Task#2-student_percentage_predictor .ipynb

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      "name": "student_percentage_predictor.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "95TOgy1jMZCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76alTRTHeS6U",
        "colab_type": "text"
      },
      "source": [
        "email:pooja.bennabhaktula@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JprUlx8kj8rQ",
        "colab_type": "text"
      },
      "source": [
        "### **SUPERVISED MACHINE LEARNING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNBYENiVdtV_",
        "colab_type": "text"
      },
      "source": [
        "####**Task2-Predicting scores of students based on hours of study using linear regression.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5Sa4e36MZXr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxL84w_YL90J",
        "colab_type": "text"
      },
      "source": [
        "### **Importing relevant libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ModzH8giGZmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "outputId": "e02e27e9-3243-477b-af15-5dddbb548576"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "url=\"http://bit.ly/w-data\"\n",
        "data_set=pd.read_csv(url)\n",
        "data_set.head(20)\n"
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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>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",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7.7</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.5</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3.3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8.9</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.9</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>6.1</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69</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\n",
              "10    7.7      85\n",
              "11    5.9      62\n",
              "12    4.5      41\n",
              "13    3.3      42\n",
              "14    1.1      17\n",
              "15    8.9      95\n",
              "16    2.5      30\n",
              "17    1.9      24\n",
              "18    6.1      67\n",
              "19    7.4      69"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "9NWsNmHEZrXx",
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          "height": 297
        },
        "outputId": "04628f23-1502-4dba-d838-8571a6a213ab"
      },
      "source": [
        "data_set.describe()"
      ],
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<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>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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdT9r-xlMerM",
        "colab_type": "text"
      },
      "source": [
        "###**Visualizing relationship between variables using matplotlib**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVIbRpo2IHnr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "79063c9b-a29f-4531-a1b3-f888f0bd63dc"
      },
      "source": [
        "\n",
        "plt.scatter(x=data_set.Hours,y=data_set.Scores)\n",
        "plt.title('Hours studied vs Scores obtained')\n",
        "plt.xlabel('Hours studied')\n",
        "plt.ylabel('Score Obtained')\n",
        "plt.show()\n"
      ],
      "execution_count": 327,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XSroHELA18Ik8G5c0Gjhw0PqzgPMkHQncB7y3pojNrON00pDH7WCtiT8iLpW0HbBDWnR7RDyTZ+MR8RSw2aBlj5D18jGzOmn3htHjD56yRo0f2nfI43aw1sQvaX3gOGDriPi4pO0kTYmI3xQfnpmtTSc0jHbSkMftIE+p5wxgEbBXut8P/Apw4jdrAdUaRtspcXbKkMftIE/j7jYR8U3gOYCIeJqs1m9mLcANo1arPIn/WUk9pJO5JG0D5Krxm1nxhmoAdcOoDSVP4j8BuBTYUtJZZOPrfKHQqMwst06aC9YaI0+vnvmSbgT2JCvxHBMRDxcemZnl4oZRq1XeOXfXAx5L6+8kiYi4uriwzKwWbhi1WuTpznkScBhwC/BCWhyAE7+ZWRvKc8Q/DZiS96QtMzNrbXkad+8BRhUdiJmZNUaeI/6ngSWSLqesG2dEfLawqMzMrDB5Ev/F6WJmZh0gT3fOOWtbx8yK1e6DsFlrqTb14nkR8V5JN1FhCsaIeG2hkZkZ0BmDsFlrqXbEf0y6fmcjAjGzyjplEDZrHUMm/tKE6BFxX+PCMbPBPAib1dtau3NK2lPSnyWtlPSspOclPdGI4MzMg7BZ/eXpx38q2by5dwI9wMeAH+TZuKQxkuZKul3SbZL2krSppPmS7kzXmww/fLP2MW9xP1NnLWDyzEuYOmsB8xb353qeB2GzesuT+ImIu4AREfF8RJwBvDXn9r8HXBoROwCvA24DZgKXR8R2ZCN9zqw9bLP2Umqg7V+xiuClBto8yX/abhM5cfouTBzTg4CJY3o4cfouru/bsOU6gUvSumQncX0TWEa+EtHGwJuBIwAi4lmysf0PBfZNq80BrgS+WGvgZu3kX22g9SBsVk95jvg/lNb7NPAUsCUwPcfzJgMDwBmSFks6TdJoYFyp4Rh4EBhX6cmSjpLUJ6lvYGAgx+7MWpcbaK2V5En80yLinxHxRER8LSKOI18Xz5HA64EfRcRuZF8aa5R1IiKocI5Aemx2RPRGRO/YsWNz7M6sdbmB1lpJnsQ/o8KyI3I8bymwNCIWpvtzyb4IHpI0HiBdL8+xLbO25gZaayXVztw9HHg/MFlS+Vg9GwGPrm3DEfGgpPslTYmIO4D9gVvTZQYwK11f9C/Eb9YWPEuWtZJqjbvXkTXkbg58u2z5k8Bfc27/M8BZqXH4HuAjZL8yzpN0JHAf8N5agzZrR26gtVZR7czd+8gS816SXgXsQVaPvyMiVufZeEQsAXorPLT/MGI1M7M6yNMt80jgBrKePO8Grpf00aIDMzOzYuTpx/8FYLeIeARA0mZkZaCfFRmYmZkVI0+vnkfI6volT6ZlZmbWhqr16jku3bwLWCjpIrIa/6Hkb9w1M7MWU63Us2G6vjtdStz90sysjVXr1fO10m1JG6RlKxsRlJmZFadq466kTwFfAkan+yuBkyLihw2IzawhPJ+tdZtqNf6vAG8C9o2Ie9KyVwPfk7RpRHy9QTGaFcbz2Vo3qtar50PA9FLSB0i33wt8uOjAzBqh2nDJZp2qWuKPiPhnhYWrgBeKC8mscTxcsnWjaom/X9LLhlaQtB/ZGD5mbc/DJVs3qta4+1ngIknXAovSsl5gKllffrO2d/zBU9ao8YOHS7bOV6075y2SdiYbmvk1afHVwCcqlYDM2pGHS7ZuVLU7Z0rwHpPHOpqHS7Zuk2esHjMz6yBO/GZmXSZX4pfUI6nm1i5J90q6SdISSX1p2aaS5ku6M11vUut2zcxs+PJMxPIuYAlwabq/66A5eNfmLRGxa0SUZuKaCVweEdsBl6f7ZmbWIHmO+L9KNu3iCnhxOsXJ/8I+DwXmpNtzgGn/wrbMzKxGeRL/cxHx+KBlkXP7AVwmaZGko9KycRFROgHsQWBcpSdKOkpSn6S+gYGBnLszM7O1yTP14i2S3g+MkLQd2Yld1+Xc/t4R0S/plcB8SbeXPxgRIanil0hEzAZmA/T29ub9ojEzs7XIc8T/GbITuJ4BzgYeB47Ns/GI6E/Xy4ELyUpGD0kaD5Cul9cetpmZDVfVxC9pBHBJRPxHRLwhXb6S58xdSaMlbVi6DRwE3AxcDMxIq83AM3qZmTXU2s7cfV7SC5I2rlDnX5txwIWSSvs5OyIulfRn4DxJRwL3kQ3zbGZmDZKnxr8SuEnSfOCp0sKI+Gy1J6Wx+19XYfkjwMtG/TRrB56tyzpBnsR/QbqYdTXP1mWdYq2JPyLmSFoX2D4tuiMinis2LLPWU222Lid+aydrTfyS9iU70epeQMCWkmZExNXFhmbWWjxbl3WKPKWebwMHRcQdAJK2B84Bdi8yMLNWM2FMD/0Vkrxn67J2k6cf/6hS0geIiL8Bo4oLyaw1HX/wFHpGjVhjmWfrsnaU54i/T9JpwJnp/geAvuJCMmtNnq3LOoUiqo+GIOkVwNHA3mnRNcAPI+KZgmN7UW9vb/T1+bvGzKwWkhaVjYz8ojxH/COB70XEd9KGRgCvqHN8ZmbWIHlq/JcD5a1XPcAfignHzMyKlifxrxcRK0t30u31iwvJzMyKlCfxPyXp9aU7knYH3HHZzKxN5anxHwv8StIDZCdwvQo4rNCozMysMHmGbPizpB2AUmdlD9nQZTwwmVlnGbLUI+kNkl4FkBL964FvAN+WtGmD4rMmKw1M1r9iFcFLA5PNW9zf7NDMbJiq1fh/AjwLIOnNwCzgF2QzcM0uPjRrBdUGJmtl8xb3M3XWAibPvISpsxb4i8qsTLVSz4iIeDTdPgyYHRHnA+dLWlJ8aNYK2nFgMg+fbFZdtSP+EZJKXwz7AwvKHsvTKAxkJ3xJWizpN+n+ZEkLJd0l6ZdpyGdrUUMNQNbKA5O1668Us0aplvjPAa6SdBFZ981rACRtS1buyesY4Lay+ycBp0TEtsBjwJE1RWwN1Y4Dk7XjrxSzRhoy8UfEN4DPAz8H9o6XBvVZB/hMno1L2gJ4B3Baui9gP2BuWmUOMG04gVtjTNttIidO34WJY3oQMHFMDydO36WlSybt+CvFrJHWNtn69RWW/a2G7X8X+AKwYbq/GbAiIlan+0uB1s0gBmTJv5UT/WDHHzxljRo/tP6vFLNGynPm7rBIeiewPCIWDfP5R0nqk9Q3MDBQ5+isk7XjrxSzRsrdSDsMU4FDJL0dWA/YCPgeMEbSyHTUvwVQsZ9dRMwmdRvt7e2tPna02SDt9ivFrJEKO+KPiC9FxBYRMQl4H7AgIj4AXAG8O602A7ioqBjMzOzlCkv8VXwROE7SXWQ1/9ObEIOZWdcqstTzooi4Ergy3b4H2KMR+zUzs5drxhG/mZk1kRO/mVmXceI3M+syTvxmZl2mIY27ZuAJXcxahRO/NYSHSjZrHS71WEN4qGSz1uHEbw3hoZLNWocTvzWEh0o2ax1O/NYQ7Tihi1mncuOuNUSpAde9esyaz4nfGsZDJZu1Bpd6zMy6jBO/mVmXceI3M+syTvxmZl3Gid/MrMsU1qtH0nrA1cAr0n7mRsQJkiYD55JNu7gI+FBEPFtUHJ2k2iBnzRoAzQOvmbWfIrtzPgPsFxErJY0CrpX0O+A44JSIOFfSj4EjgR8VGEdHqDbIGdCUAdA88JpZeyqs1BOZlenuqHQJYD9gblo+B5hWVAydpNogZ80aAM0Dr5m1p0Jr/JJGSFoCLAfmA3cDKyJidVplKVDx0FDSUZL6JPUNDAwUGWZbqDbIWbMGQPPAa2btqdDEHxHPR8SuwBbAHsAONTx3dkT0RkTv2LFjC4uxXVQb5KxZA6B54DWz9tSQXj0RsQK4AtgLGCOp1LawBdDfiBjaXbVBzpo1AJoHXjNrT0X26hkLPBcRKyT1AAcCJ5F9AbybrGfPDOCiomLoJHkGOWt07xoPvGbWnhQRxWxYei1Z4+0Isl8W50XEf0l6NVnS3xRYDHwwIp6ptq3e3t7o6+srJE4zs04laVFE9A5eXtgRf0T8FditwvJ7yOr91qLcN9+ss3lYZluD++abdT4P2WBrcN98s87nxG9rcN98s87nxG9rcN98s87nxN8h5i3uZ+qsBUyeeQlTZy1g3uLhnR7hvvlmnc+Nux2gng2y7ptv1vmc+OusGV0hqzXIDmffnhTdrLM58ddRs7pCukHWzGrhGn8dNasrpBtkzawWTvx11KwjbzfImlktnPjrqFlH3tN2m8iJ03dh4pgeBEwc08OJ03dxnd7MKnKNv46OP3jKGjV+aNyRtxtkzSwvJ/46cldIM2sHTvx15iNvM2t1TvxtxMMlm1k9OPG3CQ+XbGb1UlivHklbSrpC0q2SbpF0TFq+qaT5ku5M15sUFcNw1Wvcm3rycMlmVi9FdudcDXw+InYC9gSOlrQTMBO4PCK2Ay5P91tG6ci6f8UqgpeOrJud/H12rpnVS2GJPyKWRcSN6faTwG3AROBQsrl4SdfTiophOFr1yNpn55pZvTTkBC5Jk8jm310IjIuIZemhB4FxQzznKEl9kvoGBgYaESbQukfWPjvXzOql8MQvaQPgfODYiHii/LGICCAqPS8iZkdEb0T0jh07tugwX9SqR9Y+O9fM6qXQXj2SRpEl/bMi4oK0+CFJ4yNimaTxwPIiY6hVM8++XRufI2Bm9VBkrx4BpwO3RcR3yh66GJiRbs8ALioqhuHwkbWZdTpl1ZYCNiztDVwD3AS8kBZ/mazOfx6wFXAf8N6IeLTatnp7e6Ovr6+QOM3MOpWkRRHRO3h5YaWeiLgW0BAP71/Ufkt8lquZWWUdeeauz3I1MxtaR47H36p98c3MWkFHJv5W7YtvZtYKOjLxt2pffDOzVtCRid9nuZqZDa0jG3c9E5aZ2dA6MvGDz3I1MxtKR5Z6zMxsaE78ZmZdxonfzKzLOPGbmXUZJ34zsy5T2Oic9SRpgGwkzzw2Bx4uMJzhasW4WjEmcFy1aMWYoDXjasWYoNi4to6Il81k1RaJvxaS+ioNQ9psrRhXK8YEjqsWrRgTtGZcrRgTNCcul3rMzLqME7+ZWZfpxMQ/u9kBDKEV42rFmMBx1aIVY4LWjKsVY4ImxNVxNX4zM6uuE4/4zcysCid+M7Mu0zGJX9LPJC2XdHOzYymRtKWkKyTdKukWScc0OyYASetJukHSX1JcX2t2TCWSRkhaLOk3zY6lRNK9km6StERSX7PjKZE0RtJcSbdLuk3SXk2OZ0p6j0qXJyQd28yYSiR9Lv2t3yzpHEnrtUBMx6R4bmn0+9QxNX5JbwZWAr+IiJ2bHQ+ApPHA+Ii4UdKGwCJgWkTc2uS4BIyOiJWSRgHXAsdExPXNjAtA0nFAL7BRRLyz2fFAlviB3ohoqZN/JM0BromI0yStC6wfESuaHRdkX+BAP/DGiMh78mVRsUwk+xvfKSJWSToP+G1E/LyJMe0MnAvsATwLXAp8MiLuasT+O+aIPyKuBh5tdhzlImJZRNyYbj8J3AY0fZKAyKxMd0elS9OPACRtAbwDOK3ZsbQ6SRsDbwZOB4iIZ1sl6Sf7A3c3O+mXGQn0SBoJrA880OR4dgQWRsTTEbEauAqY3qidd0zib3WSJgG7AQubG0kmlVSWAMuB+RHRCnF9F/gC8EKzAxkkgMskLZJ0VLODSSYDA8AZqTR2mqTRzQ6qzPuAc5odBEBE9APfAv4BLAMej4jLmhsVNwP7SNpM0vrA24EtG7VzJ/4GkLQBcD5wbEQ80ex4ACLi+YjYFdgC2CP99GwaSe8ElkfEombGMYS9I+L1wNuAo1NZsdlGAq8HfhQRuwFPATObG1ImlZ0OAX7V7FgAJG0CHEr2ZTkBGC3pg82MKSJuA04CLiMr8ywBnm/U/p34C5Zq6OcDZ0XEBc2OZ7BUHrgCeGuTQ5kKHJLq6ecC+0k6s7khZdIRIxGxHLiQrC7bbEuBpWW/1OaSfRG0grcBN0bEQ80OJDkA+HtEDETEc8AFwJuaHBMRcXpE7B4RbwYeA/7WqH078RcoNaKeDtwWEd9pdjwlksZKGpNu9wAHArc3M6aI+FJEbBERk8jKBAsioqlHZQCSRqeGeVIp5SCyn+lNFREPAvdLmpIW7Q80tdNAmcNpkTJP8g9gT0nrp//J/cna25pK0ivT9VZk9f2zG7XvjplsXdI5wL7A5pKWAidExOnNjTDBjx8AAANHSURBVIqpwIeAm1I9HeDLEfHbJsYEMB6Yk3perAOcFxEt032yxYwDLszyBSOBsyPi0uaG9KLPAGel0so9wEeaHE/py/FA4BPNjqUkIhZKmgvcCKwGFtMawzecL2kz4Dng6EY2zndMd04zM8vHpR4zsy7jxG9m1mWc+M3MuowTv5lZl3HiNzPrMk781pYkrRx0/whJpzYrnhTDvpJqPjFI0s8lvTvdPk3STjXu011xrSYd04/frB4kjUyDZg3HvmQjxF433P1HxMeG+1yzvHzEbx1H0iRJCyT9VdLl6czINY6s0/2V6XpfSddIuhi4NZ2te0mar+BmSYdV2Mdn0zwLf5V0bhqE75PA59JY9PtU2Z8knSrpDkl/AF5Zts6VknrT7YMk/UnSjZJ+lcZ8QtJblY3BfyMNHNHROoeP+K1d9ZSdDQ2wKXBxuv0/wJyImCPpo8D3gWlr2d7rgZ0j4u+S/h14ICLeAS8OgTzYTGByRDwjaUxErJD0Y2BlRHwrPe/IIfb1f4ApwE5kZwbfCvysfAVJmwNfAQ6IiKckfRE4TtI3gZ8C+wF3Ab9cy+syexkf8Vu7WhURu5YuwH+WPbYXL4178r/A3jm2d0NE/D3dvgk4UNJJkvaJiMcrrP9XsuESPkg2DEAt3gyck0ZIfQBYUGGdPcm+GP6YvuBmAFsDO5ANOHZnZKfdt8RAdtZenPitm6wm/c1LWgdYt+yxp0o3IuJvZL8AbgK+Lqn8S6XkHcAP0np/ThN81LK/tRHZPAmlL7edImKoXxBmNXHit050HdkInwAfAK5Jt+8Fdk+3DyGbeexlJE0Ano6IM4GTGTTccUriW0bEFcAXgY2BDYAngQ3LVh1qf1cDh6XJcMYDb6kQxvXAVEnbpn2OlrQ92SiqkyRtk9Y7vPJbYDY01/itE32GbGaq48lmqSqNWvlT4CJJfyGb/OKpIZ6/C3CypBfIRk781KDHRwBnptq/gO+nGv+vgbmSDk0xDLW/C8lq9LeSDRn8p8EBRMSApCOAcyS9Ii3+SkT8TdksYJdIeprsS23Dwc83q8ajc5qZdRmXeszMuowTv5lZl3HiNzPrMk78ZmZdxonfzKzLOPGbmXUZJ34zsy7z/wHnP3xrg8oYrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DYSnlVGoP46p",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing relationship  between variables using Seaborn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKje-S5hP4Bd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "112522fe-a6f0-41c0-8ef5-d521e766e498"
      },
      "source": [
        "sns.regplot(x=\"Hours\",y=\"Scores\",data=data_set)\n",
        "b=sns.PairGrid(data_set)\n",
        "b.map(plt.scatter)"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fd5dc325780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tdjVoL1myVDk2LW+1S9BesmSpcmxa3mqXoL1kyVLl2LS81S5Be8mSpcqxaXmr3UFCL1myVDk2LW+1S9DgpXSWLsem5al2JQ4zs6ZwgjYzS5QiouoxDCRpEfhOyW/7C8AdJb/ncnmMw7sjIs7I+0ULiM1UPq9BPM789IzNWiToKkiaj4i5qsfRj8c4eeryeXmc5XCJw8wsUU7QZmaJcoLubWvVAxiCxzh56vJ5eZwlcA3azCxRnkGbmSXKCdrMLFFO0GZmiXKCNjNLVC0S9BlnnBGAL76McymEY9OXHC491SJB33FH6js1rakcm1akWiRoM7MmcoI2M0tULRv2W3Nt37ngM5ZYbYwbr07QVhvbdy7w2kt2sXfffgAW9uzltZfsAnCStuTkEa8ucVhtnHfZTQeCfcneffs577KbKhqRWW95xKsTtNXGbXv2Lut+syrlEa9O0FYbx89ML+t+syrlEa9O0FYbm9adxPSKqYPum14xxaZ1J1U0IrPe8ohXHyS02lg6sOJVHFYHecSrE7TVyvq1s07IVhvjxqtLHGZmiXKCNjNLlBO0mVminKDNzBLlBG1mliiv4rDacwMlK0vZseYEbbXmBkpWlipizSUOqzU3ULKyVBFrTtBWa26gZGWpItZc4rBSFFW7O35mmoUufyBuoGR5WYrdXmd3LTLWPIO2wi3V7hb27CW4v3a3fefC2K/tBkpWpPbY7aboWHOCtsL92T9dV1jtbv3aWbZsWMPszDQCZmem2bJhjQ8Q2ti271zg1R/9yiGxu6SMWHOJwwq1fecCd96zr+vv8qrduYGS5W1p5rw/uhc2BFy1+fTCx+EZtBWq3yzZdWJLVbcVG+3Kil0naCtUv1my68SWqn5xW+YxDidoK1SvmcbM9AqXJSxZveJ2Sir1GIcTtBWq1yqLNz77kRWNyGywXnH79nN+vdSJhQ8SWqF8miqro1Ti1gnaCjfsKgs3PbIq9Iq7FFYHFVrikPQHkq6T9DVJH5Z0pKQTJV0t6ZuSPiLpiCLHYPVQ5GYWs15Sj7vCErSkWeAVwFxEPAqYAp4HvBV4Z0Q8HLgTeFlRY7D6cNMjq0LqcVf0QcLDgWlJhwMrgd3A6cDF2e8vBNYXPAarATc9siqkHneFJeiIWADeBnyXVmK+C9gB7ImIe7OH3Qq4yGg9lzV5M4sVKfW4K7LEcTRwFnAicDzwAOCMZTx/o6R5SfOLi4sFjdJSUaemR47NyZF63BVZ4ngqcHNELEbEPuAS4FRgJit5ADwE6FqNj4itETEXEXOrVq0qcJiWgjo1PXJsTo7U467IZXbfBR4vaSWwF3gKMA9cCTwH+AfgRcDHCxyD1UgKy5qseVKOuyJr0FfTOhh4DbAre6+twGuAP5T0TeDngQ8WNQYzszordKNKRLwBeEPH3d8GHlvk+1pxvJnE6qTu8eqdhDY0n0Hb6mQS4tXNkmxoZS7q375zgVPPvYITN3+SU8+9IpmdXVYPvc6GktImlGF4Bm1DK2tR/yTMfKw6g86GksomlGF4Bm1DK2tRf+rbby1tqZwNJQ9O0Da0shb1p7791tKWytlQ8uAEbUMra1F/6ttvLW2pnA0lD65B27KUsah/07qTDqpBQ/1mPladXvFTt+QMTtCWoFTOZmH1NEnx4wRtSUp5+62lb1LixzVoM7NEOUGbmSXKCdrMLFGuQVuu6t6cxtLWtPhygrbceIu2FamJ8eUSh+XGW7StSE2MLydoy423aFuRmhhfLnE0WN71vONnplno8sfiLdo2jqU47d6bbrLjyzPohlqq5y3s2Utwfz1vnL7LqZ8h2eqnPU67mfT4coJuqCLqeamfIdnqp1/r0CbEl0scDVVUPW9StthaGnrFo4CrNp9e7mAq4Bl0Q7mlp9VB0+PUCbqhXC+2Omh6nLrE0VCT1JLRJlfT49QJusFcL7Y6aHKcusRhZpaooWbQkh4G3BoRP5V0GvBrwN9GxJ4iB2f5a1qzGUufY7K3YWfQ24D9kh4ObAVOAP6+sFFZIYrYnGI2Dsdkf8Mm6Psi4l7gt4D3RMQm4LjihmVFaGKzGUubY7K/YRP0PknPB14EfCK7b8WgJ0makXSxpBsl3SDpCZKOkfRZSd/Ifh496uBteZrYbMbS5pjsb9hVHC8Bfhd4c0TcLOlE4O+GeN75wKcj4jmSjgBWAq8DLo+IcyVtBjYDrxlh7NZDr5qemxlZCtrj8zCJ/XFoGyTHZMtQM+iIuJ5WEr0mu31zRLy133MkPRh4EvDB7Dk/yw4qngVcmD3sQmD9aEO3bvrV9Jq+6N+q1xmf3ZKzY/J+QyVoSc8CrgU+nd1+tKRLBzztRGARuEDSTkkfkPQA4NiI2J095vvAsaMN3brpV9NzMyOrWq/mR1OSY7KLYUscbwQeC3wOICKulfTLQ7z2KcDLI+JqSefTKmccEBEhqWubV0kbgY0Aq1evHnKYNqim1+RF/3lxbI6uV3zeF8HN555Z8mjSN/RBwoi4q+O++wY851Zaa6evzm5fTCth/0DScQDZz9u7PTkitkbEXETMrVq1ashhWtOby5TBsTk6x+fyDJugr5P0n4EpSY+Q9B7gC/2eEBHfB74naamY9BTgeuBSWqtByH5+fPnDtl5cZ7aUOT6XZ9gSx8uBPwF+SmuDymXAm4Z83kXZCo5v01oNchjwUUkvA74DnLPcQVtvTW8uY2lzfC6PostR1IMeIE0B/xwRTy5nSIeam5uL+fn5qt7eJoOKeFHHpuWgZ2wOLHFExH7gvmzZnJmZlWTYEsdPgF2SPgvcvXRnRLyikFGZmdnQCfqS7GJmZiUZKkFHxIXZgb5fye66KSL2FTcs6+SWjJY6x2j+hu0HfRqtbdm30CponyDpRRHxf4sbmi1Z2h67tANrafs24D8AS4JjtBjDroN+O/D0iPgPEfEkYB3wzuKGZe3cktFS5xgtxrAJekVEHPikI+LrDNFu1PLhloyWOsdoMYZN0PNZs6PTssv7AS/+LIm3x1rqHKPFGDZB/x6tbdqvyC7XZ/dZCbw91lLnGC3GsMvsDgfOj4h3wIHdhT9X2KjsIN4ea6lzjBZj2AR9OfBUWhtWAKaBzwC/WcSg7FBuE2qpc4zmb9gSx5ERsZScya6vLGZIZmYGwyfouyWdsnRD0hzgw7NmZgUatsTxKuBjkm7Lbh8HPLeYIZmZGQyYQUv6DUm/FBFfBk4GPgLso3VuwptLGJ+ZWWMNKnH8FfCz7PoTgNcB/wu4E9ha4LjMzBpvUIljKiJ+mF1/LrA1IrYB2yRdW+zQbBA3p7EiOb6qNzBBSzo8Iu6ldU7Bjct4rhXIzWmsSI6vNAwqcXwY+D+SPk5r1cbnASQ9HOg8y7eVyM1prEiOrzT0nQVHxJslXU5r1cZn4v4TGB5G64SwVhE3p7EiOb7SMLBMERFf6nLf14sZjg3r+JlpFrr8sbg5jeXB8ZWGYTeqWGLcnMaK5PhKgw/01ZSb01iRHF9pcIKuMTensSI5vqrnEoeZWaI8g86BF/RbHTlu0+cEPSYv6Lc6ctzWg0scYypiQf/2nQuceu4VnLj5k5x67hVs37kw7jDNDti+c4FXf/Qr3ohSA4UnaElTknZK+kR2+0RJV0v6pqSPSDqi6DEUKe8F/Uszm4U9ewnun9k4SVseluJr/4E9ZwfzRpS0lDGDfiVwQ9vttwLvjIiH0+qK97ISxlCYvM9m7C22VqRu8dXOG1HSUmiClvQQ4EzgA9ltAacDF2cPuRBYX+QYipb3gn5vsbUi9Ysjb0RJT9Ez6HcBfwzcl93+eWBP1h0P4Fag6xEJSRslzUuaX1xcLHiYo1u/dpYtG9YwOzONgNmZabZsWDPygZa8Z+SWv7rEZje94mhKGiturRiFJWhJzwRuj4gdozw/IrZGxFxEzK1atSrn0eVr/dpZrtp8OjefeyZXbT59rCD3Ftv01Sk2O/WKr7ef8+tOzgkqcpndqcCzJT0DOBJ4EHA+MNPWY/ohgI9+tfEWWyuS46teFD2O5ub6JtJpwB9FxDMlfQzYFhH/IOkvga9GxHv7PX9ubi7m5+cLH6dNNBXxoo5Ny0HP2KxiHfRrgD+U9E1aNekPVjAGM7PklbKTMCI+B3wuu/5t4LFlvK+ZWZ15J6GZWaLci6MEbkpjZXCcTR4n6IK5KY2VwXE2mVziKJi3blsZHGeTyQm6YN66bWVwnE0mJ+iCeeu2lcFxNpmcoAvmrdtWBsfZZPJBwoJ5a62VwXE2mZygS+CzI1sZHGeTxyUOM7NEeQbdoX2x/4OnVyDBnnv2Df2V0ZsFrAgveP8XuepbPzxw+9SHHcNF//UJFY7IyuAZdJvO8wHu2buPO+/ZN/S5AX0+QStCZ3IGuOpbP+QF7/9iRSOysjhBtxl0vrZBC/+9WcCK0JmcB91vk8MJus0wi/r7PcabBcwsT07QbYZZ1N/vMd4sYGZ58kHCNpvWnXRQw5lO3Rb+dx5UXDEl9u2Pvs8x66fzQPMjfvEBfOP2uw953KkPO6aC0VmZnKDbdC72H7SKo7OD2J69+1hxmDh65YplrfwwW9KtK930iqlDkrRXcTSDE3SH5Sz273ZQcN99wcojDmfnnz69iOHZhOt1oPmen93HLeeeWdGorCquQY/BBwUtb44pa+cZdB+DNp0cPzPNQpc/HB8UtOVairXo8XvHVDN5Bt3DMJtO3EHM8tAea904ppqrMTPo5W7B7rfpZOl57iBm49q+c4FXf/Qr7I/uc+dZx1SjNSJBj3K+tmFrge4gZqNaisteyVnAVZtPL3dQlpRGlDhG2YLtTSdWtEGtBRxr1ogEPcqRcdeXrWj94s+xZtCQBD3KbHj92lm2bFjD7Mw0olUL3LJhjcsZlpte8TclOdYMaEgNutsW7l4zFPdztqJ0xtaTT17Fth0Lh8Slk7MtaUSCHna1xSgHE82G0S22tu1Y4OzHzHLljYueEFhXhSVoSScAfwscCwSwNSLOl3QM8BHgocAtwDkRcWde79trBjzMaothltaZjeKNl17XNbauvHHRKzWspyJr0PcCr46IXwUeD/y+pF8FNgOXR8QjgMuz27kY94wm3mZrRdi+c4E9e/d1/Z1jy/opLEFHxO6IuCa7/mPgBmAWOAu4MHvYhcD6vN5z3DOaeGmdFWGU5ZxmUNIqDkkPBdYCVwPHRsTu7Fffp1UC6facjZLmJc0vLi4O9T7jzoC9tM6GsdzYHLSc06yXwhO0pAcC24BXRcSP2n8XEQHd+8NExNaImIuIuVWrVg31XuPOgL20zoax3NjsFX9Hr1zh2LK+Cl3FIWkFreR8UURckt39A0nHRcRuSccBt+f1fstZTteLt25b3nrF5Rue9cgKR2V1UOQqDgEfBG6IiHe0/epS4EXAudnPjy/3tfut1AA3L7Lq9FtH77i05VL0aNQy9gtLTwQ+D+wC7svufh2tOvRHgdXAd2gts+t7/vi5ubmYn58HDl1PCl7cb0NRES/q2LQc9IzNwmbQEfEvfd74KaO+rtcqW6ocm5a32vXi8FplS5Vj0/JWuwTttcqWKsem5a12CdprlS1Vjk3LW+2aJfmIuKXKsWl5q12CBq9VtnQ5Ni1PtStxmJk1hRO0mVmiCtuokidJi7Q2tZTpF4A7Sn7P5fIYh3dHRJyR94sWEJupfF6DeJz56RmbtUjQVZA0HxFzVY+jH49x8tTl8/I4y+ESh5lZopygzcwS5QTd29aqBzAEj3Hy1OXz8jhL4Bq0mVmiPIM2M0tUoxO0pBMkXSnpeknXSXpll8ecJukuSddmlz+tYJy3SNqVvf98l99L0rslfVPSVyWdUuLYTmr7bK6V9CNJr+p4TOWfYWp6xZ6kN0paaPusnlH1WKF7DEo6RtJnJX0j+3l0xWPsGoupfqbDaHSJIzvl1nERcY2ko4AdwPqIuL7tMacBfxQRz6xomEi6BZiLiK7rObOAeznwDOBxwPkR8bjyRnhgHFPAAvC4iPhO2/2nUfFnmJpesQecA/wkIt5W6QA7dItBSX8B/DAizpW0GTg6Il5T1Rjbtcci8BIS/EyH0egZdETsjohrsus/Bm4A6thI4Szgb6PlS8BMlgDK9hTgW+3J2bqbkNg7C7gwu34hrf/BpGIiYrHRCbqdpLv+5IoAAAUkSURBVIcCa2mdkqvTEyR9RdL/llTFmT4D+IykHZI2dvn9LPC9ttu3Us0f+/OAD/f4XdWfYbK6xN5/y0pVf1112aBNtxg8NiJ2Z9e/DxxbzdC66ozFFD/TwSKi8RfggbS+Ym7o8rsHAQ/Mrj8D+EYF45vNfv4i8BXgSR2//wTwxLbbl9P6OlrmGI+gtaX22BQ/w1QvnbFHK8lN0Zo8vRn466rHmI3rkBgE9nQ85s6qx5mN46BYTPUzHebS+Bm0pBXANuCiiLik8/cR8aOI+El2/VPACkm/UOYYI2Ih+3k78I/AYzsesgCc0Hb7Idl9ZfqPwDUR8YPOX6TwGaaoW+xFxA8iYn9E3Ae8n0P/W1eiRwz+YKmUlv28vboRHuSgWEz1Mx1GoxO0JAEfBG6IiHf0eMwvZY9D0mNpfWb/VuIYH5AdRELSA4CnA1/reNilwH/JVnM8Hrgr7v/qWZbn06O8UfVnmKJesddx7OC3OPS/den6xOClwIuyh70I+Hg1IzzEQbGY4mc6rFo27M/RqcALgV2Srs3uex2wGiAi/hJ4DvB7ku4F9gLPi+x7U0mOBf4xy2+HA38fEZ+W9LttY/wUrdLBN4F7aB21Lk32R/s04Hfa7msfX9WfYYp6xd7zJT2aVs33Fto+0wr1isEvAx+V9DJaHf3OqXCMQPdYBP4iwc90KI1eZmdmlrJGlzjMzFLmBG1mlignaDOzRDlBm5klygnazCxRTtCJkfSTjtsvlvQ/qxqPGYCkP8m67n016whXejOuJmr6OujGkHR4RNxb9TisfiQ9AXgmcEpE/DTbBXrEGK/nWBySZ9A1Iumhkq7IZjGXS1qd3f83kp7T9rifZD9Pk/R5SZcC12c7wj6ZNS36mqTnVvRPsXo5DrgjIn4KEBF3RMRtkn5D0heyePpXSUdJOlLSBWr1jt4p6clw4JvgpZKuAC7PYvGvs+ftlHRW9rhHZvddm8X5I6r7Z1fPM+j0TLftLAM4htaWWoD3ABdGxIWSXgq8m8EtHk8BHhURN0s6G7gtIs4EkPTgnMduk+kzwJ9K+jrwz8BHgC9mP58bEV+W9CBau0RfCURErJF0Mq0OeL+Svc4pwK9FxA8lvQW4IiJeKmkG+FdJ/wz8Lq1+5hdJOoJWk6PG8gw6PXsj4tFLF6D97CNPAP4+u/53wBOHeL1/jYibs+u7gKdJequkfx8Rd+U3bJtUWaOrxwAbgUVaifl3gN0R8eXsMT/KyhZPBD6U3XcjrS3gSwn6sxHxw+z604HN2WTkc8CRtFosfBF4naTXAP8uIvYW/y9Ml2fQk+Fesv/ZSjqMg+uDdy9diYivq3U6rGcAb5J0eUT8eakjtVqKiP20EunnJO0Cfn+El7m77bqAsyPipo7H3CDpauBM4FOSficirhhlzJPAM+h6+QKtRuQALwA+n12/hdYMB+DZwIpuT5Z0PHBPRHwIOI/WV06zvtQ61197LfjRtM4Ac5yk38gec5Skw2nF5Auy+36F1qy4MwkDXAa8vK3L4drs5y8D346Id9Pqjvdrxfyr6sEz6Hp5OXCBpE20vmouda17P/BxSV8BPs3BM5V2a4DzJN0H7AN+r+Dx2mR4IPCerFZ8L62uiRuBC7L7p2nVn58KvBd4XzbLvhd4cbbyo/M1/wfwLuCr2be+m2mtFDkHeKGkfbTO0vKWov9xKXM3OzOzRLnEYWaWKCdoM7NEOUGbmSXKCdrMLFFO0GZmiXKCNjNLlBO0mVminKDNzBL1/wHSIuW5ey0RhwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPhyRYFuMqj4",
        "colab_type": "text"
      },
      "source": [
        "### **Checking information like shape,data-types of elements and memory usage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1b_n3L2SM0c_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "4bcbd75c-d88f-4102-b926-cf1d2189ba0e"
      },
      "source": [
        "data_set.info()"
      ],
      "execution_count": 329,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsMR8iqRNFVJ",
        "colab_type": "text"
      },
      "source": [
        "### **Checking Range of values across various parameters given in the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTiXxP6tNRhP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "6d540483-aed1-442f-dd26-c61493b714b3"
      },
      "source": [
        "data_set.describe()"
      ],
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YatWRgNXUPcV",
        "colab_type": "text"
      },
      "source": [
        "### **Fixing the target variable and seggregating it from the independent variables in the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gg59THCQZRRj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "X = data_set.iloc[:, :-1].values  \n",
        "y = data_set.iloc[:, 1].values \n"
      ],
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVLblUyaUkMh",
        "colab_type": "text"
      },
      "source": [
        "### **Splitting the data into train and test / validation set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYSuI9vneh4A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "ca431078-a83c-46bd-fd1e-46ab32038dfa"
      },
      "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=0)\n",
        "print(X_test.shape)\n",
        "print(X_test)"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5, 1)\n",
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awYXunSwV4cr",
        "colab_type": "text"
      },
      "source": [
        "### **1.Simple linear regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wD-siJYdk4Xr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "84092cd8-d247-418e-8e83-97ab39d2da0a"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor=LinearRegression()\n",
        "regressor.fit(X,y)\n",
        "regressor.coef_\n"
      ],
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.77580339])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ra0fyFtrWTQ4",
        "colab_type": "text"
      },
      "source": [
        "### **Making predictions using Model 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "609THWALlbUs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f3fc093d-5a2d-4103-a783-bb921c7c08a1"
      },
      "source": [
        "mod1_pred=regressor.predict(X_test)\n",
        "mod1_pred"
      ],
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.14737849, 33.76624426, 74.8246185 , 26.92318188, 60.16091341])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A37BG-NWWvh0",
        "colab_type": "text"
      },
      "source": [
        "### **Comparing Actual vs Predicted values for Model 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gP3r8colx_5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2981ce81-54a3-4752-9065-a38147c379a3"
      },
      "source": [
        "check=pd.DataFrame(np.c_[X_test,y_test,y_pred],columns=['Hours','Actual','predicted'])\n",
        "check['Deviation']=abs(check['Actual']-check['predicted'])\n",
        "check"
      ],
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>predicted</th>\n",
              "      <th>Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.147378</td>\n",
              "      <td>2.852622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>33.766244</td>\n",
              "      <td>6.766244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>74.824618</td>\n",
              "      <td>5.824618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>26.923182</td>\n",
              "      <td>3.076818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62.0</td>\n",
              "      <td>60.160913</td>\n",
              "      <td>1.839087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Actual  predicted  Deviation\n",
              "0    1.5    20.0  17.147378   2.852622\n",
              "1    3.2    27.0  33.766244   6.766244\n",
              "2    7.4    69.0  74.824618   5.824618\n",
              "3    2.5    30.0  26.923182   3.076818\n",
              "4    5.9    62.0  60.160913   1.839087"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3z5TwjQalft",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing relationship between Actual vs Predicted values for Model 1** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRCZpT8eawab",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "98d3f835-8666-45e7-efdb-1c3b9419c705"
      },
      "source": [
        "sns.regplot('Actual','predicted',data=check,marker='D',color='green')\n",
        "plt.title('Deviation in actual vs predicted values(Simple linear regression)')"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Deviation in actual vs predicted values(Simple linear regression)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 336
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDCuoZkPOwID",
        "colab_type": "text"
      },
      "source": [
        "## **Evaluating Model 1 Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3GWiwv7Pzk0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "2f5c47ee-5d5f-4f90-88ca-3fcc8497a5d5"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score_1=r2_score(y_test,y_pred)\n",
        "print(\"r2_score for model 1 is \",r2_score(y_test,y_pred))\n",
        "from sklearn import metrics\n",
        "MAE_1=metrics.mean_absolute_error(y_test,y_pred)\n",
        "print(\"Mean Absolute error for model 1 is \",metrics.mean_absolute_error(y_test,y_pred))\n",
        "RMSE_1=np.sqrt(metrics.mean_squared_error(y_test,y_pred))\n",
        "print(\"Root Mean squared error for model 1 is \",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))\n"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2_score for model 1 is  0.9491748734859172\n",
            "Mean Absolute error for model 1 is  4.071877793635605\n",
            "Root Mean squared error for model 1 is  4.487643939746131\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5A6JFg1VdrPH",
        "colab_type": "text"
      },
      "source": [
        "## **2.Ridge Regularization regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWC9oLxZi6-0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3fbaea4c-d6af-4ea6-c60d-8f0d1f539937"
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "clf=Ridge(alpha=0.1)\n",
        "clf.fit(X,y)\n",
        "clf.coef_\n",
        "clf.intercept_"
      ],
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.515670713867742"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDxYWkKa45-n",
        "colab_type": "text"
      },
      "source": [
        "### **Making Predictions Using Model 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35liEFuYk_U6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ec31cbfe-b788-4219-ef90-f8d5799d24d6"
      },
      "source": [
        "mod2_pred=clf.predict(X_test)\n",
        "mod2_pred"
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.16979959, 33.77781232, 74.80937317, 26.93921884, 60.15524429])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZKHHThqzkoD",
        "colab_type": "text"
      },
      "source": [
        "###  **Evaluating Model 2 Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LobWxw15z4tA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0b4298d3-4054-48f6-834d-49e46dd17a55"
      },
      "source": [
        "from sklearn.metrics import r2_score,mean_absolute_error\n",
        "r2_score_2=(r2_score(y_test,mod2_pred))\n",
        "print(\"r2_score for model 2 is :\",(r2_score(y_test,mod2_pred)))\n",
        "MAE_2=mean_absolute_error(y_test,mod2_pred)\n",
        "print(\"Mean Absolute Error for model 2 is : \",mean_absolute_error(y_test,mod2_pred))\n",
        "RMSE_2=np.sqrt(metrics.mean_squared_error(y_test,mod2_pred))\n",
        "print(\"Root Mean Square  error for model 2 is : \",np.sqrt(metrics.mean_squared_error(y_test,mod2_pred)))\n"
      ],
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2_score for model 2 is : 0.9492887665444383\n",
            "Mean Absolute Error for model 2 is :  4.0645845523698085\n",
            "Root Mean Square  error for model 2 is :  4.482612981781025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUvD6khGwXbt",
        "colab_type": "text"
      },
      "source": [
        "### **Comparing Actual vs Predicted values for Model 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1OMw2AqvTWj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f75e0444-7e67-4080-9f9a-32374a8b060e"
      },
      "source": [
        "check1=pd.DataFrame(np.c_[X_test,y_test,mod2_pred],columns=['Hours','Actual','Predicted'])\n",
        "check1['Deviation']=abs(y_test-mod2_pred)\n",
        "check1"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.169800</td>\n",
              "      <td>2.830200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>33.777812</td>\n",
              "      <td>6.777812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>74.809373</td>\n",
              "      <td>5.809373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>26.939219</td>\n",
              "      <td>3.060781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62.0</td>\n",
              "      <td>60.155244</td>\n",
              "      <td>1.844756</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Actual  Predicted  Deviation\n",
              "0    1.5    20.0  17.169800   2.830200\n",
              "1    3.2    27.0  33.777812   6.777812\n",
              "2    7.4    69.0  74.809373   5.809373\n",
              "3    2.5    30.0  26.939219   3.060781\n",
              "4    5.9    62.0  60.155244   1.844756"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YH4EkyIdzfYZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHBuwkPywkdX",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing Relationship between Actual and Predicted values for Model 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90yXhvhexBwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "af5dc3ff-7249-48c1-cf71-5efc812979d7"
      },
      "source": [
        "sns.regplot('Actual','Predicted',data=check1,line_kws={'color':'green'},scatter_kws={'color':'red'},marker='*')\n",
        "plt.title('Deviation in Actual vs Predicted values(Ridge Regression)')"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Deviation in Actual vs Predicted values(Ridge Regression)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 343
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wyo0BEj66XiL",
        "colab_type": "text"
      },
      "source": [
        "### **3.Using Lasso Regularization Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s1CwUd-f6JwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "10cbe9d5-6a90-4dfb-c598-b22f38b2e044"
      },
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "clf3=Lasso(alpha=0.1)\n",
        "clf3.fit(X,y)"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 344
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4beHIq57W90",
        "colab_type": "text"
      },
      "source": [
        "### **Making Predictions using Model 3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th4ttoN07cYy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1f23bc78-e5bb-4330-9ff2-8cbcc7d29fea"
      },
      "source": [
        "model3_pred=clf3.predict(X_test)\n",
        "model3_pred"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.20475421, 33.79584699, 74.78560562, 26.96422055, 60.14640611])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 345
