the-sparks-foundations-internship-Predict-first-task-the-percentage-of-an-student-based-on-hr-study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "214112b9",
   "metadata": {},
   "source": [
    "# The spark Foundation\n",
    "## Predict the percentage of an student based on the no. of study hours.\n",
    "Importing Python llibrary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec76531a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8f5d952",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Data Successfully\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\Admin\\Desktop\\student_scores - student_scores.csv\")\n",
    "print(\"Importing Data Successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "737a5845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this we print first 10 data of Data-set \n"
     ]
    },
    {
     "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>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"For this we print first 10 data of Data-set \")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "258c3513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have correctly imported Data-set\n"
     ]
    }
   ],
   "source": [
    "print(\"You have correctly imported Data-set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4818cdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Hours',y='Scores',style='2')\n",
    "plt.title('Hours - Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbe4ee95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Scores'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.pie(x='Hours',y='Scores')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34bc1c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hours', ylabel='Scores'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VWiVHVC9tmaNv0io5YjBkxB99kVbJEYMjiT/6Iq2SIwZHEn/0RVolRwyOJP7oi7RKjhgcmdyNvkmr5IjBkMQffZVWyRHVS6knIqJlkvgjIlomiT8iomWS+CMiWiaJPyKiZUpL/JJOlvQtSfdKelDS7xTHz5K0RdIjkr4s6aSyYmii8ZqcVdUALY3XIuqlzOWcB4FLbR+QNBP4pqS/Bz4KfNr2lyT9GXAV8LkS42iM8ZqcVdUALY3XIuqntBG/Ow4UD2cWPwYuBW4pjq8HVpYVQ5OM1+SsqgZoabwWUU+l1vglTZe0HdgLbAK+C+y3fbh4yS5gzOGhpNWShiUNj4yMlBlmLYzX5KyqBmhpvBZRT6Umftsv2F4OLAYuAH5+AueutT1ke2jBggVlhVgb4zU5q6oBWhqvRdRTX1b12N4P3AVcCMyV1J1bWAzs7kcMdTdek7OqGqCl8VpEPcl2OW8sLQAO2d4vaTZwJ3ADsArYMGpy9z7bfzreew0NDXl4eLiUOOtm34GDx2xyNt5zVcUUEdWRtNX20NHHy1zVswhYL2k6nW8WN9v+qqQdwJck/S5wD7CuxBgaZ7wmZ1U1QEvjtYh6KS3x274PeOMYxx+lU++PAZZRfERzpS1zvELW5kc0W1o2xMtkbX5E8yXxx8tkbX5E8yXxx8tkbX5E8yXxN8hUNEvL2vyI5svkbkNM5YRsNkWPaLaM+EvQ7zbFZUzIzj91FucumZukH9FAGfFPsSqWQnYnZJ/jpdp8d0I2iTsijpYR/xSqailkJmQjYiKS+KdQVUshMyEbERORUs8UqnLknQnZiOhVRvxTqOqRdyZkI6IXGfFPsYy8I2LQJfGXIG2KI2KQpdRTM/2+RiAimicj/hpJu+SImAqljfglLZF0l6Qdkh6UdHVx/DRJmyQ9XNzOKyuGEzFoI+u0S46IqVLmiP8w8N9tb5P0KmCrpE3ArwObbV8vaQ2wBriuxDgmbBBH1rk6NyKmSmkjftt7bG8r7v8U2AmcAVwJrC9eth5YWVYMkzGoI+tcnRsRU6Uvk7uSltLZf3cLsND2nuKpJ4CFxzhntaRhScMjIyP9CBMY3I1Iqr5GICKao/TJXUmnAhuAa2z/RNKLz9m2JI91nu21wFqAoaGhMV9ThkEeWecagYiYCqWO+CXNpJP0v2D71uLwk5IWFc8vAvaWGcNEDfrIOlfnRsSJKm3Er87Qfh2w0/YfjHrqdmAVcH1xu7GsGCYrI+uIaLIySz0XAb8G3C9pe3Hs43QS/s2SrgIeB95bYgyTlqtvI6KpSkv8tr8J6BhPryjrc0fbd+BgRu0REUdp7JW7g7gWPyJiEDSyV8+grsWPiBgEjUz8g7oWPyJiEDQy8Q/yWvyIiKo1MvEP+lr8iIgqNXZyN2vxIyLG1tjED1mLHxExlkaWeiIi4tiS+CMiWiaJPyKiZZL4IyJaJok/IqJlZPdtj5NJkzRCp5NnL04HflRiOJM1iHENYkyQuCZiEGOCwYxrEGOCcuP6l7YXHH2wFol/IiQN2x6qOo6jDWJcgxgTJK6JGMSYYDDjGsSYoJq4UuqJiGiZJP6IiJZpYuJfW3UAxzCIcQ1iTJC4JmIQY4LBjGsQY4