titanic_test_csv.ipynb

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      "source": [
        "# <font color='#FF6347'>Project Overview: titanic_test_csv EDA</font>\n",
        "\n",
        "## <font color='#4682B4'>Objective</font>\n",
        "The primary objective of this project is to preprocess and analyze the Titanic test dataset to prepare it for predictive modeling tasks. The dataset contains various attributes about passengers on the Titanic voyage, and the goal is to clean, explore, and prepare this data for further machine learning applications.\n",
        "\n",
        "## <font color='#4682B4'>Dataset</font>\n",
        "The Titanic test dataset is used in this project and includes features about passengers. Key columns are:\n",
        "- **PassengerId**: A unique identifier for each passenger.\n",
        "- **Pclass**: The class of the passenger’s ticket (1st, 2nd, or 3rd).\n",
        "- **Name**: The passenger's full name.\n",
        "- **Sex**: The passenger's gender.\n",
        "- **Age**: The passenger's age.\n",
        "- **SibSp**: The number of siblings or spouses the passenger had aboard.\n",
        "- **Parch**: The number of parents or children the passenger had aboard.\n",
        "- **Ticket**: The ticket number.\n",
        "- **Fare**: The fare the passenger paid.\n",
        "- **Cabin**: The cabin number where the passenger stayed.\n",
        "- **Embarked**: The port where the passenger boarded the Titanic (C = Cherbourg, Q = Queenstown, S = Southampton).\n",
        "\n",
        "## <font color='#4682B4'>Steps Involved</font>\n",
        "1. **Import Necessary Libraries**:\n",
        "   - Libraries such as `pandas`, `numpy`, `matplotlib.pyplot`, and `seaborn` are imported for data handling, numerical computation, and visualization.\n",
        "\n",
        "2. **Load the Data**:\n",
        "   - The dataset is loaded into a DataFrame from a CSV file for subsequent processing.\n",
        "\n",
        "3. **Inspect the Data**:\n",
        "   - Initial inspection involves displaying the first few rows, checking data types, and summarizing statistical information to understand the dataset's structure and contents.\n",
        "\n",
        "4. **Check for Missing Values**:\n",
        "   - Identifying missing values in the dataset helps in understanding which columns require imputation or other forms of handling.\n",
        "\n",
        "5. **Handle Missing Values**:\n",
        "   - Missing values are addressed by either imputing with appropriate values or removing columns with excessive missing data.\n",
        "\n",
        "6. **Check for Duplicate Values**:\n",
        "   - Duplicate rows are detected to ensure that the dataset contains only unique records, avoiding potential biases.\n",
        "\n",
        "7. **Remove Duplicate Values**:\n",
        "   - Duplicate rows are removed from the dataset to maintain its integrity.\n",
        "\n",
        "8. **Visualize the Data**:\n",
        "   - Various visualizations, such as histograms and count plots, are created to understand the distribution of features and identify patterns.\n",
        "\n",
        "9. **Check Data Distribution**:\n",
        "   - Analyzing the distribution of numerical and categorical features provides insights into their spread and relationships.\n",
        "\n",
        "10. **Handle Categorical Variables**:\n",
        "    - Categorical variables are transformed into numerical format using techniques like one-hot encoding to facilitate machine learning.\n",
        "\n",
        "11. **Feature Scaling**:\n",
        "    - Numerical features are standardized to ensure they are on a similar scale, which is crucial for many machine learning algorithms.\n",
        "\n",
        "12. **Save Processed Data**:\n",
        "    - The cleaned and transformed dataset is saved for future analysis or model training.\n",
        "\n",
        "## <font color='#4682B4'>Tools and Libraries Used</font>\n",
        "- **pandas**: For data manipulation and cleaning.\n",
        "- **numpy**: For numerical operations and handling missing values.\n",
        "- **matplotlib.pyplot**: For creating visualizations such as plots and histograms.\n",
        "- **seaborn**: For advanced statistical visualizations and enhancing plots."
