Clustering-KMeans_Heart_Scratch_MiniProject.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KMeans-Heart-Scratch-MiniProject",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzPmj6y4M3_i",
        "outputId": "9503f331-88fa-4775-d9cd-3406d7dae812",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7f9e8c82-f2bf-4191-abba-8495d8245c42\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7f9e8c82-f2bf-4191-abba-8495d8245c42\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving heart.csv to heart.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLt69GIWNTTa"
      },
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "temp=pd.read_csv(io.BytesIO(uploaded['heart.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-2NPmwiNkK-",
        "outputId": "07005fcc-f6a0-44bd-eefa-7169a7beae3e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "temp.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nb4qTQIvNDbT",
        "outputId": "26871718-ab8e-457e-9dd6-3d16350ff054",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "a={\"F1\":temp['age'],\"F2\":temp['thalach']}\n",
        "df=pd.DataFrame(a)\n",
        "df.head()"
      ],
      "execution_count": null,
      "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>F1</th>\n",
              "      <th>F2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   F1   F2\n",
              "0  63  150\n",
              "1  37  187\n",
              "2  41  172\n",
              "3  56  178\n",
              "4  57  163"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hZukC-UNClt"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "centroids=[]\n",
        "\n",
        "centroids.append(list(df.iloc[1]))\n",
        "centroids.append(list(df.iloc[4]))\n",
        "\n",
        "distances=np.zeros([2,len(df)])\n",
        "distances\n",
        "cluster=[[],[]]\n",
        "\n",
        "previous_clusters=[]\n",
        "previous_centroids=[]\n",
        "\n",
        "\n",
        "v=0\n",
        "while(1):\n",
        "  v=v+1\n",
        "  previous_clusters.append(list(cluster))\n",
        "  previous_centroids.append(list(centroids))\n",
        "  for i in range(len(distances)):\n",
        "    for j in range(len(df)):\n",
        "      dist=np.linalg.norm(np.array(centroids[i])-np.array(df.iloc[j]))\n",
        "      \n",
        "      distances[i,j]=dist\n",
        "  cluster=[[],[]]\n",
        "  for i in range(len(df)):\n",
        "    if distances[0,i]<=distances[1,i]:\n",
        "      cluster[0].append(i)\n",
        "    else:\n",
        "      cluster[1].append(i)\n",
        "  \n",
        "  \n",
        "  for i in range(len(cluster)):\n",
        "    c_x=0\n",
        "    c_y=0\n",
        "    for j in cluster[i]:\n",
        "      c_x+=df.iloc[j,0]\n",
        "      c_y+=df.iloc[j,1]\n",
        "    centroids[i][0]=c_x/len(cluster[i])\n",
        "    centroids[i][1]=c_y/len(cluster[i])\n",
        "\n",
        "  if cluster==previous_clusters[-1]:\n",
        "    break\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WozxOjzeOBKJ",
        "outputId": "0a759f25-178f-4d83-9f7d-8076a383ff8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(\" Clusters 1 : \",cluster[0],\" Cluster 2 : \",cluster[1])\n",
        "\n",
        "print(\"Centroids  :  \",centroids)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Clusters 1 :  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 19, 20, 21, 22, 24, 25, 26, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 61, 62, 64, 65, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 102, 103, 104, 107, 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 127, 128, 130, 131, 132, 133, 134, 135, 140, 141, 142, 146, 147, 148, 149, 152, 153, 154, 156, 157, 159, 160, 161, 162, 163, 164, 167, 169, 171, 172, 173, 176, 177, 182, 183, 185, 188, 189, 194, 196, 197, 199, 200, 203, 205, 207, 209, 210, 219, 220, 222, 228, 230, 235, 236, 237, 238, 239, 245, 246, 248, 255, 258, 259, 260, 261, 263, 273, 275, 278, 281, 283, 286, 287, 290, 293, 302]  Cluster 2 :  [11, 13, 17, 23, 27, 31, 34, 40, 43, 48, 52, 60, 63, 66, 84, 89, 94, 95, 101, 105, 106, 112, 120, 126, 129, 136, 137, 138, 139, 143, 144, 145, 150, 151, 155, 158, 165, 166, 168, 170, 174, 175, 178, 179, 180, 181, 184, 186, 187, 190, 191, 192, 193, 195, 198, 201, 202, 204, 206, 208, 211, 212, 213, 214, 215, 216, 217, 218, 221, 223, 224, 225, 226, 227, 229, 231, 232, 233, 234, 240, 241, 242, 243, 244, 247, 249, 250, 251, 252, 253, 254, 256, 257, 262, 264, 265, 266, 267, 268, 269, 270, 271, 272, 274, 276, 277, 279, 280, 282, 284, 285, 288, 289, 291, 292, 294, 295, 296, 297, 298, 299, 300, 301]\n",
            "Centroids  :   [[52.02777777777778, 165.11111111111111], [57.78861788617886, 127.01626016260163]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Hzym96AOajo"
      },
      "source": [
        "f={\"F1\":[centroids[0][0],centroids[1][0]],\"F2\":[centroids[0][1],centroids[1][1]]}\n",
        "kf=pd.DataFrame(f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQ6HU_ZAT_WS",
        "outputId": "744d5e83-2d9b-42c3-e178-73517627a13f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "for i in range(len(df)):\n",
        "  if i in cluster[0]:\n",
        "    color='y'\n",
        "  else:\n",
        "    color='b'\n",
        "  plt.scatter(df.iloc[i,0],df.iloc[i,1],c=color)\n",
        "plt.scatter(kf['F1'],kf['F2'], c='red', s=50,alpha=0.5)\n",
        "plt.show()"
      ],
      "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": "k54nhL9vYpU_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}