IK4mpcjT8iIsbXxBF/RESMI4k/IqJlGpP4Jf1fSXslPVB1LF2Slki6S9IOSQ9KurrqmAAknSzpW5LuLeL6napj6pI0XdI9kr5adSxdkr4n6X5J2yUNVx1Pl6S5km6R9G1JOyVdWHE8ry/+jro/P5F0TZUxdUn6reLf+gOSbpJ08gDEdHURz4P9/ntqTI1f0sXAAeD/2T6n6ngAJC0CFtneJulVwFZgpe0dFccl4BTbByTNBL4JXG37n6qMC0DSR4Eh4NW231l1PNBJ/MCQ7YG6+EfSeuBu2zdKOgmYY3t/xWEBnV/gwG7g39vu9eLLsmI5g86/8X9j+1lJNwN/Z/uvKozpHOBLwAXA88AdwH+z/Ug/Pr8xI37b/wD8uOo4RrO9x/a24v5PgZ3AGdVGBe44UDycWfxUPgKQtBh4B3Bj1bEMOkmvAS4G1gHYfn5Qkn5hBfDdqpP+KDOA2ZJmAHOAH1Yczy8AW2z/zPZh4BvAu/r14Y1J/INO0lLgjcCWikMBXiypbAf2AptsD0JcfwhcCxw5zuv6zcCdkrZKWl11MIWzgBHgL4vS2I2STqk6qFHeD9xUdRAAtncDvw98H9gDPG37zmqj4gHgLZLmS5oDvB1Y0q8PT+LvA0mnAhuAa2z/pOp4AGy/YHs5sBi4oPjqWRlJ7wT22t5aZRzH8Gbb5wFvAz5UlBWrNgM4D/ic7TcCzwBrqg2poyg7XQF8pepYACTNA66k88vydcApkv5TlTHZ3gncANxJp8yzHXihX5+fxF+yooa+AfiC7VurjudoRXngLuBXKg7lIuCKop7+JeBSSZ+vNqSOYsSI7b3AbXTqslXbBewa9U3tFjq/CAbB24Bttp+sOpDCZcBjtkdsHwJuBX6p4piwvc72+bYvBp4CHurXZyfxl6iYRF0H7LT9B1XH0yVpgaS5xf3ZwOXAt6uMyfbHbC+2vZROmeBrtisdlQFIOqWYmKcopbyVztf0Stl+AviBpNcXh1YAlS4aGOUDDEiZp/B94E2S5hT/J1fQmW+rlKTXFrdn0qnvf7Ffn92YzdYl3QRcApwuaRfwCdvrqo2Ki4BfA+4v6ukAH7f9d9WFBMAiYH2x8mIacLPtgVk+OWAWArd18gUzgC/avqPakF70YeALRWnlUeA3Ko6n+8vxcuC/Vh1Ll+0tkm4BtgGHgXsYjPYNGyTNBw4BH+rn5HxjlnNGRERvUuqJiGiZJP6IiJZJ4o+IaJkk/oiIlknij4homST+iIKkA0c9/nVJf1xVPBFlSeKPKFnRGCxiYCTxR/RA0lJJX5N0n6TNxdWWSPorSe8Z9boDxe0lku6WdDuwo7gC+G+LPRAekPS+iv4oEc25cjdiCswedYU1wGnA7cX9zwLrba+X9J+BPwJWHuf9zgPOsf2YpHcDP7T9DnixrXJEJTLij3jJs7aXd3+A/z3quQt5qZfKXwNv7uH9vmX7seL+/cDlkm6Q9BbbT09Z1BETlMQfcWIOU/w/kjQNOGnUc89079h+iM43gPuB35U0+pdKRF8l8Uf05h/pdA0F+CBwd3H/e8D5xf0r6Oxm9gqSXgf8zPbngd9jcFooRwulxh/Rmw/T2e3qf9DZ+arbCfMvgI2S7qWzocYzxzj/3wK/J+kInW6Mv1lyvBHHlO6cEREtk1JPRETLJPFHRLRMEn9ERMsk8UdEtEwSf0REyyTxR0S0TBJ/RETL/H+ME76D69CSzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.scatter(x='Hours',y='Scores')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27ad81c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Hours'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.bar(x='Hours',y='Scores')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7c7500e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1].values\n",
    "y = data.iloc[:, 1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23b67036",
   "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, \n",
    "                            test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6928325f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor = LinearRegression() \n",
    "\n",
    "regressor.fit(X_train, y_train) \n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6f6862e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predection of Score\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([16.88414476, 33.73226078, 75.357018  , 26.79480124, 60.49103328])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test\n",
    "print(\"Predection of Score\")\n",
    "y_pred = regressor.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "909940d2",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f312bacd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[54.04910656]\n"
     ]
    }
   ],
   "source": [
    "hours = [[5.25]]\n",
    "pred = regressor.predict(hours)\n",
    "if pred >= 100:\n",
    "    pred = 100\n",
    "print(pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0451f2dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) \n"
   ]
  }
 ],
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