BloodCenter.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Data exploration\n",
        "The main dataset used is going to be explored using python, pandas and scipy,  \n",
        " due to the format of the database."
      ],
      "metadata": {
        "id": "uKtc6p66FPlj"
      }
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      "source": [
        "# Importing the libraries needed for the exploration\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.io import arff\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNVp_apG9IF-",
        "outputId": "984a2a45-3e2c-464a-bb38-8fa9af821d50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.getcwd()"
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          "height": 35
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        "id": "SCyOOQgf9aoC",
        "outputId": "89593937-7068-4c7b-8edd-3e1c2ba7f0a7"
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              "'/content'"
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    {
      "cell_type": "code",
      "source": [
        "os.chdir(\"/content/drive/MyDrive/AI/ML/datasetML\")\n",
        "os.getcwd()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "vkmbJUyX9MVr",
        "outputId": "57f109fe-84f3-4581-bc28-f07b49b7c594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/AI/ML/datasetML'"
            ],
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              "type": "string"
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          "execution_count": 17
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      "cell_type": "code",
      "source": [
        "# Transforming data into a dataframe for visualization\n",
        "data, meta = arff.loadarff('bloodinfo.arff')\n",
        "df = pd.DataFrame(data)\n",
        "df.head(30)"
      ],
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      "execution_count": null,
      "outputs": [
        {
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              "     V1    V2       V3    V4 Class\n",
              "0   2.0  50.0  12500.0  98.0  b'2'\n",
              "1   0.0  13.0   3250.0  28.0  b'2'\n",
              "2   1.0  16.0   4000.0  35.0  b'2'\n",
              "3   2.0  20.0   5000.0  45.0  b'2'\n",
              "4   1.0  24.0   6000.0  77.0  b'1'\n",
              "5   4.0   4.0   1000.0   4.0  b'1'\n",
              "6   2.0   7.0   1750.0  14.0  b'2'\n",
              "7   1.0  12.0   3000.0  35.0  b'1'\n",
              "8   2.0   9.0   2250.0  22.0  b'2'\n",
              "9   5.0  46.0  11500.0  98.0  b'2'\n",
              "10  4.0  23.0   5750.0  58.0  b'1'\n",
              "11  0.0   3.0    750.0   4.0  b'1'\n",
              "12  2.0  10.0   2500.0  28.0  b'2'\n",
              "13  1.0  13.0   3250.0  47.0  b'1'\n",
              "14  2.0   6.0   1500.0  15.0  b'2'\n",
              "15  2.0   5.0   1250.0  11.0  b'2'\n",
              "16  2.0  14.0   3500.0  48.0  b'2'\n",
              "17  2.0  15.0   3750.0  49.0  b'2'\n",
              "18  2.0   6.0   1500.0  15.0  b'2'\n",
              "19  2.0   3.0    750.0   4.0  b'2'\n",
              "20  2.0   3.0    750.0   4.0  b'2'\n",
              "21  4.0  11.0   2750.0  28.0  b'1'\n",
              "22  2.0   6.0   1500.0  16.0  b'2'\n",
              "23  2.0   6.0   1500.0  16.0  b'2'\n",
              "24  9.0   9.0   2250.0  16.0  b'1'\n",
              "25  4.0  14.0   3500.0  40.0  b'1'\n",
              "26  4.0   6.0   1500.0  14.0  b'1'\n",
              "27  4.0  12.0   3000.0  34.0  b'2'\n",
              "28  4.0   5.0   1250.0  11.0  b'2'\n",
              "29  4.0   8.0   2000.0  21.0  b'1'"
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b291810d-6d41-4515-aecf-eb7513170d29 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 748,\n  \"fields\": [\n    {\n      \"column\": \"V1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.095395635299214,\n        \"min\": 0.0,\n        \"max\": 74.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          17.0,\n          16.0,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"V2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.839307123090025,\n        \"min\": 1.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          21.0,\n          14.0,\n          43.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"V3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1459.8267807725063,\n        \"min\": 250.0,\n        \"max\": 12500.0,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          5250.0,\n          3500.0,\n          10750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"V4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.376714368614188,\n        \"min\": 2.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          71.0,\n          98.0,\n          23.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"b'1'\",\n          \"b'2'\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['V1'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ofinAqHBWyV",
