtghWkMIFG10fvrz/jv334au/f1Nv3o+eYyzunenXPSHDdZX9+rphoRmLFgFVvX1jjeRwVQnXipcrxUWal29pZa2igG/ArT+LtPGHkjk4Ll+WJ1cwuiG7hp9u858O2O8JIxp1zLEzenTEmVkJIimtKhNrLoR2MOjh/WN6FSWSENqumqbFLt7MNIk2EY2WLppIucYvcqOWnF81x037TY9SUH/pxbhx7uqeOPxyulQ3zsQGNzCzMWrMpI7ZILvFQ5tTXVrGvbkNHO3hLRGYUmSByBUUBykSI6FFasAJGYEFjynR3Y/jcN3Dr08MALYNCUDl5OpIUwqHqlrr543M6WYtooWexEUOSE7VWStZqprQ0OOAD+/e9Y01MN/+Lipetpb26hLo0xs13IC2FQTaXKsYXfKEVMEBQ5YaeIzkrNdN11cHqc3v/WW+GkkzgIOOjw1O9OXjyDpnSAzllMC2lQNVWOUW6YaqjICaO8YZSM1UxLlzqJc6JCYMwYp4j8SScFeq9XKcyRg3r7RxxGqKmu4vhhfU3tYhg5wk4ERU6YXiVpq5nWrYMhQxxBEGGfX91Gl759mPTSB4Hn4CWA5i1r8i1cA6SlakqXYvbGMox8YoKgBAhLFZGWmmnaNJgyJXb5q/opPDZwuHORpkrJTwDV+aiHomkaciUEitkbyzDyiamGKohAaqZFixw1UEQIPLTjAfQ/75EOIRDBS6XklknTr8i8X8ZPxQkgy0U2zqL1xjKMAmCCoIKoH1LH+D3rqIqkC6kSYfyekdPGmjWw1VYwdGis/x6/nsFZ4yZ5JtZP3un72QK8BFA015AXCjlZnC3Hj2F0YIKggmhY3MjsRY2xwK12VWYvauSNk05zEsF99BEAPz36Evqf/yif9ejlO17yTt/PFuDnY18/pM43AG11c0voOfv9TimGUWmYjaCCSF6ov/feq9x/9+SODr/8JQN6jU1pwAV3zyW/XXYqO8ekMQOZOHOJ67tre1SHrs+3HD+G0YGdCCqI6EK96Tdfs/yq+pgQWFPdHZqb4frrfXfEUQWRl/tmNrvs+iF1HD+sr2uaaVVC1+d7pb82Q7FRidiJoAKIukmqKpc9cS0/fumJ2L3xx1/Jh7vsyfxejhrIbaccpbZHNRcdtrPnYpntLvvS+l0Z2m/zTi6dE2cuce0fVRll6gJqgWGG4WCCoMyJGnD3XvYC82ddHGv/27CjmX7AidRUV3F53EIdH7fQ2NySENH7+dpWX5VM8rNVIgk79yCLrtviHB0vmVyojAyjEjHVUJnzj9kLWHrpD7gtIgQ+6LkFg86ZxfQDTvRUh9QPqWP+5FHU1dZ4Zv30on5IXcwlNGqUji7QmRp4vdxec6EyMoxKxE4E5Yoq/OhHzJk1K9Z06El/5rVvbwc4+v75k0f5DpGpi2XYdXi9oqv9VEaGYQTHBEE5ct99cMwxscvLRpzMjXuPT+gSxICbacK7XPjop6MyMhdQw0iPnKmGRKSPiMwTkddF5DUROculj4jIX0TkLRF5WUT2yNV8yoGUvvTvvecEf0WFwK678tAL73Dnfj9K6BbUgJtpwrt8+eh7RSWvXd+Wk2hkwyhXcmkjaAPOVdWdgGHA6SKyU1KfHwA7RH4mANfncD4ljVfUbsPiRicT6OjR0LdvxwNLl8LLL3P4XgMydpPM1MUyzIypQeZXW1Od0B41apswMIxgiGqQ8KEQXiTyEHCtqj4V1/Z34BlVvSdyvRwYoaofeI0zdOhQXbhwYc7nW2wMnzbXVQ3yizfnMeWBqzsa/v53mDAhjzNzJ5+ZPb2+m7rampR2EMOoFERkkaoOdbuXFxuBiPQHhgAvJN2qA96Lu34/0uYpCCqV5IWu/2eNPPOPX3Q0HHAAPP00VLkncMs3+fTRt7xBhpEdORcEItITmA2crapfZjjGBBzVEX3j1R95ppD566tEaFela3sbDXeeyy4fvd1xc+VK6NcvL/MoRjI1als9AsNwyGkcgYhU4wiBGar6gEuXRqBP3PU2kbYEVPVGVR2qqkN79+6dm8mmwFdHnwfaVTn1vw28dVV9TAicedgk+p//aEULAcjMJlHo36dhFBM5OxGIiAA3A0tV9RqPbg8DZ4jIvcDewBd+9oFCErZvfFq8/DIrrxgbu3z8u/vwy/rfgohv1s5KIZMqbgX9fRpGkZFL1dBw4CfAKyKyJNL2W6AvgKreAPwT+CHwFrAWODmH88mKguihW1pgxx3h3XdjTd87/U6aem4GWLbMeNK1SZhdwTA6yJkgUNXnwb82uTouS6fnag5hkqkeOmMuugimTu24fuQRGuqG0O2J5YjptLMm779PwyhiLLI4IHnLX79gAeyzT8f1KafQcPolTH/yDVY/v4Sta2v44zGDUwqAQhlCS8UAa/UIDKMDEwQByUQPnRZffQXbbANfRhyrqqqgqYmGlWvTzrBZqMLspVQQPue/