TSF_INTERNSHIP_TASK_2_UNSUPERVISED_LEARNING.ipynb

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      "source": [
        "# The Sparks Foundation - Supervised Learning ML\n",
        "- Created By Amey Mahendra Thakur"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUQSZkRicXy2"
      },
      "source": [
        "## Task: From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B3F3l7bQcXy3"
      },
      "source": [
        "## Step 1 - Importing Libraries and Loading Dataset"
      ]
    },
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      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 1,
      "outputs": []
    },
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        "trusted": true,
        "id": "V0gY_OmTcXy4"
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      "source": [
        "data = pd.read_csv('/content/Iris.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
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        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "grQJogkvcXy4",
        "outputId": "60758f5f-0ff0-4aaf-b7af-f13f873a9df2"
      },
      "source": [
        "data.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",
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              "    }\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": 3
        }
      ]
    },
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      "metadata": {
        "id": "5PxOpGulcXy5"
      },
      "source": [
        "## Step 2 - Exploratory Data Analysis"
      ]
    },
    {
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        "outputId": "f3903b99-0a29-487d-8c7c-9d9f9b5de2b3"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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      "source": [
        "data.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
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      },
      "source": [
        "data.info()"
      ],
      "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"
        }
      ]
    },
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        "execution": {
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        },
        "id": "PcXO8nOocXy6",
        "outputId": "0c8a05fd-5cef-40e0-9f1e-21c81b4a5b00"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<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": 7
        }
      ]
    },
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      "cell_type": "code",
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        "trusted": true,
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        "id": "WKl36vXIcXy7",
        "outputId": "e695a6fe-5cab-4109-9588-e07c6202f1f5"
      },
      "source": [
        "data.Species.unique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ij_wiM8tcXy7"
      },
      "source": [
        "## Step 3 - Find The Number of Clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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        "outputId": "84e1113c-09f4-41fc-a7e3-60c8b6d6c8e2"
      },
      "source": [
        "x = data.iloc[:, [0,1,2,3]].values\n",
        "wcss = []\n",
        "\n",
        "for i in range(1,11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 10)\n",
        "    kmeans.fit(x)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "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": "CRFPnH-ycXy8"
      },
      "source": [
        "From the above plot, A clear elbow is seen to be formed at 3.\n",
        "Thus, the optimum number of clusters is 3. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-enHzyVWcXy8"
      },
      "source": [
        "## Step 4 - Train The Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-07-04T09:40:20.667528Z",
          "iopub.execute_input": "2021-07-04T09:40:20.667960Z",
          "iopub.status.idle": "2021-07-04T09:40:20.704648Z",
          "shell.execute_reply.started": "2021-07-04T09:40:20.667916Z",
          "shell.execute_reply": "2021-07-04T09:40:20.703797Z"
        },
        "trusted": true,
        "id": "agfv8f0fcXy8"
      },
      "source": [
        "#Assigning the 'kmeans' classifier\n",
        "kmeans = KMeans(n_clusters= 3,init = 'k-means++',max_iter = 300,n_init = 10,random_state= 10)\n",
        "y_kmeans = kmeans.fit_predict(x)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiTKSz9jcXy8"
      },
      "source": [
        "## Step 5 - Cluster Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-07-04T09:40:20.705685Z",
          "iopub.execute_input": "2021-07-04T09:40:20.706110Z",
          "iopub.status.idle": "2021-07-04T09:40:20.988874Z",
          "shell.execute_reply.started": "2021-07-04T09:40:20.706079Z",
          "shell.execute_reply": "2021-07-04T09:40:20.987716Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "VfmTozz_cXy8",
        "outputId": "a293e8cc-f918-48ef-d460-d74061441a08"
      },
      "source": [
        "plt.figure(figsize = (6,5))\n",
        "\n",
        "\n",
        "plt.scatter(x[y_kmeans == 0,0], x[y_kmeans == 0,1] , s = 100,c = 'blue',label = 'Iris-setosa')\n",
        "plt.scatter(x[y_kmeans == 1,0], x[y_kmeans == 1,1] , s = 100,c = 'green',label = 'Iris-versicolor')\n",
        "plt.scatter(x[y_kmeans == 2,0], x[y_kmeans == 2,1] , s = 100,c = 'yellow',label = 'Iris-verginica')\n",
        "\n",
        "#Centroid Plotting of the clusters\n",
        "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s = 100,c = 'red',label = 'Centroids')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}