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      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">1. Import Necessary Libraries</span>\n",
        "\n",
        "- **Objective**: Import essential libraries to perform data manipulation, visualization, and analysis.\n",
        "  \n",
        "- **Details**:\n",
        "  - **pandas**:\n",
        "    - **Purpose**: Provides data structures like DataFrames and Series for data manipulation and analysis.\n",
        "    - **Functions**: Used for data loading, data cleaning, merging datasets, handling missing values, and data transformation.\n",
        "    - **Syntax**: `import pandas as pd` – Imports the pandas library and aliases it as `pd` for easier reference.\n",
        "\n",
        "  - **numpy**:\n",
        "    - **Purpose**: Offers support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.\n",
        "    - **Functions**: Provides numerical operations such as calculating mean, median, variance, and performing mathematical computations.\n",
        "    - **Syntax**: `import numpy as np` – Imports the numpy library and aliases it as `np` for easier reference.\n",
        "\n",
        "  - **matplotlib.pyplot**:\n",
        "    - **Purpose**: A plotting library that produces static, interactive, and animated visualizations in Python.\n",
        "    - **Functions**: Used to create various types of plots, including line plots, histograms, scatter plots, and bar charts.\n",
        "    - **Syntax**: `import matplotlib.pyplot as plt` – Imports the pyplot module from matplotlib and aliases it as `plt`.\n",
        "\n",
        "  - **seaborn**:\n",
        "    - **Purpose**: Built on top of matplotlib, seaborn provides a high-level interface for drawing attractive and informative statistical graphics.\n",
        "    - **Functions**: Enhances matplotlib plots with additional features like color palettes, themes, and advanced visualizations such as violin plots and pair plots.\n",
        "    - **Syntax**: `import seaborn as sns` – Imports the seaborn library and aliases it as `sns`."
      ],
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      "cell_type": "code",
      "source": [
        "# 1. Import Necessary Libraries\n",
        "# We import essential libraries to work with data and visualization."
      ],
      "metadata": {
        "id": "KQrvseL_TkFe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # pandas helps handle data"
      ],
      "metadata": {
        "id": "suIrfZsQjRoy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np   # numpy is used for numerical computations"
      ],
      "metadata": {
        "id": "2_8XuLrHTwdc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # matplotlib helps create plots"
      ],
      "metadata": {
        "id": "SApO63K2Twgz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns   # seaborn provides additional visualization tools"
      ],
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      "execution_count": null,
      "outputs": []
    },
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      "metadata": {
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    {
      "cell_type": "code",
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">2. Load the Data</span>\n",
        "\n",
        "- **Objective**: Read the Titanic test dataset into a DataFrame to facilitate analysis and preprocessing.\n",
        "\n",
        "- **Details**:\n",
        "  - **pd.read_csv()**:\n",
        "    - **Purpose**: Reads data from a CSV file into a pandas DataFrame.\n",
        "    - **Functionality**: Parses the CSV file, handles delimiters, and converts the data into a DataFrame structure that is easy to manipulate.\n",
        "    - **Parameters**:\n",
        "      - **`filepath_or_buffer`**: Specifies the path to the CSV file. In this case, `'/content/titanic_test.csv'`.\n",
        "      - **Additional Options**: Parameters like `header`, `index_col`, and `dtype` can be used to customize the reading process.\n",
        "    - **Syntax**: `df = pd.read_csv('/content/titanic_test.csv')` – Loads the data from the specified file path into the DataFrame `df`."
      ],
      "metadata": {
        "id": "-vMV1-Qt31rL"
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    {
      "cell_type": "code",
      "source": [
        "# 2. Load the Data\n",
        "# We load the Titanic test dataset to analyze and preprocess it."