T8MoIfIWUBYWZRlQduaZ8Ne/dlzPmwcjRgCZBUsVKsDKArsMo3jxCyizNNSF5OmnndxAUSFwzjlO1tCIEIDMjJqFMoSaAdYwShNTDRWCTz+FLbfsuN5yS1ixwikgn0QmRs1CGULNAGsYpYmdCPKJKpxwQqIQ+L//g6YmVyEAmQVL5SvpW7G81zCM7LATQb5oaIAjjui4njoVLrww5WOZGDULZQg1A6xhlCZmLM41q1dDXdxCuMMO8PLL0L174eZkGEbFUfDsoxXJhg0wbhzMmdPR9sorsMsuob+qVHz3DcMoTsxGkAvuusuJA4gKgb/8xbEP5EgIWPI0wzCywU4EYbJiBWy7bcf13nvD889D19x9zZY8zTCMbLETQRi0tcG++yYKgbfectJF5FAIgPnuG4aRPSYIsuVvf4PqavjPf5zr22931EDbbZeX1+erULxhGOWLCYJMef11Jyr4jDOc60MOcYrI//SneZ2G+e4bhpEtZiNIl3XrYPfdYfnyjrb33090Ec0BXp5B5rtvGEa2mCBIh8sug9/9ruN69mw48sicvzZVVs9MC8Wb26lhGGCCIBiLFsHQuDiMH//YcREV37o7oZELz6BSShltGEZuMUHgx5o1jifQxx93tDU1JeYKygO58Awyt1PDMKKYsdiL885zEsFFhcCTTzreQHkWApAbzyBzOzUMI4oJgmSee85R+Uyf7lyfdpojAA46qGBTyoVnkLmdGoYRpSJUQ4GMos3N8K1vQWurc92zp+MN1KtXfufhQi48g6xmr2EYUcpeEKQ0iqrChAlw000dD82f70QK53MeKcjUM8hvPDC3U8MwcpiGWkRuAcYCH6tqp2xrIjICeAhYEWl6QFWnpho33TTUvnV0d/sGDj20o/F3v4NLLw08djpYPV/DMApJodJQ3wZcC9zh0+dfqjo2h3NwNX5usaaZ+VfEvbZPH1i2DHr0yPp9yeqfkYN6M29Zk6sQ8JqfYRhGPsmZIFDV50Skf67GD0pCHV1Vrm+4nB+88e+ODkuWOJHCIeCm/rlrwaqU8zMMwygkhfYa2kdEXhKRx0RkZ69OIjJBRBaKyMKmpqa0XhD1uOmxvoWVVx4WEwKvnn2BYx8ISQiAu2++H2acNQyjGCiksfhFoJ+qfi0iPwQagB3cOqrqjcCN4NgI0nlJ1Ph5713/C8Cb39mO1xue4vC9BmQ8cS/SUfPUmXHWMIwiIac1iyOqoUfdjMUufVcCQ1X1E79+xVyz2MsgnIwZiA3DyDd+xuKCqYZEZCsRJ1mPiOwVmcunhZpPGLgFfiVj6iDDMIqNnKmGROQeYASwpYi8D1wEVAOo6g3AUcCvRKQNaAGO1VweT/KAm29+1GvIfPUNwyhWcqoaygXFrBrKNZY22jCMTClUHIERIpY22jCMXFFo91EjIH5pow3DMLLBBEGJYGmjDcPIFSYISgRLG20YRq4wQVAi5KImgWEYBpixuGSwtNGGYeQKEwQlRNg1CQzDMMBUQ4ZhGBWPCQLDMIwKxwSBYRhGhWOCwDAMo8IxQWAYhlHhlFzSORFpAtYAvnULyoAtsc9YDthnLB9K/XP2U9XebjdKThAAiMhCryx65YJ9xvLAPmP5UM6f01RDhmEYFY4JAsMwjAqnVAXBjYWeQB6wz1ge2GcsH8r2c5akjcAwDMMIj1I9ERiGYRghYYLAMAyjwik5QSAiVSKyWEQeLfRccoGIrBSRV0RkiYgsLPR8coWI1IrILBFZJiJLRWSfQs8pTERkYOR3GP35UkTOLvS8wkZEJorIayLyqojcIyLdCz2nsBGRsyKf77Vy/B1CaaahPgtYCmxa6InkkJGqWsqBK0H4M/C4qh4lIt2AHoWeUJio6nJgMDibF6AReLCQcwobEakDzgR2UtUWEbkPOBa4raATCxER2QX4ObAXsB54XEQeVdW3CjuzcCmpE4GIbAMcCtxU6LkYmSMivYDvAzcDqOp6VW0u6KRyy4HA26r6bqEnkgO6AjUi0hVHmK8u8HzCZkfgBVVdq6ptwLPAkQWeU+iUlCAA/gScB2wo8DxyiQJPisgiEZlQ6MnkiAFAE3BrRM13k4hsXOhJ5ZBjgXsKPYmwUdVG4CpgFfAB8IWqPlnYWYXOq8D+IrKFiPQAfgj0KfCcQqdkBIGIjAU+VtVFhZ5LjtlPVfcAfgCcLiLfL/SEckBXYA/gelUdgpM7anJhp5QbImqvccD9hZ5L2IjIZsDhOIJ9a2BjETmhsLMKF1VdClwBPAk8DiwB2gs5p1xQMoIAGA6ME5GVwL3AKBG5q7BTCp/ILgtV/RhHp7xXYWeUE94H3lfVFyLXs3AEQznyA+BFVf2o0BPJAaOBFarapKqtwAPAvgWeU+io6s2quqeqfh/4HHij0HMKm5IRBKo6RVW3UdX+OEftuapaVrsPEdlYRDaJ/h04GOdoWlao6ofAeyIyMNJ0IPB6AaeUS46jDNVCEVYBw0Skh4gIzu9xaYHnFDoi8q3In31x7AN3F3ZG4VOKXkPlzLeBB53/U3QF7lbVxws7pZzxa2BGRHXyDnBygecTOhFhfhDwi0LPJReo6gsiMgt4EWgDFlOeaRhmi8gWQCtwejk6NliKCcMwjAqnZFRDhmEYRm4wQWAYhlHhmCAwDMOocEwQGIZhVDgmCAzDMCocEwSG4YOIbCUi94rI25G0H/8Uke+KyA4i8mhc+7xoFLiInCQiTZGso6+LyM/jxqsXERWRQYX7VIaRiAkCw/AgEiT1IPCMqm6nqnsCU3DiPeYAN8a1/xrYNu7xmao6GBgBXCYi3460Hwc8H/nTMIoCEwSG4c1IoFVVb4g2qOpLwHeB/6jqw3Htr6rqbckDRFKFvA30E5GewH7AqTjR8YZRFJggMAxvdgHckhzujBNNmxIR2RbnpPAWToK2x1X1DeBTEdkzrIkaRjaYIDCMLBGRByMVrB6Iaz5GRJbg5Bn6hap+hqMOujdy/15MPWQUCZZryDC8eQ04yqM9lh5cVY8QkaE4ufmjzFTVM6IXIrI5MArYVUQUqAJURCap5XkxCoydCAzDm7nARvEFgkRkN5w0xMNFZFxc31SlNo8C7lTVfqraX1X7ACuA/cOetGGkiwkCw/AgslM/AhgdcRN9Dbgc+BAYC/xSRN4Rkf8AFwCX+gx3HJ1rFs/G1ENGEWDZRw3DMCocOxEYhmFUOCYIDMMwKhwTBIZhGBWOCQLDMIwKxwSBYRhGhWOCwDAMo8IxQWAYhlHh/D+ptASLemsm+gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['cgpa'],df['package'])\n",
    "plt.plot(X_train,lr.predict(X_train),color='red')\n",
    "plt.xlabel('CGPA')\n",
    "plt.ylabel('Package(in lpa)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44c1cfb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad6078f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85fd85f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.89111601])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = mx + b\n",
    "\n",
    "m * 8.58 + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91528dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.40443183])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m * 9.5 + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc040001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([54.89908542])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m * 100 + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c410c0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLR:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.m = None\n",
    "        self.b = None\n",
    "        \n",
    "    def fit(self,X_train,y_train):\n",
    "        \n",
    "        num = 0\n",
    "        den = 0\n",
    "        \n",
    "        for i in range(X_train.shape[0]):\n",
    "            \n",
    "            num = num + ((X_train[i] - X_train.mean())*(y_train[i] - y_train.mean()))\n",
    "            den = den + ((X_train[i] - X_train.mean())*(X_train[i] - X_train.mean()))\n",
    "        \n",
    "        self.m = num/den\n",
    "        self.b = y_train.mean() - (self.m * X_train.mean())\n",
