SPARKS_TASK_1_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PPARKS TASK 1- Linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSEgJVH2dcsd"
      },
      "source": [
        "**Author : Prof. Priyanka Shahane**\n",
        "\n",
        "**Problem Statement: Prediction using Supervised Machine Learning**\n",
        "\n",
        "In this regression task I tried to predict the percentage of marks that a student is expected to score based on the number of hours he/she studied.\n",
        "\n",
        "This is a simple linear regression task as it involves only two variables. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG1bHn4LqLF-"
      },
      "source": [
        "**Technical Stack : Scikit Learn, Numpy, Pandas, Matplotlib**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2-ZZuNL0s_k"
      },
      "source": [
        "# Importing the required libraries\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNO2hUK7xiYC"
      },
      "source": [
        "**Step 1 - Reading data from the source**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "c08a99jI0xud",
        "outputId": "24c78fc6-5eb5-4a60-a34f-841cea2500af"
      },
      "source": [
        "# Reading data from the given link\n",
        "url = r\"https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv\"\n",
        "s_data = pd.read_csv(url)\n",
        "print(\"Data import successful\")\n",
        "\n",
        "s_data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data import successful\n"
          ],
          "name": "stdout"
        },
        {
          "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>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.1</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.5</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.5</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.5</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9.2</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.5</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.3</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Hours  Scores\n",
              "0    2.5      21\n",
              "1    5.1      47\n",
              "2    3.2      27\n",
              "3    8.5      75\n",
              "4    3.5      30\n",
              "5    1.5      20\n",
              "6    9.2      88\n",
              "7    5.5      60\n",
              "8    8.3      81\n",
              "9    2.7      25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BHOtRpex8OC"
      },
      "source": [
        "**Step 2 - Input data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gAdn8OGA0ySl",
