The Spark Foundation -Task 1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f69fbdb3",
   "metadata": {},
   "source": [
    "# THE SPARK FOUNDATION - Data Science and Business Analytics Internship"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f37f8de8",
   "metadata": {},
   "source": [
    "# Task 1 - Prediction Using Supervised Machine Learning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "15fd3599",
   "metadata": {},
   "source": [
    "In this task we have to predict the percentage of an student based on the no. of study hours, also predict score if a student studies for 9.25 hrs/day?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f2b508f4",
   "metadata": {},
   "source": [
    "Author-Ipsita Mishra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a9214c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IMPORTING THE MODULES\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings as wg\n",
    "wg.filterwarnings(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1bf01bfb",
   "metadata": {},
   "source": [
    "# READING THE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17ca8479",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"http://bit.ly/w-data\"\n",
    "data=pd.read_csv(url)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6669a4fa",
   "metadata": {},
   "source": [
    "Printing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b265756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Hours  Scores\n",
      "0    2.5      21\n",
      "1    5.1      47\n",
      "2    3.2      27\n",
      "3    8.5      75\n",
      "4    3.5      30\n",
      "5    1.5      20\n",
      "6    9.2      88\n",
      "7    5.5      60\n",
      "8    8.3      81\n",
      "9    2.7      25\n"
     ]
    }
   ],
   "source": [
    "print(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8830abfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the Dataset (25, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of the Dataset\", data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1515fd47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hours     0\n",
       "Scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dataset wheteher there is any null values or not\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99d698b0",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#description of the data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db01900a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "#getting the info of the dataset\n",
    "data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "49f4146b",
   "metadata": {},
   "source": [
    "Lets Plot our dataset in 2-D graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4767ab91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 12,
     "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": [
    "data.plot(kind='scatter', x='Hours', y='Scores')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "40923138",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.976191</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.976191\n",
       "Scores  0.976191  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr(method = 'pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8730b6ea",
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scores</th>\n",
       "      <td>0.971891</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours    Scores\n",
       "Hours   1.000000  0.971891\n",
       "Scores  0.971891  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr(method = 'spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9475f5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "hours = data['Hours']\n",
