Regression_ice_cream_revenue_model.ipynb

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      "name": "Regression-ice-cream-revenue-model.ipynb",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mart-anthony-stark/Machine-Learning-and-Deep-Learning/blob/main/Regression_ice_cream_revenue_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# ICE CREAM REVENUE PREDICTION"
      ],
      "metadata": {
        "id": "JSo25U8765iG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing Libraries"
      ],
      "metadata": {
        "id": "Elui1zVE65iQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import seaborn as sb\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "trusted": true,
        "id": "MmkEUZRC65iR"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Getting our Data"
      ],
      "metadata": {
        "id": "SD8sfYNw65iU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('IceCreamData.csv', encoding='unicode_escape')\n",
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "6wS73Eyt65iV",
        "outputId": "e1616510-9b58-468d-d76f-c9ce2b3e0cb2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Temperature     Revenue\n",
              "0      24.566884  534.799028\n",
              "1      26.005191  625.190122\n",
              "2      27.790554  660.632289\n",
              "3      20.595335  487.706960\n",
              "4      11.503498  316.240194\n",
              "..           ...         ...\n",
              "495    22.274899  524.746364\n",
              "496    32.893092  755.818399\n",
              "497    12.588157  306.090719\n",
              "498    22.362402  566.217304\n",
              "499    28.957736  655.660388\n",
              "\n",
              "[500 rows x 2 columns]"
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              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Revenue</th>\n",
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              "      <td>27.790554</td>\n",
              "      <td>660.632289</td>\n",
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              "      <td>487.706960</td>\n",
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              "      <td>316.240194</td>\n",
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              "      <td>306.090719</td>\n",
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              "      <td>28.957736</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing"
      ],
      "metadata": {
        "id": "IwuMhMxd65iW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "df.isnull().any().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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              "0"
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          "metadata": {},
          "execution_count": 21