      ],
      "metadata": {
        "id": "kUSp8whPT6C6"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/titanic_test.csv')"
      ],
      "metadata": {
        "id": "UPeVVaXcjRuj"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kSasoUQFjRzN"
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      "cell_type": "code",
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      "metadata": {
        "id": "GVw3lahe3_h7"
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      "source": [
        "<span style=\"color:#FF6347;\">3. Inspect the Data</span>\n",
        "\n",
        "- **Objective**: Examine the structure and initial content of the dataset to understand its composition and identify potential issues.\n",
        "\n",
        "- **Details**:\n",
        "  - **df.head()**:\n",
        "    - **Purpose**: Displays the first few rows of the DataFrame to provide a quick overview of the dataset.\n",
        "    - **Functionality**: Helps in understanding the data types, column names, and sample values.\n",
        "    - **Syntax**: `print(df.head())` – Prints the first five rows of the DataFrame.\n",
        "\n",
        "  - **df.info()**:\n",
        "    - **Purpose**: Provides a summary of the DataFrame, including data types and non-null counts.\n",
        "    - **Functionality**: Helps identify missing values, understand data types (e.g., integer, float, object), and the total number of entries.\n",
        "    - **Syntax**: `print(df.info())` – Prints summary information about the DataFrame.\n",
        "\n",
        "  - **df.describe()**:\n",
        "    - **Purpose**: Generates descriptive statistics of numerical columns.\n",
        "    - **Functionality**: Provides insights into the distribution of numerical data, including measures such as mean, median, standard deviation, and quartiles.\n",
        "    - **Syntax**: `print(df.describe())` – Prints summary statistics for numerical columns in the DataFrame."
      ],
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      "cell_type": "code",
      "source": [
        "# 3. Inspect the Data\n",
        "# It's essential to inspect the data to understand its structure and contents.\n",
        "# Displaying the first few rows gives us an initial understanding of the dataset."
      ],
      "metadata": {
        "id": "gI37_HTjUGvj"
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      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")"
      ],
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          "base_uri": "https://localhost:8080/"
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        "id": "2rw6fU8xjR3q",
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())"
      ],
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        "id": "i1i1jSd4UOOt",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   PassengerId  Pclass                                          Name     Sex  \\\n",
            "0          892       3                              Kelly, Mr. James    male   \n",
            "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
            "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
            "3          895       3                              Wirz, Mr. Albert    male   \n",
            "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
            "\n",
            "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
            "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
            "1  47.0      1      0   363272   7.0000   NaN        S  \n",
            "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
            "3  27.0      0      0   315154   8.6625   NaN        S  \n",
            "4  22.0      1      1  3101298  12.2875   NaN        S  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CBMZ7_PXUORz"
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      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "# Using df.info(), we get an overview of the dataset, including column names, data types, and non-null counts.\n",
        "# This helps us identify missing values and understand the data types."
      ],
      "metadata": {
        "id": "-EvcT83VUVkT"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "print(\"\\nInformation about the dataset:\")"
      ],
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          "base_uri": "https://localhost:8080/"
        },
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          "name": "stdout",
          "text": [
            "\n",
            "Information about the dataset:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.info())"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n",
            "None\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ym2Mdkg4UgHd"
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    {
      "cell_type": "code",
      "source": [
        "# Using df.describe(), we get summary statistics of numerical columns, providing insights into the distribution of data."
