Notebooks/PYTHON_CRASH_COURSE_DAY_10.ipynb

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        "<a href=\"https://colab.research.google.com/github/Amey-Thakur/PYTHON-CRASH-COURSE/blob/main/PYTHON_CRASH_COURSE_DAY_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **Day 10**\n",
        "\n",
        "**Random Forest**\n",
        "\n",
        "Its a type of supervised machine learning alogorithm based on ensemble learning. The random forest combines multiple algorithm of the same type i.e, multiple decision trees, resulting in forest of trees, hence the name Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xd6JIhofdwXc"
      },
      "source": [
        "**Problem Statement**\n",
        "\n",
        "To predict the gas consumption(in millions of gallons) in 48 of the US states based on petrol tax(in cents), per capita income(in dollars), paved highways(in miles) and the proportion of population with the driving license"
      ]
    },
    {
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      },
      "source": [
        "#import libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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        "id": "-dZDM02VdwXd",
        "outputId": "432748c7-765b-4de6-ecae-e118ee333182"
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      "source": [
        "#import dataset, assign it to dataset and view the dataset\n",
        "dataset = pd.read_csv('/content/petrol_consumption.csv')\n",
        "dataset.head()"
      ],
      "execution_count": 2,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Petrol_tax</th>\n",
              "      <th>Average_income</th>\n",
              "      <th>Paved_Highways</th>\n",
              "      <th>Population_Driver_licence(%)</th>\n",
              "      <th>Petrol_Consumption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>9.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>8.0</td>\n",
              "      <td>4399</td>\n",
              "      <td>431</td>\n",
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              "      <td>410</td>\n",
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              "   Petrol_tax  Average_income  ...  Population_Driver_licence(%)  Petrol_Consumption\n",
              "0         9.0            3571  ...                         0.525                 541\n",
              "1         9.0            4092  ...                         0.572                 524\n",
              "2         9.0            3865  ...                         0.580                 561\n",
              "3         7.5            4870  ...                         0.529                 414\n",
              "4         8.0            4399  ...                         0.544                 410\n",
              "\n",
              "[5 rows x 5 columns]"
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      "source": [
        "#Prepare dataset for training, X = 0:4 columns, Y=4th column\n",
        "X = dataset.iloc[:, 0:4]  #feature variable\n",
        "Y = dataset.iloc[:, 4]    #target variable"
      ],
      "execution_count": 3,
      "outputs": []
    },
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      "cell_type": "code",
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      },
      "source": [
        "#divide the data in to training and test using sklearn's library\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, random_state=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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      "source": [
        "#Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 5,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "#Lets train the algorithm\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "regressor = RandomForestRegressor(n_estimators=200, random_state=0)\n",
        "regressor.fit(X_train, Y_train)\n",
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
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        "outputId": "da579650-668f-4b16-c917-d5a87a97baac"
      },
      "source": [
        "#Evaluating the algorithm\n",
        "from sklearn import metrics\n",
        "\n",
        "print('Mean Absolute Error: ', metrics.mean_absolute_error(Y_test, y_pred))\n",
        "print('Mean Squared Error: ', metrics.mean_squared_error(Y_test, y_pred))\n",
        "print('Root Mean Squared Error: ', np.sqrt(metrics.mean_squared_error(Y_test, y_pred)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error:  48.33899999999999\n",
            "Mean Squared Error:  3494.2330150000003\n",
            "Root Mean Squared Error:  59.112037818028234\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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        "id": "F8WO8WyEdwXf",
        "outputId": "dd4e869f-4917-43a5-84a8-a91820ce9829"
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[569.205 493.25  601.31  592.405 607.655 609.635 590.265 570.74  471.455\n",
