Damin01_17.ipynb

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  "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": "3.6.5"
    },
    "colab": {
      "name": "remake_damin.ipynb",
      "provenance": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsgR3-hICfIr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7js3vmjXDA90",
        "colab_type": "code",
        "outputId": "c75fbcc0-3691-43cb-f8ec-e5cdae88a253",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0cf9acd4-739d-4697-a049-f8f1fc669e41\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-0cf9acd4-739d-4697-a049-f8f1fc669e41\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Social_Network_Ads.csv to Social_Network_Ads.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlzmKLD5CfIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the dataset\n",
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "y = dataset.iloc[:, 4].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xzsncd0uM3fv",
        "colab_type": "code",
        "outputId": "130ec0a1-ddfc-4860-b84e-d780e1f3a995",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": 0,
      "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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male   19            19000          0\n",
              "1  15810944    Male   35            20000          0\n",
              "2  15668575  Female   26            43000          0\n",
              "3  15603246  Female   27            57000          0\n",
              "4  15804002    Male   19            76000          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs0jCAhrCfI3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\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 = 0.25, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK7MU2krCfI6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4Fau2ChCfJD",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rkJc3pk1CfJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fitting Decision Tree Classification to the Training set\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ar6f0sU3CfJI",
        "colab_type": "code",
        "outputId": "e639e484-d7b2-4e28-c9f1-a4da947b84fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "results = confusion_matrix(y_test, y_pred) \n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Confusion Matrix :'); print(results)\n",
        "print('Accuracy Score :'); print(accuracy)\n",
        "print('Report : '); print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[62  6]\n",
            " [ 3 29]]\n",
            "Accuracy Score :\n",
            "0.91\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.91      0.93        68\n",
            "           1       0.83      0.91      0.87        32\n",
            "\n",
            "    accuracy                           0.91       100\n",
            "   macro avg       0.89      0.91      0.90       100\n",
            "weighted avg       0.91      0.91      0.91       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaDG3n1LCfJK",
        "colab_type": "code",
        "outputId": "406c1ca0-c3bf-41cd-a6a4-f0eb5873c5fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Decision Tree Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "yeJX4sJkCfJQ",
        "colab_type": "code",
        "outputId": "8cec0bb6-5b9f-413d-f641-d27d17039594",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Decision Tree Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": "b0zyEDTvCfJU",
        "colab_type": "text"
      },
      "source": [
        "# K-NN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZkuK28XCfJV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "modt30FxCfJY",
        "colab_type": "code",
        "outputId": "4cdba405-03ca-4307-d601-71be72e800bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "results = confusion_matrix(y_test, y_pred) \n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Confusion Matrix :'); print(results)\n",
        "print('Accuracy Score :'); print(accuracy)\n",
        "print('Report : '); print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[64  4]\n",
            " [ 3 29]]\n",
            "Accuracy Score :\n",
            "0.93\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.94      0.95        68\n",
            "           1       0.88      0.91      0.89        32\n",
            "\n",
            "    accuracy                           0.93       100\n",
            "   macro avg       0.92      0.92      0.92       100\n",
            "weighted avg       0.93      0.93      0.93       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnUDjr_oCfJa",
        "colab_type": "code",
        "outputId": "5fcf895a-0b1a-4628-8871-bce0da3649fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "1Fbz5VoBCfJk",
        "colab_type": "code",
        "outputId": "0e557dd4-d92e-48c2-d4e1-512cc47f157c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('K-NN (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": "5sg-f9fjCfJp",
        "colab_type": "text"
      },
      "source": [
        "# Naive Bayes (Gaussian)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpPmLzmpCfJp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fitting Naive Bayes to the Training set\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPJRaSQSCfJs",
        "colab_type": "code",
        "outputId": "0d793fd2-9de1-46dc-e2f7-f4300ff33b4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "results = confusion_matrix(y_test, y_pred) \n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Confusion Matrix :'); print(results)\n",
        "print('Accuracy Score :'); print(accuracy)\n",
        "print('Report : '); print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[65  3]\n",
            " [ 7 25]]\n",
            "Accuracy Score :\n",
            "0.9\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.96      0.93        68\n",
            "           1       0.89      0.78      0.83        32\n",
            "\n",
            "    accuracy                           0.90       100\n",
            "   macro avg       0.90      0.87      0.88       100\n",
            "weighted avg       0.90      0.90      0.90       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xM3GGsorCfJv",
        "colab_type": "code",
        "outputId": "f9c442e9-99be-4552-9e6b-5dd91d51c348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Naive Bayes (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "HTdNHT20CfJ2",
        "colab_type": "code",
        "outputId": "8466213b-c480-4088-f8bb-71ce6f3a0a89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Naive Bayes (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": "kuorW4dLCfJ7",
        "colab_type": "text"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYiS5Z3uCfJ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fitting SVM to the Training set\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Olgy1lh0CfJ_",
        "colab_type": "code",
        "outputId": "a7c8e186-2ade-4711-8634-3d9a96feac05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "results = confusion_matrix(y_test, y_pred) \n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Confusion Matrix :'); print(results)\n",
        "print('Accuracy Score :'); print(accuracy)\n",
        "print('Report : '); print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[66  2]\n",
            " [ 8 24]]\n",
            "Accuracy Score :\n",
            "0.9\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.97      0.93        68\n",
            "           1       0.92      0.75      0.83        32\n",
            "\n",
            "    accuracy                           0.90       100\n",
            "   macro avg       0.91      0.86      0.88       100\n",
            "weighted avg       0.90      0.90      0.90       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XcNM53ACfKD",
        "colab_type": "code",
        "outputId": "cb531265-6bdc-4f53-bcd9-cb08956ae908",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('SVM (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "HgjmEdP3CfKG",
        "colab_type": "code",
        "outputId": "5be5b734-2e47-4f0f-d6bd-0f026bd80b7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('SVM (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": "K7R22luHCfKJ",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWMKAalNCfKJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Fitting Random Forest Classification to the Training set\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)   "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs7esAx1X-N0",
        "colab_type": "code",
        "outputId": "5edc9362-ca8b-407d-c023-bdbd3cb28d9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "results = confusion_matrix(y_test, y_pred) \n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print('Confusion Matrix :'); print(results)\n",
        "print('Accuracy Score :'); print(accuracy)\n",
        "print('Report : '); print(report)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[63  5]\n",
            " [ 4 28]]\n",
            "Accuracy Score :\n",
            "0.91\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.93      0.93        68\n",
            "           1       0.85      0.88      0.86        32\n",
            "\n",
            "    accuracy                           0.91       100\n",
            "   macro avg       0.89      0.90      0.90       100\n",
            "weighted avg       0.91      0.91      0.91       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVUIO4ouYNjI",
        "colab_type": "code",
        "outputId": "ecaea4b3-6903-4fef-d590-aa022506c11d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Training set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_train, y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Random Forest Classification (Training set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "99Y-VmoZYZTa",
        "colab_type": "code",
        "outputId": "1c4523de-a635-4fc3-9079-e7a69ae5aea4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Visualising the Test set results\n",
        "from matplotlib.colors import ListedColormap\n",
        "X_set, y_set = X_test, y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
        "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
        "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
        "plt.xlim(X1.min(), X1.max())\n",
        "plt.ylim(X2.min(), X2.max())\n",
        "for i, j in enumerate(np.unique(y_set)):\n",
        "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
        "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
        "plt.title('Random Forest Classification (Test set)')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Estimated Salary')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "w2kJW2tTCfKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sm0DGglOCfKN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCJbrCYxCfKW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}