      ],
      "metadata": {
        "id": "fwV3yf8zjR6B"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics of numerical columns:\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mn7Ud0YKUlyY",
        "outputId": "464f52af-3bac-4ca0-a498-49009164d065"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics of numerical columns:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHXp0PTtUoek",
        "outputId": "803079ae-8e4a-4ca6-8669-17f29c7202b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
            "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
            "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
            "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
            "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
            "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
            "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
            "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
            "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tXFLUNaXUrPf"
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      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CRjykgwiUrrH"
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      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xdaPhUIz4Jgs"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">4. Check for Missing Values</span>\n",
        "\n",
        "- **Objective**: Identify missing values in the dataset to understand which columns require data imputation or other handling.\n",
        "\n",
        "- **Details**:\n",
        "  - **df.isnull()**:\n",
        "    - **Purpose**: Detects missing values in the DataFrame.\n",
        "    - **Functionality**: Returns a DataFrame of the same shape as `df`, with `True` where values are missing and `False` where they are not.\n",
        "    - **Syntax**: `df.isnull()` – Produces a boolean DataFrame indicating missing values.\n",
        "\n",
        "  - **df.isnull().sum()**:\n",
        "    - **Purpose**: Summarizes the count of missing values for each column.\n",
        "    - **Functionality**: Provides a quick way to identify which columns have missing values and how many.\n",
        "    - **Syntax**: `print(df.isnull().sum())` – Prints the number of missing values for each column in the DataFrame."
      ],
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    {
      "cell_type": "code",
      "source": [
        "# 4. Check for Missing Values\n",
        "# Missing values can affect our analysis, so it's crucial to handle them appropriately."
      ],
      "metadata": {
        "id": "zBwKHbIJjR8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing Values Before Handling:\")"
      ],
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        "id": "jCxwlpyEjR-a",
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          "base_uri": "https://localhost:8080/"
        },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values Before Handling:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "Hz2gp0Dnjf1v",
        "outputId": "adb73325-a466-4823-dd29-8f9a3a8ce332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age             86\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             1\n",
            "Cabin          327\n",
            "Embarked         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "azkFNg2YU2zL"
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      "outputs": []
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      "cell_type": "code",
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      "metadata": {
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lTt-8Q8T4TWI"
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    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">5. Handle Missing Values</span>\n",
        "\n",
        "- **Objective**: Address missing values to ensure the dataset is complete and suitable for analysis or modeling.\n",
        "\n",
        "- **Details**:\n",
        "  - **Filling Missing Values**:\n",
        "    - **Purpose**: Impute missing values with a central measure like the mean to avoid losing data.\n",
        "    - **Functionality**: Applies the mean of each numerical column to replace missing values.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      numeric_columns = df.select_dtypes(include=np.number).columns\n",
        "      df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].mean())\n",
        "      ```\n",
        "    - **Explanation**:\n",
        "      - `df.select_dtypes(include=np.number).columns` selects all numerical columns.\n",
        "      - `df[numeric_columns].fillna(df[numeric_columns].mean())` fills missing values with the mean of respective columns.\n",
        "\n",
        "  - **Dropping Columns**:\n",
        "    - **Purpose**: Remove columns with too many missing values that cannot be reasonably imputed.\n",
        "    - **Functionality**: Deletes columns that contain a significant proportion of missing values.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      df.drop(columns=['Cabin'], inplace=True)\n",
        "      ```\n",
        "    - **Explanation**: Drops the 'Cabin' column as it had many missing values and was considered not useful for the analysis.\n",
        "\n",
        "  - **Verification**:\n",
        "    - **Purpose**: Ensure that all missing values have been addressed.\n",
        "    - **Functionality**: Rechecks for any remaining missing values to confirm they have been handled.\n",
        "    - **Syntax**: `print(df.isnull().sum())` – Verifies that no missing values remain."
      ],
      "metadata": {
        "id": "WR_BYwO94TrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Handle Missing Values (Fill missing values with the mean for numeric columns)\n",
        "# We identify numeric columns to fill missing values with their respective means."