            " 509.78 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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        "outputId": "f0feeb6f-3ca8-4c4f-d756-6fa59cffe6aa"
      },
      "source": [
        "Y_test.shape"
      ],
      "execution_count": 9,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
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      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GN_l62P1dwXf"
      },
      "source": [
        "**Support Vector Machines(SVM)**\n",
        "\n",
        "It is a type of supervised machine learning classification algorithm, which tries to find a boundary that divides the data in such a way that the misclassification error can be minimized."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrwOLCwLdwXg"
      },
      "source": [
        "**Problem statement**\n",
        "\n",
        "To classify Muffins and Cupcakes using SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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      "source": [
        "#import libaries, numpy, pandas and sklearn's svm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import svm\n",
        "\n",
        "#libraries for visualization, matplotlib and seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns;\n",
        "sns.set(font_scale=1.2)\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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        "id": "U8gcgQnHdwXg",
        "outputId": "924343f9-e037-4000-da0f-d71015e6ec10"
      },
      "source": [
        "#load the dataset as recipes variable\n",
        "recipes = pd.read_csv('/content/recipes_muffins_cupcakes.csv')\n",
        "recipes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Type</th>\n",
              "      <th>Flour</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Sugar</th>\n",
              "      <th>Butter</th>\n",
              "      <th>Egg</th>\n",
              "      <th>Baking Powder</th>\n",
              "      <th>Vanilla</th>\n",
              "      <th>Salt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>55</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>47</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
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              "      <th>2</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>47</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>45</td>\n",
              "      <td>11</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>50</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>55</td>\n",
              "      <td>27</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>54</td>\n",
              "      <td>27</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>47</td>\n",
              "      <td>26</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Muffin</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>42</td>\n",
              "      <td>21</td>\n",
              "      <td>16</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>34</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>39</td>\n",
              "      <td>13</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>38</td>\n",
              "      <td>15</td>\n",
              "      <td>23</td>\n",
              "      <td>15</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>42</td>\n",
              "      <td>18</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>36</td>\n",
              "      <td>14</td>\n",
              "      <td>21</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>38</td>\n",
              "      <td>15</td>\n",
              "      <td>31</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>36</td>\n",
              "      <td>16</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Cupcake</td>\n",
              "      <td>34</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Type  Flour  Milk  Sugar  Butter  Egg  Baking Powder  Vanilla  Salt\n",
              "0    Muffin     55    28      3       7    5              2        0     0\n",
              "1    Muffin     47    24     12       6    9              1        0     0\n",
              "2    Muffin     47    23     18       6    4              1        0     0\n",
              "3    Muffin     45    11     17      17    8              1        0     0\n",
              "4    Muffin     50    25     12       6    5              2        1     0\n",
              "5    Muffin     55    27      3       7    5              2        1     0\n",
              "6    Muffin     54    27      7       5    5              2        0     0\n",
              "7    Muffin     47    26     10      10    4              1        0     0\n",
              "8    Muffin     50    17     17       8    6              1        0     0\n",
              "9    Muffin     50    17     17      11    4              1        0     0\n",