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWXMS8VA-6RW",
        "colab_type": "text"
      },
      "source": [
        "### **Evaluating Model 3 Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Pom0272-_0G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "25530228-5816-49e6-c014-1720b5775452"
      },
      "source": [
        "from sklearn.metrics import r2_score,mean_absolute_error\n",
        "r2_score_3=r2_score(y_test,model3_pred)\n",
        "MAE_3=mean_absolute_error(y_test,model3_pred)\n",
        "RMSE_3=np.sqrt(metrics.mean_squared_error(y_test,model3_pred))\n",
        "print(\"Mean Absolute Error for Model 3 is :\",mean_absolute_error(y_test,model3_pred))\n",
        "print(\"r2_score for Model 3 is :\",r2_score(y_test,model3_pred))\n",
        "print(\"Root Mean squared error for model 3 is \",np.sqrt(metrics.mean_squared_error(y_test,model3_pred)))\n"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error for Model 3 is : 4.053214347328307\n",
            "r2_score for Model 3 is : 0.9494639940937134\n",
            "Root Mean squared error for model 3 is  4.47486167163936\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtrxk1od7sD7",
        "colab_type": "text"
      },
      "source": [
        "### **Comparing Actual vs Predicted values for Model 3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNZZQXWl74L1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "10d03c6f-e7f9-421a-e60f-908ef1c401aa"
      },
      "source": [
        "check3=pd.DataFrame(np.c_[X_test,y_test,model3_pred],columns=['Hours','Actual','Predicted'])\n",
        "check3['Deviation']=abs(y_test-model3_pred)\n",
        "check3"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.204754</td>\n",
              "      <td>2.795246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>33.795847</td>\n",
              "      <td>6.795847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>74.785606</td>\n",
              "      <td>5.785606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>26.964221</td>\n",
              "      <td>3.035779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62.0</td>\n",
              "      <td>60.146406</td>\n",
              "      <td>1.853594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Actual  Predicted  Deviation\n",
              "0    1.5    20.0  17.204754   2.795246\n",
              "1    3.2    27.0  33.795847   6.795847\n",
              "2    7.4    69.0  74.785606   5.785606\n",
              "3    2.5    30.0  26.964221   3.035779\n",
              "4    5.9    62.0  60.146406   1.853594"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SA6w38I_wNL",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing Relationship between Actual and Predicted Values for Model 3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nkqOF2F_uaH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "2d6782c4-d745-4581-f7ae-c9cb6d804712"
      },
      "source": [
        "sns.regplot('Actual','Predicted',data=check3,line_kws={'color':'blue'},scatter_kws={'color':'orange'},marker='*')\n",
        "plt.title('Deviation in Actual vs Predicted Values for Model 3(Lasso Regression)')"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Deviation in Actual vs Predicted Values for Model 3(Lasso Regression)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O/wv2BLmoqy9Q1QbspF6MnRwfx7yNxPLXsZ2yWswFHt9p/TewwfafYk9AF2JpA829/I2p4GLMO/1teGrbpKqbgP+LPR2eg+27xdjJWI+NJwwL698I3Bp+3xxVfRPri34EG3frEMEIfAH4bjg236Lrm8q++D3Wh578cJGHXWTvYd06p2IPIAMiXMwXYML+LnaMfoV1v4F1Aa0Nyx5i32HSfdmHzwKfw7p8dmDn6aeCTc2YV/Wp8FvnYh5XT9yKPWUmeyMPYl3qb4bfsZduuupUtQ47dr/Cnsp3YeNECX6CnfsPhWP7JDbGCHbTXxJ+9yosCq+3IeVfCt+1GjuXfo+dm31GVfeo6iOquqeLZY9gY9a3Yx7CAcCVYdk2LJDiJqy78SDgiaR1lwI/wLpO6zFP6NxemnUJ1uXcgN0sfxr+EvyCqFs4wTzsmk38vaOqrwE/wu5Zm7GAqieS1vkw8JSINGLH6TpVXY09uPwSO8/Wht/3r2Gdge77fwXmB2fhk9iD/2vhu5ZgvSeE738IeAl4HuvVasWE5gOE++5Z2PF5DxtC+QHRg9UngDXhWHwe63YEO26PYA+6fwH+W1WXdfEV3wOeDfa8DDwX2nokeKCHYZ7gvj/bcXjAcRwnexELO/9iGEPLekLP1s9VdUqPH04zRORH2IPFf/f4WRcyx3Gc7EAsf3cW5pVVY17xk6r6lVgNG2RcyBzHcbIEscjwx4BDsK7SP2Bdn/WxGjbIuJA5juM4GU3a1tlyHMdxnN6QkVN8dMXo0aN16tSpcZvhOI6TUaxcuXKbqo7p+ZPpS9YI2dSpU3n22Wd7/qDjOI7zV0Skp2o2aY93LTqO4zgZjQuZ4ziOk9G4kDmO4zgZjQuZ4ziOk9G4kDmO4zgZjQuZ4ziOk9G4kDmO4zgZjQuZ4ziOk9FkTUK04ziO03v27oWWlritSA0uZI7jODlEUxO8/z40NsKIEXFbkxpcyBzHcXKAlhYTsPosnNDFhcxxHCeLaW2F7duhrg6yddYuFzLHcZwspK0Nduywv2wVsAQuZI7jOFlEezvs3GleWHt73NYMDS5kjuM4WYBqJGBtbXFbM7S4kDmO42Q4dXUWyNHaGrcl8eBC5jiOk6E0NJiANTfHbUm8xF7ZQ0T+l4i8KiKviEiNiJSIyDQReUpE3haRRSJSFLedjuM46cKuXbB2LdTWuohBzEImIhOALwPTVfUIIB+4EvgBcLOqHgjsAK6Jz0rHcZz0YM8eWL8eNm60xGbHiN0jw7o3h4lIAVAK1AKnAUvC8luBi2OyzXEcJ3aamky81q83MXM6EusYmapuFJF/A9YBe4CHgJXATlVNDFtuACZ0tb6IzAfmA0yePHnwDXYcxxlCmpttDKyhIW5L0pu4uxZHALOBacB4oAw4p7frq+oCVZ2uqtPHjBkzSFY6juMMLa2tsHmzjYO5iPVM3FGLZwDvqupWABG5AzgJqBKRguCVTQQ2xmij4zjOkNDWZnlgO3dmfzWOVBL3GNk6YIaIlIqIAKcDrwHLgMvDZ64G7orJPsdxnEGnvd26EN99NzdKSqWauMfInhKRJcBzQCvwPLAA+AOwUES+F9puic9Kx3GcwSGXq3Gkkri7FlHVbwPf7tS8GjghBnMcx3EGHVWbTiWXq3GkktiFzHEcJ5dICFi2zM6cDriQOY7jDAG7dsG2bZ7IPBi4kDmO4wwiu3ebgO3dG7cl2YsLmeM4ziCwd68J2O7dcVuS/biQOY7jpJDmZhOwxsa4LckdXMgcx3FSQEuLBXHU18dtSR9oaeSg/TggbjMGiguZ4zjOAGhrMwGrq8vAROb3n6K0iLK4zRgoLmSO4zj9oK3NqnDs3GmVOTKKVT+Czctg2BYAiducgeJC5jiO0wdUTcC2b89AAUsw5ePQ8Ba0b6a1jYyfmjPuWouO4zgZQaKc1OrVFsyRsSIGUDoOpn0S2vaQl0d+3OYMFPfIHMdxeiArq3FsXQHDxrJp56vvxW3KQHEhcxzH6YbGRvO+mjO+860LJl0K1Z9ja8PI7XGbMlBcyBzHcTqRE9U4yg+E4riNSA0uZI7jOAGvxpGZuJA5jpPzNDWZgO3aFbclQ0vG5b11gwuZ4zg5S0uLCVhDQ9yWDC2NjXDXXXDbbXFbkhpcyBzHyTlaW6NyUtnilfSG116DhQvhnnuyq/s0ViETkYOBRUlN+wPfAn4b2qcCa4A5qrpjqO1zHCe7aGuzROadO3NHwPbuhfvuMwF78cWovaQELrsMfve7+GxLFbEKmaq+ARwDICL5wEZgKXAD8Kiq3iQiN4T3X4/NUMdxMpr2dqvGsWNHhicy94HVq2HRIli61OpAJjjgAJg3D2bPhilTXMhSzenAO6q6VkRmAzND+63AclzIHMfpI4lqHNu3mzeW7bS0wKOPQk0NPPlk1F5YCGefDVdeCdOng2R8dcWOpJOQXQnUhNfVqlobXm8CquMxyXGcTEQ1qsbR2hq3NYNPba15X0uWwNatUfuECSZel10Go0bFZ99gkxZCJiJFwEXANzovU1UVkS57s0VkPjAfYPLkyYNqo+M4mUFDgwlYVlbjSKK9HR5/3Lyvxx6LukxFYOZM6z48+WTIz/hKij2TFkIGnAs8p6qbw/vNIjJOVWtFZBywpauVVHUBsABg+vTpOTJ06zhOV+zaZaH0TU1xWzK4vP8+3H67eWAbNkTtY8bA5ZfDFVeYJ5ZLpIuQzSPqVgS4G7gauCn8vysOoxzHSX/27DEB27MnbksGD1V49lnzvh56qGPx4hkzrPvwjDNsLCwXiV3IRKQMOBO4Nqn5JmCxiFwDrAXmxGGb4zjpSy5U46ivt8TlhQvh7bej9ooKuOQSmDvXohBzndiFTFV3AaM6tb2PRTE6juN0oLnZuteyuRrHK6+Y9/WHP3T0NI86ysa+zjvP8sAcI3YhcxzH6Q2treaB1dfHbcngsGePJS7X1MDLL0ftpaVw4YXWfXjYYfHZl864kDmOk9a0tZkHVleXndU43nnHug7vvLOjSB90UJS4PHx4fPZlAi5kjuOkJe3tUTmpbKvG0dwMjzxi3tfTT0fticTlefPg+OOzL3F5sHAhcxwnrVCNykllWzWOjRth8WJLXN62LWqfNMkCNy67DEaOjM++TMWFzHGctEDVug+3b8+uahxtbR0TlxPdo3l5MGuWeV8nnWTvnf7hQuY4Tuwkykkl50dlOtu2mee1eLF5YgnGjIE5cyxxedy4+OzLJlzIHMeJjcZGu+FnSzkpVRvzqqmBhx/u6FmeeKJ5X6edlruJy4OFC5njOEPO7t0mYHv3xm1Jaqirs6jDhQtt+pQElZVw6aU2/jVtWnz2dUdBlihAlvwMx3Eygb17TcCyZXbil14y7+u++zqK8rHHWt7XOeekX+JyUZFVBqmocCFzHMfpNc3NJmCNjXFbMnB277aKGzU18OqrUXtpKVx0kXUfHnJIfPZ1RV4elJebh5huwpoKXMgcxxk0WlosiCMbqnG8/XaUuJxcHuvgg028Lrww/RKXS0vN8yovz+6cNBcyx3FSTmurhdFnejWO5mYL2qipgWeeidqLiuDcc6378Nhj00skCgtNvCors6frsCdy5Gc6jjMUtLVFycyZLGDr10eJy9u3R+2TJ5t4XXJJeiUu5+WZN1hZCcOGxW3N0ONC5jjOgGlvt1JS27dnbjmptjZYvty6Dx9/PBLi/HwLmZ83z0Lo0ylxOdF1OHx4etk11LiQOY7Tb1QjAcvUclJbtsBtt9lfbW3UPnashc1fcQVUV8dnX2cSXYcVFZ6PlsCFzHGcflFXZ4EcmVhOShWefNLGvh59tONvOPlk875mzkyfMSYRC9ioqDAvzOlImhwmx3EyhYYGE7BMrMaxc6dFHdbUwJo1UXtVlRXsnTsXpkyJzbwPMGxYFHWYy12HPeFC5jhOr9i1y3LBmpritqRvqHZMXE62/7jjzPs6+2woLo7PxmQKCqKoQ+867B2xC5mIVAG/Ao4AFPgM8AawCJgKrAHmqOqOmEx0nJxmzx4TsD174rakb+zaBffea8Ebr70WtZeV2WSVV15pOWADomUXvPA1OOaHUFjW780kEpYrKnIz6nCgxC5kwE+AB1T1chEpAkqBbwKPqupNInIDcAPw9TiNdJxco6nJBGzXrrgt6RtvvGHiddddHW0/9FDzvs4/P4WJy9tXwo4XYPtzUP2xPq0qYqJaUWH/0ykXLdMQjTHZQ0QqgReA/TXJEBF5A5ipqrUiMg5Yrqr7fHaaPn26Pvvss4NrsOPkAM3NNgaWXL0i3WlqggcftO7D556L2ouLLXF53jw4+ugUisWqH8HmZdC2