      ],
      "metadata": {
        "id": "VSw9-JnTjf49"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns = df.select_dtypes(include=np.number).columns"
      ],
      "metadata": {
        "id": "EGXxStoijf-B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].mean())"
      ],
      "metadata": {
        "id": "fon_B-r9jgBV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4krJdaGmVBfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify missing values after handling"
      ],
      "metadata": {
        "id": "CdRysfx4jgEJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing Values After Handling:\")"
      ],
      "metadata": {
        "id": "-18MvJfzjgHZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0a61ade-3a81-4841-b4f4-8f1bc2c06287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values After Handling:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90RMjOr-juBK",
        "outputId": "c0449c78-dfc3-417c-ee14-b731472392cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age              0\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             0\n",
            "Cabin          327\n",
            "Embarked         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kPWvApp2VL43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if 'Cabin' column still has missing values"
      ],
      "metadata": {
        "id": "5bFbQPzAjuEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if df['Cabin'].isnull().sum() > 0:\n",
        "    print(\"Warning: 'Cabin' column still has missing values.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUS6ZtdzVTQ7",
        "outputId": "82ad616e-ebe6-446a-e3a3-2c883814ba0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: 'Cabin' column still has missing values.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Od5JszVGVVwf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the 'Cabin' column as it contains a significant number of missing values."
      ],
      "metadata": {
        "id": "qUiQETqxVV5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['Cabin'], inplace=True)"
      ],
      "metadata": {
        "id": "kBTqmq6_VaOv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ivjb4-jHVfOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_jZk6x-_4bXU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZKs_9YaA4bam"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">6. Check for Duplicate Values</span>\n",
        "\n",
        "- **Objective**: Identify and address duplicate rows in the dataset to prevent bias and redundancy in the analysis.\n",
        "\n",
        "- **Details**:\n",
        "  - **df.duplicated()**:\n",
        "    - **Purpose**: Detect duplicate rows in the DataFrame.\n",
        "    - **Functionality**: Returns a boolean Series indicating whether each row is a duplicate.\n",
        "    - **Syntax**: `df.duplicated().sum()` – Counts the number of duplicate rows in the DataFrame.\n",
        "\n",
        "  - **Handling Duplicates**:\n",
        "    - **Purpose**: Remove duplicate rows to maintain data integrity.\n",
        "    - **Functionality**: Eliminates redundant data entries to ensure each observation is unique.\n",
        "    - **Syntax**: `df.drop_duplicates(inplace=True)` – Removes duplicate rows from the DataFrame."
      ],
      "metadata": {
        "id": "2dhAL0Xm4bwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# 6. Check for Duplicate Values\n",
        "# Duplicate rows can bias our analysis, so it's essential to identify and remove them."
      ],
      "metadata": {
        "id": "OE-gn3pdVfRK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Duplicate Rows Before Removal:\", df.duplicated().sum())"
      ],
      "metadata": {
        "id": "H0Pph3ivjuHC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "350152f2-66be-44b6-8299-a70cd5588911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate Rows Before Removal: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JN8rh7JkVmDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XfvuZM6n4kTD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9pVbdUKE4kWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">7. Remove Duplicate Values</span>\n",
        "\n",
        "- **Objective**: Remove duplicate rows to ensure the dataset's uniqueness and reliability.\n",
        "\n",
        "- **Details**:\n",
        "  - **df.drop_duplicates()**:\n",
        "    - **Purpose**: Deletes duplicate rows from the DataFrame.\n",
        "    - **Functionality**: Maintains only the first occurrence of each unique row, eliminating redundancy.\n",
        "    - **Syntax**: `df.drop_duplicates(inplace=True)` – Removes duplicates directly from the DataFrame without creating a new object."
      ],
      "metadata": {
        "id": "GAGTV6CS4ktL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Remove Duplicate Values"
      ],
      "metadata": {
        "id": "x4pS_fTtVmRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "A5ZKmrNsVndd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sc15BSX2VqLS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "McIkJZ6I4q1z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X7lDH1NF4q4_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">8. Visualize the Data</span>\n",
        "\n",
        "- **Objective**: Visualize the distribution of each feature to understand its spread and identify patterns or anomalies.\n",
        "\n",
        "- **Details**:\n",
        "  - **sns.histplot()**:\n",
        "    - **Purpose**: Creates histograms with Kernel Density Estimation (KDE) to visualize data distributions.\n",
        "    - **Functionality**: Plots the frequency of data points within bins and adds a smooth curve to represent the data distribution.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      for column in df.columns:\n",
        "          sns.histplot(df[column], kde=True)\n",
        "          plt.title(f\"Distribution of {column}\")\n",
        "          plt.show()\n",
        "      ```\n",
        "    - **Explanation**: Iterates through each column, plots its distribution, and adds a KDE curve for a smooth representation of the data spread."
