14. Unsupervised_Learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xq-No3Cs1Kgs"
      },
      "source": [
        "# **K-means Clustering method used on Point Dataset.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWeYVKWr1W2T"
      },
      "source": [
        "1. Upload the dataset file.\n",
        "2. Pandas used for read the dataset file.\n",
        "3. Numpy used for changine dataset from points to arrays.\n",
        "4. Matplot library for plotting the scatter plot of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "WsrlG7N5oI_l",
        "outputId": "c635ae72-ce2b-4ee1-cd19-a8bc4ba19314"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7cc3eb07fe50>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pointsdf = pd.read_csv(\"/content/points.csv\");\n",
        "\n",
        "import numpy as np\n",
        "points = np.array(pointsdf)\n",
        "\n",
        "xs = points[:, 0]\n",
        "ys  = points[:, 1]\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(xs, ys)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZby5nKS2WZW"
      },
      "source": [
        "**1.** **K-means clustering algorithm download from the SK learn.**\n",
        "\n",
        "**2.** **A K-Means clustering model is created with the specified number of clusters, which is set to 3. This means the algorithm will try to cluster the data points into three distinct groups.**\n",
        "\n",
        "\n",
        "**3.** **Predict the cluster labels for the new data points new_points using the previously trained KMeans model.**\n",
        "\n",
        "\n",
        "**4.** **The predicted cluster labels for the new data points are printed to the console.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uaR4isPoI_o",
        "outputId": "b6e38a49-0d4a-4451-e2fb-a9da078d7940"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 0 1]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "model = KMeans( n_clusters=3)\n",
        "t=model.fit(points)\n",
        "clusters = model.fit_predict(points)\n",
        "clusters\n",
        "new_points = [[4,3],[4,1],[-5,1]]\n",
        "j = model.predict(new_points)\n",
        "print(j)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OudUNe4r3RWl"
      },
      "source": [
        "**Original data points and their cluster assignments**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "LDSOsTV5oI_o",
        "outputId": "e45aa0e2-39aa-4f51-e584-32c9d84691b2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Original data points and their cluster assignments\n",
        "plt.scatter(points[:, 0], points[:, 1], c=clusters, cmap='viridis', label='Original Data')\n",
        "plt.scatter(new_points[0][0], new_points[0][1], c='red', marker='x', label='New Point 1')\n",
        "plt.scatter(new_points[1][0], new_points[1][1], c='blue', marker='x', label='New Point 2')\n",
        "plt.scatter(new_points[2][0], new_points[2][1], c='green', marker='x', label='New Point 3')\n",
        "\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.title('Cluster Assignments')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osrsOf0xoI_p"
      },
      "source": [
        "Using Dataset\n",
        "https://archive.ics.uci.edu/ml/datasets/seeds\n",
        "\n",
        "# **seeds**\n",
        "\n",
        "**Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.**\n",
        "\n",
        "Dataset Characteristics\n",
        "Multivariate\n",
        "\n",
        "# Instances\n",
        "210\n",
        "\n",
        "# Attributes\n",
        "7\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzJsYIK65EY2"
      },
      "source": [
        "**Additional Information**\n",
        "\n",
        "The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for the experiment. High quality visualization of the internal kernel structure was\n",
        "detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates. Studies were conducted using combine harvested wheat grain originating from experimental fields, explored at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CkYX93IoI_q",
        "outputId": "7e08f76b-1224-44b9-b6b5-8f83e3dee49c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-09-10 14:25:48--  https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt\n",
            "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
            "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified\n",
            "Saving to: ‘seeds_dataset.txt’\n",
            "\n",
            "\rseeds_dataset.txt       [<=>                 ]       0  --.-KB/s               \rseeds_dataset.txt       [ <=>                ]   9.08K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-09-10 14:25:48 (108 MB/s) - ‘seeds_dataset.txt’ saved [9300]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e23scV7U5eFy"
      },
      "source": [
        "## last few lines of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ng0sRcNmoI_q",
        "outputId": "0666fbe6-db6e-428f-d9f6-620e9d95acad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "12.38\t13.44\t0.8609\t5.219\t2.989\t5.472\t5.045\t3\n",
            "12.67\t13.32\t0.8977\t4.984\t3.135\t2.3\t\t4.745\t3\n",
            "11.18\t12.72\t0.868\t5.009\t2.81\t4.051\t4.828\t3\n",
            "12.7\t13.41\t0.8874\t5.183\t3.091\t8.456\t5\t\t3\n",