    "scores = data['Scores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6be7610b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hours', ylabel='Density'>"
      ]
     },
     "execution_count": 16,
     "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.distplot(hours)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b16eca5e",
   "metadata": {},
   "source": [
    "# Using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18781923",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,:-1].values\n",
    "y = data.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4216177d",
   "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=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "42213594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "36ea680b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m=reg.coef_\n",
    "c=reg.intercept_\n",
    "line = m*x+c\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x,line)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92adbd56",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "17895048",
   "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>Target</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95</td>\n",
       "      <td>88.211394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>28.718453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>69.020122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>39.273652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>13.365436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Target  Predicted\n",
       "0      95  88.211394\n",
       "1      30  28.718453\n",
       "2      76  69.020122\n",
       "3      35  39.273652\n",
       "4      17  13.365436"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_predicted = pd.DataFrame({'Target':y_test,'Predicted':y_pred})\n",
    "actual_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4dd860d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5l8LyWqPLEukSCjoiIh0gr7SGl7/Yh7PBQ0KfIBacOYiggO5zv4fNauHa1HgG9w2m3uXhlY25VDgbjC5LpNMp6IiInKaCslpe/nIfdS4PgyOD+fHUQd1ysk27zcr1EwcSGRJAeW0DKzfm0uD2GF2WSKdS0BEROQ3FlXW8dPRMzoDeQcyfMhB/e/c9tDr8bdw0ZSAOPxv5ZbW8t6PA6JJEOlX3/TaKiHRzFc4GXv5yHzX1bmIjHPx4agIB9u53Juc/9QkJ4EcTB2ABthydK0vErBR0RKRbsdm6f1AAcDa4+cuXORypaaBPsD83TU3olperWjK0XwgzRvYD4N0d+RwsdxpckUjn6D495UTkOOW1DVT6UIdRr8eLKyCMgjInlg6YjNJqgbqGju+D4vF6Wf1VHoXlToID7Nx85iBCulHH49Y6Z3g/cktqyDxUxRtb8kg7Zwh2m/7+FXPped9MER9S6WxgW26Zz3QY9Xg85BcUEVtlx2o9/V+4wf52BkZ2/BxPH+06yHdFlditFm6aMpDewac/MacRGm87j2PZPzI5WOHk/3YXMWtMjNFliXQoBR2Rbq7B7aHO5TtBp7beRZ3LQwfkHPxtHd9u2/YfYUNmMQBzk+N6/GSZoYF+XJEUx6ubc/k8s5jh0aEMjgwxuiyRDqNzlCIirbS/tIa12/MBOGd4X8bFRRhbUAcZ1T+MlIG98AJvbT1AvY8Ea/ENCjoiIq1QXtvAq5tycXu8jIoJY+bIKKNL6lCXjI0h3OHHkZoG/rG7yOhyRDqMgo6IyCk0uD28uimXqjoX0WGBXJ0Sh7WLJ+jsbAF+Ni4b3x+Az7OKyT+iKSLEHBR0RERO4e87C8kvqyXI38b8yQN7xFg57TEiOoxxceF4gbe3H8Dt8RpdkshpU9ARETmJbw6UsWlvKQBXJ8fTq4feYdVal47rj8PPRmG5ky+zi40uR+S0KeiIiLSguKquqfPx2cP6Mjw61OCKOl9IgJ0LRjf2P/pw10GOVNcbXJHI6VHQERE5gQa3h9fT91PnapyN3Gydj08mNaE3/SMCcTZ4+N/12UaXI3JaFHRERE7gg4zCxpGP/W1ckzoAWweM1NxTWC0W5oxr7Ji8LuMg2/drLizpuRR0RET+wzcHykjfV4oFmJcST7jDz+iSutyAPsGkDOwFwJIPduP1qmOy9EwKOiIiP1BWU8/fvj7aL2d4XxKjzN8vpyWzxkQTYLeyJfcIH+06aHQ5Iu2ioCMicpTH62XN1gM4GzzE9XIwY4Tv9Ms5kYggf65NjQfgN3/foxGTpUdS0BEROeqLrGL2Flfjb7MyLyXep/rltOT6SQOIDAkgp6SGVZtzjS5HpM0UdEREgMLyWj7e1Tj1wSXjYogMCTC4ou4hKMDO4pmJADz9aTa19W6DKxJpGwUdEfF5DW4Pq7/Kw+31MjImrKkTrjSalxJPXC8HxVV1rNyUY3Q5Im2ioCMiPu/DXQc5VFlHaICdK5NisZhsHqvT5W+3smhG41mdZ9bvpbrOZXBFIq2noCMiPi37cBUbs0sAuCo5juAAu8EVdU9XJsWS0CeI0up6VnyZY3Q5Iq2moCMiPsvZ4OatbQcAmDioN8N8+FbyU7HbrNxztK/Oc5/tpdLZYHBFIq2joCMiPuvvOw9SVtNAryA/Zo2JNrqcbm/OGbEM7RdCeW0DL32eY3Q5Iq2ioCMiPimzqJKvchpnJb9qQhwBdpvBFXV/Nqul6Q6sFz7fS3mNzupI96egIyI+x9ng5u2js5JPGdKHwX1DDK6o57h4TAwjokOpdLp44fO9RpcjckoKOiLicz74ppDy2gb6BPtz4ShdsmoLq9XC4pnDAHjp832U1dQbXJHIySnoiIhP+e5gBVv3H8FC4yUrf7sOg2114egoRsaEUV3v5tVNGi1Zujd9w0XEZzgb3Kw9eslq6pA+JEQGG1xRz2SxWLjj7MEAvPxFDs4GjZYs3ZeCjoj4jA93HaTC6aJ3sD/n65LVablkbAyxEQ5KqutZs/WA0eWItEhBR0R8wt7iKtL3Nd5ldUVSrC5ZnSa7zcqt0wcB8PyGvbg9XoMrEjkxfdNFxPQa3B7Wbmu8ZJWa0JshusuqQ8xLjadXkB+5JTX8fWeh0eWInFC7gk5eXl5H1yEi0mk+2nWQkup6wgLtGhiwAwX527lxSgIAz67fi9erszrS/bQr6MyaNYv58+fzzjvv4HQ6O7omEZEOk1lUyfrvDwNw2fhYAv00MGBHumlqAoF+VjLyy/ny6JxhIt1Ju4LO+vXrOffcc3nxxReZNm0aDz30ENu3b+/o2kRETovb4+WPn2Ti8cLY2HBGxoQZXZLp9A7259rUAQA8sz7b4GpEjteuoNOnTx8WLFjAu+++yyuvvEJYWBgPPPAAs2bN4oUXXqC0tPSUr1FSUkJaWhopKSlMmjSJJUuW4HK5Trju+vXrmT17NuPHj2fWrFl8+umnzZ5/7bXXOP/880lKSmL27NnHPS8ivumT3UXsLa4myN/G7DP6G12Oaf1k2iBsVgsbMovZmV9udDkizZxWZ2SXy0VBQQEFBQWUlJTgcDjYsWMHF1xwAWvXrj3ptosXLyYoKIgNGzawZs0aNm7cyIoVK45bLycnh4ULF3LPPfewZcsWFi5cyOLFiykqKgJg7dq1PP300zz55JNs27aN22+/nYULFzY9LyK+6VClkw93HQTg8vGxhATYDa7IvOJ7B3HpuBig8Q4ske6kXUHn66+/5tFHH226bNW3b19effVV3n77bZ566ikef/xxHn/88Ra3z83NJT09nfvvvx+Hw0F8fDxpaWmsWrXquHXXrl1LSkoKM2fOxG63c/HFF5Oamsrq1asBeOmll7jnnnsYN24cFouFSy+9lNWrVxMSorsqRHyVx+vl7W35uDxeUgb2YsKACKNLMr1bpzcOILguo5BDFeq7Kd1Hu/7Euf7665k2bRqPPvoo5513Hn5+fs2eHzlyJOedd16L22dmZhIREUFUVFTTsiFDhlBQUEBFRQVhYf++jp6VlcWwYcOabT906FD27NlDbW0tmZmZWK1Wrr/+erKyshg0aBD33XcfwcFtG/HU7W79yJ7H1m3LNr5E7XNybWkfr8eLx+PB4/F0dlndgsfr+ff/p7HLm/aWsL+0hgC7lYXnDeFQRZ0p2rCj2qfV7+dp/Ay25rM6MjqE5IERbM0t49VNOdwzI7HzC/wPOva0zIxt09p9aVfQWblyJRMmTDhu+WeffcZZZ51FXFwcv/nNb1rcvrq6GofD0WzZscc1NTXNgs6J1g0MDKSmpoaKigq8Xi8vvfQSf/rTnxg4cCBvvPEGt956K++99x5xcXGt3qeMjIxWr3s62/gStc/Jnap9bDYbroAw8guKqK0/cf81s8rPz2/3tpV1Hj78tgqAsxOCCLK6KSwspLrOPJNPnk77tIXD305RiIuSvIpW/VI5O8bL1lx45Yt9TI2ows9m6YIqj6djT8t8sW3aFXRuueUWtm3b1mxZVVUV99xzT6vuvgoKCqK2trbZsmOP//NMjMPhOO4WdqfTSXBwcNOZpJtvvpnExMa/Hm644QZef/111q9fz/XXX9/qfRo7diw2W+tuO3W73WRkZLRpG1+i9jm5trRPQZmT2Co7da6efzaiNTxeD/n5+cTGxmK1tP3Kutfr5ZVN+3F5YGDvIGZNGExIcDAxMTGmaMPTbZ+2CrBbierXh/4RA1u1/uixHl77dj0HK+rIt0Vx+fiu7QCuY0/LzNg2x/bpVFoddHJzc7nkkktwu914vV5Gjhx53DonOstzIomJiZSVlVFcXExkZCQA2dnZREdHExoa2mzdYcOGsWvXrmbLsrKyGDNmDL1796ZPnz7U1zf/S609p+ZsNlubf/jt2caXqH1OrjXtY7FasFqtWH1lDPOjWcRqsWJtx05v23+EzENV2K0WrpwQh91qA4uJ2vA026etrFYrFqul1d9jm83G/CkJ/O6j7/jLxlyunBCHxdL1Z3V07GmZL7ZNq4POwIEDefPNN6moqOC2227j+eefb/Z8QEDAcX1pWpKQkEBycjJLly7lscce48iRIyxfvpy5c+cet+6cOXN4+eWXWbduHRdccAEff/wx6enpPPjggwBce+21PP3000yYMIHExERee+01ioqKmDlzZmt3TURMoNLZwAffNE5DcN6IfvQNDTC4It90bWo8f/pHJt8cKGd7XhkTBvQyuiTxcW26dHXsLM77779PfHz8ab3xsmXLeOyxx5gxYwZWq5XLL7+ctLQ0AJKSknj00UeZM2cOQ4YM4emnn+aJJ57gwQcfJDY2lqeeeopBgxonk7v77rsJCQlh8eLFHDp0iMGDB/P888836+gsIub3/jeF1Da4iQkPZHpiX6PL8Vl9QgK47Iz+vLn1ACu+yFHQEcO1Keg88sgjPPLIIyxfvrzFdU52W/kPRUZGsmzZshM+95/9fKZPn8706dNPuK7VamXBggUsWLCgVe8rIubzbUE5GfnlWC1w5YQ4bFZjOsFKo5umJvDm1gOsyyjkwUtGEhUWaHRJ4sPadJFXE7aJSHdTW+/mnR0FAExP7EtshOMUW0hnGxMbzsSE3rg8XlZtyjW6HPFxbTqj8+ijjwKtP2sjItLZ1u0spNLpok+wP+eN6Gd0OXLUTVMTSM8pZdXm/dx13lAC7L7VAVa6j3Z12y8uLmbp0qUAbNmyhalTp3LppZeSna0J3USk62QeqmRr7hGg8ZKVn80Mt1aZw4Wjo4gJD6Skup4Pdx40uhzxYe06Kjz66KNkZ2fj9XpZsmQJF198Meeeey6PPfZYR9cnInJCdS43a7c3Dpw3eXBvBkW2bTR06Vx2m7VpVvNVm/cbXI34snYNGJiRkcG6des4fPgwe/bs4aWXXiI0NJRJkyZ1dH0iIif08a4iymoaiHD4ceGoaKPLkRO4JjWeZf/MJH1fKZlFlSRGhZ56I5EO1q4zOrW1tQQGBrJx40aGDRtGr169cDqd2O2aHVhEOl9uSTWb9pYAcEVSLAF+6v/RHUWHBzLjaL8pndURo7Qr6IwbN45HHnmE5557jvPPP5/i4mL++7//m4kTJ3Z0fSIizTS4Pby1LR8vkDygl84SdHPXT26cPuKtbQeorTfPhJLSc7Qr6CxZsoT6+npSUlK4/fbbyc/Pp76+nocffrij6xMRaeafew5RXFVHaICdi8fGGF2OnML0oZHE93ZQ6XTx/jcFRpcjPqhd15r69evXbHbyM844g2eeeabDihIROZH8slo2ZB4G4LLx/XH465JVd2e1WvjRxAH89sPvWLV5P1ennN6o+iJt1a6gU11dzWuvvUZOTg4eT/MZgTXGjoh0BrfHy9vbDuDxwtjYcEb1Dze6JGmlq5Pj+cP/fc/XeWXszC9nTKx+dtJ12nXp6r/+67945ZVXqKur6+h6REROaP33hyksdxLkb2P2Gf2NLkfaoG9oABeObrwz7rV0dUqWrtWuMzqbN29mzZo1pz2xp4hIaxRVOPn0u0MAXDouhpAA3eHZ01w/aSDvf1PIO9vz+e+LR+pnKF2mXWd0AgICNDu4iHQJj7fxkpXb42V4VChnxEUYXZK0w+TBvRncN5jqejfvfJ1vdDniQ9oVdK677jp+85