k_nearest_neighbors.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "k_nearest_neighbors.ipynb",
      "provenance": [],
      "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/antonio2505/SuperDataScience_Projects_ML/blob/main/k_nearest_neighbors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MRC0e0KhQ0S"
      },
      "source": [
        "# K-Nearest Neighbors (K-NN)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWd1UlMnhT2s"
      },
      "source": [
        "## Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvGPUQaHhXfL"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1VMqkGvhc3-"
      },
      "source": [
        "## Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M52QDmyzhh9s"
      },
      "source": [
        "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
        "X = dataset.iloc[:, [2, 3]].values\n",
        "y = dataset.iloc[:, -1].values"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvxIPVyMhmKp"
      },
      "source": [
        "## Splitting the dataset into the Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVzJWAXIhxoC"
      },
      "source": [
        "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": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW3c7UYih0hT"
      },
      "source": [
        "## Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fQlDPKCh8sc"
      },
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bb6jCOCQiAmP"
      },
      "source": [
        "## Training the K-NN model on the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0pFVAmciHQs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53b92e40-4bf4-460f-f7c2-885bca68cb15"
      },
      "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": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmePIEiiiKyw"
      },
      "source": [
        "## Predicting the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWK4AcCgiNwF"
      },
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5e6bCLa3fWj",
        "outputId": "477fad8f-dd5c-4339-fa92-368ef05af39b"
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n",
            " 0 0 0 0 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRQB3W1-3uen",
        "outputId": "fb6967f9-d693-440f-8048-d95c72193693"
      },
      "source": [
        "print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)), 1))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0]\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4Hwj34ziWQW"
      },
      "source": [
        "## Making the Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6bpZwUiiXic",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be6c4ac3-5663-4268-c16f-6140a51b273e"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[64  4]\n",
            " [ 3 29]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OMC_P0diaoD"
      },
      "source": [
        "## Visualising the Training set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NOjKvZRid5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "365cf200-6f1a-4981-d2d7-cfd5dc960017"
      },
      "source": [
        "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": 8,
      "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": "markdown",
      "metadata": {
        "id": "SZ-j28aPihZx"
      },
      "source": [
        "## Visualising the Test set results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeTjz2vDilAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "f6b15d93-5d75-419f-d36c-42dff1f15163"
      },
      "source": [
        "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": 9,
      "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"
          }
        }
      ]
    }
  ]
}