      ],
      "metadata": {
        "id": "3g5LRgrr4rLq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Visualize the Data (Histogram with kernel density estimation)\n",
        "# Visualizing the distribution of each feature helps us understand its spread and identify potential patterns."
      ],
      "metadata": {
        "id": "UMG-vJ5jVqZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    sns.histplot(df[column], kde=True)\n",
        "    plt.title(f\"Distribution of {column}\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XMHN4tCQVuim",
        "outputId": "9c18ddcd-53e4-4a49-c12b-146dc86788c2"
      },
      "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "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": [],
      "metadata": {
        "id": "EfRjnLbJVx2O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nghWQGvKVyHr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I6dAvE394wRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">9. Check Data Distribution</span>\n",
        "\n",
        "- **Objective**: Analyze the distribution of numerical features to understand their spread and central tendencies.\n",
        "\n",
        "- **Details**:\n",
        "  - **sns.histplot()**:\n",
        "    - **Purpose**: Plots histograms to visualize the distribution of numerical data.\n",
        "    - **Functionality**: Displays the frequency of different data ranges and can include KDE for smoother distribution representation.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      sns.histplot(df['Age'].dropna(), kde=True)\n",
        "      plt.title('Age Distribution')\n",
        "      plt.show()\n",
        "      ```\n",
        "    - **Explanation**: Plots the distribution of the 'Age' column, excluding missing values, and adds a KDE curve.\n",
        "\n",
        "  - **sns.countplot()**:\n",
        "    - **Purpose**: Visualizes the count of categorical variables.\n",
        "    - **Functionality**: Displays the number of occurrences for each category in a categorical feature.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      sns.countplot(x='Pclass', data=df)\n",
        "      plt.title('Passenger Class Distribution')\n",
        "      plt.show()\n",
        "      ```\n",
        "    - **Explanation**: Shows the distribution of passenger classes in the Titanic dataset."
      ],
      "metadata": {
        "id": "vr5nM2uR4wh0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 9. Check Data Distribution\n",
        "# Visualizing histograms for numerical features provides insights into their distributions."
      ],
      "metadata": {
        "id": "04ldokb-VykN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(bins=30, figsize=(20,15), density=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "26n3nIjrovqT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "outputId": "2c897eb8-1b7c-43c8-8fd2-26b5ad4eaff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Vj6IzKFV48E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M44Ft9rxV5PK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 10. Check Correlations (Correlation matrix)\n",
        "# Understanding correlations between features helps us identify relationships within the data."
