Copy_of_Revenue_Prediction_Using_Simple_Linear_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.8"
    },
    "colab": {
      "name": "Copy of Revenue Prediction Using Simple Linear Regression.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mayorofdata/Revenue-Prediction/blob/master/Copy_of_Revenue_Prediction_Using_Simple_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tyc3USF0ULZr",
        "colab_type": "text"
      },
      "source": [
        "# Revenue Prediction Using Simple Linear Regression\n",
        "## Problem Statement\n",
        "### An ice cream business will like to predict the daily revenue generated in dollars based on the outside air temperature(degC). In this project, I applied Linear Regression model to solve the task.\n",
        "\n",
        "### Dataset is obtained from Kaggle - https://www.kaggle.com/ahmednour/ice-cream-dataset\n",
        "### In the dataset, there are two fields - \n",
        "### - Independent variable X: Outside Air Temperature in degree Celsius and \n",
        "### - Dependent variable Y: Overall daily revenue generated in dollars. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVhF1EvjULZv",
        "colab_type": "text"
      },
      "source": [
        "### Step #1: Libraries Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NgSpUjQULZw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "22c06661-46a5-4e7a-de7d-f4e7a39a7b47"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgQUqWjXULZ4",
        "colab_type": "text"
      },
      "source": [
        "### Step #2: Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJcqIRT_ULZ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "f5cae904-d781-47c1-d696-2a29fd13cc2b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "IceCream = pd.read_csv(\"/content/drive/My Drive/DATA-475/IceCreamData.csv\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xg623VD4ULaD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5189eea3-64cd-49c1-bf7f-893b49938b09"
      },
      "source": [
        "IceCream.head(5)"
      ],
      "execution_count": 3,
      "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>Temperature</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.566884</td>\n",
              "      <td>534.799028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26.005191</td>\n",
              "      <td>625.190122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.790554</td>\n",
              "      <td>660.632289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.595335</td>\n",
              "      <td>487.706960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.503498</td>\n",
              "      <td>316.240194</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdioVB1BULaM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "076c64d2-f49f-4fc5-edeb-91a0b4cb4632"
      },
      "source": [
        "IceCream.tail(5)"
      ],
      "execution_count": 4,
      "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>Temperature</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>22.274899</td>\n",
              "      <td>524.746364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>32.893092</td>\n",
              "      <td>755.818399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>12.588157</td>\n",
              "      <td>306.090719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>22.362402</td>\n",
              "      <td>566.217304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>28.957736</td>\n",
              "      <td>655.660388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Temperature     Revenue\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVAIdG3eULaU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a0aa42c2-f28d-4afa-fa77-5098316480f5"
      },
      "source": [
        "IceCream"
      ],
      "execution_count": 5,
      "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>Temperature</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.566884</td>\n",
              "      <td>534.799028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26.005191</td>\n",
              "      <td>625.190122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.790554</td>\n",
              "      <td>660.632289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.595335</td>\n",
              "      <td>487.706960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.503498</td>\n",
              "      <td>316.240194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>22.274899</td>\n",
              "      <td>524.746364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>32.893092</td>\n",
              "      <td>755.818399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>12.588157</td>\n",
              "      <td>306.090719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>22.362402</td>\n",
              "      <td>566.217304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>28.957736</td>\n",
              "      <td>655.660388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 2 columns</p>\n",
              "</div>"
            ],
            "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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqI6hlvQULaY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d1c8848d-051e-4b50-bdd9-c2a4ea1533ea"
      },
      "source": [
        "IceCream.describe()"
      ],
      "execution_count": 6,
      "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>Temperature</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.232225</td>\n",
              "      <td>521.570777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.096388</td>\n",
              "      <td>175.404751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.122258</td>\n",
              "      <td>405.558681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.392791</td>\n",
              "      <td>529.368565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>27.740674</td>\n",
              "      <td>642.257922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Temperature      Revenue\n",
              "count   500.000000   500.000000\n",
              "mean     22.232225   521.570777\n",
              "std       8.096388   175.404751\n",
              "min       0.000000    10.000000\n",
              "25%      17.122258   405.558681\n",
              "50%      22.392791   529.368565\n",
              "75%      27.740674   642.257922\n",
              "max      45.000000  1000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AZOZboWULaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "b28b479f-113d-4ba3-88c4-31f48d29ce26"
      },
      "source": [
        "IceCream.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Temperature  500 non-null    float64\n",
            " 1   Revenue      500 non-null    float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 7.9 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96Sp8nr6ULan",
        "colab_type": "text"
      },
      "source": [
        "### Step #3: Visualize Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYpMeS1CULao",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "af983b29-7dc4-414a-ed61-f50767b0f8b7"
      },
      "source": [
        "sns.jointplot( x= 'Temperature', y='Revenue', data = IceCream, color = 'blue')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7feb57fa0160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b14X-72XULas",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "6d4d2be4-c85b-40b1-efb9-ecfc04ed208c"
