Task_2_Prediction_using_unsupervised_ML.ipynb

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    "colab": {
      "name": "Task 2_Prediction using unsupervised ML.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Csk-g9nFa9m1"
      },
      "source": [
        "**Name : Hasti Shah**"
      ],
      "id": "Csk-g9nFa9m1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52d68767"
      },
      "source": [
        "The Sparks Foundation\n",
        "\n",
        "Task2: Prediction Using Unsupervised ML\n",
        "\n",
        "Dataset: https://bit.ly/3kXTdox\n",
        "\n",
        "Problem Definition: Predict the optimum number of clusters\n",
        "\n",
        "Goal: From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually."
      ],
      "id": "52d68767"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cb265ad"
      },
      "source": [
        "Importing Libraries"
      ],
      "id": "4cb265ad"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78de6c9d"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "id": "78de6c9d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eaeeebc"
      },
      "source": [
        "Loading Dataset"
      ],
      "id": "1eaeeebc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4827a02f",
        "outputId": "3c21ed6f-9613-4b8f-f076-2c64806f0b12"
      },
      "source": [
        "dataframe=pd.read_csv(\"Iris.csv\")\n",
        "print(dataframe.head())"
      ],
      "id": "4827a02f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   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\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aad03a05",
        "outputId": "484ce0ce-7107-4337-93e0-f26ae8e1c92f"
      },
      "source": [
        "dataframe.info()"
      ],
      "id": "aad03a05",
      "execution_count": null,
      "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": {
        "id": "3a20de70",
        "outputId": "905847cb-c2c0-4ea7-c8e5-9688f4e8566f"
      },
      "source": [
        "dataframe.Species.value_counts()"
      ],
      "id": "3a20de70",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-virginica     50\n",
              "Iris-versicolor    50\n",
              "Iris-setosa        50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5e237cb3",
        "outputId": "f56a84ab-9240-4cef-991c-3bd67d243906"
      },
      "source": [
        "dataframe.describe()"
      ],
      "id": "5e237cb3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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",
              "    </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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7dd2b2a"
      },
      "source": [
        "KMeans Clustering"
      ],
      "id": "c7dd2b2a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "fdb2fef4"
      },
      "source": [
        "X=dataframe[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]"
      ],
      "id": "fdb2fef4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59ae1c4a"
      },
      "source": [
        "#Segrating the columns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "X=scaler.fit_transform(X)\n"
      ],
      "id": "59ae1c4a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f76f1086"
      },
      "source": [
        "Elbow Method"
      ],
      "id": "f76f1086"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6b6e4091",
        "outputId": "94fc2cad-b6f0-46f3-c61c-7b84c2a0e042"
      },
      "source": [
        "wcss=[]\n",
        "for i in range(1,11):\n",
        "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10)\n",
        "    y_kmeans=kmeans.fit_predict(X)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "    dataframe['Cluster']=y_kmeans\n",
        "plt.plot(range(1,11),wcss)   \n",
        "plt.title(\"Elbow Method\")\n",
        "plt.xlabel(\"No. of Clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()\n"
      ],
      "id": "6b6e4091",
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "0ec2bba8",
        "outputId": "61add2f3-f539-4e45-8060-00293f3a5899"
      },
      "source": [
        "kmeans=KMeans(n_clusters=3,init='k-means++',max_iter=300,n_init=10)\n",
        "y_kmeans=kmeans.fit_predict(X)\n",
        "kmeans.fit(X)\n",
        "y_kmeans"
      ],
      "id": "0ec2bba8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
              "       2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0,\n",
              "       0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,\n",
              "       0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31645def"
      },
      "source": [
        "Visualizing clusters"
      ],
      "id": "31645def"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29cffab6",
