Exemplo_K_Means.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Exemplo K-Means.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKsN-EwEoqMR",
        "outputId": "d23f12f2-b54d-472a-fa3a-fe0ec92ccd28"
      },
      "source": [
        "from google.colab import drive, files\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8uGXseRpDQT"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split #to split the dataset for training and testing\n",
        "from sklearn.cluster import KMeans# implements the K-Means algorithm for clustering.\n",
        "from sklearn import metrics"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN6QXZ-ipR87"
      },
      "source": [
        "iris = pd.read_csv(\"/content/drive/My Drive/ia/Iris.csv\") #load the dataset"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "EvvDwe4VpWFQ",
        "outputId": "1534cd99-2251-46ce-96ca-f288c1c22737"
      },
      "source": [
        "iris.head(5) #show the first 5 rows from the dataset"
      ],
      "execution_count": 5,
      "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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaP2qfwUpayl",
        "outputId": "ef533da0-63a8-4da1-9ab0-9449ea294298"
      },
      "source": [
        "iris.info()  #checking if there is any inconsistency in the dataset\n",
        "#as we see there are no null values in the dataset, so the data can be processed"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EU8NRDApflc",
        "outputId": "ece77601-1072-4372-8b0a-eab55b146151"
      },
      "source": [
        "train, test = train_test_split(iris, test_size = 0.3)# in this our main data is split into train and test\n",
        "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(105, 6)\n",
            "(45, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQPzB7Wlpkvt"
      },
      "source": [
        "train_X = train[['SepalLengthCm','PetalWidthCm']]# taking the training data features\n"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8Yby_OWppX2"
      },
      "source": [
        "kmeans = KMeans(n_clusters=3)\n",
        "kmeans.fit(train_X)\n",
        "X_clustered = kmeans.predict(train_X)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h62WbWve_9_S",
        "outputId": "2f4dcf14-8da7-4b68-b6b5-f6b02aaa453a"
      },
      "source": [
        "X_clustered "
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 2, 1, 1, 2, 1, 2, 1, 0, 1, 0, 0, 0, 2, 0, 2, 1, 0, 1, 0,\n",
              "       0, 1, 2, 0, 0, 1, 2, 0, 2, 1, 1, 1, 0, 1, 2, 1, 0, 0, 1, 0, 2, 2,\n",
              "       0, 0, 0, 0, 2, 2, 2, 2, 0, 1, 2, 1, 1, 0, 1, 1, 1, 2, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 1, 0, 2, 1, 2, 2, 1, 1, 2, 2, 0, 0, 2, 0, 0, 1, 0, 0,\n",
              "       1, 2, 2, 0, 0, 0, 1, 0, 1, 2, 2, 1, 2, 2, 1, 1, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "vdqASfdiH6Zb",
        "outputId": "229c1bf1-6bd4-4b89-c709-eea33fc181c9"
      },
      "source": [
        "# Plot clusters - this is done by colour coding the data points according to which cluster the data point belongs to\n",
        "sns.scatterplot(data=train, x=\"SepalLengthCm\", y=\"PetalWidthCm\", hue= X_clustered, palette = \"coolwarm_r\")\n",
        "centers = kmeans.cluster_centers_\n",
        "# Plot centers\n",
        "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha = 0.6);\n",
        "plt.xlabel(\"SepalLengthCm\")\n",
        "plt.ylabel(\"PetalWidthCm\")"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'PetalWidthCm')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6RbjV6TF-uW",
        "outputId": "2e5868e3-d815-4811-b441-5aa137cf2c54"
      },
      "source": [
        "new_data = [[4.5, 0.8]]\n",
        "y_pred = kmeans.predict(new_data)\n",
        "y_pred"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    }
  ]
}