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    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization"
      ],
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        "id": "cmkODHBr65iZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['Temperature'], df['Revenue'])\n",
        "plt.title(\"Regression of Temperature vs Ice Cream Revenue\")\n",
        "plt.xlabel(\"Temperature\")\n",
        "plt.ylabel(\"Sales / Revenue\")\n",
        "plt.show() "
      ],
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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76ent27EyxxB7+W761ZiLohwULEREEmS7WRvQt61/dNhWwnHSqjEXRTkoWIhI0ylkTok5MyeRyihfSg2znNN7lnLTr8ZcFOWgprMi0lSK6lCXWReRo24C4KKTJpfUBLYROukpWIhIU8lVcR2/YV+8dB19W/vnI/q2OsMMtiVkL0a1pRripl8qFUOJSFMptPVRtv22OaRaBmYx3Km7vhHlpGAhIk2lvS2VuD6zIjpbxXRHeysXn3IIozKO09PbV5ed6cpFwUJEmkZnVzcvvrxlwPpUiw2oiM7VSmn21A7aRgwsxa/HznTlojoLEWkaFy9dN6DZK8DIEcMH1DWkl7NVWDdKZ7pyUbAQkaaR7Ub+XOhg19nVzbwla7d3uBvVluKCWQcnVlqPbW+lO+F49daZrlxUDCUiTSNXb+nOru4BPbM3be7j3OvuztoPoxE605WLgoWINI0jDxyTdX22Iqqt2zxx9NhG6UxXLiqGEpGm0NnVzTV3bUjctvy+jTnrGjZt7ktcP5T7VWRSzkJEhrx0r+305ESZ0hXYkp2ChYgMeUm9tuPSLZ2yaW9N7pvRTFQMJSINL2laUtjR7DXf4H/pJrErH36Wq1Y80m97apgx74SDK5j6xqBgISINLWlgwDnX3w0+cCjxTC1m/Sqlp+07mlv/8vj2Oor21hTzTkhuOttsFCxEpCaScgOl3JSTipgyBwBMYsDX33fI9nNmBh2AV7bkmqmiuShYiEjVFTVMeMbz4gHmyAPHJHaMK4RnnKvQ0WiblSq4RaTq8s1vnSQdYLpDHUR3T++A+oVidGS0fmq24TuKVZNgYWbnmNlaM/urmV1jZjub2UQzu8vM1pvZIjMbEfbdKSyvD9sn1CLNIlI+pdyY87VoKkZST+tGmQu7VqoeLMysA/gUMM3d3wi0AO8HvgosdPf9gE3AGeEpZwCbwvqFYT8RaWCF3Jgzpz4ttbgJojGe2ltTOXtaN9vwHcWqVZ3FcKDVzPqANuBx4Cjgg2H7FcA84DLgxPAY4Hrg22Zm7ll614hI3ZszcxJzrru7X2ul1LAdw4Qn1WkY5GwCm01Heyt3zj0q7375RpltdlUPFu7ebWZfAx4BeoFfAauAHndPDzT/KJB+hzqADeG5W8zsOWAP4On4cc3sTOBMgPHjx1f6ZYjIYOWY3zqpyMnDLsUEjGJzBs00fEexalEMNYootzARGAuMBI4Z7HHd/XJ3n+bu08aMSR4sTETqQ7b5rc9dfDcT596atcip2JzFUB7Yr9pqUcH9LuBBd9/o7n3AjcB0oN3M0jmdcUB6TOBuYB+AsH134JnqJllEyiFdD5EtGGx1zxkQOtpbB7Riyqa9NaVAUUa1CBaPAIebWZuZGfBO4B5gOXBK2Od04KbweElYJmxfpvoKkcYTb/paqs2vbuHIA8cMKMHKpCE6yq8WdRZ3mdn1wJ+BLUAXcDlwK3Ctmc0P634UnvIj4Gdmth54lqjllIg0mHI0fd20uY8bVnXnzX2oYrr8atIayt0vAC7IWP0A8NaEfV8G3luNdIlI5ZSrc1tv31ZazBKHGy+05ZMUTz24RaQqytm5LSlQqE9EZSlYiEhVJHV6K5f21pRaPlWYBhIUkbLKNppstvkiymHkTsMVKCpMwUJEyibb3BLzlqzlud7keawL0WLGthzNajXYX+WpGEpEyibb3BI9vX04pQ3XAbDNnQcXHJe1j4UG+6s8BQsRKZtK/cJPBwMN9lc7KoYSEaA8M9eNbW8dVKe7JPFgoMH+akfBQkRyzlwHhd+c58ycNGBq0kKNakvRNmI43T292/tRJHWw02B/taFgISJZZ6678Oa1vNy3bUAQWfnwsyy/b2NiiyeATy9aXdT5W1MtXDDrYAWBOqY6CxHJWtewaXNfYhC5esUj/aY3Pe/GNXR2RWN/zp7aUdBgf6Pack9GJPVFOQsRKbquIbNVU2/fVs5ZvJoLb15Lz+Y+2ttSeY/Rs7mv5NZRUn3KWYhI1lZG7a35b/pp7lFOxIn+590//M/MmUh9UrAQEWZP7eCikybT0d7ar2jo+EP2zjsceDn09m3l4qXrqnAmKZWKoUQEGNgsdd6Stbz06paqFRWpF3Z9U85CRID+kxM50NPbN2Dq00oaZqaiqDpWcLAws7ZKJkREaqsckxMNxlZ31V3UsbzBwszeZmb3APeF5UPM7LsVT5mIFC09x/XEubcyfcGyom689VAMpLqL+lVIzmIhMBN4BsDd7wb+sZKJEpHiZRYjFdvKqF4G46uHoCUDFVTB7e4bzPq1iahdXlVEEmXrhX3x0nVZO7zFx4Nqb0sxzGBbjTs/