              "10  Cupcake     39     0     26      19   14              1        1     0\n",
              "11  Cupcake     42    21     16      10    8              3        0     0\n",
              "12  Cupcake     34    17     20      20    5              2        1     0\n",
              "13  Cupcake     39    13     17      19   10              1        1     0\n",
              "14  Cupcake     38    15     23      15    8              0        1     0\n",
              "15  Cupcake     42    18     25       9    5              1        0     0\n",
              "16  Cupcake     36    14     21      14   11              2        1     0\n",
              "17  Cupcake     38    15     31       8    6              1        1     0\n",
              "18  Cupcake     36    16     24      12    9              1        1     0\n",
              "19  Cupcake     34    17     23      11   13              0        1     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:44.459767Z",
          "iopub.execute_input": "2021-08-08T20:29:44.460140Z",
          "iopub.status.idle": "2021-08-08T20:29:56.496689Z",
          "shell.execute_reply.started": "2021-08-08T20:29:44.460107Z",
          "shell.execute_reply": "2021-08-08T20:29:56.495463Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "krBFo0VadwXg",
        "outputId": "3241de48-9c24-4e3f-8e2b-3e07bc9fa3bc"
      },
      "source": [
        "sns.pairplot(recipes)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f010875f750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 72 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:56.497969Z",
          "iopub.execute_input": "2021-08-08T20:29:56.498283Z",
          "iopub.status.idle": "2021-08-08T20:29:57.198134Z",
          "shell.execute_reply.started": "2021-08-08T20:29:56.498252Z",
          "shell.execute_reply": "2021-08-08T20:29:57.197341Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "JFo4znGxdwXh",
        "outputId": "c6839929-563f-4396-9c57-e4bb53eb773e"
      },
      "source": [
        "#plot the two ingrdients using sns.lmplot('Flour', 'Sugar',data=recipes,hue='Type', palette='Set1',fit_reg=False,scatter_kws={'s':70})\n",
        "sns.lmplot('Flour', 'Sugar',data=recipes,hue='Type', palette='Set1',fit_reg=False,scatter_kws={'s':70});"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 464.77x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.199288Z",
          "iopub.execute_input": "2021-08-08T20:29:57.199878Z",
          "iopub.status.idle": "2021-08-08T20:29:57.206920Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.199835Z",
          "shell.execute_reply": "2021-08-08T20:29:57.205981Z"
        },
        "trusted": true,
        "id": "cDkh93kQdwXh"
      },
      "source": [
        "#specifying inputs to the model as ingredients, convert it to matrix\n",
        "ingredients = recipes[['Flour','Sugar']].to_numpy()\n",
        "type_label = np.where(recipes['Type'] == 'Muffin',0,1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.208323Z",
          "iopub.execute_input": "2021-08-08T20:29:57.208739Z",
          "iopub.status.idle": "2021-08-08T20:29:57.220872Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.208682Z",
          "shell.execute_reply": "2021-08-08T20:29:57.219652Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ko-r7JbdwXh",
        "outputId": "8e4729c7-6fad-4a2b-a0f9-667bcaf1a71a"
      },
      "source": [
        "#feature names\n",
        "recipe_features = recipes.columns.values[1:].tolist()\n",
        "recipe_features"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Flour', 'Milk', 'Sugar', 'Butter', 'Egg', 'Baking Powder', 'Vanilla', 'Salt']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.222787Z",
          "iopub.execute_input": "2021-08-08T20:29:57.223541Z",
          "iopub.status.idle": "2021-08-08T20:29:57.238353Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.223495Z",
          "shell.execute_reply": "2021-08-08T20:29:57.237306Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGrRm6gPdwXi",
        "outputId": "c1755644-84e7-4a16-8027-6f7e47a105c9"
      },
      "source": [
        "#lets fit the model\n",
        "model = svm.SVC(kernel='linear')\n",
        "model.fit(ingredients, type_label)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.239989Z",
          "iopub.execute_input": "2021-08-08T20:29:57.240559Z",
          "iopub.status.idle": "2021-08-08T20:29:57.246514Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.240514Z",
          "shell.execute_reply": "2021-08-08T20:29:57.245522Z"
        },
        "trusted": true,
        "id": "Q2C-RlghdwXi"
      },
      "source": [
        "#lets visualize the results\n",
        "#Get the seperating hyperplane\n",
        "w = model.coef_[0]\n",
        "a = -w[0]/w[1]\n",
        "xx = np.linspace(30, 60)\n",
        "yy = a * xx - (model.intercept_[0])/w[1]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.247894Z",