        "outputId": "4fa6a98d-3aed-4399-b254-ecfd8b5f9f34"
      },
      "source": [
        "# Plotting the distribution of scores\n",
        "s_data.plot(x='Hours', y='Scores', style='o')  \n",
        "plt.title('Hours vs Percentage')  \n",
        "plt.xlabel('Hours Studied')  \n",
        "plt.ylabel('Percentage Score')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bnIN617G2hSRfiaUtl3rMzApQTaUt3/GbmRWgmkpbTvxmZgWqltKWSz1mZjWmoMQv6QRJ56TL/SUNzTYsMzPLSruJX9IlJGPofz3d1AO4PsugzMwsO4Xc8X8SOB3YAhARLwP7ZBmUmZllp5DEvz0iAggASb0KObGkIyUtz3ttlHS+pD6SFkpalf7c7938AmZm1jGFJP5bJf2EZJL0fwDuB37a3kER8WxEjIyIkcAHgTeBO4FZwAMRMQx4IF03M7MiabM7ZzpJ+i3AUcBG4EjgmxGxsIPXmQg8HxEvSToDGJ9unwc8RNKGYGZmRdBm4o+IkPTfETEC6Giyz3cWcFO6fEBErEuXXwEOeBfnNbMqUS1DHleCQko9SyUd19kLSNqDpHH4l83fy287aOG46ZIaJDU0NjZ29vJmVgGqacjjSlBI4h8DPCbpeUlPSFoh6YkOXONjwNKIeDVdf1XSAID05/qWDoqIORFRHxH1/fv378DlzKzSVNOQx5WgkCEbTnmX1/g075R5AO4GpgGz0593vcvzm1mFq6YhjytBu3f8EfES0Bv4RPrqnW5rV9r18yTgjrzNs4GTJK0CJqXrZlbDam02r1Ir5MndmcANwP7p63pJMwo5eURsiYi+EfFG3rYNETExIoZFxKSI+EtngzezxPxlaxk3exFDZ93DuNmLKq42Xk1DHleCQko95wJjImILgKQrgMeAq7IMzMwKk2sYzdXIcw2jQMX0iqmmIY8rQSGJX0B+q8uudJuZlYG2GkYrKXFWy5DHlaCQxP9zYLGkO9P1ycDc7EIys45ww6h1VLuJPyK+L+kh4IR00zkRsSzTqMysYNU0F6wVRyGNu8cDqyLihxHxQ+B5SWOyD83MCuGGUeuoQh7g+jGwOW99c7rNzMrA5FEDuXzKCAb2rkPAwN51XD5lhOvl1qqCGnfToRUAiIi3JHmuXrMy4oZR64hC7vhfkPRVST3S10zghawDMzOzbBSS+L8EfAhYm77GANOzDMrMzLJTSK+e9STDKpuZWRVo9Y5f0j9IGpYuS9LPJL2RjtD5geKFaGZmXamtUs9M4MV0+dPAscChwAXAD7INy8zMstJWqWdnROxIl08D/isiNgD3S/r37EMzsxzPTmVdqa07/rckDZDUk2TO3Pvz3vMjgWZF4tmprKu1lfi/CTSQlHvujoinACT9L9yd06xoPDuVdbVWSz0RsUDSIcA+EfF63lsNwNTMIzMzwIOwWddrsx9/ROxslvRzk6tsbu0YM+tanp3KulohD3B1mqTekm6T9IyklZLGSuojaaGkVenP/bKMwaxcdHaWLA/CZl0t08RP0u3z3og4iqQ76EpgFvBARAwDHkjXzarau2mg9SBs1tWUN/5ayztIAs4GDo2ISyUNBg6MiN+3c9x7geXpcZG3/VlgfESskzQAeCgi2rx1qa+vj4aGhsJ+I7MyNG72ohbHzB/Yu47/mTWhBBFZLZC0JCLqm28v5I7/R8BYkoe4ADYBVxdw3FCgEfi5pGWSrpXUCzggItal+7wCHNBKwNMlNUhqaGxsLOByZuXLDbRWTgpJ/GMi4jxgG0Da2LtHAcd1Bz4A/DgiRgFbaFbWSb8JtPiVIyLmRER9RNT379+/gMuZlS830Fo5KSTx75DUjTRBS+oPvFXAcWuANRGxOF2/jeSD4NW0xEP6c32HozarMG6gtXJSSOL/IXAnsL+ky4BHgX9r76CIeAX4s6Tcv+yJwNPA3cC0dNs04K6OBm1WadxAa+Wk3cZdAElHkSRukfTIWVnQyaWRwLUkpaEXgHNIPmxuBQYDLwFnRsRf2jqPG3fNzDqutcbddsfjl9SHpBxzU962HnkDuLUqIpYDf3NRkg8RMzMrgUJKPUtJeuc8B6xKl1+UtFTSB7MMzszMul4hiX8hcGpE9IuIvsDHgAXAV0i6epqZWQUpJPEfHxG/zq1ExH3A2Ij4HbBnZpGZmVkm2q3xA+skXQTcnK5PJemS2Y3CunWamVkZKeSO/zPAIGB++hqcbusGnJldaGZmloV27/gj4jVgRitv/7FrwzEzs6wV0p2zP/DPwPuAnrntEeGRpawqeD5bqzWFlHpuAJ4hGXTt2yRTMT6eYUxmReP5bK0WFZL4+0bEXGBHRDwcEZ8HfLdvVcHz2VotKqRXT+4J3XWSPg68DPTJLiSz4vFwyVaLCkn830knVflH4CpgX+D8TKMyK5KDete1OEGKh0u2alZIqef1iHgjIp6MiI9ExAeBNgdVM6sUHi7ZalEhif+qAreZVRwPl2y1qNVSj6SxwIeA/pIuyHtrX5KHt8yqwuRRA53oraa0VePfA9g73WefvO0bgU9lGZSZmWWn1cQfEQ8DD0u6LiJeKmJMZmaWoUJ69ewpaQ4wJH//Qp7clfQisAnYBeyMiPp0Ypdb0vO9SDID1+sdDdzMzDqnkMT/S+AakikUd7Wzb0s+ko73kzOLZPrG2ZJmpesXdeK8ZmbWCYUk/p0R8eMuvOYZwPh0eR7wEE78ZmZFU0h3zl9J+oqkAZL65F4Fnj+A+yQtkTQ93XZARKxLl18BDmjpQEnTJTVIamhsbCzwcmZm1p5C7vinpT8vzNsWwKEFHHtCRKyVtD+wUNIz+W9GREiKlg6MiDnAHID6+voW9zEzs44rZDz+oZ09eUSsTX+ul3QnMJpk9q4BEbFO0gBgfWfPb2ZmHdduqUfSXpK+kfbsQdIwSacVcFwvSfvkloGTgSeBu3nnW8Q04K7OBm9mZh1XSKnn58ASkqd4AdaS9PRZ0M5xBwB3Sspd58aIuFfS48Ctks4FXsLTN5qZFVUhif+wiJgq6dMAEfGm0mzeloh4ATi2he0bgIkdjtSsDHi2LqsGhST+7ZLqSBp0kXQY8NdMozIrQ7nZunITt+Rm6wKc/K2iFNKd8xLgXuBgSTcAD5DMwWtWUzxbl1WLQnr1LJS0FDgeEDCz2ZO4ZjXBs3VZtSikV88nSZ7evSciFgA7JU3OPjSz8tLarFyercsqTUGlnoh4I7cSEU0k5R+zmuLZuqxaFNK429KHQyHHmVWVXAOue/VYpSskgTdI+j5wdbp+Hkm/frOa49m6rBoUUuqZAWwnGUP/ZmAbSfI3M7MK1OYdv6RuwIKI+EiR4jEzs4y1eccfEbuAtyS9t0jxmJlZxgqp8W8GVkhaCGzJbYyIr2YWlZmZZaaQxH9H+jIzsypQyJO789KxegZHhJ9Nr0EemMysuhTy5O4ngOUk4/UgaaSku7MOzMpDbmCytU1bCd4ZmGz+srWlDs3MOqmQ7pzfIpk5qwkgIpZT2LSLVgUqdWCy+cvWMm72IobOuodxsxf5g8osTyE1/h0R8UazIfjfyigeKzOVODCZh082a1shd/xPSfoM0C2ddvEq4LeFXkBSN0nLJC1I14dKWizpj5JukbRHJ2O3IqjEgckq9VuKWbEU+uTu+0gmX7kReAM4vwPXmAmszFu/ArgyIg4HXgfO7cC5rMgqcWCySvyWYlZMrSZ+ST0lnQ/8O7AaGBsRx0XENyJiWyEnlzQI+DhwbbouYAJwW7rLPMBDPJexyaMGcvmUEQzsXYeAgb3ruHzKiLIumVTitxSzYmqrxj8P2AE8AnwMGE7H7vQB/oNktq590vW+QFNE7EzX1wDlm0EMqLyByS485cjdavxQ/t9SzIqprcR/dESMAJA0F/h9R04s6TRgfUQskTS+o4FJmg5MBxg8eHBHD7ca5uGTzdrWVuLfkVuIiJ3NevUUYhxwuqRTgZ7AvsAPgN6Suqd3/YOAFvvZRcQcYA5AfX19dPTiVtsq7VuKWTG11bh7rKSN6WsTcExuWdLG9k4cEV+PiEERMQQ4C1gUEWcDDwKfSnebBtz1Ln8HMzPrgFbv+COiW2vvvUsXATdL+g6wDJib0XXMzKwFRZlCMSIeAh5Kl18geRLYzMxKoJB+/GZmVkWc+M3MaowTv5lZjXHiNzOrMUVp3DUDT+hiVi6c+K0oPFSyWflwqceKwkMlm5UPJ34rCg+VbFY+nPitKDxUsln5cOK3oqjECV3MqpUbd60oPFSyWflw4rei8VDJZuXBpR4zsxrjxG9mVmOc+M3MaowTv5lZjXHiNzOrMZn16pHUE/gNsGd6ndsi4hJJQ4Gbgb7AEuCzEbE9qziqSVuDnJVqADQPvGZWebLszvlXYEJEbJbUA3hU0v8DLgCujIibJV0DnAv8OMM4qkJbg5wBJRkAzQOvmVWmzEo9kdicrvZIXwFMAG5Lt88DJmcVQzVpa5CzUg2A5oHXzCpTpjV+Sd0kLQfWAwuB54GmiNiZ7rIGaPHWUNJ0SQ2SGhobG7MMsyK0NchZqQZA88BrZpUp08QfEbsiYiQwCBgNHNWBY+dERH1E1Pfv3z+zGCtFW4OclWoANA+8ZlaZitKrJyKagAeBsUBvSbm2hUHA2mLEUOnaGuSsVAOgeeA1s8qUZa+e/sCOiGiSVAecBFxB8gHwKZKePdOAu7KKoZoUMshZsXvXeOA1s8qkiMjmxNIxJI233Ui+WdwaEZdKOpQk6fcBlgF/FxF/betc9fX10dDQkEmcZmbVStKSiKhvvj2zO/6IeAIY1cL2F0jq/Vam3DffrLp5WGbbjfvmm1U/D9lgu3HffLPq58Rvu3HffLPq58Rvu3HffLPq58RfJeYvW8u42YsYOusexs1exPxlnXs8wn3zzaqfG3erQFc2yLpvvln1c+LvYqXoCtlWg2xnru1J0c2qmxN/FypVV0g3yJpZR7jG34VK1RXSDbJm1hFO/F2oVHfebpA1s45w4u9CpbrznjxqIJdPGcHA3nUIGNi7jsunjHCd3sxa5Bp/F7rwlCN3q/FD8e683SBrZoVy4u9C7gppZpXAib+L+c7bzMqdE38F8XDJZtYVnPgrhIdLNrOuklmvHkkHS3pQ0tOSnpI0M93eR9JCSavSn/tlFUNnddW4N13JwyWbWVfJsjvnTuAfI+Jo4HjgPElHA7OAByJiGPBAul42cnfWa5u2ErxzZ13q5O+nc82sq2SW+CNiXUQsTZc3ASuBgcAZJHPxkv6cnFUMnVGud9Z+OtfMukpRHuCSNIRk/t3FwAERsS596xXggFaOmS6pQVJDY2NjMcIEyvfO2k/nmllXyTzxS9obuB04PyI25r8XEQFES8dFxJyIqI+I+v79+2cd5tvK9c7aT+eaWVfJtFePpB4kSf+GiLgj3fyqpAERsU7SAGB9ljF0VCmfvm2PnxEws66QZa8eAXOBlRHx/by37gampcvTgLuyiqEzfGdtZtVOSbUlgxNLJwCPACuAt9LNF5PU+W8FBgMvAWdGxF/aOld9fX00NDRkEqeZWbWStCQi6ptvz6zUExGPAmrl7YlZXTfHT7mambWsKp/c9VOuZmatq8rx+Mu1L76ZWTmoysRfrn3xzczKQVUm/nLti29mVg6qMvH7KVczs9ZVZeOuZ8IyM2tdVSZ+8FOuZmatqcpSj5mZtc6J38ysxjjxm5nVGCd+M7Ma48RvZlZjMhudsytJaiQZybMQ/YDXMgyns8oxrnKMCRxXR5RjTFCecZVjTJBtXIdExN/MZFURib8jJDW0NAxpqZVjXOUYEziujijHmKA84yrHmKA0cbnUY2ZWY5z4zcxqTDUm/jmlDqAV5RhXOcYEjqsjyjEmKM+4yjEmKEFcVVfjNzOztlXjHb+ZmbXBid/MrMZUTeKX9DNJ6yU9WepYciQdLOlBSU9LekrSzFLHBCCpp6TfS/pDGte3Sx1TjqRukpZJWlDqWHIkvShphaTlkhpKHU+OpN6SbpP0jKSVksaWOJ4j079R7rVR0vmljClH0tfSf+tPSrpJUs8yiGlmGs9Txf47VU2NX9KHgc3Af0XE+0sdD4CkAcCAiFgqaR9gCTA5Ip4ucVwCekXEZkk9gEeBmRHxu1LGBSDpAqAe2DciTit1PJAkfqA+Isrq4R9J84BHIuJaSXsAe0VEU6njguQDHFgLjImIQh++zCqWgST/xo+OiK2SbgX+OyKuK2FM7wduBkYD24F7gS9FxB+Lcf2queOPiN8Afyl1HPkiYl1ELE2XNwErgZJPEhCJzelqj/RV8jsASYOAjwPXljqWcifpvcCHgbkAEbG9XJJ+aiLwfKmTfk/uyUsAAAU2SURBVJ7uQJ2k7sBewMsljmc4sDgi3oyIncDDwJRiXbxqEn+5kzQEGAUsLm0kibSkshxYDyyMiHKI6z+AfwbeKnUgzQRwn6QlkqaXOpjUUKAR+HlaGrtWUq9SB5XnLOCmUgcBEBFrge8Bq4F1wBsRcV9po+JJ4ERJfSXtBZwKHFysizvxF4GkvYHbgfMjYmOp4wGIiF0RMRIYBIxOv3qWjKTTgPURsaSUcbTihIj4APAx4Ly0rFhq3YEPAD+OiFHAFmBWaUNKpGWn04FfljoWAEn7AWeQfFgeBPSS9HeljCkiVgJXAPeRlHmWA7uKdX0n/oylNfTbgRsi4o5Sx9NcWh54EPhoiUMZB5ye1tNvBiZIur60ISXSO0YiYj1wJ0ldttTWAGvyvqndRvJBUA4+BiyNiFdLHUhqEvCniGiMiB3AHcCHShwTETE3Ij4YER8GXgeeK9a1nfgzlDaizgVWRsT3Sx1PjqT+knqny3XAScAzpYwpIr4eEYMiYghJmWBRRJT0rgxAUq+0YZ60lHIyydf0koqIV4A/Szoy3TQRKGmngTyfpkzKPKnVwPGS9kr/n5xI0t5WUpL2T38OJqnv31isa1fNZOuSbgLGA/0krQEuiYi5pY2KccBngRVpPR3g4oj47xLGBDAAmJf2vHgPcGtElE33yTJzAHBnki/oDtwYEfeWNqS3zQBuSEsrLwDnlDie3IfjScAXSx1LTkQslnQbsBTYCSyjPIZvuF1SX2AHcF4xG+erpjunmZkVxqUeM7Ma48RvZlZjnPjNzGqME7+ZWY1x4jczqzFO/FaRJG1utv45Sf9ZxOsfL2lxOgrlSknfSrePl9Thh4MkXSfpU+nytZKO7sCx48tpNFMrf1XTj9+sK0jqng6a1Z55wJkR8Yf0eYjcg1TjSUaJ/W1nY4iIL3T2WLNC+I7fqo6kIZIWSXpC0gPpk5G73VWn65vTn+MlPSLpbuDp9Gnde9L5Cp6UNLWFy+xPMuBXbtyjp9OB+L4EfC39JnBiG9eUpP+U9Kyk+9Pz5fZ5SFJ9unyypMckLZX0y3TcJyR9VMk4/Esp4qiOVh2c+K1S1Slv0g/g0rz3rgLmRcQxwA3ADws43wdI5iQ4gmTcopcj4th0boeWntS9EnhW0p2SviipZ0S8CFwDXBkRIyPikTau90mSbwlHA39PC2PHSOoHfAOYlA4S1wBcoGQSkZ8CnwA+CBxYwO9n9jYnfqtUW9PkOjIdZfSbee+N5Z1xT34BnFDA+X4fEX9Kl1cAJ0m6QtKJEfFG850j4lKSCWPuAz5Dyx8ObfkwcFP6beFlYFEL+xxP8sHwP+mH2zTgEOAokkHHVkXy6H1ZDGZnlcOJ32rJTtJ/85LeA+yR996W3EJEPEfyDWAF8B1J+R8q5O33fET8mGTQr2PTcVc6cs32iGSuhNwH3NERcW4HjjdrkRO/VaPfkozwCXA2kCu5vEhSGoFkvPgeLR0s6SDgzYi4HvguLQx3LOnj6UiPAMNIxlJvAjYB++Tt2to1fwNMTSfEGQB8pIVQfgeMk3R4es1eko4gGUl1iKTD0v0+3dLvYdYa9+qxajSDZGaqC0lmqcqNWvlT4C5JfyApzWxp5fgRwHclvUUycuKXW9jns8CVkt4kuas/OyJ2SfoVcJukM9I4WrvmncAEkqGUVwOPNb9ARDRK+hxwk6Q9083fiIjnlMwEdk96/UfY/cPGrE0endPMrMa41GNmVmOc+M3MaowTv5lZjXHiNzOrMU78ZmY1xonfzKzGOPGbmdWY/w8LO4HQkVYJNQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4OyJ970ylyY"
      },
      "source": [
        "**Step 3 - Data Preprocessing**\n",
        "\n",
        "In this step we divide our data into \"attributes\" (inputs) and \"labels\" (outputs)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQN2yVkw0ybp"
      },
      "source": [
        "X = s_data.iloc[:, :-1].values  \n",
        "y = s_data.iloc[:, 1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aod_zOg2eeI"
      },
      "source": [
        "**Step 4 - Model Training**\n",
        "\n",
        "Splitting the data into training and testing sets, and training the algorithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyRdCEtZ0yfI",
        "outputId": "16cc4a2f-1878-47a9-f153-617da5e376e2"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) \n",
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train.reshape(-1,1), y_train) \n",
        "\n",
        "print(\"Training complete.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training complete.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shQZANRP-fPn"
      },
      "source": [
        "**Step 5 - Plotting the Line of regression**\n",
        "\n",
        "Now since our model is trained now, its the time to visualize the best-fit line of regression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ApWLN1UQ0yib",
        "outputId": "c03dcc14-4645-434a-cf29-c277680ffe50"
      },
      "source": [
        "# Plotting the regression line\n",
        "line = regressor.coef_*X+regressor.intercept_\n",
        "\n",
        "# Plotting for the test data\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, line,color='red');\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7THcf5u6jgAuBxe5+MfAkcEHmy6YCD1atSonP6NE9Lw1UeIvErpJ14FcTTGiuJRgTvy2ckiQROjqC4F6/vuvaD36gx+BFEqSkJzHd/SngqczH64CTwy9JYqf9S0RSQU9iSpdXXskP75deUniLJJT2QpGAum6R1FEHXu9++tP88G5vV3iLpIA68HqWG9yHHQZbthT8tpblbXq4RiQBFOD1aNAgeOed7GtFdtza4lUkOTSEUk/a24Ouu3t433RTScMl2uJVJDnUgdeLkCYptcWrSHKoA691q1blh/f69WVPUmqLV5HkUIDXMjMYOzb7mjuMHFn2W1b1YAgRKYkCvBbddFN+193REcrSwKodDCEiJdMYeK3JDe5zzoFHHw31FtriVSQZFOC14ogj4NVXs6/pYRyRmqYhlLTbuzfouruH96JFCm+ROqAOPM20f4lIXVMHnkYrV+aH99atCm+ROqMOPAb72kuk4D4jVeq6tb+JSPoowCO2r71EgN73GXn0Dvjud7PfrPPUnCrWpBAXSS4FeMQK7SXS0+cmnzgs+00uvhjuvjuSmhTgIsmlAI9YKXuJrPz55zloT871Koxza38TkXTSJGbE9rWXSOfn9vtgD+tvmJQd3o89VrVJSu1vIpJO6sAjNmPimKzxZsjeS+RfThpBX+/I+p6WZZuqOpRRqCYRSSYFeMQ6gzhvxcdH3ocjj8z62onXP8i/Tz6p6uPQvdak8W+RRDOPcO1wc3Ozt7a2Rna/1MhdSXLeefDggxW/rZYGitQGM1vq7s2519WBx+n+++HCC7OvhfQfqpYGitQ+TWLGxSw7vO+4I9RJSh19JlL7FOBRu/LK/CETd/jKV0K9jZYGitQ+DaFE5f33Yf/9s6+99BKMqc5Kj6GNDbT1ENZaGihSO9SBR2HIkOzwHjIk6Lp7CO+W5W2Mn72Y0dc8yvjZi2lZ3lbWLXX0mUjtUwdeTevW5S0NZPdu2G+/Hr88zIlHLQ0UqX0K8F5UvAQvd5z7iivgZz/b57eEvSeJjj4TqW0K8B5U1AkvXAgXXJB9rcjVJZp4FJFSaAy8B2UvwTPLDu9HHilpaaD2JBGRUijAe1ByJzx9es9LA889t6T7auJRREqhIZQeFL0Eb9cuGDgw+9rGjTB8eFn31cSjiJRCAd6DonbnO/BAePfdrtdHHw0vv1zxvTXxKCLFUoD3YJ+d8Jo18PGPZ3/Dnj3Qv38MlYpIPVOA96LHTjh3nPvaa+EHPyj5vbVLoIiEQQFejPnzg3Mouytz4yntEigiYdEqlH1xD7ru7uH9+OMV7RqoXQJFJCwFA9zMhpvZk2b2opmtMrPLM9c/amZ/MLO/Zn4/uPrllqaifUVuvhn65PzxuMOECRXVpId1RCQsxXTgHwD/6e6fAE4BvmVmnwCuAZ5w96OBJzKvE6NzqKJtxy6crqGKgiG+e3fQdV92Wde1118Pba9uPawjImEpGODu/oa7L8t8/A6wGmgCzgfmZb5sHjC5WkWWo6yhigsugIZuQXrVVUFwDxkSWl16WEdEwlLSJKaZjQLGAUuAw939jcynNgOH9/I904BpACNGjCi3zpKVNFSxcSOMHJl9rb09fwglBHpYR0TCUnSAm9mBwELg2+7+d+u2pM7d3cx6HGNw9znAHAgONa6s3OIV/TTlfvsF67g7LVoEn/tcVWvTwzoiEoaiWkwz608Q3ve4+6LM5S1mNiTz+SHA1uqUWJ6CQxWLFwdj3d3D273q4S0iEpZiVqEYcBuw2t27b2j9EDA18/FU4MHwyyvf5HFNzJpyLE2NDRjQ1NjArCnHMvmEoUFwd19N8uKLoR4oLCISBfMCwWVmpwHPACuBjszl7xCMgz8AjAA2AF9w97f29V7Nzc3e2tpaUoGhPrV4443BxGSn8ePh2WfLey8RkYiY2VJ3b869XnAM3N2fBayXT1e2KLqA0J5afO89OOCA7Gtvvw2DBoVVqohI5BL9JGYoTy1+4xvZ4X399cFwicJbRFIu0XuhVPTU4vbt8NGPZl/r6MjfkEpEJKUS3YGX/dTi9ddnh/fq1V37moiI1IhEB3jJTy2uXRuE9H//d/B65swguHP37xYRqQGJHkIp+qlFd5g8GR56qOva3/6WP4QiIlJDEh3gUMRTi08/Daef3vV63jz48perX5iISMwSH+C9ev/94BzK114LXh9xRDDWPWBAvHWJiEQk0WPgvbrzTth//67wfvppeOUVhbeI1JV0deBvvgmDB3e9njIFFizQ6hIRqUvp6cCvvjo7vNeuhYULFd4iUrfSEeCXXgo//nHw8fe/H6w6OfLIWEsSEYlbOoZQzjkHVqyAhx+Gxsa4qxERSYR0BPjZZwe/RETkQ+kYQhERkTwKcBGRlFKAi4iklAJcRCSlFOAiIimlABcRSSkFuIhISinARURSytw9upuZbQM2FPnlhwJvVrGcciWxriTWBKqrFEmsCZJZVxJrgurWNdLdB+dejDTAS2Fmre7eHHcduZJYVxJrAtVViiTWBMmsK4k1QTx1aQhFRCSlFOAiIimV5ACfE3cBvUhiXUmsCVRXKZJYEySzriTWBDHUldgxcBER2bckd+AiIrIPCnARkZRKXICb2e1mttXMXoi7lk5mNtzMnjSzF81slZldHndNAGa2v5n9n5n9JVPXf8VdUycz62tmy83skbhr6WRm681spZmtMLPWuOvpZGaNZrbAzF4ys9VmdmrM9YzJ/Bl1/vq7mX07zpo6mdkVmb/rL5jZvWa2fwJqujxTz6qo/5wSNwZuZp8GdgL/4+5j464HwMyGAEPcfZmZHQQsBSa7+4sx12XAAe6+08z6A88Cl7v783HWBWBmVwLNwCB3nxR3PRAEONDs7ol6CMTM5gHPuPtcMxsADHT3HXHXBcF/xEAb8Cl3L/YhvGrV0kTwd/wT7r7LzB4Afuvud8ZY01jgPuBkYA/wO+Ab7r42ivsnrgN396eBt+Kuozt3f8Pdl2U+fgdYDTTFWxV4YGfmZf/Mr9j/RzazYcC5wNy4a0k6M/sI8GngNgB335OU8M6YALwSd3h30w9oMLN+wEDg9Zjr+Qdgibu/5+4fAP8LTInq5okL8KQzs1HAOGBJvJUEMkMVK4CtwB/cPQl1/QK4CuiIu5AcDvzezJaa2bS4i8kYDWwD7sgMOc01swPiLqqbC4F74y4CwN3bgJ8AG4E3gLfd/ffxVsULwD+Z2SFmNhA4Bxge1c0V4CUwswOBhcC33f3vcdcD4O7t7n4CMAw4OfMjXWzMbBKw1d2XxllHL05z9xOBs4FvZYbr4tYPOBG4xd3HAe8C18RbUiAznHMe8Ou4awEws4OB8wn+0xsKHGBmX4qzJndfDdwA/J5g+GQF0B7V/RXgRcqMMS8E7nH3RXHXkyvzY/eTwGdjLmU8cF5mvPk+4EwzuzvekgKZDg533wr8hmDcMm6bgE3dfnJaQBDoSXA2sMzdt8RdSMZngFfdfZu77wUWAf8Yc024+23ufpK7fxrYDrwc1b0V4EXITBbeBqx295/FXU8nMxtsZo2ZjxuAs4CX4qzJ3We6+zB3H0Xw4/did4+1SwIwswMyE9Bkhij+meDH31i5+2bgNTMbk7k0AYh1crybi0jI8EnGRuAUMxuY+Tc5gWA+KlZmdljm9xEE49/zo7p3v6huVCwzuxc4AzjUzDYB33P32+KtivHAvwIrM+PNAN9x99/GWBPAEGBeZqVAH+ABd0/Msr2EORz4TfDvnn7AfHf/XbwlfehS4J7MkMU64JKY6+n8T+4sYHrctXRy9yVmtgBYBnwALCcZj9UvNLNDgL3At6KchE7cMkIRESmOhlBERFJKAS4iklIKcBGRlFKAi4iklAJcRCSlFOAiIimlABcRSan/B48OsIbL69lWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myUxxkhX_BgA"
      },
      "source": [
        "**Step 6 - Making Predictions**\n",
        "\n",
        "Now that we have trained our algorithm, it's time to test the model by making some predictions.\n",
        "\n",
        "For this we will use our test data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKjPWk3N0ylr",
        "outputId": "1f92ec1d-b7bd-423f-c9ae-932ba10fdc85"
      },
      "source": [
        "# Testing data\n",
        "print(X_test)\n",
        "# Model Prediction \n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.5]\n",
            " [3.2]\n",
            " [7.4]\n",
            " [2.5]\n",
            " [5.9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbyY54xB_1px"
      },
      "source": [
        "**Step 7 - Comparing actual result with the result predicted by model**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8PJm7ZeV0ypG",
        "outputId": "c4617d28-53e7-4332-9db1-c1a8670727f0"
      },
      "source": [
        "# Comparing Actual vs Predicted\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred}) \n",
        "df"
      ],
      "execution_count": null,
      "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20</td>\n",
              "      <td>16.884145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27</td>\n",
              "      <td>33.732261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>75.357018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>26.794801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>60.491033</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Actual  Predicted\n",
              "0      20  16.884145\n",
