Honey_Production_Project_.ipynb

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  "metadata": {
    "colab": {
      "name": "Honey Production Project_.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPzANNGBRDGZmDRCzLLPywb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devzohaib/Honey-Production/blob/master/Honey_Production_Project_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGNxv0x7IhpU"
      },
      "source": [
        "\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np\r\n",
        "from sklearn import linear_model\r\n",
        "\r\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/devzohaib/Honey-Production/master/honeyproduction.csv\")"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBReFtLOJ3xF"
      },
      "source": [
        "### Use .head() to get a sense of how this DataFrame is structured."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "yWDI2uEaJxOp",
        "outputId": "eb9611d3-f3a8-4643-9262-5d101e409043"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 2,
      "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>state</th>\n",
              "      <th>numcol</th>\n",
              "      <th>yieldpercol</th>\n",
              "      <th>totalprod</th>\n",
              "      <th>stocks</th>\n",
              "      <th>priceperlb</th>\n",
              "      <th>prodvalue</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AL</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>71</td>\n",
              "      <td>1136000.0</td>\n",
              "      <td>159000.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>818000.0</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AZ</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>60</td>\n",
              "      <td>3300000.0</td>\n",
              "      <td>1485000.0</td>\n",
              "      <td>0.64</td>\n",
              "      <td>2112000.0</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AR</td>\n",
              "      <td>53000.0</td>\n",
              "      <td>65</td>\n",
              "      <td>3445000.0</td>\n",
              "      <td>1688000.0</td>\n",
              "      <td>0.59</td>\n",
              "      <td>2033000.0</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CA</td>\n",
              "      <td>450000.0</td>\n",
              "      <td>83</td>\n",
              "      <td>37350000.0</td>\n",
              "      <td>12326000.0</td>\n",
              "      <td>0.62</td>\n",
              "      <td>23157000.0</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CO</td>\n",
              "      <td>27000.0</td>\n",
              "      <td>72</td>\n",
              "      <td>1944000.0</td>\n",
              "      <td>1594000.0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1361000.0</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state    numcol  yieldpercol  ...  priceperlb   prodvalue  year\n",
              "0    AL   16000.0           71  ...        0.72    818000.0  1998\n",
              "1    AZ   55000.0           60  ...        0.64   2112000.0  1998\n",
              "2    AR   53000.0           65  ...        0.59   2033000.0  1998\n",
              "3    CA  450000.0           83  ...        0.62  23157000.0  1998\n",
              "4    CO   27000.0           72  ...        0.70   1361000.0  1998\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWZTy8UqKSwL"
      },
      "source": [
        "### For now, we care about the total production of honey per year. Use the `.groupby()` method provided by pandas to get the mean of `totalprod` per year. Store this in a variable called `prod_per_year.`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "eYlOvpYLKMcb",
        "outputId": "d0d7d633-62fa-49bc-e20c-f9ac785e57dc"
      },
      "source": [
        "prod_per_year = df.groupby('year').totalprod.mean().reset_index()\r\n",
        "prod_per_year.head()"
      ],
      "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>year</th>\n",
              "      <th>totalprod</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1998</td>\n",
              "      <td>5.105093e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1999</td>\n",
              "      <td>4.706674e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000</td>\n",
              "      <td>5.106000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2001</td>\n",
              "      <td>4.221545e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2002</td>\n",
              "      <td>3.892386e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year     totalprod\n",
              "0  1998  5.105093e+06\n",
              "1  1999  4.706674e+06\n",
              "2  2000  5.106000e+06\n",
              "3  2001  4.221545e+06\n",
              "4  2002  3.892386e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ua8WBCdmKlch"
      },
      "source": [
        "### Create a variable called `X` that is the column of `years` in this `prod_per_year` DataFrame. After creating `X`, we will need to reshape it to get it into the right format, using this command: \r\n",
        "`X = X.values.reshape(-1, 1)`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8JVIOAAKcoI"
      },
      "source": [
        "X = prod_per_year['year']\r\n",
        "X = X.values.reshape(-1,1)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PP7fbhkoMBMu"
      },
      "source": [
        "### Create a variable called `y` that is the `totalprod` column in the `prod_per_year` dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUj43E73MAg2"
      },
      "source": [
        "y = prod_per_year['totalprod']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BomjOmOMQBE"
      },
      "source": [
        "\r\n",
        "### Using `plt.scatter()`, plot y vs X as a scatterplot. Display the plot using `plt.show()`. Can you see a vaguely linear relationship between these variables?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "-00gqH6UMXbg",
        "outputId": "38e01190-c43c-4366-f57c-a3050cc3ccff"
      },
      "source": [
        "plt.scatter(X,y)\r\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GogdoBO6MpcI"
      },
      "source": [
        "# Create and Fit a Linear Regression Model\r\n",
        "\r\n",
        "### Create a linear regression model from scikit-learn and call it `regr`. Use the `LinearRegression`() constructor from the `linear_model` module to do this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WKqcAJqaMyqd",
        "outputId": "12b38fa7-2185-46c1-e6f7-2949e5d91ae0"
      },
      "source": [
        "regr = linear_model.LinearRegression()\r\n",
        "regr.fit(X,y)\r\n",
        "regr.coef_\r\n",
        "regr.intercept_"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181208083.10732976"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFH_Nv00Qu32"
      },
      "source": [
        "y_predict = regr.predict(X)\r\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJkZ1HDvQ0qL"
      },
      "source": [
        "Plot y_predict vs X as a line, on top of your scatterplot using plt.plot().\r\n",
        "\r\n",
        "Make sure to call plt.show() after plotting the line."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "9QhFeb1ZQ1k-",
        "outputId": "c765caaf-6423-49eb-899a-c946a283adb9"
      },
      "source": [
        "plt.scatter(X,y)\r\n",
        "plt.plot(X,y_predict)\r\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dw7lW44MTREx"
      },
      "source": [
        "###So, it looks like the production of honey has been in decline, according to this linear model. Let’s predict what the year 2050 may look like in terms of honey production. Our known dataset stops at the year 2013, so let’s create a NumPy array called X_future that is the range from 2013 to 2050. \r\n",
        "\r\n",
        "You can think of reshape() as rotating this array. Rather than one big row of numbers, X_future is now a big column of numbers — there’s one number in each row."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rUU_XsnTZCy"
      },
      "source": [
        "X_future = np.array(range(2013, 2051))\r\n",
        "X_future = X_future.reshape(-1, 1)\r\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K86tu1kTT2MI"
      },
      "source": [
        "###Create a list called `future_predict` that is the y-values that your regr model would predict for the values of `X_future`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qOF2Rd9TA47"
      },
      "source": [
        "future_predict = regr.predict(X_future)\r\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwQ2hYdRUG12"
      },
      "source": [
        "\r\n",
        "### Plot future_predict vs X_future on a different plot.\r\n",
        "\r\n",
        "####How much honey will be produced in the year 2050, according to this?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "XOq49B6VUMTc",
        "outputId": "7573ce94-1267-43a8-cf09-425fc55722fd"
      },
      "source": [
        "plt.plot(X_future,future_predict)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f24ebf23438>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}