      },
      "source": [
        "sns.pairplot(IceCream)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7feb57f8cb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBbY4pgRULa2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "a1ee8ca8-7526-4399-d583-9a13ed228f43"
      },
      "source": [
        "sns.lmplot(x='Temperature', y='Revenue', data=IceCream)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7feb54fa2198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "zyxL_YNuULa-",
        "colab_type": "text"
      },
      "source": [
        "### Step #4: Create Testing and Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38BAUnb3ULa_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "206a7a3c-6efc-4a3d-8631-294e3b2891bb"
      },
      "source": [
        "X = IceCream[['Temperature']]\n",
        "X"
      ],
      "execution_count": 11,
      "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>Temperature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.566884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26.005191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27.790554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.595335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11.503498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>22.274899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>32.893092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>12.588157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>22.362402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>28.957736</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Temperature\n",
              "0      24.566884\n",
              "1      26.005191\n",
              "2      27.790554\n",
              "3      20.595335\n",
              "4      11.503498\n",
              "..           ...\n",
              "495    22.274899\n",
              "496    32.893092\n",
              "497    12.588157\n",
              "498    22.362402\n",
              "499    28.957736\n",
              "\n",
              "[500 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqko3cKRULbH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "4788e9d4-a166-47aa-cfa2-414c74aaf1e6"
      },
      "source": [
        "Y = IceCream[['Revenue']]\n",
        "Y"
      ],
      "execution_count": 12,
      "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>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>534.799028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>625.190122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>660.632289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>487.706960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>316.240194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>524.746364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>755.818399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>306.090719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>566.217304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>655.660388</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Revenue\n",
              "0    534.799028\n",
              "1    625.190122\n",
              "2    660.632289\n",
              "3    487.706960\n",
              "4    316.240194\n",
              "..          ...\n",
              "495  524.746364\n",
              "496  755.818399\n",
              "497  306.090719\n",
              "498  566.217304\n",
              "499  655.660388\n",
              "\n",
              "[500 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtNFfxVeULbM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "714231a9-888a-4786-b049-503b97f0dc4f"
      },
      "source": [
        "y = IceCream['Revenue']\n",
        "y"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      534.799028\n",
              "1      625.190122\n",
              "2      660.632289\n",
              "3      487.706960\n",
              "4      316.240194\n",
              "          ...    \n",
              "495    524.746364\n",
              "496    755.818399\n",
              "497    306.090719\n",
              "498    566.217304\n",
              "499    655.660388\n",
              "Name: Revenue, Length: 500, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPCOjJE7ULbT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaFbGN3yULbY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryOPcMFgULbd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "176c11a6-b107-4dcf-ae27-86d6ddafc6da"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 16,
      "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>Temperature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>18.148952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>8.756004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>21.775948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>18.439982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>15.843022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>23.761436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>24.628611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>38.185199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>15.916678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>14.287196</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Temperature\n",
              "249    18.148952\n",
              "433     8.756004\n",
              "19     21.775948\n",
              "322    18.439982\n",
              "332    15.843022\n",
              "..           ...\n",
              "106    23.761436\n",
              "270    24.628611\n",
              "348    38.185199\n",
              "435    15.916678\n",
              "102    14.287196\n",
              "\n",
              "[400 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoyKBcBAULbj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "e469986a-707d-43f6-ccdd-f965126db2f2"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 17,
      "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>Temperature</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>29.704183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>27.375401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>23.984641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>21.144047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>26.595054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>24.960446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>27.780500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>28.790102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438</th>\n",
              "      <td>17.132795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>18.483141</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Temperature\n",
              "361    29.704183\n",
              "73     27.375401\n",
              "374    23.984641\n",
              "155    21.144047\n",
              "104    26.595054\n",
              "..           ...\n",
              "347    24.960446\n",
              "86     27.780500\n",
              "75     28.790102\n",
              "438    17.132795\n",
              "15     18.483141\n",
              "\n",