        "outputId": "ace79efb-6bb1-4d7b-d65c-d39fba6f1348"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.506684491978609"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# General exploration of the variables\n",
        "print(df.mean(axis=0))                   # numeric mean\n",
        "print(df.describe())                     # Basic statistic summary (numerical only)\n",
        "print(df.count(axis=0))                  # Counter of not null data\n",
        "print(df.V1.corr(df.V3))                   # Correlation between V1 and V3\n",
        "print(df.corr())                         # Correlation matrix of the columns\n",
        "#print(df.corrwith(df.V2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CD5tfBUBtyX",
        "outputId": "333cc684-68e0-4577-d940-cca74a70bd31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "V1          9.506684\n",
            "V2          5.514706\n",
            "V3       1378.676471\n",
            "V4         34.282086\n",
            "Class            inf\n",
            "dtype: object\n",
            "               V1          V2            V3          V4\n",
            "count  748.000000  748.000000    748.000000  748.000000\n",
            "mean     9.506684    5.514706   1378.676471   34.282086\n",
            "std      8.095396    5.839307   1459.826781   24.376714\n",
            "min      0.000000    1.000000    250.000000    2.000000\n",
            "25%      2.750000    2.000000    500.000000   16.000000\n",
            "50%      7.000000    4.000000   1000.000000   28.000000\n",
            "75%     14.000000    7.000000   1750.000000   50.000000\n",
            "max     74.000000   50.000000  12500.000000   98.000000\n",
            "V1       748\n",
            "V2       748\n",
            "V3       748\n",
            "V4       748\n",
            "Class    748\n",
            "dtype: int64\n",
            "-0.18274546808203906\n",
            "             V1        V2        V3        V4     Class\n",
            "V1     1.000000 -0.182745 -0.182745  0.160618 -0.279869\n",
            "V2    -0.182745  1.000000  1.000000  0.634940  0.218633\n",
            "V3    -0.182745  1.000000  1.000000  0.634940  0.218633\n",
            "V4     0.160618  0.634940  0.634940  1.000000 -0.035854\n",
            "Class -0.279869  0.218633  0.218633 -0.035854  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here it's the information about the different blood donors <br>\n",
        "**Attribute information**  \n",
        "- V1: Recency - months since last donation\n",
        "- V2: Frequency - total number of donation\n",
        "- V3: Monetary - total blood donated in c.c.\n",
        "- V4: Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood).\n"
      ],
      "metadata": {
        "id": "iL2XKF0aAPpR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMeqZnQ0BAuY",
        "outputId": "880ce3cc-2c87-4166-de30-51a02cbe0bdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 748 entries, 0 to 747\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   V1      748 non-null    float64\n",
            " 1   V2      748 non-null    float64\n",
            " 2   V3      748 non-null    float64\n",
            " 3   V4      748 non-null    float64\n",
            " 4   Class   748 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 29.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "sns.histplot(df['V1'], kde=True)\n",
        "plt.title('Distribution of Recency (months)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RPo-JkicDrZ3",
        "outputId": "3cbe8fc4-fc1f-42dc-e3b5-4abe3849a0ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df['V2'], kde=True)\n",
        "plt.title('Distribution of Frequency (times)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "r7fh4PY4GoLk",
        "outputId": "e21cd941-f29c-4cca-a8df-45451fc60f8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = df.corr()\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "G5icX2grG3lD",
        "outputId": "6f3eb16c-008a-43e8-f38c-b67327a7c82f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ghg9UTd_G7wd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}