            "12.37\t13.47\t0.8567\t5.204\t2.96\t3.919\t5.001\t3\n",
            "12.19\t13.2\t0.8783\t5.137\t2.981\t3.631\t4.87\t3\n",
            "11.23\t12.88\t0.8511\t5.14\t2.795\t4.325\t5.003\t3\n",
            "13.2\t13.66\t0.8883\t5.236\t3.232\t8.315\t5.056\t3\n",
            "11.84\t13.21\t0.8521\t5.175\t2.836\t3.598\t5.044\t3\n",
            "12.3\t13.34\t0.8684\t5.243\t2.974\t5.637\t5.063\t3"
          ]
        }
      ],
      "source": [
        "!tail seeds_dataset.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDr1AbIOOYNf"
      },
      "source": [
        "# create the numpy array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lg1OK3xCoI_r"
      },
      "outputs": [],
      "source": [
        "data = np.fromfile(\"seeds_dataset.txt\", sep=\"\\t\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsb-_rCToI_r",
        "outputId": "3555fc0b-3813-4114-9ffa-118a9b5807a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([15.26 , 14.84 ,  0.871, ...,  5.637,  5.063,  3.   ])"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "RoSz0z7JoI_r"
      },
      "outputs": [],
      "source": [
        "data = data.reshape(-1, 8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AvI-cm0coI_s"
      },
      "outputs": [],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "81Ed22NJoI_s"
      },
      "outputs": [],
      "source": [
        "data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PDn-ardOoI_s"
      },
      "outputs": [],
      "source": [
        "samples = data[:, 0:7]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iv_emvLioI_t",
        "outputId": "5dbbad9d-a3e0-413f-824c-72c12b0e103d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(210, 7)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "samples.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZMfxKQtPvL-"
      },
      "source": [
        "# \"elbow method\" for determining an appropriate number of clusters (k) in a KMeans clustering algorithm.\n",
        "\n",
        "#\"elbow curve\" that helps you visually determine an appropriate number of clusters for your data.\n",
        "#The idea is to look for the \"elbow point\" on the curve where the inertia starts to decrease at a slower rate. This point often corresponds to a suitable number of clusters, as adding more clusters beyond this point may not significantly improve the clustering quality."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "2R01NigUoI_t",
        "outputId": "aa69289c-43d3-4ab9-e628-2eb2d371ddef"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ks = range(1, 10)\n",
        "inertias = [] # An empty list named inertias is created to store the inertia values for each value of k.\n",
        "\n",
        "for k in ks:\n",
        "    # Create a KMeans instance with k clusters: model\n",
        "    model = KMeans(n_clusters=k)\n",
        "\n",
        "    # Fit model to samples\n",
        "    model.fit(samples)\n",
        "\n",
        "    # Append the inertia to the list of inertias\n",
        "    inertias.append(model.inertia_)\n",
        "\n",
        "# Plot ks vs inertias\n",
        "plt.plot(ks, inertias, '-o')\n",
        "plt.xlabel('number of clusters, k')\n",
        "plt.ylabel('inertia')\n",
        "plt.xticks(ks)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7Kh4iKmoI_t"
      },
      "source": [
        "## Using Cross Tab - in this method creating a cross-tabulation (crosstab) of the cluster labels and the original varieties or categories of the data.\n",
        "## This method helps you understand how well the KMeans clustering algorithm has grouped data points into clusters and how these clusters relate to the known categories or varieties of the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "uWOq2NwMoI_u",
        "outputId": "4423bb2f-fa7a-4876-c9a5-1d17ee1921e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "varieties  1.0  2.0  3.0\n",
            "labels                  \n",
            "0            1   60    0\n",
            "1            9    0   68\n",
            "2           60   10    2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "\n",
        "samples = data[:, 0:7]\n",
        "varieties = data[:, 7]\n",
        "\n",
        "# Create a KMeans model with 3 clusters: model\n",
        "model = KMeans(n_clusters=3)\n",
        "\n",
        "# Use fit_predict to fit model and obtain cluster labels: labels\n",
        "labels = model.fit_predict(samples)\n",
        "\n",
        "# Create a DataFrame with labels and varieties as columns: df\n",
        "df = pd.DataFrame({'labels': labels, 'varieties': varieties})\n",
        "\n",
        "# Create crosstab: ct\n",
        "ct = pd.crosstab(df['labels'], df['varieties'])\n",
        "\n",
        "# Display ct\n",
        "print(ct)"
      ]
    },
    {
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        "id": "4fq-fvgboI_u",
        "outputId": "c28b8fcf-996e-4326-a8e9-e123e4babc9f"
      },
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              "varieties  1.0  2.0  3.0\n",
              "labels                  \n",
              "0            1   60    0\n",
              "1            9    0   68\n",
              "2           60   10    2"
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