K_Means_Practice.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "K_Means_Practice.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMEqpBxyj0OkFLhLf0Hr7VU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shaikh-Raza/PYTHON-TUTORIAL/blob/master/K_Means_Practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkyhGL8ejmfW"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oqDOiytkj5B"
      },
      "source": [
        "df = pd.read_csv(\"/K-Means_data.csv\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "r9TiEvH6lMid",
        "outputId": "0cd97d25-8384-465c-ee49-44c342a97c26"
      },
      "source": [
        "df"
      ],
      "execution_count": 8,
      "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rob</td>\n",
              "      <td>23</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>web</td>\n",
              "      <td>24</td>\n",
              "      <td>48000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>shah</td>\n",
              "      <td>38</td>\n",
              "      <td>60000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rav</td>\n",
              "      <td>33</td>\n",
              "      <td>55000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>shahu</td>\n",
              "      <td>25</td>\n",
              "      <td>35000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>naya</td>\n",
              "      <td>28</td>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>add</td>\n",
              "      <td>40</td>\n",
              "      <td>50000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>aslam</td>\n",
              "      <td>44</td>\n",
              "      <td>80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>situ</td>\n",
              "      <td>45</td>\n",
              "      <td>100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>raza</td>\n",
              "      <td>26</td>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Name   Age  Income($)\n",
              "0    Rob   23      50000\n",
              "1    web   24      48000\n",
              "2   shah   38      60000\n",
              "3    rav   33      55000\n",
              "4  shahu   25      35000\n",
              "5   naya   28      40000\n",
              "6    add   40      50000\n",
              "7  aslam   44      80000\n",
              "8   situ   45     100000\n",
              "9   raza   26      40000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "W-EiByFQliAA",
        "outputId": "d51cecab-865d-4d82-cf8e-cdc39fce01d2"
      },
      "source": [
        "plt.scatter(df['Age'],df['Income($)'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f86c5b7bc90>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GyxS7Z3l2qQ",
        "outputId": "97adda70-d2d5-4604-f213-788ce81987a0"
      },
      "source": [
        "km = KMeans(n_clusters =3)\n",
        "km"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1wKKXAlmFmS",
        "outputId": "e5aee26c-c7c3-4284-8a5a-09aa06cc413e"
      },
      "source": [
        "y_predicted = km.fit_predict(df[['Age','Income($)']])\n",
        "y_predicted"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 2, 2, 0, 1, 1, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "353iq35wmiWr",
        "outputId": "d3dc0a04-efb9-47c7-ddf1-3f71add97485"
      },
      "source": [
        "df['cluster'] = y_predicted\n",
        "df.head()"
      ],
      "execution_count": 16,
      "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income($)</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rob</td>\n",
              "      <td>23</td>\n",
              "      <td>50000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>web</td>\n",
              "      <td>24</td>\n",
              "      <td>48000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>shah</td>\n",
              "      <td>38</td>\n",
              "      <td>60000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rav</td>\n",
              "      <td>33</td>\n",
              "      <td>55000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>shahu</td>\n",
              "      <td>25</td>\n",
              "      <td>35000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Name   Age  Income($)  cluster\n",
              "0    Rob   23      50000        0\n",
              "1    web   24      48000        0\n",
              "2   shah   38      60000        0\n",
              "3    rav   33      55000        0\n",
              "4  shahu   25      35000        2"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lrDqa6zmnSDB",
        "outputId": "463b4b0d-61f8-43fb-80e9-553843d23c47"
      },
      "source": [
        "plt.scatter(df['Age'],df['Income($)'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f86c547ce50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "rUaUNXrjnouy",
        "outputId": "2c8b6c57-0a58-47ea-d036-17ae804de0b3"
      },
      "source": [
        "df1 = df[df.cluster==0]\n",
        "df2 = df[df.cluster==1]\n",
        "df3 = df[df.cluster==2]\n",
        "\n",
        "plt.scatter(df1.Age,df1['Income($)'],color='green')\n",
        "plt.scatter(df2.Age,df2['Income($)'],color='red')\n",
        "plt.scatter(df3.Age,df3['Income($)'],color='black')\n",
        "\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Income($)')\n",
        "\n",
        "plt.legend()\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f86c3f6ec10>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "up2y4jKbqY_z",
        "outputId": "bee2ee92-734a-4a72-ff7a-afb5cb62a7eb"
      },
      "source": [
        "plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],color='purple',marker='+',label='centroied')\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f86c3f64210>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z682Eszpqusv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}