    "        print(self.m)\n",
    "        print(self.b)       \n",
    "    \n",
    "    def predict(self,X_test):\n",
    "        \n",
    "        print(X_test)\n",
    "        \n",
    "        return self.m * X_test + self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e7c1caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0].values\n",
    "y = df.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2a810ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.89, 5.12, 7.82, 7.42, 6.94, 7.89, 6.73, 6.75, 6.09, 8.31, 5.32,\n",
       "       6.61, 8.94, 6.93, 7.73, 7.25, 6.84, 5.38, 6.94, 7.48, 7.28, 6.85,\n",
       "       6.14, 6.19, 6.53, 7.28, 8.31, 5.42, 5.94, 7.15, 7.36, 8.1 , 6.96,\n",
       "       6.35, 7.34, 6.87, 5.99, 5.9 , 8.62, 7.43, 9.38, 6.89, 5.95, 7.66,\n",
       "       5.09, 7.87, 6.07, 5.84, 8.63, 8.87, 9.58, 9.26, 8.37, 6.47, 6.86,\n",
       "       8.2 , 5.84, 6.6 , 6.92, 7.56, 5.61, 5.48, 6.34, 9.16, 7.36, 7.6 ,\n",
       "       5.11, 6.51, 7.56, 7.3 , 5.79, 7.47, 7.78, 8.44, 6.85, 6.97, 6.94,\n",
       "       8.99, 6.59, 7.18, 7.63, 6.1 , 5.58, 8.44, 4.26, 4.79, 7.61, 8.09,\n",
       "       4.73, 6.42, 7.11, 6.22, 7.9 , 6.79, 5.83, 6.63, 7.11, 5.98, 7.69,\n",
       "       6.61, 7.95, 6.71, 5.13, 7.05, 7.62, 6.66, 6.13, 6.33, 7.76, 7.77,\n",
       "       8.18, 5.42, 8.58, 6.94, 5.84, 8.35, 9.04, 7.12, 7.4 , 7.39, 5.23,\n",
       "       6.5 , 5.12, 5.1 , 6.06, 7.33, 5.91, 6.78, 7.93, 7.29, 6.68, 6.37,\n",
       "       5.84, 6.05, 7.2 , 6.1 , 5.64, 7.14, 7.91, 7.19, 7.91, 6.76, 6.93,\n",
       "       4.85, 6.17, 5.84, 6.07, 5.66, 7.57, 8.28, 6.3 , 6.12, 7.37, 7.94,\n",
       "       7.08, 6.98, 7.38, 6.47, 5.95, 8.71, 7.13, 7.3 , 5.53, 8.93, 9.06,\n",
       "       8.21, 8.6 , 8.13, 8.65, 9.31, 6.22, 8.01, 6.93, 6.75, 7.32, 7.04,\n",
       "       6.29, 7.09, 8.15, 7.14, 6.19, 8.22, 5.88, 7.28, 7.88, 6.31, 7.84,\n",
       "       6.26, 7.35, 8.11, 6.19, 7.28, 8.25, 4.57, 7.89, 6.93, 5.89, 7.21,\n",
       "       7.63, 6.22])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84272bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.26, 1.98, 3.25, 3.67, 3.57, 2.99, 2.6 , 2.48, 2.31, 3.51, 1.86,\n",
       "       2.6 , 3.65, 2.89, 3.42, 3.23, 2.35, 2.09, 2.98, 2.83, 3.16, 2.93,\n",
       "       2.3 , 2.48, 2.71, 3.65, 3.42, 2.16, 2.24, 3.49, 3.26, 3.89, 3.08,\n",
       "       2.73, 3.42, 2.87, 2.84, 2.43, 4.36, 3.33, 4.02, 2.7 , 2.54, 2.76,\n",