        "outputId": "def1300d-515d-40e0-9605-0c2f1f243087"
      },
      "source": [
        "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 50, c = 'orange', label = 'Cluster1')\n",
        "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 50, c = 'green', label = 'Cluster2')\n",
        "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1],s = 50, c = 'blue', label = 'Cluster3')\n",
        "\n",
        "# Plotting the centroids of the clusters\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 200, c = 'red', label = 'Centroids')\n",
        "\n",
        "plt.title(\"Clusters\")\n",
        "plt.xlabel(\"X\")\n",
        "plt.ylabel(\"Y\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "29cffab6",
      "execution_count": null,
      "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": "9da0e3ec"
      },
      "source": [
        "Performance Measures"
      ],
      "id": "9da0e3ec"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12a7b134"
      },
      "source": [
        "from sklearn.metrics import davies_bouldin_score\n",
        "from sklearn.metrics import silhouette_score"
      ],
      "id": "12a7b134",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "367bcf91"
      },
      "source": [
        "silhouette_coefficients=[]\n",
        "for k in range(2,11):\n",
        "    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10)\n",
        "    kmeans.fit(X)\n",
        "    score=silhouette_score(X,kmeans.labels_)\n",
        "    silhouette_coefficients.append(score)\n",
        "    \n",
        "   "
      ],
      "id": "367bcf91",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c40fd9e3",
        "outputId": "eeaa0bc9-b6ac-4087-e101-87d938507cf1"
      },
      "source": [
        "plt.plot(range(2,11),silhouette_coefficients,c='blue')\n",
        "plt.title(\"Silhouette Coefficients\")\n",
        "plt.xlabel(\"No. of Clusters\")\n",
        "plt.ylabel(\"silhouette_coefficients\")\n",
        "plt.show()\n",
        "\n"
      ],
      "id": "c40fd9e3",
      "execution_count": null,
      "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": "code",
      "metadata": {
        "id": "6798e343",
        "outputId": "1b2efe8f-fefd-4262-b6c8-ca07b5506175"
      },
      "source": [
        "#No.of clusters=3\n",
        "kmeans=KMeans(n_clusters=3,init='k-means++',max_iter=300,n_init=10)\n",
        "y_kmeans=kmeans.fit_predict(X)\n",
        "print(\"KMeans Inertia Value=\",kmeans.inertia_)\n",
        "kmeans_silhouette=silhouette_score(X,kmeans.labels_)\n",
        "print(\"Silhouette value=\",kmeans_silhouette)\n",
        "labels=kmeans.labels_\n",
        "print(\"DB Index=\",davies_bouldin_score(X,labels))\n",
        "    "
      ],
      "id": "6798e343",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KMeans Inertia Value= 140.96581663074699\n",
            "Silhouette value= 0.4589717867018717\n",
            "DB Index= 0.8354098493935597\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74654c9b",
        "outputId": "7ae56e79-0e1e-48d6-ca1d-c64ca54c393b"
      },
      "source": [
        "#No.of clusters=5\n",
        "kmeans=KMeans(n_clusters=5,init='k-means++',max_iter=300,n_init=10)\n",
        "y_kmeans=kmeans.fit_predict(X)\n",
        "print(\"KMeans Inertia Value=\",kmeans.inertia_)\n",
        "kmeans_silhouette=silhouette_score(X,kmeans.labels_)\n",
        "print(\"Silhouette value=\",kmeans_silhouette)\n",
        "labels=kmeans.labels_\n",
        "print(\"DB Index=\",davies_bouldin_score(X,labels))\n",
        "    "
      ],
      "id": "74654c9b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KMeans Inertia Value= 91.2578427971818\n",
            "Silhouette value= 0.3442962563138737\n",
            "DB Index= 0.9475331385285999\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85683ff8",
        "outputId": "ae0fdb71-4343-4fd1-a988-1cf06226db10"
      },
      "source": [
        "#No.of clusters=7\n",
        "kmeans=KMeans(n_clusters=7,init='k-means++',max_iter=300,n_init=10)\n",
        "y_kmeans=kmeans.fit_predict(X)\n",
        "print(\"KMeans Inertia Value=\",kmeans.inertia_)\n",
        "kmeans_silhouette=silhouette_score(X,kmeans.labels_)\n",
        "print(\"Silhouette value=\",kmeans_silhouette)\n",
        "labels=kmeans.labels_\n",
        "print(\"DB Index=\",davies_bouldin_score(X,labels))\n",
        "    "
      ],
      "id": "85683ff8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "KMeans Inertia Value= 72.16178866465833\n",
            "Silhouette value= 0.3380271842997504\n",
            "DB Index= 1.0399636505324612\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84d69b19"
      },
      "source": [
        ""
      ],
      "id": "84d69b19",
      "execution_count": null,
      "outputs": []
    }
  ]
}