              "[100 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3OcQ7DJULbo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43b5aee7-6f3e-419e-fde1-2760f456b855"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTRE-x7UULbs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b6f96aa6-c989-42ef-bd01-42f05a287249"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QaYya2d8ULbx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b06e815-a862-46c3-cbbe-f43e83e81395"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-1raiLdULb4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7e3c70e8-36f7-4fe6-c5ee-3ddf4720d9dd"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsXsOVp8ULb-",
        "colab_type": "text"
      },
      "source": [
        "### Step #5: Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9cStiOtULb_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d7ea3ea-8b15-4ce6-e618-ba3d2db759c3"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression(fit_intercept = True)\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxyVQwZTULcI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "617dc60d-1c11-4053-b8b1-c4cc3a2bc4c8"
      },
      "source": [
        "print('Linear Model Coefficeint (m)', regressor.coef_)\n",
        "print('Linear Model Coefficeint (b)', regressor.intercept_)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Model Coefficeint (m) [21.38197386]\n",
            "Linear Model Coefficeint (b) 46.80464127742374\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbVFeQMKULcQ",
        "colab_type": "text"
      },
      "source": [
        "### Step #6: Test the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkomaEoOULcR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "898e3c18-5503-4b19-c077-b976fd323c77"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RyQEYQcULca",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_predict = regressor.predict(X_test)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38Z_8yQJULcg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "b0962f9f-14d3-484c-8a75-c992e35af8d4"
      },
      "source": [
        "y_predict"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([681.93870622, 632.14474995, 559.64360486, 498.90609993,\n",
              "       615.45939165, 304.72417213, 285.79420948, 315.60842003,\n",
              "       506.26427154, 685.69744019, 723.96030494, 400.84625166,\n",
              "       431.61634142, 671.36600031,  52.51422053, 198.77682819,\n",
              "       523.08595488, 563.90495549, 583.83579144, 682.24517768,\n",
              "       556.2285085 , 783.88766163, 655.44109362, 570.0735459 ,\n",
              "       912.21841974, 664.74778505, 315.96428407, 631.00983241,\n",
              "       731.1169322 , 698.02984963, 435.08453949, 471.22058847,\n",
              "       710.29440571, 799.81874771, 641.02153776, 493.66044553,\n",
              "       744.9247237 , 318.10831232, 262.69083171, 132.04437543,\n",
              "       582.55738268, 230.41054193, 547.29799783, 393.00654153,\n",
              "       436.66704225, 593.02780835, 572.09312165, 125.16251036,\n",
              "       474.79444877, 530.45025905, 623.19251713, 503.4813544 ,\n",
              "       484.60103791, 498.58705266, 551.22738648, 630.80646076,\n",
              "       573.12703075, 679.486141  , 171.29759926, 660.49458416,\n",
              "       583.75017395, 704.57274239, 535.82240023, 630.12763484,\n",
              "       676.79854535, 411.36168509, 508.88286115, 494.71237942,\n",
              "       731.12113825, 312.64970607, 318.45658383, 454.48599433,\n",
              "       795.88908859, 424.3427197 , 520.47071347, 688.86698747,\n",
              "       804.96958246, 448.21981638, 493.70018478, 493.61560689,\n",
              "       546.05307987, 520.86863586, 663.24179618, 547.66983485,\n",
              "       589.3531778 , 697.41089087, 239.06622088, 626.63543807,\n",
              "       585.44702174, 768.32242947, 428.92615255, 522.0216248 ,\n",
              "       610.64187899, 353.88023698, 562.40093312, 580.5082378 ,\n",
              "       640.80655591, 662.39383884, 413.13762416, 442.01067869])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBMhf7qWULco",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "de9febe5-fdc7-419e-a3b1-5f17e7ad3be0"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "361    659.873287\n",
              "73     623.248701\n",
              "374    559.135869\n",
              "155    497.752318\n",
              "104    627.901841\n",
              "          ...    \n",
              "347    564.310532\n",
              "86     643.944327\n",
              "75     653.986736\n",
              "438    412.082357\n",
              "15     469.909033\n",
              "Name: Revenue, Length: 100, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJi8Tg0kULct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "d28ea460-75bf-4c82-af2c-725eaf619663"
      },
      "source": [
        "plt.scatter(X_train,y_train, color=\"blue\")\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'red')\n",
        "plt.xlabel(\"Temperature [DegC]\")\n",
        "plt.ylabel(\"Revenue [Dollars]\")\n",
        "plt.title(\"Revenue vs Temperature at Ice Cream Stand (Training set)\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Revenue vs Temperature at Ice Cream Stand (Training set)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "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": "code",
      "metadata": {
        "scrolled": true,
        "id": "eeNQJcunULcy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "2660b726-b16c-4621-8c08-902010c73fa2"
      },
      "source": [
        "plt.scatter(X_test,y_test, color=\"blue\")\n",
        "plt.plot(X_test, regressor.predict(X_test), color = 'black')\n",
        "plt.xlabel(\"Temperature [DegC]\")\n",
        "plt.ylabel(\"Revenue [Dollars]\")\n",
        "plt.title(\"Revenue vs Temperature at Ice Cream Stand (Testing set)\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Revenue vs Temperature at Ice Cream Stand (Testing set)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "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": "code",
      "metadata": {
        "scrolled": true,
        "id": "p2Z32dcTULc5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "56d85374-a7a4-4b58-ee6a-7c20c1a833c9"
      },
      "source": [
        "# Predict the revenue in dollars that will be generated when the temperature is 40 degC\n",
        "T = np.array([40]).reshape(1, 1)\n",
        "revenue = regressor.predict(T)\n",
        "revenue"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([902.08359549])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JoUnwEATULdB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "24db7b89-a69d-401d-e7ea-0c9984319c12"
      },
      "source": [
        "# Predict the revenue in dollars that will be generated when the temperature is 10 degC\n",
        "T = np.array([10]).reshape(1, 1)\n",
        "revenue = regressor.predict(T)\n",
        "revenue"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([260.62437983])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59NPAgxNULdF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e8753310-3955-4cb6-b4f2-e09c490f52f0"
      },
      "source": [
        "# Predict the revenue in dollars that will be generated when the temperature is 25 degC\n",
        "T = np.array([25]).reshape(1, 1)\n",
        "revenue = regressor.predict(T)\n",
        "revenue"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([581.35398766])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    }
  ]
}