          "iopub.execute_input": "2021-08-08T20:29:57.248285Z",
          "iopub.status.idle": "2021-08-08T20:29:57.259868Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.248246Z",
          "shell.execute_reply": "2021-08-08T20:29:57.258853Z"
        },
        "trusted": true,
        "id": "WK_tUYQ_dwXj"
      },
      "source": [
        "#plot the parallel to the seperating hyperplane that pass through the support vectors\n",
        "b = model.support_vectors_[0]\n",
        "yy_down = a * xx + (b[1] - a * b[0])\n",
        "b = model.support_vectors_[-1]\n",
        "yy_up =  a * xx + (b[1] - a * b[0])"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.263627Z",
          "iopub.execute_input": "2021-08-08T20:29:57.264108Z",
          "iopub.status.idle": "2021-08-08T20:29:57.845097Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.264074Z",
          "shell.execute_reply": "2021-08-08T20:29:57.844183Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "aRTS4VIedwXj",
        "outputId": "2ceb8bdb-f559-4545-e05c-12df081d2dae"
      },
      "source": [
        "#lets plot the hyperplane\n",
        "sns.lmplot('Flour', 'Sugar',data=recipes,hue='Type', \n",
        "           palette='Set1',fit_reg=False,\n",
        "           scatter_kws={'s':70});\n",
        "plt.plot(xx,yy,linewidth=2,color='black');"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 464.77x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:57.846537Z",
          "iopub.execute_input": "2021-08-08T20:29:57.846880Z",
          "iopub.status.idle": "2021-08-08T20:29:58.445501Z",
          "shell.execute_reply.started": "2021-08-08T20:29:57.846852Z",
          "shell.execute_reply": "2021-08-08T20:29:58.444558Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "tkjDmU5CdwXj",
        "outputId": "d0cf7060-d8c2-4ffb-979d-8385afbcc2ff"
      },
      "source": [
        "#lets plot the margins and support vectors\n",
        "sns.lmplot('Flour', 'Sugar',data=recipes,hue='Type', \n",
        "           palette='Set1',fit_reg=False,\n",
        "           scatter_kws={'s':70});\n",
        "plt.plot(xx,yy,linewidth=2,color='black')\n",
        "plt.plot(xx,yy_down,'k--')\n",
        "plt.plot(xx,yy_up, 'k--')\n",
        "\n",
        "plt.scatter(model.support_vectors_[:,0], model.support_vectors_[:,1], s= 80, facecolors= 'none')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f00fbf8e710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 464.77x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:58.446795Z",
          "iopub.execute_input": "2021-08-08T20:29:58.447085Z",
          "iopub.status.idle": "2021-08-08T20:29:58.451275Z",
          "shell.execute_reply.started": "2021-08-08T20:29:58.447059Z",
          "shell.execute_reply": "2021-08-08T20:29:58.450597Z"
        },
        "trusted": true,
        "id": "PqVvQNwtdwXk"
      },
      "source": [
        "#lets predict the new case\n",
        "#lets define a function to guess when a recipe is muffin or a cupcake\n",
        "def muffin_or_cupcake(flour, sugar):\n",
        "  if(model.predict([[flour,sugar]])) == 0:\n",
        "    print('You\\'re looking at a muffin recipe')\n",
        "  else:\n",
        "    print('You\\'re looking at a cupcake recipe')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:58.452308Z",
          "iopub.execute_input": "2021-08-08T20:29:58.452580Z",
          "iopub.status.idle": "2021-08-08T20:29:58.465510Z",
          "shell.execute_reply.started": "2021-08-08T20:29:58.452556Z",
          "shell.execute_reply": "2021-08-08T20:29:58.464415Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCH_wZXcdwXk",
        "outputId": "65670568-6a49-4bc8-e1f9-67d05de6367d"
      },
      "source": [
        "#predict for 50 parts of flour and 20 parts of sugar\n",
        "muffin_or_cupcake(50,20)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You're looking at a muffin recipe\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-08T20:29:58.467249Z",
          "iopub.execute_input": "2021-08-08T20:29:58.467686Z",
          "iopub.status.idle": "2021-08-08T20:29:59.076851Z",
          "shell.execute_reply.started": "2021-08-08T20:29:58.467655Z",
          "shell.execute_reply": "2021-08-08T20:29:59.075983Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "37J1FiBJdwXl",
        "outputId": "3553a0f8-11e6-443c-de90-ad3ccda9be07"
      },
      "source": [
        "#lets plot the new point visually to see where the point lies\n",
        "sns.lmplot('Flour', 'Sugar',data=recipes,hue='Type', \n",
        "           palette='Set1',fit_reg=False,\n",
        "           scatter_kws={'s':70});\n",
        "plt.plot(xx,yy,linewidth=2,color='black')\n",
        "\n",
        "plt.plot(50,20,'yo',markersize='9')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f00fbef6090>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 464.77x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}