vfUFpa2tH1iIg082V2CXlHagmwW7k8KRaLRTOT90QWi4XrJjaOlPy6Ll9JF2rXucM33niDgoICXn/99eOe271792kXJSICUFxZx/992zhP0qwxMYQ7/AyuSE7HlRPi+H8f7mFnfgXfFlQwqn+Y0SWJD2hX0PnhreUiIp3B7fHy5tY8GtxehvYNITWhl9ElyWnqHezPBaOi+SCjkDe25PHInNFGlyQ+oF1B59gIyOXl5eTl5TFq1ChcLhf+/v4dWpyI+K4NmYebLlldOUGXrMzi6pQ4PsgoZO32fB6YNYJATd8hnaxdfXSqq6u59957mTRpEjfccAM5OTmcf/757N27t6PrExEfVFju5B+7G++ymj2uPxFB+iPKLKYn9qV/eCDltQ3837dFRpcjPqBdQee3v/0tNTU1/P3vf8fPz4/4+HjOPfdclixZ0tH1iYiPcXu8vLXtAG6vl5ExYSQNiDC6JOlANquFuclxALyxJc/gasQXtCvofPrpp/zmN79h0KBBWCwW/Pz8eOCBB8jIyOjo+kTEx2wprONgRR1B/jau0F1WpjQ3uXEMts+zijlwpMbgasTs2hV0PB5PU38cr9d73DIRkfbYX1rDjoP1AFw+PlaDypnUgD5BTB3SB68X1mw9YHQ5YnLtCjqTJ0/mscceo7a2tumvrT/+8Y9NnZRFRNqq3uXhrW35eIEz4sI1H5LJXZPaeFbnzS0H8Hi8BlcjZtbuua727t1LamoqlZWVJCUl8dVXX/GLX/yio+sTER/x0a6DlFTXE+xn4dKxMUaXI53swtHRhAXayS+r5YvsYqPLERNr13nhwMBA0tLSyMjIYMiQIfTt25ekpCRsNt0mKCJtl324io17SwA4e6ADh7+OJWYX6GfjsvGxrNyUyxtbDjA9sa/RJYlJtTnovPDCC/z5z3+mrq6uqX9OcHAwP/vZz7j++us7vEARMbeaeldTP42JCb2ID3cbXJF0lWtS41m5KZePdh2krKZewwhIp2jTpas333yTZ555hgcffJDPPvuMnTt3sn79eu677z7+9Kc/8dFHH3VWnSJiQl6vl7Xb8ymvbaBPsD8XjtZkwb5kTGw4o2LCqHd5+Nt2TfQpnaNNQee1117j8ccf5+qrr6Zv377Y7XaioqL40Y9+xCOPPMLKlSs7q04RMaGtuUfYVVCBzWLh2tQBBNh1ycrXzEs5NqaO7r6SztGmoJOTk8O55557wudmzpypkZFFpNUOV9bx3jcFAJw/KorYXg6DKxIjXJ4Ui7/dyreFFezMLze6HDGhNgUdi8WC3X7ibj3+/v44nc4OKUpEzM3l8bB6y34a3F4G9w1mWmKk0SWJQSKC/LlwdDQAq7/SSMnS8dp1e7mIyOn4v2+LKChz4vCzcXVyPFaNfuzTrklpHFPnb1/n42xQZ3TpWG2668rlcvG3v/2txefdbn1AReTkMosq2ZDZOG7KVRNiCXf4GVyRGG3qkD7ERjjIL6vlw50HuTwp1uiSxETaFHQiIyNZtmxZi8/36dPntAsSEfMqr21g9dGJHCcO6s2o/hr9WMBqtXB1Shx//CSTNVsPKOhIh2pT0PnnP//ZWXWIiMm5PV5Wf7Wfmno3MeGBXKLRj+UHrprQGHS+yC4mv6yW2Ah1TpeOoT46ItIlPtldRE5JDQF2K9dNHICfTYcf+bf43kFMGdw40efbmuhTOpCONCLS6b47WMn67w8DcEVSLH1CAgyuSLqjq4+OqbNm24GmkfdFTpeCjoh0qrKaet7c2tgvZ9Kg3oyLizC2IOm2LhoTTUiAndySGtL3lRpdjpiEgo6IdBqXx8Nfv8qjpt5N/wj1y5GTC/K3N31G1ujylXQQBR0R6TQffFPI/tIaAv2s/Ch1AHb1y5FTOHb56oOMQqrrXAZXI2ago46IdIqtuaVs3leKBZiXEq9+OdIqyQN7MSgymJp6N+syCo0uR0xAQUdEOtyBIzW883XjPFYzRvZjRHSYwRVJT2GxWJibfLRTsi5fSQdQ0BGRDlVV52LV5v24PF5GRodyzvB+RpckPcyVE2KxWGDzvlL2l9QYXY70cAo6ItJh3B4vr6fvp7y2gcgQf65O0TxW0nYx4Q6mDW2c6HXNVk30KadHQUdEOsz73xSwr7gaf7uVGyYNJNDPZnRJ0kNdfXSiz7e25ePxaEwdaT8FHRHpEBuzi//d+Tg5jn5hgUaXJD3YBaOiCA20k19Wy8a9JUaXIz2Ygo6InLbviyp5/5vGO2QuGB2tyTrltAX62ZhzRn8A3tyiy1fSfgo6InJaiiqcvJ6+Hy8wYUAEZyVGGl2SmMSxy1cf7jpIhbPB4Gqkp1LQEZF2q6pz8crGHOpcHhL6BHH5+Fgs6nwsHeSMuHCG9gvB2eDhg280po60j4KOiLRLg9vDqk25HKlpoHewP9dPGqiRj6VDWSwWrj46po4uX0l76agkIm3m8Xp5Y0seuUend5g/eSDBAXajyxITuiIpFpvVwrb9ZWQfrjK6HOmBFHREpE28Xi/vf1PIroIKbFYLN0waSJTusJJO0i8skLOH9QU0UrK0j4KOiLTJhsxiNh293ffq5DgG9w0xuCIxu2OXr97edgC3xtSRNlLQEZFW277/CB/uOgjAxWNjGBcXYWxB4hNmjIyiV5AfRRV1bMg8bHQ50sMo6IhIq2QWVfLWtsZLB9OGRjYN0S/S2fztVi4bHwvAm7p8JW2koCMip5RbUs2rm3PxeGFsbDgXjYk2uiTxMcdmNP+/XUWU12hMHWk9BR0ROamCslpWfJlDg9vLsKgQrk6J00Sd0uXGxIYzMiaMereHd3fkG12O9CAKOiLSokOVTl7+Yl/TgIDXTRyI3arDhhjj2FkdXb6StjDsiFVSUkJaWhopKSlMmjSJJUuW4HK5Trju+vXrmT17NuPHj2fWrFl8+umnJ1zvzTffZPjw4Z1ZtojPOFJTz8tf5FBd76Z/RCA3TknA366QI8a5fHx/7FYL3xwo57uDlUaXIz2EYUetxYsXExQUxIYNG1izZg0bN25kxYoVx62Xk5PDwoULueeee9iyZQsLFy5k8eLFFBUVNVsvMzOTpUuXdlH1IuZWXtvAi5/vo7y2gb6hAdw8dRCBfjajyxIf1yckgPNG9ANgzVaNlCytY0jQyc3NJT09nfvvvx+Hw0F8fDxpaWmsWrXquHXXrl1LSkoKM2fOxG63c/HFF5Oamsrq1aub1qmtreVnP/sZN954Y1fuhogpVdQ28MKGvZRW19MryI8FZw7SqMfSbRyb6HPt9nwa3B6Dq5GewJCjV2ZmJhEREURFRTUtGzJkCAUFBVRUVBAWFta0PCsri2HDhjXbfujQoezZs6fp8WOPPcY555zD1KlTeeaZZ9pVk9vtbvO6bdnGl6h9Tq4t7eP1ePF4PHg8XXNAr3Q28OIXOZRU1xPh8GPBmQmEBti67P09Xs+//++At/R4PeDt2jbsTB3dPqd8P0/jZ7A7fZenD+1Nn2B/iqvq+XR3ETNG9mt6TseelpmxbVq7L4YEnerqahwOR7Nlxx7X1NQ0CzonWjcwMJCamhoA3nnnHbKzs/n1r3/N1q1b211TRkZGl2zjS9Q+J3eq9rHZbLgCwsgvKKK2/sT91zpSTYOH976voczpIcTfwqwhAVSVFlFV2ulvfZz8/I65q6ZXSCD9Q6wUFhZSXVffIa/ZHXRU+5yKw99OUYiLkryKbvULcmqsnfe+r+fFT3fRp67guOd17GmZL7aNIUEnKCiI2traZsuOPQ4ODm623OFw4HQ6my1zOp0