7_predict_missing_values.ipynb

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            "--2022-10-23 20:04:28--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/titanic.csv\n",
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            "2022-10-23 20:04:29 (5.05 MB/s) - ‘titanic.csv’ saved [61194/61194]\n",
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            "--2022-10-23 20:04:29--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/graphing.py\n",
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
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              "3            4         1       1   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
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              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
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          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load data from our dataset file into a pandas dataframe\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/titanic.csv\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/graphing.py\n",
        "dataset = pd.read_csv('titanic.csv', index_col=False, sep=\",\", header=0)\n",
        "\n",
        "# Let's take a look at the data\n",
        "dataset.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcHw991Yfe-C",
        "outputId": "172c8b73-bf27-4298-bcc4-bb27a3c22298"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphing\n",
        "\n",
        "graphing.histogram(dataset, label_x='Pclass', label_y='Survived', histfunc='avg', include_boxplot=True)\n"
      ],
      "metadata": {
        "colab": {
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          "height": 417
        },
        "id": "0lBaahWOfl1j",
        "outputId": "2c26bc84-2e7a-4582-a195-b50571849dc2"
      },
      "execution_count": 3,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "graphing.multiple_histogram(dataset, \n",
        "                            label_x='Pclass', # group by ticket class\n",
        "                            label_group=\"Parch\", # colour by no parents or children\n",
        "                            label_y='Survived', \n",
        "                            histfunc=\"avg\")"
      ],
      "metadata": {
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          "height": 417
        },
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        "outputId": "4f8882b6-4d0a-4f92-f430-cee00bbd7157"
      },
      "execution_count": 4,
      "outputs": [
        {
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    {
      "cell_type": "code",
      "source": [
        "# Plot Fare vs Survival\n",
        "graphing.histogram(dataset, label_x=\"Fare\", label_y=\"Survived\", histfunc=\"avg\", nbins=30, title=\"Fare vs Survival\", include_boxplot=True, show=True)\n",
        "\n",
        "# Plot Age vs Survival\n",
        "graphing.histogram(dataset, label_x=\"Age\", label_y=\"Survived\", histfunc=\"avg\", title=\"Age vs Survival\", nbins=30, include_boxplot=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "mq9y7_DHf0vY",
        "outputId": "9bae9899-8b40-4306-d39f-10e48a55f28b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "# Create some simple functions\n",
        "# Read their descriptions to find out more \n",
        "def get_rows(sex, port):\n",
        "    '''Returns rows that match in terms of sex and embarkment port'''\n",
        "    return dataset[(dataset.Embarked == port) & (dataset.Sex == sex)]\n",
        "\n",
        "def proportion_survived(sex, port):\n",
        "    '''Returns the proportion of people meeting criteria who survived'''\n",
        "    survived = get_rows(sex, port).Survived\n",
        "    return np.mean(survived)\n",
        "\n",
        "# Make two columns of data - together these represent each combination\n",
        "# of sex and embarkment port\n",
        "sexes = [\"male\", \"male\", \"male\", \"female\",\"female\", \"female\"]\n",
        "ports = [\"C\", \"Q\", \"S\" ] * 2\n",
        "\n",
        "# Calculate the number of passengers at each port + sex combination\n",
        "passenger_count = [len(get_rows(sex, port)) for sex,port in zip(sexes, ports)]\n",
        "\n",
        "# Calculate the proportion of passengers from each port + sex combination who survived\n",
        "passenger_survival = [proportion_survived(sex, port) for sex,port in zip(sexes, ports)]\n",
        "\n",
        "# Combine into a single data frame\n",
        "table = pd.DataFrame(dict(\n",
        "    sex=sexes,\n",
        "    port=ports,\n",
        "    passenger_count=passenger_count,\n",
        "    passenger_survival_rate=passenger_survival\n",
        "))\n",
        "\n",
        "# Make a bubble plot\n",
        "# This is just a scatter plot but each entry in the plot \n",
        "# has a size and colour. We set colour to passenger_survival \n",
        "# and size to the number of passengers \n",
        "graphing.scatter_2D(table, \n",
        "                    label_colour=\"passenger_survival_rate\", \n",
        "                    label_size=\"passenger_count\", \n",
        "                    size_multiplier=0.3,\n",
        "                    title=\"Bubble Plot of Categorical Data\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "gPl7-od_f5_0",
        "outputId": "8798f527-08ee-4176-c491-c48102cac775"
      },
      "execution_count": 8,
      "outputs": [
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