              "1      27  33.732261\n",
              "2      69  75.357018\n",
              "3      30  26.794801\n",
              "4      62  60.491033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6UuXSOW0ysO",
        "outputId": "e52e9195-64f3-4950-a7f6-ecacbfa12a60"
      },
      "source": [
        "#Estimating training and test score\n",
        "print(\"Training Score:\",regressor.score(X_train,y_train))\n",
        "print(\"Test Score:\",regressor.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Score: 0.9515510725211553\n",
            "Test Score: 0.9454906892105354\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "xI44BZuv1iua",
        "outputId": "2a7fb314-fbc9-4391-da41-71ed24bfcfe3"
      },
      "source": [
        "# Plotting the Bar graph to depict the difference between the actual and predicted value\n",
        "\n",
        "df.plot(kind='bar',figsize=(5,5))\n",
        "plt.grid(which='major', linewidth='0.5', color='red')\n",
        "plt.grid(which='minor', linewidth='0.5', color='blue')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hy_Qu5hd1i6G",
        "outputId": "43d97dc5-92ce-4cee-ad13-31124bb3de5d"
      },
      "source": [
        "# Testing the model with our own data\n",
        "hours = 9.25\n",
        "test = np.array([hours])\n",
        "test = test.reshape(-1, 1)\n",
        "own_pred = regressor.predict(test)\n",
        "print(\"No of Hours = {}\".format(hours))\n",
        "print(\"Predicted Score = {}\".format(own_pred[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of Hours = 9.25\n",
            "Predicted Score = 93.69173248737539\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hZGyX9NE4E7"
      },
      "source": [
        "**Step 8 - Evaluating the model**\n",
        "\n",
        "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. Here different errors have been calculated to compare the model performance and predict the accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VG70WQgI1jCZ",
        "outputId": "48d1f2d8-7b1c-4709-f052-c95651af2758"
      },
      "source": [
        "from sklearn import metrics  \n",
        "print('Mean Absolute Error:',metrics.mean_absolute_error(y_test, y_pred)) \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
        "print('R-2:', metrics.r2_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 4.183859899002982\n",
            "Mean Squared Error: 21.598769307217456\n",
            "Root Mean Squared Error: 4.647447612100373\n",
            "R-2: 0.9454906892105354\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}