F9p2Q34p5JdA9Sw49Pp9rlpaat5Xuox7ichKVZ0etx0DIW6PbBqwFfi1iBwNrASuA6pVNREIuwnoMvhVROYD8wEmT548+NY6ThbT2hqVk8qUZOZ168z7uv12C+RIMHWqidfFF1sgR8qZ8nFoeAvq34KS/aC5DkonwtSruvx4cXEUtJEukZDZRNy7tAA4DviSqj4lIj/BuhH/iqqqiHR5WanqAmABmEc22MY6TjbS1mZ5YDt3ZoaAtbZa4nJNDaxYEbXn58MZZ5iAzZgxyF11peNg2ifh+X+AFoH2Jns/bL+/fiQRtFFenj6BJNlK3EK2Adigqk+F90swIdssIuOSuha3xGah42Qp7e1ROalMqMaxebMlLS9ebK8T7Lefzbh8+eVDnLi8dQUUj4H9Pw2rfw1bV5C/30cZPtyDNoaaWIVMVTeJyHoROVhV3wBOB14Lf1cDN4X/d8VopuNkFZlUjaO9Hf7yF+s+fPTRyF4ROOmkmBOXJ10KB85HiqsYfsAplBduomy8B23EQdweGcCXgN+FiMXVwKeBPGCxiFwDrAXmxGif42QFqjb+lQnVOHbsgKVLTcDWro3aR46MEpcnTYrPPoDS6gOT6hyOAEbEa1AOE7uQqeoLQFcRM6cPtS2Ok600NFgofUtL3JZ0jyq88IKNfd1/f8fKIR/+sOV9nXWWTaESFx60kZ74oXCcLCYTqnE0NsI995j39frrUfvw4RZ1eOWVcNBB8dlXWBglK8cpok73uJA5Thaye7cJ2N69cVvSPa+/HiUu794dtR9+eJS4HFeBXJGowrwHbaQ/LmSOk0Xs3WsCliwM6URTEzzwgHUfPv981F5cbMI1bx4ceWS8ARPFxTBunHtfmYQLmeNkAc3NJmCNjXFb0jVr1sCiRXDHHR0Tl6dNi2ZcrqyMzby/MnIkjBrlkYeZhguZ42QwLS1RNY50o6UFli0z7+vPf47aCwqixOWPfCQ9RCM/37wwn+srM3Ehc5wMpK3NBKyuLv2qcWzaZEnLt91msy8nGD8+SlweMyY++zpTXGy2+ZQpmYsLmeNkEO3tUTmpdKrG0d4OTzxhwRvLlnVMXD71VOs+POUU83zSifJyqwySDl6h038GLGQiMnJfy1V1+0C/w3FyHdWonFQ6VePYvt3GvRYtsgK+CUaNgiuusL+JE+Ozb1+MGQMjPIc5K0iFR7YSmxBTgMnAjvC6CliHVbh3HKcfqFr34fbt6VONQxVWrjTv64EHOiZZn3CCjX2dcUb6Rv3l55sXVtb/eTCdNGPAQqaq0wBE5JfAUlW9L7w/F7h4oNt3nFwlUU4qXapxNDZaztfChfDmm1F7ebklLs+bBwccEJ99vaGoCCZM8PGwbCOVY2QzVPVziTeqer+I/DCF23ecnKCx0ULpk0s0xclrr5l43XNPx/y0I46IEpczIWl4+HDzxNJhMksntaRSyN4TkX8C/ie8vwp4L4Xbd5ysJp2qcezdC/fdZwL24otRe0kJXHCBBW8ceWR89vWVUaPsz8lOUilk84BvA0uxMbM/hTbHcfZBOlXjWL3aAjeWLrWxuQQHHGDe1+zZVrYpU8jLMy9s+PC4LXEGk5QJWYhOvE5EylR1V6q26zjZSlOTjYHFXY2jpcXm+qqpgSefjNoLC63a/JVXWvX5TAtRLyqy/LB0DTpxUkfKhExEPgr8ChgOTBaRo4FrVfULqfoOx8kG0qUaR22teV9LlsDWrVH7hAkmXpdeCqNHx2ffQCgrs0odPh6WG6Sya/Fm4GzgbgBVfVFETknh9h0no2ltjQQsrs4A0q8AAB9+SURBVGoc7e3w+OPmfT32WJRUnZdnicvz5sHJJ6df4nJfGDkycwXY6R8preyhquulY/9DGqVuOk48tLVF1TjiErD334fbbzcPbMOGqH3MGCsZNWeOdcNlMiI2HlZeHrclzlCTSiFbH7oXVUQKgeuAVT2tJCJrgAZM9FpVdXqoFrIImAqsAeao6o4U2uo4g057e1SNI45yUonE5ZoaePDBjvloM2ZY9+EZZ2RHTlVhoQlxcXHcljhxkEoh+zzwE2ACsBF4COjt+NgsVd2W9P4G4FFVvUlEbgjvv55CWx1n0FA172v79njKSTU0wJ13Wuj8229H7RUVNl3KlVfC/vsPvV2DRWmpjYdlcneoMzBSKWQHq+pVyQ0ichLwRD+2NRuYGV7fCizHhcxJc1SjahxxlJN65RXzvv7wB9izJ2o/6igb+zrvPMsDyyZGjLDxsEyLqHRSSyqF7KfAcb1o64wCD4mIAr9Q1QVAtarWhuWbgOquVhSR+cB8gMmTJ/fXbscZEC0tlnNVVzf0HtiePSZcCxfCyy9H7cOGwYUXmvd1+OFDa9NQIALV1ZmV0+YMHqmofn8i8FFgjIh8NWlRBdAbZ/9kVd0oImOBh0Xk9eSFqqpB5D5AEL0FANOnT0+zWZmcbEbV8r/q6uJJZH7nHROvO+/sGMZ/0EHmfV10UfYGPRQU2HhYtnmXTv9JhUdWhOWOFQDJl049cHlPK6vqxvB/i4gsBU4ANovIOFWtFZFxwJZ9bsRxhoi9e028GhqGPoCjuRkeecS6D59+OmovLISzzzYBO/747O5mGzbMxsMKfCZFJ4lUVL9/DHhMRH6jqmv7sq6IlAF5qtoQXp8FfBfLRbsauCn8v2ugdjpOf2ltNa+nvj6eQr4bN9qMy0uWWCmrBBMnWtfhZZdZ7lS2U1kJY8dmt1A7/SOVzzW/EpErVHUngIiMABaq6tn7WKcaWBpyzwqA36vqAyLyDLBYRK4B1gJzUmin4/RIouuwvh52xVBwra2tY+JyIv8sLw9mzTIBO/nk3KhcIWICVlkZtyVOupJKIRudEDEAVd0Rxr26RVVXA0d30f4+cHoKbXOcXrF3r4lXQ0M8ofNbt1ri8uLF5oklGDPGkpbnzLGk31whP9/GwzJhmhgnPlIpZO0iMllV1wGIyBQsItFx0pq2NhOvurp4ug5VbcyrpgYefrhj6P5JJ5n3NWtWdiQu94WSEhMxHw9zeiKVp8g/AitE5DFAgI8RQuMdJ91QtS7DRNRhHKWj6uqixOXVq6P2ykor2HvllTB16tDblQ5UVFh4vY+HOb0hldO4PCAixwEzQtNXOlXrcJzYaWqKog7j6DoEeOkl877uu6/jJJrHHGORh+eck9uh5WPGWKKz4/SWVOSRHaKqrwcRg2hW6Mmhq/G5gX6H4wyERNdhfb0JWRzs3m2JyzU18OqrUXtpqSUuz5sHhx4aj23pQn6+hdaXlsZtiZNppMIjux74HPCjLpYpcFoKvsNx+kSi6zARdRhX1fm3344SlxsaovYPfShKXPbZi63Y7/jxuTcO6KSGVOSRfS78nzVwcxxnYDQ1Rd5XXF2Hzc0WtFFTA888E7UXFcG559rY17HH+vhPgvJyGw/LhVQCZ3BIRdfipftarqp3DPQ7HGdftLWZt1NXF1/XIdg8X4sWWfj8++9H7ZMnm3hdckluJC73hdGjfZ84AycVXYsXhv9jsZqLfwzvZwF/BlzInJSjauNOdXXxdh22tVnCck2NJTAn7MjPh9NOMwH76Efd2+hMXp6Nh5WVxW2Jkw2komvx0wAi8hBwWKJqfaiR+JuBbt9xkmlujqIO45gqJcHWrXDbbZa4XFsbtY8dC3PnwhVXWHeZ80GKimw8rKgobkucbCGVeWSTkqZeAdgM+NwqzoBJdB3W13cMVx9qVOHJJy1445FHOgrpySdb8MbMmZ7Auy/KyswTcw/VSSWpvOQeFZEHgZrwfi7wSAq37+QYiajDxsb4ug7BZnu+807rPlyzJmqvqooSl6dMic28jGHkSBsTc5xUk8qE6C+KyCXAKaFpgaouTdX2ndyguTmKOoyz61C1Y+JychDJcceZ93X22RY27uybvDyrD+lpBs5gkepOkOeABlV9RERKRaRcVRt6XMvJadrbo6jDOLsOwbzAe+81AVu1KmovK4PZs23865BD4rMv0ygstPEwF3xnMEmZkInI57DaiiOBA4AJwM/xKvZONySiDuPuOgR4880ocTl52pZDD7WuwwsucI+ir5SW2nhYfm/miXecAZBKj+zvsNmdnwJQ1bd6msbFyT3SpeswYcsDD5iArVwZtRcVwXnnWffh0Ud74nJ/GDHCaiY6zlCQSiFrUtXmMEkmIlKAT+PiBBLTpOzZE7clsG5dlLi8Y0fUPnVqlLhcVRWbeRmNiKUdVFTEbYmTS6RSyB4TkW8Cw0TkTOALwD0p3L6TgTQ2wrZt8czzlUxrKyxfbmNfK1ZE7fn5cMYZ5n3NmOHe10AoKLDxsFyu3O/EQyqF7OvAZ4GXgWuB+4BfpXD7TgaxZ48JWNwe2ObNlrh8222waVPUvt9+Ntvy5Zd74nIqGDbMRMzHw5w4SImQiUg+8KqqHgL8sp/rPwtsVNULRGQasBAYBawEPqGqMT/TO72hudkErLExPhva2+Evf7Gxr0cfjYoHi0SJy6ee6onLqaKqysbD3Jt14iIll7KqtonIG2H+sXX92MR1wCog0bP+A+BmVV0oIj8HrgF+lgpbncGhtdUK5dbVxWfDjh2wdKkJ2Nq1UfuIEeZ5zZ0LkybFZ1+2IWIluSor47bEyXVS+Uw6AnhVRJ4G/hrArKoX7WslEZkInA98H/iqWLTIacDHw0duBW7EhSwtaW+H7dtNROIIoVeFF16wsa/77+84FvfhD1vwxllneV2/VOPjYU46kUoh++d+rvdj4GtAeXg/Ctipqong7A1YTtoHEJH5WO4akyd7WcehpL3dSjft2BHPvF+NjXDPPeZ9vf561D58OFx8sQnYQQcNvV25QEmJiZh3zTrpQirmIysBPg8ciAV63JIkQj2tewGwRVVXisjMvn63qi4AFgBMnz7dQ/2HgNZWE7CdO03MhprXXzfv6+67LaE6weGH29jX+edbIq4zOFRUWHCMj4c56UQqnqluBVqAx4FzgcOwMa/ecBJwkYicB5RgY2Q/AapEpCAI4kRgYwrsdPpJooRUQ0NH8RgqmposcbmmBp5/PmovKYkSl4880m+ug4mIBXR4fp2TjqRCyA5T1SMBROQW4Onerqiq3wC+EdadCfy9ql4lIrcBl2ORi1cDd6XATqcPqFqppoaG+EpIrV1rXYd33GEeYIJp00y8Lr7YAw2Ggvx8KzXlnq6TrqRCyFoSL1S1VVLzWPx1YKGIfA94HrglFRt1embPnmjqlDjGvlpbYdky876eeCJqLyiIEpc/8hH3voaK4mIbDyssjNsSx+meVAjZ0SJSH14LVtmjPrxWVe1VsRpVXQ4sD69XY3UbnSEgUf+woQFaWnr+/GCwebPNtrx4MWzZErWPHx8lLnvtvqGlvNwSx/2hwUl3Bixkquq5/BlIW1skXnFNndLebl7XwoXmhSUnLp9yinlfp5zi1SLiYPRomwjTcTIBD6DNIdrbrcuwoaHjVCVDzfbtNu61aJEV8E0wahRccYX9TZwYn325TH6+eWFlZXFb4ji9x4UsB0gO2ogjZB4sWOS552zs64EHOnZhnnCCeV9nnOGJy3FSVAQTJvh4mJN5uJBlKXv3mnjV18cTtJGgsRHuusu6D998M2ovL7eow3nz4IAD4rPPMYYPN08sLy9uSxyn77iQZRF795pwNDbGP23KqlXmfd1zT8fcsyOOMPE67zwP504XRo2yP8fJVFzIMhhVE4ldu0y84p5xee9eq3dYUwMvvhi1l5TABRdY2agjj4zPPqcjeXnmhQ0fHrcljjMwXMgyjOSAjd2740lU7sy771rX4dKlHavfH3CAeV+zZ/uMwelGUZGlNviYpJMNuJClAy2N8JdPwom/hcIPPh63t0cBG7t2pYd4tbTYXF8LF9rcXwkKC63a/JVXWvV5z0FKP8rKrFKHj4c52YILWTqwdQVs+wtsfQLGnw2kR4morqittbD5JUtg69aofcIEE69LL7UcJCc9GTnSj4+TfbiQxckL34CN90LbHkBpefI6mtoraKyaTePUf4wtVL4z7e3w+OM29vXYY1EIf16ezbQ8b57NvOyJy+mLiI2HlZf3/FnHyTRcyGKkecoXqFu7nl2b19GSPwptqoeKg2DMNZAGIrZ9u3leixbBhg1R+5gxVjJqzhwbZ3HSm8JCO07FxXFb4jiDgwvZENPWFuV37d07CUZ/Adb/A+Q3QHsTTPskDNsvNvtU4dlnbezrwQc7Ji7PmGHe1+mne9JsplBaauNh7i072YwL2RCxa1dUVb7DeNfWFVA8Bvb/NKz+tb0f89Eht6+hIUpcfuutqL2iAi65xMa/9t9/yM1yBsCIETYe5gE3TrbjQjaIJKrK19fvI8dr0qVw4HwoqoLqU6Fp25Da+MorJl733mtTuCQ46ijzvs49F4YNG1KTnAEiYrM4e8qDkyu4kKWYxGzKdXW9rCpffmD0uqjK/gaZPXvgD38wAXv55ah92DC48ELzvg4/fNDNcAaBggIbDyspidsSxxk6XMhSRLddh2nEO++YeN15p9ma4KCDzPu66CKPastkhg2z8bACv6qdHMNP+QHQq67DmGluhkcesdD5p5+O2gsL4eyzTcCOP97HUTKdykoYO9aPo5ObxCpkIlIC/AkoDrYsUdVvi8g0YCEwClgJfEJVYy6Da/S56zAmNmyA226z8PltScNuEyfC3LkWPu8TJ2Y+IiZglZVxW+I48RG3R9YEnKaqjSJSCKwQkfuBrwI3q+pCEfk5cA3ws7iMTMcSUV3R1tYxcTlhZ14ezJpl3tdJJ3lpomwhP9/GwzwYx8l1YhUyVVWgMbwtDH8KnAZ8PLTfCtzIEAtZupaI6opt28zzWrwYNm6M2seMsaTlK66wsRMneygpMRHz8TDHid8jQ0Tyse7DA4H/At4BdqpqYtRpAzChm3XnA/MBJk+ePGBbEtOixD2bcm9QtTGvmhobA0tOXD7xRPO+TjvNE5ezkYoKC6/38TDHMWIXMlVtA44RkSpgKXBIH9ZdACwAmD59er99pmTxinM25d5QVxclLr/zTtReWWkFe+fOhWnT4rPPGVzGjLFEZ8dxImIXsgSqulNElgEnAlUiUhC8sonAxn2v3Xf27jXxamhI34jDZF56ybyv++7rGGRy7LGW93XOOZ47lM3k51v3sM+q7TgfJO6oxTFASxCxYcCZwA+AZcDlWOTi1cBdqfi+pqZIvJK74tKV3bstcbmmBl59NWovLbXE5Xnz4NBD47PPGRqKi208zLuJHadr4vbIxgG3hnGyPGCxqt4rIq8BC0Xke8DzwC39/YLm5ki8mtMigL9n3n47SlxuaIjaP/ShKHHZp6fPDcrLbTzMI00dp3vijlp8CTi2i/bVwAn93W5LSyReTU0DsXDoaG6Ghx827+uZZ6L2wkKrdzhvnnUj9mqAv2UXvPA1OOaHUFg2aDY7g8vo0Z7r5zi9IW6PLGWowo4dJl7pnKjcmfXrLWx+yRKb/yvB5Mk29nXJJf24mW1fCTtegO3PQfXHUmqvM/jk5dl4WJk/gzhOr8gaIWtqgq1b47aid7S1WcJyTY0lMCdy1PLzLWR+3jwLoe9zd9KqH8HmZdC2F1B45TuwqgSqZ8Gh16f6ZziDQFGRjYcVFcVtieNkDlkjZJnA1q1WNmrxYqitjdqrq6PE5erqAXzBlI9Dw1tQ/xaU7AfNdVA6EaZeNWDbncGnrMw8MR8Pc5y+4UI2yKjCk0+a9/Xoox1D/U8+2boPZ81KUYWG0nE2w/Tz/wAtkhYzTju9Y+RIGxNzHKfvuJANEjt3WtRhTQ2sWRO1V1