11. K_Mean_Clustering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "K Mean Clustering.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyODmZLJkRddkCje8iSkTTH8",
      "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_Mean_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ThshhO5hwl4"
      },
      "source": [
        "# K Mean Clustering\n",
        "\n",
        "<hr>\n",
        "\n",
        "### K Mean clustering is one of the most famous unsupervised learning techniques used in Machine Learning.It clusters similar types of data at k Euclidean distances and forms a cluster."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2nthRgeiFVp"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfmegpBMhta6",
        "outputId": "723eaccd-f46a-42c9-c261-350447917e86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "print(\"Basic packages included successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic packages included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oO0Mof2NjOEf"
      },
      "source": [
        "## 2. Importing the datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIDnQhu9iWfF",
        "outputId": "842cea59-a4f4-431c-e945-212d352adf03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Mall_Customers.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head()"
      ],
      "execution_count": 3,
      "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>CustomerID</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ks5M-9yPjR4a"
      },
      "source": [
        "## 3. Splitting the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiCTXMcUjJtr"
      },
      "source": [
        "X = df.iloc[:, [3, 4]].values"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anXIgQ2Mjh8D"
      },
      "source": [
        "## 4. Using the elbow method to find the optimal number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPqUfEeQjZVH",
        "outputId": "23968ef2-9e74-4508-b3d8-52d5c3803b33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1K-zIQwbjnuA"
      },
      "source": [
        "## 5. Training the K-Means model on the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kxk-8ll3jjnz"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 42)\n",
        "y_kmeans = kmeans.fit_predict(X)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtFwdWoPj0VQ"
      },
      "source": [
        "## 6. Visualising the clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HXtHOM6jtDk",
        "outputId": "057aa276-8bec-4218-8b3e-11718909c0e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        }
      },
      "source": [
        "plt.figure(figsize=(18,8))\n",
        "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of customers\\n',size=20)\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score (1-100)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PnaNPnQj33w"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}