EBwezd+9ennzySVatWoXdfnq7MnbsWGy21vVBcLvdZGRktGkbX6L2Obm2tE9BmZPYKjt1rs79872qzsXaL3Ioc3oIC7Rzy7RB9A7279T3PBGP10N+fj6xsbFYLad/ZT000E5IcDAxMTGd3oZdoaPb51QC7Fai+vWhf8TATn+vtrgzppL3vv+CrYX1xCeOos/Rz6qOPS0zY9sc26dTMSToJCYmUlZWRnFxMZGRjaOrZmdnEx0dTWhoaLN1hw0bxq5du5oty8rKYsyYMXz00UdUVFRwxRVXAP8+jZWSksLDDz/M7NmzW12TzWZr8w+/Pdv4ErXPybWmfSxWC1arlc68o7uspp6XvsihuKqOsEA7t04fTJ+QgM57w5M5mkWsFivWDthpq8UKls5vwy7Twe1zKlarFYvV0u2+x6P6RzAuLpxvDpTz/jcHWTBtULPndexpmS+2jSFf/YSEBJKTk1m6dClVVVXk5eWxfPly5s6de9y6c+bMIT09nXXr1uFyuVi3bh3p6elcdtll3HnnnXz99dds2bKFLVu2NPXP2bJlS5tCjoivKq6q47nP9lJcVUeEw49bjAw5Im2gMXWktQz7G2fZsmW4XC5mzJjBvHnzmD59OmlpaQAkJSXx7rvvAo2dlJ9++mmeffZZUlNTWb58OU899RSDBg062cuLyCkcLHfy3Gd7KattIDLEn9vOGkykQo70EHPO6I+/zcruwgp25pcbXY50Y4bdMxoZGcmyZctO+Nz27dubPZ4+fTrTp08/5WtOmjSJ7777rkPqEzGzvNIaVnyZQ22Dm5jwQH48NYHQQD+jyxJptYggf84fHcUH3xSyZusBxsSGG12SdFNmuGotIm2w93AVL36xj9oGN/G9HNwybbBCjvRIxy5fvfN1PvUm6GwunUNBR8SH7CmsYMWXOdS7PAzpG8yCaYNw+PtWx0Qxj7MS+xIVFsCRmgb+sbvo1BuIT1LQEfEROw6U8ermXFweLyNjwrhxSgIBdoUc6blsVgtXTmg8q7NGnZKlBQo6Ij7gq32lvPFVHh4vjI+P4LqJA/Cz6esvPd+xy1f/+v4whyvrDK5GuiMd6URMbkPmYdZ+nY8XmDioN3OT47BZLUaXJdIhhvQNYcKACNweL2u/Pn6UZBEFHRGT8nq9/N+3Rfx950GgsT/DZWf0x2pRyBFzmZvcONHn29vy8Xq9Blcj3Y2CjogJebxePsgo5NPvDgFwwagoLhoTjUUhR0zo0jNiCPSzknmoiqwjDUaXI92Mgo6IyXi8XtZuy+fL7BIAZp/Rn3OG9zvFViI9V1igHxeNjgbgn/tqT7G2+BoFHRETcXk8/DV9P1v3H8FCY0fNKYP7GF2WSKc7dvnq8zwndQ3dZ6Z1MZ6CjohJ1Ls8vLopl50FFdisFq6bNIAJA3oZXZZIl5g6pA/9IwKpafDy8e5DRpcj3YiCjogJOBvcrPhyH98XVeFns3Dj5IGM7q8h8cV3WK0WrkyKBeAtjakjP6CgI9LDVde5ePHzfeSU1BBgt7LgzEEkRoUaXZZIlzsWdD7PLqGwXH11pJGCjkgPVlHbwPMb9pJfVkuQv41bpw9mYJ9go8sSMcTAPkGMivTD62281VwEFHREeqzS6nqe/SybQ5V1hAXaue2swfSPcBhdloihzh3U+B1Ys/WAxtQRQEFHpEcqqnDy3GfZHKlpoHewP7efNYR+oYFGlyViuClxgQT529hXXM3W3CNGlyPdgIKOSA+Tf6SW5zfspcLpol9oALedNZhewf5GlyXSLTjsVmaNaRxT580t6pQsCjoiPcq+4mpe+HwvNfVu4no5uG36YMIC/YwuS6RbmTuhsVPyBxmF1NS7DK5GjKagI9JDZB6qZMWX+6hzeRgUGcyCMwcRFGA3uiyRbic1oRcDegdRVefiw6NzvYnvUtAR6QF2F1bwysZcGtxehkWF8OOpCQT62YwuS6RbslgszE2OA+CNLXkGVyNGU9AR6eZ2HChj1eZc3B4vo/uHccPkgfjZ9NUVOZmrkuOwWGDT3lL2FVcbXY4YSEdLkW7so10HWbkxF48XxsWFc23qAOxWfW1FTiU2wsE5w/oC8Nf0/QZXI0bSEVOkm/pr+n7+5/3deIHkAb2YlxKPzWoxuiyRHuO6SQMBeHPrAepcmujTVynoiHRDf/kyhwfezsBL42SFV0yIxWpRyBFpi3OH9yU6LJDS6no+2lVkdDliEAUdkW7muc+yefjdXQBckxrPlUkKOSLtYbdZmZcaD8Brm3MNrkaMoqAj0o0891k2S9ftAWDheUO5+9whWBRyRNrt2tR4rEc7JWcfrjK6HDGAgo5IN/HChr1NIWfxzETuvWC4Qo7Iaeof4eDc4f0AdUr2VQo6It3AS5/v438+2A3AovOGsnjmMIMrEjGP6yYNABon+nQ2qFOyr1HQETHYX77M4bH3vwXgrnOH8NPzFXJEOtI5w/sREx7IkZoGPtqlkZJ9jYKOiIFWbspt6nh8x9lDuE+Xq0Q6nM1q4ZqjnZJXbdblK1+joCNikNfT83jobzsBuO2swfziIoUckc5yzdFOyen7Ssk6VGl0OdKFFHREDPDJvhp++U7jmZyfTBvEf80aoZAj0oliwh2cNyIKgFc36ayOL1HQEeli739TyDNbKgC4+cwEfnnJSIUckS5w45TGkZLf2nqAqjqXwdVIV1HQEelCn+45xL1vfoMXuDY1jl9dOkohR6SLTBsayeDIYCrrXKzdnm90OdJFFHREusjmvSXc8epWXB4vZ8YH8tic0Qo5Il3IarUw/+hZnVe+zMHr9RpckXQFBR2RLpBxoJyf/GULdS4P5w7vy6KJ4ZqgU8QAVyXHEeRvI/NQFRv3lhhdjnQBBR2RTpZZVMmNL22mqs7F5MG9+fOPxmNXyBExRFigH1ckxQLwypea/8oXKOiIdKK80hpueHEzR2oaOCMunBduSiXQz2Z0WSI+7cYpCQB8/O1BCspqjS1GOp2CjkgnOVTh5IYXN1NUUcewqBBW3DyRkAC70WWJ+Lzh0aFMHtwbjxde0wCCpqegI9IJKpwN3PTyV+SW1DCgdxArfzKJXsH+RpclIkfddPSszuvp+6lzaf4rM1PQEelgdS43d6zcyu7CCiJDAnj1J5OICgs0uiwR+YHzR0UREx5ISXU96zIKjS5HOpGCjkgH8ni83PfmN3yZXUKwv40VN6cyoE+Q0WWJyH+w26xcN7FxVvMV6pRsago6Ih1o6brdvLejALvVwjPzkxkTG250SSLSgmsnDsDfZmVHXhlbc0uNLkc6iYKOSAd5/rO9vPD5PgCeuPoMpif2NbgiETmZvqEBXJ7UH4AXNuwzuBrpLAo6Ih3gna/zWbJuNwD/ffEILj86ToeIdG8/mTYYgI92HWR/SY3B1UhnUNAROU1fZBVz35s7AFhw5iBunT7Y4IpEpLWGR4dy1rC+eLzw0hc6q2NGCjoip+HbggpuX7mVBreXS8fFaCZykR7olmmDAHhjSx7ltQ0GVyMdTUFHpJ2KKpz85C9fNU3t8OS8M7BqageRHmd6YiTDo0KpqXfzeroGEDQbBR2Rdqipd/GTv3xFYbmTIX2DefaGFALsmtpBpCeyWCz8ZHrjWZ0VX+TQ4PYYXJF0JAUdkTZye7wsev1rduZX0CfYn5d/PJHwID+jyxKR03DZ+P5EhgRwsMKpAQRNRkFHpI2WrtvNJ7uL8Ldbee7GFA0IKGICAXYbN00ZCMDzG/bi9XoNrkg6ioKOSBus3JTLi0fHynny6jNIHtjL4IpEpKNcP3kggX5WduZXsGmvBhA0CwUdkVb613eHeOTdXQDcd8EwZp/R3+CKRKQj9Q7256oJcQD87/psg6uRjqKgI9IKew5WcPdr23F7vFw1IY67zh1qdEki0gluP2sINquFz74/TMaBcqPLkQ6goCNyCocqnCx4+d+3kT9+5ViNlSNiUgP6BDHn6Nnapz/NMrga6QgKOiInUVPv4pZXtlBQ7mRwZDDP3JCMv11fGxEzSztnCAAf7jpIZlGlwdXI6dIRW6QFHo+Xn67+mm8OlNMryI+Xb04lIsjf6LJEpJMlRoVy4egoAJb/S311ejrDgk5JSQlpaWmkpKQwadIklixZgsvlOuG669evZ/bs2YwfP55Zs2bx6aefNj1XV1fHkiVLOOuss0hOTubqq69m06ZNXbUbYmK/+XAPH+0qwt/WeBv5wD7BRpckIl3k7nMTAXh3R4Em++zhDAs6ixcvJigoiA0bNrBmzRo2btzIihUrjlsvJyeHhQsXcs8997BlyxYWLlzI4sWLKSoqAuCJJ55g27ZtrF69mvT0dK6++mruuOMOCgoKuniPxExe27yf5z7bC8Dvrh5HakJvgysSka40Ni6cs4b1xe3x8sxnOqvTkxkSdHJzc0lPT+f+++/H4XAQHx9PWloaq1atOm7dtWvXkpKSwsyZM7Hb7Vx88cWkpqayevVqoPGMzqJFi4iJicFmszFv3jz8/f3ZtWtXV++WmMRn3x/moXd2AvDTmcO4bHyswRWJiBHuPnp35ZotBzhY7jS4GmkvuxFvmpmZSUREBFFRUU3LhgwZQkFBARUVFYSFhTUtz8rKYtiwYc22Hzp0KHv27AHgsccea/bcxo0bqaysZMSIEW2qye12t3ndtmzjS3py+3xXVEnaqm24PV4uH9+fu84Z1OH70Zb28Xq8eDwePB7fmHvH4/X8+/8O2GWP1wNe87RhR7fPKd/P0/gZ7Cnf5Y4+9iQPCCdlYC+25B7huc+yePDikR3yukboycfllrR2XwwJOtXV1TgcjmbLjj2uqalpFnROtG5gYCA1NcdfM/36669ZvHgxd999N/Hx8W2qKSMjo03rt3cbX9LT2qfM6eaBf5RQVedhVKQf1w52s2PHjk57v1O1j81mwxUQRn5BEbX1J+6/Zlb5+fkd8jq9QgLpH2KlsLCQ6rr6DnnN7qCj2udUHP52ikJclORV9KhfkB157LloAGzJhVWb9jO1dw29Anv25L097bjcEQwJOkFBQdTW1jZbduxxcHDzDp8OhwOns/kpQ6fTedx6b775JkuXLmXRokXcfPPNba5p7Nix2Gyt+wC73W4yMjLatI0v6YntU1vv5voX0zlc4yGhTxCv3D6ZXp10h1Vb2qegzElslZ06V88/G9EaHq+H/Px8YmNjsVpO/8p6aKCdkOBgYmJiTNGGHd0+pxJgtxLVrw/9IwZ2+nt1hM449pzh9fLevk3sOFDOhuIgfnVpzzyr0xOPy6dybJ9OxZCgk5iYSFlZGcXFxURGRgKQnZ1NdHQ0oaGhzdYdNmzYcf1tsrKyGDNmDNC4o48++igff/wxTz/9NFOnTm1XTTabrc0//PZs40t6Svt4PF7uf+trdhwoJyLIj5dvnkhkqOPUG56m1rSPxWrBarVi9ZWBII5mEavFirUDdtpqsYLFRG3Ywe1zKlarFYvV0iO+xz/U0cee+y4czvwX03k9PY/bzh5CbETnHx86S085LnckQ776CQkJJCcns3TpUqqqqsjLy2P58uXMnTv3uHXnzJlDeno669atw+VysW7dOtLT07nssssAePzxx/nss89466232h1yxLf99qPv+PvOg/jZLDx7QzKDInUbuYj827ShkUwa1Jt6t4en/pFpdDnSRob9jbNs2TJcLhczZsxg3rx5TJ8+nbS0NACSkpJ49913gcZOyk8//TTPPvssqampLF++nKeeeopBgwZRWlrKqlWrKC4u5tJLLyUpKanp37HtRU7mr+n7eebo5H2/nTuOSYP7GFyRiHQ3FouF+y8cDsCbWw+wr7ja4IqkLQy5dAUQGRnJsmXLTvjc9u3bmz2ePn0606dPP2693r17s3v37k6pT8xvQ+ZhHvxb423ki2YkckVSnMEViUh3lZLQm3OG9+Vf3x3mj598z5+uTTK6JGklM1y1Fmmz7w5WkvZq423kVyTF8tOZiUaXJCLd3H0XNJ7VeXdHAd8d1BxYPYWCjvicQ5VOFqz4iso6FxMTevObqzQbuYic2pjYcC4eG43XC09+/J3R5UgrKeiIT6mtd3PLX7aQX1bLoMhgnp2fTIDdt+5AEJH2+9n5w7Ba4ONvi9iRV2Z0OdIKCjriM9weL/f8dfu/ZyP/cSq9gjUbuYi03tB+oVye1DgtzNJ1u/F6vQZXJKeioCM+4/F1u/n423/PRp6g28hFpB3uvWA4AXYrm/eV8tGug0aXI6egoCM+YeXGHF74fB+g2chF5PTERji4/azBACxZt5s6V8+ZHsMXKeiI6X265xAPv9s4uvZ9F2g2chE5fbefPYSosADySmt5+Ysco8uRk1DQEVP7tqCCu1/bhscLc5PjuOvcoUaXJCImEBxg5+cXjgDgz//M4nBlncEVSUsUdMS0CstrWbDiK6rr3UwZ3IelV+g2chHpOFckxTIuLpyqOhe//7/vjS5HWqCgI6ZUXtPATS+lc7DCyZC+wTxzQzL+dn3cRaTjWK0WHrp0FACrv9rPtwUVBlckJ6Ijv5iOs8HNLa98xfdFVfQLDeAvCyYSHuRndFkiYkKpCb25ZFwMHi/8zwff6nbzbkhBR0zF7fGy6PXtfJVzhNBAO39ZMJG4XkFGlyUiJvbARSPwt1v5MruEd3cUGF2O/AcFHTENr9fLQ+/sbBor5/kbUxgZE2Z0WSJicvG9g1h0XuONDo+99y1lNfUGVyQ/pKAjprHsH1m8tnk/Fgv88drxTB7cx+iSRMRH3HbWEIZFhVBSXc/j6/YYXY78gIKOmMLr6fv5wyeNdz08Nmc0F4+NMbgiEfEl/nYrj185FoDVW/LYtLfE4IrkGAUd6fE+3nWQB9dmAHD3uUOZPyXB2IJExCclD+zN9ZMGAPDfazNwNmjE5O5AQUd6tI3ZJSx8fTseL8xLiePeC4YZXZKI+LCfXzSCvqEB7D1czfJ/ZRtdjqCgIz3Y9v1HuOUvX1Hn8jBzZD8NCCgihgt3+PHonNEA/O+/ssg6VGlwRaKgIz3StwUV3PRSOtX1bs4c2oc/XzcBu00fZxEx3qwx0cwY0Y8Gt5d739hBg9tjdEk+Tb8ZpMfJPlzF/Bc3U+F0kTywF8/NTyHQz2Z0WSIiAFgsFv7nijGEO/zYcaCcZf/INLokn6agIz1KXmkNN7ywmZLqekb3D+OlH6cSHGA3uiwRkWZiwh0svaLxLqynP83iq5xSgyvyXQo60mPkldZw7XObKCx3MrRfCK8smEi4Q1M7iEj3dMm4GK6aEIfHCz9d/TUVzgajS/JJCjrSIxwLOflltQyKDObVn0yiT0iA0WWJiJzUI3NGEd/bwYEjtTzyzi6jy/FJCjrS7f1nyHn91slEhwcaXZaIyCmFBvrxh3njsVrg7e35vKe5sLqcgo50a/8Zcv56m0KOiPQsKQm9uevcxrmwHlybwf6SGoMr8i0KOtJt5RRXN4WcwUdDTlSYQo6I9DyLZiSSNCCCCqeL21ZuoabeZXRJPkNBR7ql3YUVzH1mY1PIeV0hR0R6MD+blf+9PpnIkAD2HKzk52u+wev1Gl2WT1DQkW5na+4Rrnl2I8VVdYyKCeONO6Yo5IhIjxcdHsjy6ydgt1p4/5tCnt+w1+iSfIKCjnQrGzIPc8MLjYMBpgzsxeu3TSZSd1eJiElMHNSbh2ePAuA3f9/D55nFBldkfgo60m2syyjkJyu2UNvg5uxhfVn5k0kaJ0dETOeGyQO5OrlxfJ27X99GXqk6J3cmBR0xnNfr5dn12aSt2ka928MlY2N4/sYUHP6a1kFEzMdisfDry8dwRnwEZTUNLFjxFeU1GkywsyjoiKFcbg+//NtOHv/7HgB+PDWBZT9Kwt+uj6aImFegn41nb0gmOiyQzENV3LZyC3Uut9FlmZJ+m4hhqupc3PLKFlZt3o/FAr+6dBSPzBmNzWoxujQRkU4XHR7IyzenEhpgZ/O+Uu59Ywduj+7E6mgKOmKIA0dqmPfMRv713WEC/aw8c0MyC6YNMrosEZEuNTImjGfmJ+Nna7wT65d/y9Bt5x1MQUe63OeZxcx+6nO+LawgMsSf1bdN4cLR0UaXJSJiiDOHRvLHa5KwWuD19DyWfLBbYacD2Y0uQHyH1+vluc/28v8+3IPHC2Njw3lmfjKxEQ6jSxMRMdQl42KorhvHz9/6hhc+34fNZuGBi0ZgsehS/ulS0JEuUV3n4udvfcMH3xQCMDc5jv+5fAyBfrqzSkQEYF5qPLUNbh5+dxfPrt+L2+3lwUtGKuycJgUd6XQ788tZ9Nft7D1cjd1q4eE5o7lh0gB9eUVE/sNNUxOwWuChd3bxwuf7qG1w89hlY3STxmlQ0JFO4/F4efnLxktVDW4vUWEB/Pm6CaQm9Da6NBGRbmv+lARsVisP/i2DVZv3U1bTwO+vOYMAu86At4eCjnSKI043C17Zyoajw5tfMCqK/3fVOHoF+xtcmYhI93fdpAGEOez8dPXXfJBRSEl1Hc/ckExEkI6hbaW7rqRDeb1e3v+mkJ99XMKGzGIC/awsuWIMz85PVsgREWmDS8f15+UfTyQkwM6mvaVcsfxL9hVXG11Wj6OgIx2msLyWW1/Zwj2rd1BR52FEdCjv3T2N6ycNVH8cEZF2mJYYyZo7pxAb4WBfcTVz/vw5H+48aHRZPYqCjpw2j8fLqs25XPD7z/hk9yH8bBauGRXC2junkBgVanR5IiI92ojoMP5215lMGBBBpdPFHa9u5bH3vqXe5TG6tB5BQUdOy468Mq5+diMPrt1JZZ2L8fERvHvXVOaNDtF8VSIiHaRvaACrb5/CrdMbR5B/6Yt9XP3sRs183gr6TSTtUlTh5GdvfM1lT3/B1twjOPxs/OrSUbx151SG6SyOiEiH87NZefCSUTx/YwphgXZ25JVxybINvPN1vkZSPgkFHWmT6joXT/0jk3N+9y/e3pYPwJUTYvn0vnNYMG2QxnoQEelk54+K4oNF0zkjPoIKp4t7/vo1N6/4Smd3WqDby6VVaupdvLIxl+c+20tpdT0AEwZE8KvZoxkfH2FscSIiPia+dxBv3j6FZ9Zn8+d/ZvGv7w5zwR8+494LhnHzmfqj84cUdOSkaupdvLopl2fX76XkaMBJ6BPET88fxpwz+utuKhERg/jbrSyakcjFY2P477czSM8p5X8+2M3a7fk8MGsE0xP7Gl1it6CgIydUWF7LKxtzeW3zfsprGwAY2CeIheclcvn4/thtuuopItIdDO0Xwl9vm8xfv8rj8b/vZldBBfNfTOfMoX34+YUjOMPHz7or6EgTr9fL9rwyXv4ih3UZhbg9jZ3bBvYJ4q5zh3JFUix+CjgiIt2O1WrhukkDuGB0FE9/msWrm3L5IquEy7K+4OKx0aSdPdjoEg2joCMcqnTyzvYC1mw9wHdFlU3LJw/uzYIzBzFjZJSu94qI9ACRIQE8PHs0C84cxB8++Z612/NZl3GQdRkHGdPXn0UBh5g5KhqrDx3TFXR8VFWdi3/uOcQ72/P51/eHm87eBNitzD6jPzefmcDo/uEGVykiIu0R3zuI388bz+1nDeGpf2by950H2Xm4ntte3UZCnyBumprAZeNj6e0DU/Mo6PiQ0up6Pvm2iA93HeTzzGLq3f8eVXPCgAjmJsdzybgYwh1+BlYpIiIdZXh0KH++bgJ5JVU88e4W/plbR05JDY++9y1LPtjNOcP7ckVSHDNG9iPQz5yzoyvomFi9y8O2/Uf4PLOYDVnFZBwow/ODMaUGRQYza0w0V06IY2i/EOMKFRGRTtU/wsGN40L59TVT+NuOQt7YksfO/Ao+2X2IT3YfIjTQzrnD+zFjZD/OHtbXVLOkK+iYSKWzgR155WzNPcLW/UfYklNKTb272TqjYsK4aEw0F42JJrFfiG4PFxHxIcEBdm6cksCNUxLILKpk7fZ83vm6gPyyWt7dUcC7OwqwWmDCgF6cM7wvEwf1YVxceI8+26Og00OV1dTzbWEF3xZUsLuwkl0F5XxXVMl/jgLeJ9ifM4dGMi0xkumJkcSEO4wpWEREupXEqFB+ftEI7rtgONv2H+Efew7x6Z5D7DlYyZbcI2zJPQKAv83KuLhwUgf15oy4cEb3Dyeul6PH/KFsWNApKSnhoYceIj09HZvNxpw5c/jFL36B3X58SevXr+eJJ54gLy+PmJgYfv7zn3Puuec2Pf/888+zcuVKKioqGDt2LI8++iiDB/f8W+lq693kllaTU1zNvuKaxv9LGh8fqqw74TbxvR1MGNCL5IGN/0ZGh/lU73oREWkbq9VCSkJvUhJ684uLRnDgSA2ffneYjdnFpO87QnFVXbPgAxAaaGdUTBgjY8IY3DeYQZGN//qHO7rd7xzDgs7ixYuJiopiw4YNFBcXc+edd7JixQpuueWWZuvl5OSwcOFCfv/733POOefw8ccfs3jxYj7++GOioqJYu3YtK1eu5MUXX2TAgAH84Q9/YNGiRbz33nvdMm16PF4qnS7Kauspq2mgtKaeQxVODpbXcbDCSdEP/hVX1Z/0teJ7OxgVE8aomHBGxoQyfkAE/UIDu2hPRETEjOJ6BTF/8kDmTx6I1+slt6SG9JxStuYcYWdBOd8XVVLpdLF5Xymb95U22zbAbiW2l4PYCAf9wx30j3AwNi6Mc4f3M+x3siFBJzc3l/T0dD777DMcDgfx8fGkpaXxu9/97rigs3btWlJSUpg5cyYAF198MW+//TarV69m0aJFvPHGG1x33XUkJiYCcO+99/LGG2+wefNmJk+e3OX7dszuwgqe/2wvxdX1lNfUU1bbQFlNAxXOhuMuL51MuMOPhMhgEvoEkdCnMTEnRAYzuG8wYYG6O0pERDqPxWJp/B0UGcy8lHig8UaXrENVfFtYwfdFlew9XM2+4ir2l9ZQ5/Kw93A1ew9XN3ud9+6extg4Y4YsMSToZGZmEhERQVRUVNOyIUOGUFBQQEVFBWFhYU3Ls7KyGDZsWLPthw4dyp49e5qev/XWW5ue8/PzIyEhgT179rQq6Byb2r6+vh6brXWdrdxu9ym3Wb05h79nFBy3PNDWmGiD/G2EBdqJCPIjMiSQvqH+RIUF0C80gL6hgUSFBhAVFnCSnu9e6utPfsbHKK1pH1/WlvZxu1zYLR68Vs9J1zMLj8VDSIAdf5uHjjj7bbd48LrdpmnDjm6fU7FbGj+D3fVY85907GlZR7fN0MhAhkYGwth+Tctcbg+F5U4Kyp0UVtRSVFZHYYWTALuVhN4BHf45OrZP3lOcPTAk6FRXV+NwNO8Ue+xxTU1Ns6BzonUDAwOpqalp1fOn4vE0Hvy+/fbbtu3EKbaZHQ+z46NafP54bqCm8Z8T6p2Qdxjy2lxV99GeNvUlrW2fOAAfmnljSKw/cOSU67WKC2qLzNWGHdo+p+KF4v2HKe6ad+swOva0rCvaJggYYoUhvYHeAG6+372r097v2O/xlhgSdIKCgqitrW227Njj4ODgZssdDgdOp7PZMqfT2bTeqZ4/FbvdztixY7Fard2yT4+IiIgcz+v14vF4TngT0w8ZEnQSExMpKyujuLiYyMhIALKzs4mOjiY0NLTZusOGDWPXruZJMCsrizFjxjS9VmZmZtNdWA0NDeTk5Bx3uaslVqsVf3/zDIwkIiIi/2bIydyEhASSk5NZunQpVVVV5OXlsXz5cubOnXvcunPmzCE9PZ1169bhcrlYt24d6enpXHbZZQBcddVVvPrqq+zZs4e6ujqefPJJIiMjSUlJ6erdEhERkW7G4j1VL55OUlxczGOPPcbmzZuxWq1cfvnl3HfffdhsNpKSknj00UeZM2cOABs2bOCJJ55g//79xMbGcv/993P22WcDjaeuXn75ZVatWkVpaWnTODqDBg0yYrdERESkGzEs6IiIiIh0NpPchyAiIiJyPAUdERERMS0FHRERETEtBR0RERExLQWddqitreWaa67h7bffbrb8ueeeY/To0SQlJTX9+8Mf/mBQlcZpqX327dvHTTfdRFJSEtOmTeOZZ54xqMLuYceOHYwYMaLZ5+X66683uizDlJSUkJaWRkpKCpMmTWLJkiW4XC6jy+o21q1bx6hRo5p9Xu6//36jyzJcaWkp559/Pps3b25atmPHDq6++mqSkpI477zzePPNNw2s0DgnapuHH36YMWPGNPscrV692sAqO59hs5f3VJmZmfziF79g165dXHPNNc2e27lzJ3feeSd33323QdUZr6X2aWho4I477uD888/n+eefJysri9tvv52BAwcya9YsAys2TkZGBqmpqaxcudLoUrqFxYsXExUVxYYNGyguLubOO+9kxYoVx03066syMjK47LLLePzxx40updvYunUrDzzwAPv3729aVl5ezm233caiRYu45ppr+Oqrr7jrrrsYPnw448aNM7DarnWitoHGz9Gvf/1rrrjiCoMq63o6o9MGGzdu5KabbuKKK66gf//+xz2fkZHRNGKzLzpZ+3z11VccOnSIRYsW4e/vz6hRo5g/fz6rVq0yqFrj+frn5Ydyc3NJT0/n/vvvx+FwEB8fT1pamk9/Pv6TPi/NrV27lvvuu4+f/vSnzZZ//PHHREREcP3112O325kyZQqzZ8/2qc9SS21TX1/P999/73OfI53R+QGn00lRUdEJn+vbty8jRozg008/JSAggJdffrnZ8yUlJRQUFPDGG2/wy1/+En9/fy666CLuueceAgICuqL8Tnc67ZOZmcmgQYOaTbcxdOhQnnvuuU6t2Uinaq+MjAwiIyO54IILqKqqYuLEiTzwwANER0d3caXGy8zMJCIigqiof0+EO2TIEAoKCqioqGg20a8v8ng87Nq1C4fDwQsvvIDb7ebss8/mvvvuIzw83OjyDDFt2jRmz56N3W5v9gs9MzPzuCmAhg4dypo1a7q6RMO01DZ79uzB5XKxbNkytm7dSmhoKFdddRW33HILVqt5z3so6PzAjh07uPHGG0/43NNPP83MmTNb3Pbw4cOkpKRw5ZVX8sc//pG8vDwWL15MbW0tDz/8cGeV3KVOp31amrG+tbPM90Qna69ly5bRr18/pk6dyo9+9CMaGhr49a9/zW233cbatWux2WxdXK2xWvp8ANTU1Ph80CktLWXUqFFceOGFLFu2jCNHjvCLX/yC+++/39R/LJxM3759T7j8RJ+lwMBAUx9r/lNLbVNZWcnEiROZP38+v//979m9ezd33XUXVqvV1JeIFXR+YNKkSXz33Xft2nbEiBHNTo0OGTKEtLQ0HnnkEdMEndNpn5ZmrG/tLPM90ana68ILL2z2+KGHHmLKlClkZ2e3elJas2jp8wGY+jPSWpGRkc2OLw6Hg/vvv5958+ZRVVVFSEiIgdV1Lw6Hg8rKymbLnE6nPkfAmWeeyZlnntn0eNy4cdx0002sW7fO1EHHvOequlh6ejrPPvtss2X19fUEBgYaVFH3kpiYSE5OTrO7aLKyskhMTDSwKuMUFhby+OOPU11d3bSsvr4ewCc/M4mJiZSVlVFcXNy0LDs7m+joaEJDQw2srHvYs2cPTzzxBD+csae+vh6r1drscrDAsGHDyMzMbLbMl481P/TJJ5/w17/+tdkyX/g9paDTQRwOB0899RTvvfceHo+HzMxMli9fftydWb5q0qRJ9OrViyeffJK6ujr27NnDypUrTzhjvS/o1asXH3zwAX/4wx+oq6ujtLSURx99lClTpjBgwACjy+tyCQkJJCcns3TpUqqqqsjLy2P58uU++/n4TxEREaxatYoXXngBl8tFQUEBv/vd77jiiisUdP7D+eefT3FxMStWrKChoYFNmzbx3nvvcdVVVxldmuG8Xi+PP/44GzduxOv1sn37dl555RXT/55S0OkgY8eO5fe//z0vvPACycnJ/OQnP2H27NnccccdRpfWLdjtdl566SW+//57zjzzTG677Tbmz5/PlVdeaXRphggMDOSFF14gOzubadOmceGFFxISEsIf//hHo0szzLJly3C5XMyYMYN58+Yxffp00tLSjC6rW4iOjubZZ5/lH//4BxMnTuSqq65i7Nix/OpXvzK6tG6nV69evPTSS3z44YdMmjSJX/7yl/zyl79k8uTJRpdmuPPPP5//+q//4pFHHmkah2nhwoVcdtllRpfWqTR7uYiIiJiWzuiIiIiIaSnoiIiIiGkp6IiIiIhpKeiIiIiIaSnoiIiIiGkp6IiIiIhpKeiIiIiIaSnoiIiIiGkp6IiIiIhpKeiIiIiIaSnoiIiIiGkp6IiIiIhp/X+6q+AHoaD5XAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.distplot(np.array(y_test-y_pred))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6168eaff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IF THE STUDENT STUDIES FOR 9.25 HOURS PER DAY HE/SHE WILL SCORE [91.56986604]%.\n"
     ]
    }
   ],
   "source": [
    "h = 9.25\n",
    "s = reg.predict([[h]])\n",
    "print(\"IF THE STUDENT STUDIES FOR {} HOURS PER DAY HE/SHE WILL SCORE {}%.\".format(h,s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "df65586e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.5916495300630285\n",
      "R2 Score 0.971014141329942\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"Mean Absolute Error:\",metrics.mean_absolute_error(y_test,y_pred))\n",
    "print(\"R2 Score\",r2_score(y_test,y_pred))"
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}