Task_02_Sparks.ipynb

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      "name": "Task-02-Sparks.ipynb",
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      "authorship_tag": "ABX9TyPmaY6QECYZ0RwCzPtUZf4Y",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parnika-dotcom/Sparks_Tasks/blob/main/Task_02_Sparks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWMlTBL_iOaM"
      },
      "source": [
        "#**Data Science and Business Analytics Intern**\r\n",
        "\r\n",
        "#**The Sparks Foundation**\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "###**Task 2:- Prediction using Unsupervised Machine Learning**\r\n",
        "\r\n",
        "***Name: Parnika Sharma (GRIPMAR21 - Intern)***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdW0SCBVjMgc"
      },
      "source": [
        "## **AIM:- To predict the optimum number of clusters and represent it visually in the given iris dataset.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAW6gX1lh-bR"
      },
      "source": [
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import pandas as pd\r\n",
        "from sklearn import datasets"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "yl5sN3Ru2Ecb",
        "outputId": "544ddc26-aa9d-421e-9164-b1d8491ba4e9"
      },
      "source": [
        "iris = datasets.load_iris()\r\n",
        "iris_df = pd.DataFrame(iris.data, columns = iris.feature_names)\r\n",
        "iris_df.head() # The first 5 rows"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAp5YAVcGkEv"
      },
      "source": [
        "#**Exploring the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6g1cevz0Gf5z",
        "outputId": "9b070803-9bc9-4de4-e5e6-1c6fd44b8cc5"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCO_AOryHWaa",
        "outputId": "eeeaee86-b264-4afb-d913-aaeb9e00435f"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJTHXpbyHw33",
        "outputId": "bb57d22e-3670-4649-eb03-d5e5ac6792d2"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 17,
      "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/",
          "height": 282
        },
        "id": "Hv9eOjtxHyiQ",
        "outputId": "128c6692-3c8f-48da-974a-478e2361c417"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJ27R3peH2G9",
        "outputId": "f54b53fc-6486-4740-c9c0-9c8047497167"
      },
      "source": [
        "data.Species.unique()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTAR8N89ImW7"
      },
      "source": [
        "#**Finding the optimal number of clusters for K-Means and determining the value of K**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qq28DbyGH7ch"
      },
      "source": [
        "x = iris_df.iloc[:, [0, 1, 2, 3]].values\r\n",
        "from sklearn.cluster import KMeans\r\n",
        "wcss = []\r\n",
        "for i in range(1, 11):\r\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', \r\n",
        "                    max_iter = 300, n_init = 10, random_state = 0)\r\n",
        "    kmeans.fit(x)\r\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJxkhvosJVBw"
      },
      "source": [
        "#**Plot the Graph and Find the Optimum Number of Clusters using the Elbow Method**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "GSZxlqGUJZTR",
        "outputId": "26275c41-7435-45ac-c832-ef742c433a07"
      },
      "source": [
        "plt.plot(range(1, 11), wcss)\r\n",
        "plt.title('The elbow method')\r\n",
        "plt.xlabel('Number of clusters')\r\n",
        "plt.ylabel('WCSS') # Within cluster sum of squares\r\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "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": "EUpipQtaJnRE"
      },
      "source": [
        "From this we choose 3 clusters.\r\n",
        "\r\n",
        "\r\n",
        "#**Applying kmeans to the dataset / Creating the kmeans classifier**\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JUpkoQPJtlE"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\r\n",
        "                max_iter = 300, n_init = 10, random_state = 0)\r\n",
        "y_kmeans = kmeans.fit_predict(x)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "na1VfK_sJ4JJ"
      },
      "source": [
        "#**Visualizing the Cluster Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "vjcImPwdRc-D",
        "outputId": "7f816ebb-36f5-45bc-d7b8-6dba3b923584"
      },
      "source": [
        "#Ploting to depict Centroids of the Cluster\r\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \r\n",
        "            s = 100, c = 'orange', label = 'Centroids')\r\n",
        "plt.legend()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1f1acf8690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "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/",
          "height": 430
        },
        "id": "dalJaXlTJzFT",
        "outputId": "a0e9229c-35ea-4ad2-8656-51f13a26bac3"
      },
      "source": [
        "plt.figure(figsize = (10,7))\r\n",
        "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \r\n",
        "            s = 100, c = 'red', label = 'Iris-setosa')\r\n",
        "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \r\n",
        "            s = 100, c = 'blue', label = 'Iris-versicolour')\r\n",
        "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\r\n",
        "            s = 100, c = 'green', label = 'Iris-virginica')\r\n",
        "# Plotting the centroids of the clusters\r\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \r\n",
        "            s = 100, c = 'yellow', label = 'Centroids')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7L4J5VfRPeK"
      },
      "source": [
        "#**----------END OF CODE------------**"
      ]
    }
  ]
}