       "       1.86, 3.58, 2.26, 3.26, 4.09, 4.62, 4.43, 3.79, 4.11, 2.61, 3.09,\n",
       "       3.39, 2.74, 1.94, 3.09, 3.31, 2.19, 1.61, 2.09, 4.25, 2.92, 3.81,\n",
       "       1.63, 2.89, 2.99, 2.94, 2.35, 3.34, 3.62, 4.03, 3.44, 3.28, 3.15,\n",
       "       4.6 , 2.21, 3.  , 3.44, 2.2 , 2.17, 3.49, 1.53, 1.48, 2.77, 3.55,\n",
       "       1.48, 2.72, 2.66, 2.14, 4.  , 3.08, 2.42, 2.79, 2.61, 2.84, 3.83,\n",
       "       3.24, 4.14, 3.52, 1.37, 3.  , 3.74, 2.82, 2.19, 2.59, 3.54, 4.06,\n",
       "       3.76, 2.25, 4.1 , 2.37, 1.87, 4.21, 3.33, 2.99, 2.88, 2.65, 1.73,\n",
       "       3.02, 2.01, 2.3 , 2.31, 3.16, 2.6 , 3.11, 3.34, 3.12, 2.49, 2.01,\n",
       "       2.48, 2.58, 2.83, 2.6 , 2.1 , 3.13, 3.89, 2.4 , 3.15, 3.18, 3.04,\n",
       "       1.54, 2.42, 2.18, 2.46, 2.21, 3.4 , 3.67, 2.73, 2.76, 3.08, 3.99,\n",
       "       2.85, 3.09, 3.13, 2.7 , 3.04, 4.08, 2.93, 3.33, 2.55, 3.91, 3.82,\n",
       "       4.08, 3.98, 3.6 , 3.52, 4.37, 2.87, 3.76, 2.51, 2.56, 2.99, 3.5 ,\n",
       "       3.23, 3.64, 3.63, 3.03, 2.72, 3.89, 2.08, 2.72, 3.14, 3.18, 3.47,\n",
       "       2.44, 3.08, 4.06, 2.69, 3.48, 3.75, 1.94, 3.67, 2.46, 2.57, 3.24,\n",
       "       3.96, 2.33])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69b8ac64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ccfd1c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "689f0eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = MyLR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f71808f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5579519734250721\n",
      "-0.8961119222429152\n"
     ]
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ca731649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "70f80451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.14"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "22383c25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.989937500000001"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "01c49abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.58"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6677c0d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.58\n",
      "3.891116009744203\n"
     ]
    }
   ],
   "source": [
    "print(lr.predict(X_test[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c78bcb0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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