task 2 linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GzfdMfk10NE6"
   },
   "source": [
    "## **Task 2**\n",
    "##### What will be predicted score if a student study for 9.25 hrs in a day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V9QN2ZxC38pB"
   },
   "outputs": [],
   "source": [
    "# Importing all libraries required in this notebook\n",
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2534,
     "status": "ok",
     "timestamp": 1544113345787,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "LtU4YMEhqm9m",
    "outputId": "5b4b36af-1545-497e-a6dc-7658bab71dbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data from remote link\n",
    "url = \"http://bit.ly/w-data\"\n",
    "data = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 718,
     "status": "ok",
     "timestamp": 1544113350499,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "qxYBZkhAqpn9",
    "outputId": "37264af1-786d-4e0c-a668-383264d1ddd1"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fiQaULio4Rzr"
   },
   "source": [
    "**From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WWtEr64M4jdz"
   },
   "source": [
    "### **Preparing the data**\n",
    "\n",
    "The next step is to divide the data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LiJ5210e4tNX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30,\n",
       "       24, 67, 69, 30, 54, 35, 76, 86], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.iloc[:, :-1].values  \n",
    "y = data.iloc[:, 1].values  \n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Riz-ZiZ34fO4"
   },
   "source": [
    "Now that we have our attributes and labels, the next step is to split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "udFYso1M4BNw"
   },
   "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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a6WXptFU5CkC"
   },
   "source": [
    "### **Training the Algorithm**\n",
    "We have split our data into training and testing sets, and now is finally the time to train our algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 701,
     "status": "ok",
     "timestamp": 1544113358086,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "qddCuaS84fpK",
    "outputId": "befbd977-772c-4bd1-bb48-ee5dd6bae73c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 985,
     "status": "ok",
     "timestamp": 1544113360867,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "J61NX2_2-px7",
    "outputId": "d20ec1fd-3e2d-4eae-84a2-a0df57d31009"
   },
   "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": [
    "# Plotting the regression line\n",
    "line = regressor.coef_*X+regressor.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.grid()\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JCQn-g4m5OK2"
   },
   "source": [
    "### **Making Predictions**\n",
    "Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 753,
     "status": "ok",
     "timestamp": 1544113366918,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "6bmZUMZh5QLb",
    "outputId": "8ea11a9e-c1b7-4fab-ab62-4dcbd2c8607b"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
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       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
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       "      <th>2</th>\n",
       "      <td>69</td>\n",
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       "      <th>4</th>\n",
       "      <td>62</td>\n",
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      "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": [
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 862,
     "status": "ok",
     "timestamp": 1544113370494,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
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    },
    "id": "KAFO8zbx-AH1",
    "outputId": "fcb3830f-3cda-4dcb-f122-84b71f101fae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Hours = [[9.25]]\n",
      "Predicted Score = 93.69173248737538\n"
     ]
    }
   ],
   "source": [
    "# You can also test with your own data\n",
    "hours = [[9.25]]\n",
    "own_pred = regressor.predict(hours)\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0AAsPVA_6KmK"
   },
   "source": [
    "### **Evaluating the model**\n",
    "\n",
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 834,
     "status": "ok",
     "timestamp": 1544113374919,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "r5UOrRH-5VCQ",
    "outputId": "7b9ddcf1-2848-408f-d81f-7a60652c381e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.18385989900298\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>17.053665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.694229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>74.806209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.842232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.123359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>39.567369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>20.969092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>86</td>\n",
       "      <td>78.721636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  17.053665\n",
       "1      27  33.694229\n",
       "2      69  74.806209\n",
       "3      30  26.842232\n",
       "4      62  60.123359\n",
       "5      35  39.567369\n",
       "6      24  20.969092\n",
       "7      86  78.721636"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.3, random_state=0) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.419727808027654\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Changing test data size and evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.499999999999998\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.3, random_state=42) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "0      20  17.185746\n",
      "1      76  69.583940\n",
      "2      25  28.829789\n",
      "\n",
      "Mean Absolute Error: 4.353367640871281\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.1, random_state=47) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print(df)\n",
    "print()\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "changing test size does not decreas the error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Changing random state and evaluating\n",
    "#### Random state ensures that the splits that you generate are reproducible. Scikit-learn uses random permutations to generate the splits. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "0      17  13.315725\n",
      "1      30  26.782388\n",
      "2      21  26.782388\n",
      "3      67  61.410950\n",
      "4      95  88.344276\n",
      "\n",
      "Mean Absolute Error: 4.985809772445338\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=32) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print(df)\n",
    "print()\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "0      81  83.188141\n",
      "1      30  27.032088\n",
      "2      21  27.032088\n",
      "3      76  69.633232\n",
      "4      62  59.951153\n",
      "\n",
      "Mean Absolute Error: 3.920751190209927\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=42) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print(df)\n",
    "print()\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "0      47  52.612655\n",
      "1      41  46.829599\n",
      "2      20  17.914320\n",
      "3      85  77.672564\n",
      "4      35  40.082701\n",
      "\n",
      "Mean Absolute Error: 5.187614275768402\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=102) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print(df)\n",
    "print()\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Actual  Predicted\n",
      "0      20  17.902485\n",
      "1      76  69.643392\n",
      "2      25  29.400464\n",
      "3      30  29.400464\n",
      "4      21  27.484134\n",
      "\n",
      "Mean Absolute Error: 3.9876515607532896\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=47) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print(df)\n",
    "print()\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## the best random state value is 42 which gives the least error that is 3.92"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  FINAL MODEL AND PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3.920751190209927\n",
      "No of Hours = [[9.25]]\n",
      "Predicted Score = 92.38611528261495\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=42) \n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) \n",
    "y_pred = regressor.predict(X_test) # Predicting the scores\n",
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred)) \n",
    "#  test with your own data\n",
    "hours = [[9.25]]\n",
    "own_pred = regressor.predict(hours)\n",
    "print(\"No of Hours = {}\".format(hours))\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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