1EvQkv4KCRYbzOxtgJtZCjgbuLeyyRKRYmX7Rf5YT++AoOAeVWDHFdI3olhGccOSawTZ+lVIMdRZwCeADqAbmBKWRaSO7J6lA93wYXDOotXbi6c2be4bECgqYVRbioWnTuGhHPNQjGpLDejboWE/6lPenIW7Pw2cVoW0iEiJOru6ee7l5ADQt63y54/nIFrM+MBh+zB/9uTt25NGo9XggY0lb7Aws5+QkJN0949WJEUiUpR0xbbXsK4hfuqt7tywqptp+47eHgg0D0XjK6TO4pbY452BfwIeq0xyRKRYte4fkSSpYl3zUDS2Qoqhbogvm9k1wP9VLEUiUpR6bWpar+mS0pQy3Mf+wGvKnRARKU29NjWt13RJaQqps3iBqEgyXYf1BPC5CqdLRPJIN4ct95zX5aAmsENPIcVQu1YjISJSuMw5s2ttVOi78Vxvnyqvh6iCenCbWQewb3x/d/9tpRIlIrnVS6V2a6pFfSOaRCHFUF8FTgXuYccwHw4oWIhUWb0VPSlQNI9CchazgUnu/kqlEyMi/cdrGtveyoQ9WlnxwCa21rIjRRYKFM2jkNZQDwCFT8RbADNrN7Przew+M7vXzI4ws9FmdpuZ/T38HxX2NTO71MzWm9lfzOzN5UyLSD1JGjn2zvufrVmgGNWWyjoPd7YhPGRoKiRYbAZWm9n3w037UjO7dJDn/SbwS3c/EDiEaGDCucDt7r4/cHtYBngPUXPd/YEzgcsGeW6RulUvdRFpbSOGM++Eg2lNtfRbr9ZOzaeQYqgl4a8szGx3ovkw/gXA3V8FXjWzE4EZYbcrgDuImuieCFzp7g6sCLmSvd398XKlSaRe1KIjW0d7a9Y6kMd6ejVUhwCFNZ29wsxagfHuXo4prCYCG4GfmNkhwCqiYc/3igWAJ4C9wuMOYEPs+Y+Gdf2ChZmdSZTzYPz48WVIpkjlZdZPtKaGsbkaI/8FHzp8PPNnT2b6gmWJASPdsU5DdUgh06rOAlYDvwzLU8xsMDmN4cCbgcvcfSrwEjuKnAAIuYiiCmnd/XJ3n+bu08aMGTOI5IlUR1L9RDUDBbB9ZNg5MyepqElyKqTOYh7wVqAHwN1XA68bxDkfBR5197vC8vVEweNJM9sbIPx/KmzvBvaJPX9cWCfS0GpdPxGvoJ49tYOLTpqsuSUkq0LqLPrc/bmMaVVL/vnj7k+Y2QYzmxSKtd5J1IfjHuB0YEH4f1N4yhLgP8zsWuAw4DnVV0ijyCxmipf113KgPYMBuQYVNUkuhQSLtWb2QaDFzPYHPgX8fpDn/SRwtZmNIGqa+xGiXM5iMzsDeBh4X9j3F8CxwHqillkfGeS5Raoic0iO7p5ePr1oNRfevJYLZh3M2BwVy5XmqI+EFMc8T/ttM2sDvgAcHVYtBea7+8sVTlvJpk2b5itXrqx1MqTJZas0hqg+4ORDO7hhVXdNiqJazNjmrpZN0o+ZrXL3aUnbCqmzONDdv+Dubwl/59dzoBCpF7mKmXr7trL8vo2cfGhtbtJb3bdXqp934xo6u1QNKLkVEiy+HnpZf9nM3ljxFIkMEe1tuQc+6O7p5aoVj1QlLSNHtGBEOYpM6VntRHLJGyzc/UjgSKK+Ed83szVmdn7FUybSoDq7upn6pV+xaXNfrZOyXXvbCB5ccBzbshQ7a1Y7yaegmfLc/Ql3vxQ4i6jPxRcrmiqROtPZ1c30BcuYOPdWpi9YlrXYJl2pXU+BAnYEg2yz12lWO8mnkE55bzCzeWb2V+BbRC2hxlU8ZSJ1IqnzXFI5f2dXN+cuvrsmFdYPLTiOhxYcl3XQv3QwUOc7KVUhOYsfA5uAo919hrtf5u5P5XuSyFCR1Hkus5w/HVBqMTpsvHNdvkH/1PlOSlXI2FBHpMeGqkJ6ROpOtvL8+Pp5S9bWJEeRmSsoZNA/db6TUhQyU94s4GvACGCimU0BvuTuJ1Q6cSL1IFvnuXTRzvmda+jprU0dRVKuQMFAKqGQHtzziMaGugOisaHMbGIF0yRSE9mG5pgzc1K/ntgQDZfR3dPLlAt/VbNAAeqFLdVT6thQ9Te/o8ggJA3Ncd6Na4D+RTvdPb0YO74AtQwUmqlOqqlWY0OJ1JVsldjnLF7NpxetBqC9NcWotlRdNItNtZhaMElVFRIsPkk0NtQrwDVE81p8uZKJEhmsXKO9JslWiR1v3FTLXERcW2oY/33Sm1QEJVVVSGuozUTB4gsAZjYJ+DbwscomTaQ0hRQpZarlCLDZdLS3cuSBY1h+30ZNZyo1lzVYmNmbiFpBjQU6ge8QBYnDgK9XJXUiJcjVLyLbjTapErvW7px7VK2TILJdrk55PwB+DpwMPE00zMf9wH7uvrAKaRMpSSH9IjLFO6uJyEC5gsVO7v5Td1/n7pcAL7n7ZzU8udS7wYx/tPnVLeVOTklG5RmxVqTactVZ7GxmU4malAO8El929z9XOnEipUgqUor3dE6q/L5u5SPcef+zVU9re2tqQMV5qsW4YNbBVU+LSC5ZZ8ozs+U5nufuXrcFqpopT7K1hsqs/K6lUW0pur54dNEtt0QqJddMeXmnVW1EChaSTa6pTqsp1WJcfMohCgpSVwY7rarIkFGrSX7aUsP6jfSqQCGNppBOeSJDRiX7U3S0t7LppVfY3LdtwLYRw1vUFFYaWq5+Fil3r48uqyJFyqwHSHduq1SgSFegnxOGBsn0XJ30/hYpVa5iqD+YWaeZnWVmE6qUHpFBS5rZ7qoVj1QsUBhw8qHRsOCatlSGqqzBIlRyfDosXmJmfzKzhWZ2tJntVJ3kiRQvqQd3JTmw/L6NgKYtlaErZ52Fuz8EfA/4npmlgLcDxwDzzWyjux9X+SSKFKcWrZ3SFeeFzFQn0ogKruAO9RfLwh9mpk+/1KUWs6rPhR0vZtJMdTIUldx01t27y5kQkXKpdqAw4MgDx1T1nCLVpn4WMqR0dnVj+XcrKwduWNVNZ5d+P8nQVVSwMLNhZrZbpRIjMhidXd2cu/jumsz5mx4CXWSoyltnYWY/B84CtgJ/AnYzs2+6+8WVTpxIkqSxlADOu3FN1Yug4mrVO1ykGgqp4D7I3Z83s9OA/wXmAqsABQupuqRZ8OZcfzdbtnpNchRx6kshQ1khxVCp0Gx2NrAktIoa9PfSzFrMrMvMbgnLE83sLjNbb2aLzGxEWL9TWF4ftk8Y7LmlcV1489oBfSj66iBQAOpLIUNaIcHi+8BDwEjgt2a2L/B8Gc59NnBvbPmrwEJ33w/YBJwR1p8BbArrF4b9pAl1dnWzaXN9DpvR3ppSc1kZ0vIGC3e/1N073P1YjzwMHDmYk5rZOOA44Idh2YCjgOvDLlcQ5WQATgzLhO3vDPtLk6nXCuTWVAvzTtBkRTK0FVLBvRfw38BYd3+PmR0EHAH8aBDnvQT4LLBrWN4D6HH39JyWjwLpn2kdwAYAd99iZs+F/Z/OSOeZwJkA48ePH0TSpF5kVmTXah6KUW0p2kYMHzAooXpoSzMppIL7p8BPgC+E5b8BiygxWJjZ8cBT7r7KzGaUcowk7n45cDlEkx+V67hSG+d3ruHqFY9sr4vo7unFKENlWUxrahijR+6UMwi1plq4YNbBCgbS9Aqps9jT3RcD2yD6dU/UjLZU04ETzOwh4Fqi4qdvAu1mlg5e44B0D6duYB+AsH134JlBnF/qXGdXd79AkVbuXwA7p6I5Ji45dcqAwf8gylFcdNJkBQoRCstZvGRmexC+q2Z2OPBcqSd09/OA88KxZgD/6e6nmdl1wClEAeR04KbwlCVh+Q9h+zIfinPBClDdjtzWU7MAAA+jSURBVHU9obJcg/+J5FdIsPgM0Q379WZ2JzCG6KZdbp8DrjWz+UAXO4q5fgT8zMzWA88C76/AuaUOpPtQVKtjnQb/Eylc3mDh7n82s3cAk4jGTFtXrhn03P0O4I7w+AHgrQn7vAy8txznk/qVzlFUK1BojgmR4uSaVvWkLJsOMDPc/cYKpUmaTCVzFK2pFi46aTKgYiaRwciVs5iVY5sDChZSFpWa2a4jIygoOIiULmuwcPePVDMh0jwq2X+ixYwPHLYP82dPLtsxRaTAmfLM7DjgYGDn9Dp3/1KlEiVDz/mda7jmrg0DiprK0X+ixYz7Lzp2UOkTkdwK6cH9PaCNaIiPHxK1hPpjhdMlDayzq5vPXn83r24tLAQ4DCpg1HJYcpFmUUinvLe5+4eJBvO7kGiojwMqmyxpVJ1d3ZyzaHXBgSJtMLf7Dg0NLlJxhRRDpQuUN5vZWKLe03tXLknSSDLrHza/uqWqw4WrCaxIdRQSLG4xs3aiyY7+TPQj8AcVTZXUlaSZ6WZP7UiciKga0kVWma2dRKRyCumU9+Xw8IYwUdHO7l7ycB/SWJICwnk3rgHK2+R1mMG2ArMk6UBx59yjynJuEckva52Fmb3FzF4bW/4wsBj4spmNrkbipPaSAkJv31YuXrqurDmJQgNFmua7FqmuXBXc3wdeBTCzfwQWAFcSDSJ4eeWTJvUg2025VnNLpGm+a5HqyhUsWtz92fD4VOByd7/B3f8L2K/ySZN6UKmbcssgJjtUpbZI9eUMFrH5Jd4JLIttK6gznzS+OTMnJc71MBij2lJsK7JvxLAQWzraWzXHhEgN5LrpXwP8xsyeJmo++zsAM9uPQcxnIfUpW4unzLkeytEsdtPmPjqKHOZjm+/IUShQiFSf5ZpHKEx0tDfwK3d/Kaw7ANjF3f9cnSQWb9q0ab5y5cpaJ6NhZLZ4Aki1GCNHDOe53r5+wWP6gmWDrq9Ij9901YpHBmyb/vrRPPRMb9ZzqBWUSOWY2Sp3n5a0LWcPbndf4e7/kw4UYd3f6jlQSPGSWjz1bXV6evtwosrscxatZsLcW9n86pZBn2+rO8vv25i47aFnerlz7lFkq9FQKyiR2ihkuA8Z4grJKaTzn5s2D37eq4721qw3/fT6bBXragUlUhsKFjKolkmlmLBHK8OynDMdDJIq1tUKSqR21KqpScUrtKs9Zuvv73828ZzxYJBZsa7Z7URqS8GiCSVVaFdTUqBoMRvQJDbeGktEakvFUE2oUtOYDsY2dwUGkTqmnEUTKPc0ptNfP5q1j71AT29yZXcpExmp4lqkvilY1KlsneRKOU7mqLHZbuaF3OQvOXVKv3Qk9buIDx9+4c1rt7egam9Ncfwhe3PDqu5+ORtVXIvUPwWLOpRrWPBiA0ZSkVO2gOBEdQfZpim1hPPnagKbrc5h2r6jVXEt0mAULOpQrmHBi72pFtuJLdd81klFRdmKtXIVK6niWqTxqIK7DuXrsFaMctUFZCsqUn8IkeagnEUdKuXXelpmXceRB44ZUEdQLANOPjQ5N6D+ECLNIedAgo2q0QcSTOoH0ZpqyTs09/mda7h6xSP96iRaUy2cfGgHy+/byGM9vQzLUSeRj+a8FhnaSh5IUGpj9tQOLjppMh3trRiFzeHQ2dXNVRmBAqK6jmvu2sCcmZN4cMFxfOCwfUpOV7qivbOru+RjiEhjUjFUnSq2EvjCm9dm3bbVfXtrqmyjvRaq1Ip2EWlsylkMEflGg03f5MsxxLeGCRdpPlUPFma2j5ktN7N7zGytmZ0d1o82s9vM7O/h/6iw3szsUjNbb2Z/MbM3VzvN9ayzq5vpC5bl35GoGKnQ1lEdOfZTb2uR5lOLnMUW4Fx3Pwg4HPiEmR0EzAVud/f9gdvDMsB7gP3D35nAZdVPcn06v3MN5yxaXfDwHQYceeCYvHNqp2eju+TUKWoWKyJADYKFuz+enmnP3V8A7gU6gBOBK8JuVwCzw+MTgSs9sgJoN7O9q5zsqkjnEibOvZXpC5blrEg+7Qd/SKzQzsWJ6izSlefAgBnpMocJL7aiXUSGpppWcJvZBGAqcBewl7s/HjY9AewVHncAG2JPezSsezy2DjM7kyjnwfjx4yuW5krJN8RHvP9Ea2oYm/u2lXSezGE48o1Bpd7WIgI1DBZmtgtwA/Bpd3/eYjOnububWVGdAdz9cuByiPpZlDOt1ZBriA+gXyApNVDAwPoGBQMRKURNWkOZWYooUFzt7jeG1U+mi5fC/6fC+m4g3jlgXFg3pOQa4qNc80+ovkFESlWL1lAG/Ai4192/Edu0BDg9PD4duCm2/sOhVdThwHOx4qohI1sLo7HtrWVpqqr6BhEZjFrkLKYD/wwcZWarw9+xwALg3Wb2d+BdYRngF8ADwHrgB8DHa5Dmiss1IN/uramCjrH/a0YmHuOSU6dw59yjFChEpGRVr7Nw9/9jYCOctHcm7O/AJyqaqDqQa0C+XL2zIZqD4gOH7cP82ZPLNmmSiEicBhJsABPn3pq1iWwhAwyKiBRCAwk2uFw9puMtpkREKkUDCdaxdJFSrnmzQWM1iUjlKVjUqcxOerkKCzVWk4hUmoqh6lS2vhW5hucQEakU5SxqJF+rpWxFS07UZ0KtnUSkmhQsaiDfOFCQfR7u9IiwIiLVpGKoGsg2DtS8JTv6U+TqpCciUm0KFjWQrYipp7dv+7DkGh5cROqJiqFqIFsRE9BvfmuNCCsi9UI5ixrIVZSkPhMiUo8ULGpg9tQORrUlDw6oPhMiUo8ULGrkglkHqwJbRBqG6ixqJNcosyIi9UbBooZUgS0ijULFUCIikpeChYiI5KViqCrSLHYi0qgULKqkkPGgRETqlYqhqiTbeFCa5U5EGoFyFgUoR/FRtp7Z6rEtIo1AOYs80sVH3T29ODuKj9ID/hUqW89s9dgWkUagYJFHuYqPNOS4iDQyFUPlUa7iI/XYFpFGpmCRIbN+YvfWFD29fQP2K6X4SD22RaRRKVjEJDVvTbUYqWFG3zbfvp+Kj0Sk2ajOIiapfqJvq7PLzsM1Y52INDXlLGKyTne6uY+uLx5d5dSIiNQP5Sxi1LxVRCSZgkXMkQeOwTLWqX5CRETBYrvOrm5uWNWNx9YZcPKhasEkItIwwcLMjjGzdWa23szmlvv4SZXbDiy/b2O5TyUi0nAaIliYWQvwHeA9wEHAB8zsoHKeQ2M3iYhk1xDBAngrsN7dH3D3V4FrgRPLeQJVbouIZNcowaID2BBbfjSs287MzjSzlWa2cuPG4ouONHaTiEh2jRIs8nL3y919mrtPGzNmTNHPnz21g4tOmqzOdyIiCRqlU143sE9seVxYV1Yau0lEJFmj5Cz+BOxvZhPNbATwfmBJjdMkItI0GiJn4e5bzOw/gKVAC/Bjd19b42SJiDSNhggWAO7+C+AXtU6HiEgzapRiKBERqSEFCxERycvcPf9eDcbMNgIPD+IQewJPlyk5jU7Xoj9djx10LfobCtdjX3dP7HswJIPFYJnZSnefVut01ANdi/50PXbQtehvqF8PFUOJiEheChYiIpKXgkWyy2udgDqia9GfrscOuhb9DenroToLERHJSzkLERHJS8FCRETyUrCIqfTUrfXOzH5sZk+Z2V9j60ab2W1m9vfwf1Qt01gtZraPmS03s3vMbK2ZnR3WN+v12NnM/mhmd4frcWFYP9HM7grfmUVhoM+mYGYtZtZlZreE5SF9LRQsgmpM3doAfgock7FuLnC7u+8P3B6Wm8EW4Fx3Pwg4HPhE+Dw06/V4BTjK3Q8BpgDHmNnhwFeBhe6+H7AJOKOGaay2s4F7Y8tD+looWOxQ8alb6527/xZ4NmP1icAV4fEVwOyqJqpG3P1xd/9zePwC0U2hg+a9Hu7uL4bFVPhz4Cjg+rC+aa6HmY0DjgN+GJaNIX4tFCx2yDt1a5Pay90fD4+fAPaqZWJqwcwmAFOBu2ji6xGKXVYDTwG3AfcDPe6+JezSTN+ZS4DPAtvC8h4M8WuhYCEF86iddVO1tTazXYAbgE+7+/Pxbc12Pdx9q7tPIZqp8q3AgTVOUk2Y2fHAU+6+qtZpqaaGmc+iCqoydWsDetLM9nb3x81sb6JflU3BzFJEgeJqd78xrG7a65Hm7j1mthw4Amg3s+HhF3WzfGemAyeY2bHAzsBuwDcZ4tdCOYsdNHVrsiXA6eHx6cBNNUxL1YQy6B8B97r7N2KbmvV6jDGz9vC4FXg3UT3OcuCUsFtTXA93P8/dx7n7BKL7xDJ3P40hfi3Ugzsm/FK4hB1Tt36lxkmqKjO7BphBNNTyk8AFQCewGBhPNOz7+9w9sxJ8yDGzfwB+B6xhR7n054nqLZrxeryJqNK2hehH5mJ3/5KZvY6oMchooAv4kLu/UruUVpeZzQD+092PH+rXQsFCRETyUjGUiIjkpWAhIiJ5KViIiEheChYiIpKXgoWIiOSlTnnSdMxsD6JBAAFeC2wFNoblt4axwepCaJr5qrv/vtZpkeamYCFNx92fIRo5FTObB7zo7l+rVXpivX6TzABeBAoOFnmOJ1ISFUOJAGZ2qJn9xsxWmdnSMJQHZnaHmS00s5Vmdq+ZvcXMbgzzWcwP+0wws/vM7Oqwz/Vm1lbAcS8xs5XA2WY2K8yF0GVmvzazvcIAhmcB55jZajN7u5n91MxOiaX7xfB/hpn9zsyWAPeEQf8uNrM/mdlfzOzfqnk9ZehRsBABA74FnOLuhwI/BuK9919192nA94iGcPgE8EbgX0KRFsAk4Lvu/gbgeeDjYWypXMcd4e7T3P3rwP8Bh7v7VKJewJ9194fCORe6+xR3/12e1/Fm4Gx3P4BoLoXn3P0twFuAj5nZxOIvjUhExVAisBPRzf+2aEgoWoDHY9vTY4StAdamhyg3sweIBp/sATa4+51hv6uATwG/zHPcRbHH44BFIecxAniwhNfxR3dPP+9o4E2xXMjuwP4lHldEwUKEKGex1t2PyLI9Pb7Pttjj9HL6O5Q5bo4XcNyXYo+/BXzD3ZeESu15WZ6zhVAiYGbDiAJL0vEM+KS7L81yHJGiqBhKJAoAY8zsCIiGJjezg4s8xvj084EPEhUrrSviuLuzY0jr02PrXwB2jS0/BBwaHp9ANGNdkqXAv4eiMMzsADMbWfjLEelPwUIkyiGcAnzVzO4GVgNvK/IY64jm6b4XGAVcFprgFnrcecB1ZrYKeDq2/mbgn9IV3MAPgHeE4x1B/9xE3A+Be4A/m9lfge+jkgQZBI06KzJIodXSLe7+xhonRaRilLMQEZG8lLMQEZG8lLMQEZG8FCxERCQvBQsREclLwUJERPJSsBARkbz+P/6pSsq5Ly+6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This shows that there is a linear relationship between the two"
      ],
      "metadata": {
        "id": "-le5Jxnn8fhA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),cmap=\"YlGnBu\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "HioJDZ7J8ADp",
        "outputId": "ccb367e7-34d5-481e-eaa8-4ef2e0943c15"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7e3d55810>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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GhQPMzKqJa9RmZjnnRG1mlnNO1GZmOVdF6wFk5kRtZoXiGrWZWc6514eZWc65Rm1mlnNO1GZmOedEbWaWc+71YWaWczW1HR1B23OiNrNCcdOHmVnOqYCZ2onazAqlgHnaidrMisWJuhXWvP6jSn+EVaGtdxnX0SFYDq15/debfQ0najOznOtSfnXxquNEbWaFUqPo6BDanBO1mRWKB7yYmeVcAVs+nKjNrFjc9GFmlnNu+jAzy7kuTtRmZvkmN32YmeWbmz7MzHLOvT7MzHLOvT7MzHLODxPNzHLObdRmZjnnpg8zs5wrYo26iA9IzawTq2lBKUfSCEmLJNVJuqCJ/f0kzZS0QNIsSX1L9k2UtDAtJzVx7lWS3s96T2ZmhVGjyFyaI6kWuAY4EhgEnCxp0EaHXQbcHBGDgfHApem5RwNDgSHA/sB5knqUXHsYsF3me8p6oJlZNehSk72UsR9QFxEvR8Ra4BbgmI2OGQT8MX39YMn+QcCciFgXEauBBcAI2PAPwCTg/Kz35ERtZoXSkqYPSaMlzS8po0sutROwuOT9knRbqaeA49PXxwHbSOqVbh8hqZuk3sCXgJ3T484GpkfEm1nvyQ8TzaxQWtLrIyKmAFM24+POAyZLOh2YAywF6iNihqR9gYeBt4FHgHpJnwFOBA5pyYc4UZtZobRhr4+lfFwLBuibbtsgIt4grVFL6g6MjIh30n0TgAnpvl8BLwB7A58D6pSswttNUl1EfK65QJyozaxQ2rA9dx4wQFJ/kgQ9CvhG6QFps8aKiFgPXAhMTbfXAj0jYrmkwcBgYEZErAN2LDn//XJJGpyozaxg2qpGHRHrJJ0N3A/UAlMj4hlJ44H5ETGdpAnjUiVzq84BzkpP7wrMTWvNq4BT0yTdKk7UZlYotTVtNzIxIu4D7tto28Ulr+8A7mjivA9Jen6Uu373LHE4UZtZoRSxK5sTtZkViuf6MDPLuSLO9eFEbWaF4kRtZpZzXd30YWaWb65Rm5nlnBO1mVnO1TpRm5nlm2vUZmY5537UZmY517WANepMoy0l9ZF0o6Tfpe8HSfpOZUMzM2u5GmUv1SLrsPibSGaQ+kz6/gXgnysRkJnZ5mirNRPzJGui7h0RtwHrIZn+D6ivWFRmZq1Uq+ylWmRto16drgMWAJKGA+9WLCozs1aqpiaNrLIm6nOA6cCukh4CdgBOqFhUZmatlGF18aqTKVFHxBOSDgZ2BwQsioiPKhqZmVkr1FZR23NWmRK1pG9ttGmoJCLi5grEZGbWagWsUGdu+ti35PVWwGHAE4ATtZnlSqdto46I75e+l9QTuKUiEZmZbYZOm6ibsBro35aBmJm1hc7cRn03adc8kiagQcBtlQrKzKy1Om2vD+CyktfrgNciYkkF4jEz2yydtukjImZXOhAzs7ZQTSMOs8o6KdPxkl6U9K6kVZLek7Sq0sGZmbVUEef6yNr08X+Br0XEc5UMpprNmfM4EyZcz/r16znxxCMYPfrERvuXLv0zF110JStWrKJnz+5MmnQuO+7YG4BJk25i9ux5AIwdO4qjjvpiu8dv7e/aSWdy5GF78/byVQw74vyODqcwCthEnfmeljlJb1p9fT3jx1/LDTf8kHvvvYZ77plDXd3rjY6ZOHEqxx57KHfffTVjx47i8sunATBr1jyeffYl7rrrKm677XJuvPE3vP/+Bx1xG9bOfn77bI751k86OozC6czTnM6XdKukk9NmkOMlHV/RyKrIggUv0q/fp9l55x3ZYouuHH303zNz5qONjnnppdcZPnwwAMOHD96wv65uMcOG7UGXLrV067YVu+/enzlzHm/3e7D299Bjz7Pinfc7OozC6VoTmUu1yJqoewAfAF8GvpaWr1YqqGqzbNnyDc0YAH369GLZsuWNjhk4sD8zZjwCwAMPPMLq1WtYuXIVAwd+lrlzn2DNmg9ZseJdHn10AW+99Zd2jd+sSIpYo87a6+MfW3JRSaOB0QDXXTee0aNPakVoxXL++d/mkkuu4847ZzJs2B706dOL2toaDjpoKE8//SKjRp3P9ttvy5AhA6mpKWIrm1n7qKYEnFXWAS+7AT8D+kTEnpIGA1+PiB83dXxETAGmJO9eqJ7vF63Up0+vRrXgZcuW06dPr08cM3nyRQCsXr2GGTMepkeP7gCMGXMSY8Yk/5ide+4k+vffqZ0iNyueIlZzst7T9cCFwEcAEbEAGFWpoKrNXnsN4NVX32Dx4rdYu/Yj7r13Doceul+jY1aseJf169cDMGXK7YwceTiQPIhcuTLp6fj886+waNGrHHjg3u17A2YFImUv1SJr97xuEfGYGt/ZugrEU5W6dKnl4ou/xxlnjKO+fj0jRx7OgAH9uPLKX7DnngM47LD9eeyxhVxxxTQkMWzYHowbNwaAdevqOeWUCwDo3r0bkyadS5cutR15O9ZOpl39fb54wOfpvd021D06mUuuuINpt87q6LCqXhGbPhRRvmUiXX38bOD2iBgq6QTgOxFxZPmPKH7Th7Xc1ruM6+gQLIfWvP7rzU6zT/zl3sw5Z2jvo6sirWdt+jgLuA4YKGkpyQrk36tYVGZmrSRF5lL+WhohaZGkOkkXNLG/n6SZkhZImiWpb8m+iZIWpuWkku2/TK+5UNJUSV3LxZE1Ub8WEYeTrJU4MCIOiojXMp5rZtZu1ILS7HWkWuAa4EiSGUNPljRoo8MuA26OiMHAeODS9NyjgaHAEGB/4DxJPdJzfgkMBPYCtgbOKHdPWRP1K5KmAMMB99A3s9xqw4eJ+wF1EfFyRKwlWSzlmI2OGQT8MX39YMn+QcCciFgXEauBBcAIgIi4L1LAY0BfysiaqAcCfyBpAnlF0mRJB2U818ys3bSkRi1ptKT5JWV0yaV2AhaXvF+Sbiv1FNAwSvs4YBtJvdLtIyR1k9Qb+BKwc6M4kyaPbwK/L3dPWQe8fECyUMBtkrYDrgRmA+6eYGa50pJpThuP+WiV84DJkk4H5gBLgfqImCFpX+Bh4G3gEaB+o3N/SlLrnlvuQzL3DZd0sKSfAo+TLHD7D1nPNTNrL23Y9LGUxrXgvum2DSLijYg4PiL2Bv5Xuu2d9OeEiBgSEUeQVOBf+DhGjSN55ndOlnvKOjLxVeBJklr1v6ZtLmZmudOG/e3mAQMk9SdJ0KOAbzT6rKRZY0VErCcZFDg13V4L9IyI5elI7sHAjHTfGcBXgMPS88rKOuBlcER4oQAzy722StQRsU7S2cD9JM28UyPiGUnjgfkRMR04BLhUSV+/OSTP8QC6AnPTQYKrgFMjomGQ4LXAa8Aj6f7fRMT45mLJmqh3lHQnGef6MDPrKG05MjEi7gPu22jbxSWv7wDuaOK8D0l6fjR1zax5dwPP9WFmhdJW/ajzxHN9mFmhVNNaiFllTdR/kbQrEADpXB9vViwqM7NWqqZZ8bLKmqjPIulr2DDXxyvAKRWLysyslYo4H3XWAS8vA4dL+hTJ/4cPSNqoPd+HmeVKEWvUzf7jI6mHpAvTIeNHkCTo04A6PODFzHKoMz5M/DmwkmT443dJRt4IOC4i/lTh2MzMWqyICweUS9R/GxF7AUi6geQB4i5pH0Ezs9zpjIn6o4YXEVEvaYmTtJnlWQHzdNlE/QVJDUPHBWydvhcQEdFj06eambW/LCu3VJtmE3VEeBpTM6sqnbFGbWZWVYrYPc+J2swKpYjNAE7UZlYorlGbmeVe8TK1E7WZFYqcqM3M8k0q3rRMTtRmVjCuUZuZ5ZoKONGpE7WZFYqbPszMcs9NH2ZmueZeH2ZmOedEbWaWc1LxBpE7UZtZwbhGbWaWa276MDPLPXfPMzPLNdeozcxyTgWc59SJ2swKRQVcOsCJ2swKxjVqM7Ncc9OHmVnuFS9RF68fi5l1aqImcyl7LWmEpEWS6iRd0MT+fpJmSlogaZakviX7JkpamJaTSrb3l/Roes1bJW1RLg4najMrGLWgNHOVZCz6NcCRwCDgZEmDNjrsMuDmiBgMjAcuTc89GhgKDAH2B86T1CM9ZyLwHxHxOWAl8J1yd+REbWaFUqOazKWM/YC6iHg5ItYCtwDHbHTMIOCP6esHS/YPAuZExLqIWA0sAEYoaUA/FLgjPW4acGzZeyp3gJlZdalpQWnWTsDikvdL0m2lngKOT18fB2wjqVe6fYSkbpJ6A18CdgZ6Ae9ExLpmrtnkHZmZFYZa8p80WtL8kjK6hR93HnCwpCeBg4GlQH1EzADuAx4Gfg08AtS39p7c68PMCiZ7r4+ImAJM2cTupSS14AZ9022l579BWqOW1B0YGRHvpPsmABPSfb8CXgCWAz0ldUlr1Z+4ZlNcozazQpGUuZQxDxiQ9tLYAhgFTN/os3rr40UaLwSmpttr0yYQJA0GBgMzIiJI2rJPSM85DfhtuUCcqM2sUERt5tKctMZ7NnA/8BxwW0Q8I2m8pK+nhx0CLJL0AtCHtAYNdAXmSnqWpMZ+akm79L8B50iqI2mzvrHsPSUJ3tqDpNHpVy2zDfx7YeW4Rt2+WvqgwjoH/15Ys5yozcxyzonazCznnKjbl9shrSn+vbBm+WGimVnOuUZtZpZzTtRmZjnX6YaQp6OFZqZvdyQZf/92+n6/dJasXJB0CLA2Ih7u6FgsIakeeJrk784rwDcbhgybVUqnq1FHxPKIGBIRQ4BrSeaFHZKWdk/Skpr7x/IQ4O/a8Hq2+dakvyt7AiuAszo6ICu+TpeomyJpH0mzJT0u6X5Jn063z5L0H+msWs9J2lfSbyS9KOnH6TGflfS8pF+mx9whqVuG6/4/SfOBf5L0tXTFhycl/UFSH0mfBb4H/IukP0n6oqSbJJ1QEvf76c9DJM2VNB14Np1nYJKkeenKE2e25//PTuQR0ikqJe0q6ffpn/VcSQMlbSvptYa5ICR9StJiSV2bOj495iZJV0l6WNLLDX/e6Z/xPQ0fLGmypNPT103+nllxOFEnU21dDZwQEfuQTKoyoWT/2ogYRlL7/i1JDWpP4PSGSVeA3YGfRsTngVXAWEldy1x3i4gYFhGXA/8FDI+IvUkmJz8/Il6lcY1/bpn7GAr8U0TsRrJixLsRsS+wL/BdSf1b/r/GNkXJ6h+H8fEkPVOA76d/1ueR/D68C/yJZPpLgK8C90fER00dX3L5TwMHpcf/pEwc5X7PrAD8NRm2JEm8D6SzadUCb5bsb/iL+DTwTES8CSDpZZIpEN8BFkfEQ+lxvwB+APy+zHVvLXndF7g1rQltQdL22VKPRUTDeV8GBpfUvrcFBrTyutbY1pL+RFKTfo7kz7c7SRPV7SUzsm2Z/rwVOIlkxrRRwE/LHA9wV0SsJ/l21KdMPLvT/O+ZFYATdVKjfiYiDtjE/r+mP9eXvG543/D/b+PO6JHhuqtLXl8NXBER09MHiD/cxDnrSL8FpV+nSxfFLL2eSGpr92/iOtZ6ayJiSNq8dT/JN6ybSFbtGNLE8dOBf5e0PbAPybJNn2rmeGj8e9aQyTf82ae2Ktnf3O+ZFYCbPpK/FDtIOgCSr5KS9mjhNXZpOB/4BklTxqIWXHdbPp48/LSS7e8B25S8f5XkLzvA10mmUmzK/cCY9GsxknaT9Knst2PlRMQHJN+czgU+AF6RdCKAEl9Ij3ufZF7jK4F7IqI+IlZt6vhmvAYMkrSlpJ4kzS7Qst8zq1JO1EnN+ARgoqSnSNoUW9TTguQvy1mSngO2A36W9iDJet0fknwNfhz4S8n2u4HjGh4mAteTLPvzFHAAjWvRpW4AngWekLQQuA5/e2pzEfEkyaKlJwOnAN9J/2yeofEiqLcCp9K4uau545v6rMXAbcDC9OeT6faW/J5ZlfIQ8s2U9s64J+2uZWbW5lyjNjPLOdeozcxyzjVqM7Occ6I2M8s5J2ozs5xzojYzyzknajOznPsfvtJfo8BXmKwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "pearson r = 0.99\n",
        "A correlation of r = 0.9 suggests a strong, positive association between two variables"
      ],
      "metadata": {
        "id": "KegAbuQD8eFT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting Data for Training and Testing"
      ],
      "metadata": {
        "id": "vtQBNVQ765if"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.values\n",
        "X, y = data[:,:-1], data[:,-1]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)  # splitting in the ratio 80:20 "
      ],
      "metadata": {
        "trusted": true,
        "id": "ZJkLA68K65if"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model"
      ],
      "metadata": {
        "id": "nbknh8Ut65ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "trusted": true,
        "id": "495eeucM65ih"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6PmJ96Y65ii",
        "outputId": "df67c909-2707-44d3-9b54-c5514678f69d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making Predictions and Checking Accuracy"
      ],
      "metadata": {
        "id": "3na-ZxwG65ii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "trusted": true,
        "id": "kW2m3pEo65ij"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, y_pred)"
      ],
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeaLXc2G65ik",
        "outputId": "a2d536ef-4392-46db-aa69-2b4badc3eddc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9837324255882577"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predictions are 98.37% accurate."
      ],
      "metadata": {
        "id": "KJcGzvhZ65ik"
      }
    }
  ]
}