8. K_Nearest_Neighbours.ipynb

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  "metadata": {
    "colab": {
      "name": "K Nearest Neighbours.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM/jnkv3c8PBgEhn4Du0/Eq",
      "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/MainakRepositor/ML-Algorithms/blob/master/K_Nearest_Neighbours.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_RBsYRtvlQc"
      },
      "source": [
        "# K Nearest Neighbours\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRBGzB7wvpfK"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKHrMtlEvXZF",
        "outputId": "1c81c8f0-afd1-4cb3-da16-04f0f925b4a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "print(\"Basic libraries included successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic libraries included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fg7vZYqCv24p"
      },
      "source": [
        "## 2. Importing datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aknvg9mSv1HU",
        "outputId": "8dcf0da6-4fa5-49b9-b123-ccf6f772dfed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Social_Network_Ads.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head(10)"
      ],
      "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>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>19000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35</td>\n",
              "      <td>20000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27</td>\n",
              "      <td>57000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19</td>\n",
              "      <td>76000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>27</td>\n",
              "      <td>58000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>27</td>\n",
              "      <td>84000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>32</td>\n",
              "      <td>150000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25</td>\n",
              "      <td>33000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>35</td>\n",
              "      <td>65000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0\n",
              "5   27            58000          0\n",
              "6   27            84000          0\n",
              "7   32           150000          1\n",
              "8   25            33000          0\n",
              "9   35            65000          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9JKkwTqwFSO"
      },
      "source": [
        "## 3. Splitting the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFfyl2LOwDu1"
      },
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHrcluM9wMP8"
      },
      "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)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv9nHKfwX1d"
      },
      "source": [
        "## 4. Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c_s1B6BwTOS"
      },
      "source": [
        "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": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nV8sMeG1wdy0"
      },
      "source": [
        "## 5. Training the K-NN model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-LDY8EswXUB",
        "outputId": "04be40ca-b781-4951-9fe0-a2c79ed81e22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpE5Bjkgwlrr"
      },
      "source": [
        "## 6. Predicting a new result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhWZYtYvwhqJ",
        "outputId": "a30ceea5-442d-4797-ba82-4ae55795fb11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classifier.predict(sc.transform([[32,150000]])))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6wWi9X2w577"
      },
      "source": [
        "## 7. Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCwY0AvowozE",
        "outputId": "1826b32e-1d31-461b-ef39-b339c0e3aae4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Xo1XN91xBQN"
      },
      "source": [
        "## 8. Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgE_UpAFw9sT",
        "outputId": "d6242150-1d1e-4790-bad9-b9f096a62d82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion matrix :\")\n",
        "print(cm)\n",
        "print(\"The accuracy of the model is :\",accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix :\n",
            "[[64  4]\n",
            " [ 3 29]]\n",
            "The accuracy of the model is : 0.93\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_7bT-5JxYJm"
      },
      "source": [
        "## 9. Visualizing results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_moqL3BxSc2",
        "outputId": "f521e049-6575-4ddf-ff24-8830157dee8b",
        "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 = sc.inverse_transform(X_train), y_train\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 1),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], 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": 16,
      "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": "ydR7SsaFxa9B",
        "outputId": "d574b805-a80f-498f-8927-4943b1e74b9f",
        "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 = sc.inverse_transform(X_test), y_test\n",
        "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 1),\n",
        "                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\n",
        "plt.contourf(X1, X2, classifier.predict(sc.transform(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], 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": 17,
      "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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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 the *color* keyword-argument or provide a 2-D array with a single row if you intend 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": "J2HbzVUA0lQy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}