XBZZeZgKWgGMkHkG0rKK0cTsHBnyN/9X+T1/IgO/I/mvaJ3rlKXp554T59juP0HxeyFKLaMXE5OWLyuONs7Ovssy0vKNXk5Vl1jxEnX0DB8M9C8Ug4+jTYu4nhw6wGYybkzuUShYU2HjYY54Pj5BIuZClg1y64917L/Xrttai9rAxmzzbv6+CDB+e78/PNy6uqstdwWLSweCQUj6QI8/42bsysiM5sprTUxsPsmDmOMxBcyAbAm29Gicu7dkXthxxi3tcFFwxe4nJhodXcq6zsXW5Zfr7NRVZb29FWZ+gZMcJqJjqOkxpcyPpIczM88IAJ2MqVUXtREZx3ngnY0UcPXmXy4mK7EZaX9/078vJgwgTYvNmKDztDi4iNh1VUxG2J42QXLmS9ZN06WLQIbr/dEq8TTJ1qFecvuWRwq5IPG2aRbalIkq2uNo8ueeZoZ3ApKLCHCB8Pc5zU40K2D1pbYflyC95YsSJqz8+HM84w72vGjMGdF6qszAQs1bMAjxxpN9fNm9N70tBsYNgwC+rw8TDHGRxcyLpg82ZLXL7tNti0KWrfbz9LXL788gEmLveAiHUdjhgxuE/wFRUmZu+9l96TiGYyVVU2HuaTYDrO4OFCFmhv75i4nMi7ErHE5Xnz4NRTB3dqeZEQQj9i6KbsKC2FSZMsojET5mXLFERg7Fg7no7jDC45L2Q7dsDSpTb+lZy4PGKEeV5z59qNfjDJz48ELI7up+LiKDw/U2YLSGcKCqwr0Sc6dZyhISeFTBVeeMG8r/vv7zhP2fTp5n2dddbgF24tKIhC6OOur1dQYIL93ns2oafTP0pKTMQG03N3HKcjOXW5NTbCPfdY6Pzrr0ftw4fDxRdb4vJBBw2+HUVFJmAVFek1dpIcnl9fH7c1mUdFhY2dptMxdZxcICeE7PXXzfu6++6O3sZhh5n3df75QzP3U0mJRQum8+zOIhbUUlgI778ftzWZgYgFdFRVxW2J4+QmsQqZiEwCfgtUAwosUNWfiMhIYBEwFVgDzFHVHd1tpyuamqzbcOFCeP75qL242IRr3jw48siheXouLTUBKy0d/O9KFaNGWffYli0enr8v8vOt1FQmHVvHyTbi9shagetV9TkRKQdWisjDwKeAR1X1JhG5AbgB+HpvNrh2rYnXHXdYBfoE++9vXYcXXzx0kWSJEPpMHfSvrDQxq6318PyuKC628bChijB1HKdrYhUyVa0FasPrBhFZBUwAZgMzw8duBZbTg5DV18NnPgNPPBG1FRREicsf+cjQeF8iNlYyYkR2zPJbVubh+V1RXm5dsD4e5jjxE7dH9ldEZCpwLPAUUB1EDmAT1vXY1Trzgfn27njWr7dX48dHictDVZz1r9OojMi+iLXi4kjMkiM8c5XRo62r2HGc9CAtbrkiMhy4HfiKqtZL0mOuqqqIdDlKo6oLgAW2jel66qnmfZ1yytDlY+Xnm3hVVcUfQj+YFBZGuWZ79sRtTTzk55sXNhSBQY7j9J7YhUxECjER+52q3hGaN4vIOFWtFZFxwJaetvOhD8GCBYNpaUf6Oo1KNpCXZ1PBbNoEDQ1xWzO0FBVZaoKPhzlO+hGrDyHmet0CrFLVf09adDdwdXh9NXBXT9saqhtMcbE9lU+dal5YrohYAhGL0sulrrXhw80bdRFznPQkbo/sJOATwMsi8kJo+yZwE7BYRK4B1gJzYrLvr6RyGpVsYPToKDw/mxk1yv4cx0lf4o5aXAF059Oc3qeN7V4HLbugMLVKM1jTqGQDVVVReH625Zrl5Znnnc7J647jGNkTntC6G7Y/l5JNJULop0yxcREXse4ZPtwiGrNprq2iIutKdBFznMwg7q7F1PLKd2BVCVTPgkOv7/PqcUyjkg2UlNiNf8MGaGmJ25qBUVZmY4DZHIHqONlG9ghZXqH1b5VOhKlX9WnVuKdRyQaSw/P37o3bmv4xcqSN/TmOk1lkj5BpG7Q3wbRPwrD9erVKOk2jkg3k51s3Y22tzTSQKSQKJZeXx22J4zj9IXtu3yVjoXg0bF3R40eLimy6jWnTTMhcxFKHiFVWyZRK8AlP0kXMcTKX7PHICqvgxN9A07ZuP5IJ06hkC2PHmkhs3Rq3Jd1TWmrjYd6d7DiZTfYIGUBRlf11IhOnUckGEnUnN21Kv/D8ESNsPCzXEtodJxvJLiHrRKZPo5INlJebmL33HrS1xW2NCVd1taVXOI6THWSdkGXbNCrZwLBhUfX8OMPzCwps/M4fbBwnu8gaIRMx8crGaVSygUSScVzh+cOG2XiYnxuOk31kTbxecbHNPeY3qvQlP9+q5w91vcrKSvtePzccJzvJGiFzMoO8PCv7VVk5+N8lYtGT1dUe1OE42Yw/ozqxUF1t4fnbus+WGBD5+TYe5nUyHSf7cSFzYmPkSOvu27w5teH5JSUmYt6V6Di5gV/qTqxUVETh+e3tqdmedyU6Tm7hY2RO7JSWWnj+QD2oMWOsZqKLmOPkFi5kTlpQXGzh+cXFfV83EQ05YkTq7XIcJ/1xIXPShoIC88z6UkosIYBefsxxcpdYhUxE/q+IbBGRV5LaRorIwyLyVvjvz9k5RCI8vzclpMrLTfh8ElTHyW3i9sh+A5zTqe0G4FFVPQh4NLx3cojE/GCjRnX/mdGjfSZnx3GMWG8DqvonYHun5tnAreH1rcDFQ2qUkzaMGmVjX8njZgmPbeTI+OxyHCe9SMfw+2pVrQ2vNwHV3X1QROYD8wEmT548BKY5Q01pKUyZAvX19jd2rBeDdhynI2ndMaOqCnSbKquqC1R1uqpOHzNmzBBa5gw1FRXmnbmIOY7TmXQUss0iMg4g/N8Ssz2O4zhOGpOOQnY3cHV4fTVwV4y2OI7jOGlO3OH3NcBfgINFZIOIXAPcBJwpIm8BZ4T3juM4jtMlsQZ7qOq8bhadPqSGOI7jOBlLOnYtOo7jOE6vcSFzHMdxMhoXMsdxHCejcSFzHMdxMhoXMsdxHCejEU3lHPMxIiINwBtx25FGjAa2xW1EGuH744P4PulIru6PKaqa0aWR0rHWYn95Q1Wnx21EuiAiz/r+iPD98UF8n3TE90fm4l2LjuM4TkbjQuY4juNkNNkkZAviNiDN8P3REd8fH8T3SUd8f2QoWRPs4TiO4+Qm2eSROY7jODmIC5njOI6T0WSckInIJBFZJiKvicirInJdaB8pIg+LyFvh/4i4bR0qRKRERJ4WkRfDPvlOaJ8mIk+JyNsiskhEcmp+ZRHJF5HnReTe8D5n94eIrBGRl0XkBRF5NrTl8jVTJSJLROR1EVklIifm8v7IdDJOyIBW4HpVPQyYAfydiBwG3AA8qqoHAY+G97lCE3Caqh4NHAOcIyIzgB8AN6vqgcAO4JoYbYyD64BVSe9zfX/MUtVjknKlcvma+QnwgKoeAhyNnSe5vD8ymowTMlWtVdXnwusG7AScAMwGbg0fuxW4OB4Lhx41GsPbwvCnwGnAktCeU/tERCYC5wO/Cu+FHN4f3ZCT14yIVAKnALcAqGqzqu4kR/dHNpBxQpaMiEwFjgWeAqpVtTYs2gRUx2RWLIRutBeALcDDwDvATlVtDR/ZgAl+rvBj4GtAe3g/itzeHwo8JCIrRWR+aMvVa2YasBX4deh6/pWIlJG7+yPjyVghE5HhwO3AV1S1PnmZWk5BTuUVqGqbqh4DTAROAA6J2aTYEJELgC2qujJuW9KIk1X1OOBcrDv+lOSFOXbNFADHAT9T1WOBXXTqRsyx/ZHxZKSQiUghJmK/U9U7QvNmERkXlo/DPJOcI3SRLANOBKpEJFFPcyKwMTbDhpaTgItEZA2wEOtS/Am5uz9Q1Y3h/xZgKfawk6vXzAZgg6o+Fd4vwYQtV/dHxpNxQhbGOm4BVqnqvyctuhu4Ory+GrhrqG2LCxEZIyJV4fUw4Exs7HAZcHn4WM7sE1X9hqpOVNWpwJXAH1X1KnJ0f4hImYiUJ14DZwGvkKPXjKpuAtaLyMGh6XTgNXJ0f2QDGVfZQ0ROBh4HXiYa//gmNk62GJgMrAXmqOr2WIwcYkTkKGxwOh97OFmsqt8Vkf0xj2Qk8DzwN6raFJ+lQ4+IzAT+XlUvyNX9EX730vC2APi9qn5fREaRu9fMMVggUBGwGvg04dohB/dHppNxQuY4juM4yWRc16LjOI7jJONC5jiO42Q0LmSO4zhORuNC5jiO42Q0LmSO4zhORuNC5ji9REQuFhEVkX1WTRGRr4hI6QC+51Mi8p/9Xd9xcg0XMsfpPfOAFeH/vvgK0G8hcxynb7iQOU4vCLU9T8amfrkytOWLyL+JyCsi8pKIfElEvgyMB5aJyLLwucak7VwuIr8Jry8M86M9LyKPiIgXqXWcflDQ80ccx8Gm+HhAVd8UkfdF5HisXuFU4BhVbRWRkaq6XUS+is39ta2Hba4AZqiqishnsWr91w/mj3CcbMSFzHF6xzys8DBYmat52HQgP09MDdOPckYTgUWhQG0R8G6KbHWcnMKFzHF6QERGYhX0jxQRxWpaKvBMLzeRXAeuJOn1T4F/V9W7Q03IGwdurePkHj5G5jg9cznw/1R1iqpOVdVJmPf0InBtYmqYIHgADUB50vqbReRQEckDLklqrySaSuZqHMfpFy5kjtMz84iqxye4HRgHrANeEpEXgY+HZQuABxLBHtikjfcCfwZqk7ZxI3CbiKwEehpPcxynG7z6veM4jpPRuEfmOI7jZDQuZI7jOE5G40LmOI7jZDQuZI7jOE5G40LmOI7jZDQuZI7jOE5G40LmOI7jZDT/H9wbCzJUI55aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yr3RPrL3Nbng",
        "colab_type": "text"
      },
      "source": [
        "### **4.Using Elastic Net Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cBU36YkNwjm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "65de15a8-3d57-4ae0-90f7-43a7c33a157d"
      },
      "source": [
        "from sklearn.linear_model import ElasticNet\n",
        "clf4=ElasticNet(alpha=0.1)\n",
        "clf4.fit(X,y)"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElasticNet(alpha=0.1, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
              "           max_iter=1000, normalize=False, positive=False, precompute=False,\n",
              "           random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0q1tBXEIOXOQ",
        "colab_type": "text"
      },
      "source": [
        "### **Predicting values using Model 4**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhgoM7nTOdZN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e9b7c24-241b-4acd-88da-af2a25e2884f"
      },
      "source": [
        "model4_pred=clf4.predict(X_test)\n",
        "model4_pred"
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([17.45400852, 33.92444859, 74.61612405, 27.14250268, 60.08338281])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYItIlPnQJke",
        "colab_type": "text"
      },
      "source": [
        "### **Evaluating Model 4 Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIDf_dGiQIk6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8596eb6d-844a-4afc-a1c3-c81a5f199ff2"
      },
      "source": [
        "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n",
        "r2_score_4=r2_score(y_test,model4_pred)\n",
        "MAE_4=mean_absolute_error(y_test,model4_pred)\n",
        "RMSE_4=np.sqrt(mean_squared_error(y_test,model4_pred))\n",
        "print(\"r2 score for Model 4 is : \",r2_score(y_test,model4_pred))\n",
        "print(\"Mean Absolute Error for Model 4 is:\",mean_absolute_error(y_test,model4_pred))\n",
        "print(\"Root Mean Squared Error for Model 4 is :\", np.sqrt(mean_squared_error(y_test,model4_pred)))"
      ],
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2 score for Model 4 is :  0.9506311212563421\n",
            "Mean Absolute Error for Model 4 is: 3.9721357252308236\n",
            "Root Mean Squared Error for Model 4 is : 4.422886445906904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1vsA6yeOmO3",
        "colab_type": "text"
      },
      "source": [
        "### **Comparing Actual vs Predicted values for Model4**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M23kEripOvjt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "3deb8e36-f414-4e91-a119-7823951d53f8"
      },
      "source": [
        "check4=pd.DataFrame(np.c_[X_test,y_test,model4_pred],columns=['Hours','Actual','Predicted'])\n",
        "check4['Deviation']=abs(y_test-model4_pred)\n",
        "check4"
      ],
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "      <th>Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.454009</td>\n",
              "      <td>2.545991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>33.924449</td>\n",
              "      <td>6.924449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.4</td>\n",
              "      <td>69.0</td>\n",
              "      <td>74.616124</td>\n",
              "      <td>5.616124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>27.142503</td>\n",
              "      <td>2.857497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.9</td>\n",
              "      <td>62.0</td>\n",
              "      <td>60.083383</td>\n",
              "      <td>1.916617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Actual  Predicted  Deviation\n",
              "0    1.5    20.0  17.454009   2.545991\n",
              "1    3.2    27.0  33.924449   6.924449\n",
              "2    7.4    69.0  74.616124   5.616124\n",
              "3    2.5    30.0  27.142503   2.857497\n",
              "4    5.9    62.0  60.083383   1.916617"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OpDt7bYAREmL",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing Relationship between Actual an Predicted Values for Model 4**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_oWknc1RS9r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "9f732da8-4658-41b9-89d7-5815bfad270e"
      },
      "source": [
        "sns.regplot('Actual','Predicted',data=check4,line_kws={'color':'grey'},marker='*',scatter_kws={'color':'blue'})\n",
        "plt.title('Deviation in Actual vs Predicted values for Model 4 (Elastic Net Regression)')"
      ],
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Deviation in Actual vs Predicted values for Model 4 (Elastic Net Regression)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 353
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4P7LigSBVWVW",
        "colab_type": "text"
      },
      "source": [
        "### **Comparing all Models with Actual and predicted values together**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zr7K_-QdVVOT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a38e627d-46d9-46b1-d9a4-5b76be7e17c8"
      },
      "source": [
        "final=pd.DataFrame()\n",
        "final['Valid']=y_test\n",
        "final['LinearRegression']=mod1_pred\n",
        "final['Bayesian Regression Model']=mod2_pred\n",
        "final['Lasso']=model3_pred\n",
        "final['ElasticNet']=model4_pred\n",
        "final.head()\n"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Valid</th>\n",
              "      <th>LinearRegression</th>\n",
              "      <th>Bayesian Regression Model</th>\n",
              "      <th>Lasso</th>\n",
              "      <th>ElasticNet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>17.147378</td>\n",
              "      <td>17.169800</td>\n",
              "      <td>17.204754</td>\n",
              "      <td>17.454009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.766244</td>\n",
              "      <td>33.777812</td>\n",
              "      <td>33.795847</td>\n",
              "      <td>33.924449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>74.824618</td>\n",
              "      <td>74.809373</td>\n",
              "      <td>74.785606</td>\n",
              "      <td>74.616124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.923182</td>\n",
              "      <td>26.939219</td>\n",
              "      <td>26.964221</td>\n",
              "      <td>27.142503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.160913</td>\n",
              "      <td>60.155244</td>\n",
              "      <td>60.146406</td>\n",
              "      <td>60.083383</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Valid  LinearRegression  Bayesian Regression Model      Lasso  ElasticNet\n",
              "0     20         17.147378                  17.169800  17.204754   17.454009\n",
              "1     27         33.766244                  33.777812  33.795847   33.924449\n",
              "2     69         74.824618                  74.809373  74.785606   74.616124\n",
              "3     30         26.923182                  26.939219  26.964221   27.142503\n",
              "4     62         60.160913                  60.155244  60.146406   60.083383"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_ZDJKTyYnyP",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing all models with thier Actual vs Predicted Values(In order to decide for the best one)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOBhlFinY4ER",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "65ea42e7-ec9f-4827-e1c7-c2bc12e4d089"
      },
      "source": [
        "N=np.arange(5)\n",
        "plt.bar(N+0.35,y_test,width=0.35,label='Actual',color='red')\n",
        "plt.bar(N,mod1_pred,width=0.35,label='Linear Regression',color='purple')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Variation In Prediction & Actual Values(Linear Regression Model)\\n r2_score: {} \\nMAE: {}\\nRMSE: {}'.format(r2_score_1.round(2),MAE_1.round(2),RMSE_1.round(2)))\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "N=np.arange(5)\n",
        "plt.bar(N+0.35,y_test,width=0.35,label='Actual',color='red')\n",
        "plt.bar(N,mod2_pred,width=0.35,label='Bayesian Regression',color='green')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Variation In Prediction & Actual Values(Ridge Regression Model)\\n r2_score: {} \\nMAE: {}\\nRMSE: {}'.format(r2_score_2.round(2),MAE_2.round(2),RMSE_2.round(2)))\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "N=np.arange(5)\n",
        "plt.bar(N+0.35,y_test,width=0.35,label='Actual',color='red')\n",
        "plt.bar(N,model3_pred,width=0.35,label='Lasso Regression',color='blue')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Variation In Prediction & Actual Values(Lasso Regression Model)\\n r2_score: {}\\nMAE: {}\\nRMSE: {}'.format(r2_score_3.round(2),MAE_3.round(2),RMSE_3.round(2)))\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n",
        "N=np.arange(5)\n",
        "plt.bar(N+0.35,y_test,width=0.35,label='Actual',color='red')\n",
        "plt.bar(N,model4_pred,width=0.35,label='Elastic Net Regression',color='yellow')\n",
        "plt.ylabel('Scores')\n",
        "plt.title('Variation In Prediction & Actual Values(Elastic Net Model)\\n r2_score: {}\\nMAE: {}\\nRMSE: {}'.format(r2_score_4.round(2),MAE_4.round(2),RMSE_4.round(2)))\n",
        "plt.legend(loc='best')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHDbRpwbaYtp",
        "colab_type": "text"
      },
      "source": [
        "  **Here we can observe that Elastic Net Model gives Accurate output with minimum error out of all the models. \n",
        "  Hence we declare Model 4 as the best performer.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4eA9II3lDJ6",
        "colab_type": "text"
      },
      "source": [
        "### Here you can give the model number of hours of study to tell you how  many marks you probably should gain!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egsdTA25aX1A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "ef2c8b2a-dda5-43ac-f504-fc8820bf1d75"
      },
      "source": [
        "Hours=float(input())\n",
        "print(\"If a student studies for {} number of hours,\".format(Hours))\n",
        "pred=clf4.predict([[Hours]])\n",
        "print(\"Expected Score should be{}\".format(pred))"
      ],
      "execution_count": 365,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12\n",
            "If a student studies for 12.0 number of hours,\n",
            "Expected Score should be[119.18319717]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}