      ],
      "metadata": {
        "id": "wunRezfAV7yu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "rgUOFElSV89Y",
        "outputId": "795bb7a4-81e9-4733-9bac-9266c405d85c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-ce5af845e35e>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(df.corr(), annot=True)\n"
          ]
        },
        {
          "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": "3jiR0IE0WBAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nAM4yoGxWBYw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qkk9sS584-Xa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">11. Handle Categorical Variables</span>\n",
        "\n",
        "- **Objective**: Convert categorical variables into numerical values to prepare the dataset for modeling.\n",
        "\n",
        "- **Details**:\n",
        "  - **pd.get_dummies()**:\n",
        "    - **Purpose**: Converts categorical variables into dummy/indicator variables.\n",
        "    - **Functionality**: Creates binary columns for each category in a categorical feature.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      df = pd.get_dummies(df, columns=['Sex', 'Embarked'])\n",
        "      ```\n",
        "    - **Explanation**: Converts 'Sex' and 'Embarked' columns into dummy variables, creating new columns with binary values for each category.\n",
        "\n",
        "  - **Label Encoding**:\n",
        "    - **Purpose**: Assigns numerical values to categorical labels.\n",
        "    - **Functionality**: Converts categorical labels into a range of numbers.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      from sklearn.preprocessing import LabelEncoder\n",
        "      le = LabelEncoder()\n",
        "      df['Sex'] = le.fit_transform(df['Sex'])\n",
        "      ```\n",
        "    - **Explanation**: Uses LabelEncoder to convert the 'Sex' column into numerical labels.\n",
        "\n",
        "  - **Handling Rare Categories**:\n",
        "    - **Purpose**: Group infrequent categories to avoid sparse features.\n",
        "    - **Functionality**: Combines infrequent categories into an \"Other\" category.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      df['Embarked'] = df['Embarked'].replace({'S': 0, 'C': 1, 'Q': 2, 'R': 3})\n",
        "      ```\n",
        "    - **Explanation**: Maps 'Embarked' categories to numerical values, with an additional category for rare cases."
      ],
      "metadata": {
        "id": "ZdYHHywo4-wF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 11. Handle Categorical Variables (One-hot encoding)\n",
        "# Categorical variables need to be converted into a numerical format for analysis."
      ],
      "metadata": {
        "id": "7WBEcQvUWBmU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "pXc_9j5YWFY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0AaPGYB-WIRO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oLc0Y12_5XaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9t_QA6cG5XdF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">12. Feature Scaling</span>\n",
        "\n",
        "- **Objective**: Normalize features to ensure they are on the same scale, which can improve the performance of some algorithms.\n",
        "\n",
        "- **Details**:\n",
        "  - **StandardScaler**:\n",
        "    - **Purpose**: Standardizes features by removing the mean and scaling to unit variance.\n",
        "    - **Functionality**: Transforms features to have a mean of 0 and a standard deviation of 1.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      from sklearn.preprocessing import StandardScaler\n",
        "      scaler = StandardScaler()\n",
        "      df[['Age', 'Fare']] = scaler.fit_transform(df[['Age', 'Fare']])\n",
        "      ```\n",
        "    - **Explanation**: Scales the 'Age' and 'Fare' columns to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "  - **MinMaxScaler**:\n",
        "    - **Purpose**: Scales features to a specified range, usually [0, 1].\n",
        "    - **Functionality**: Normalizes the data to be within a specific range.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      from sklearn.preprocessing import MinMaxScaler\n",
        "      scaler = MinMaxScaler()\n",
        "      df[['Age', 'Fare']] = scaler.fit_transform(df[['Age', 'Fare']])\n",
        "      ```\n",
        "    - **Explanation**: Scales 'Age' and 'Fare' columns to the range [0, 1].\n",
        "\n",
        "  - **Normalization**:\n",
        "    - **Purpose**: Adjusts the scale of features to ensure equal importance in model training.\n",
        "    - **Functionality**: Adjusts data values to a common scale without distorting differences.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      from sklearn.preprocessing import Normalizer\n",
        "      normalizer = Normalizer()\n",
        "      df[['Age', 'Fare']] = normalizer.fit_transform(df[['Age', 'Fare']])\n",
        "      ```\n",
        "    - **Explanation**: Normalizes 'Age' and 'Fare' columns to have unit norm."
      ],
      "metadata": {
        "id": "t9srAFO65Xsd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 12. Feature Scaling (Standardization)\n",
        "# Standardizing features ensures they have the same scale, essential for many machine learning algorithms."
      ],
      "metadata": {
        "id": "SO0VUVO9ovtO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "6pKIIr8Oovvs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "ekcBZqkVovyn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data = scaler.fit_transform(df)"
      ],
      "metadata": {
        "id": "PU38TzbPo4mE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eH2UuSHnWXm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert scaled data back to DataFrame"
      ],
      "metadata": {
        "id": "Vvw_6yl_WXqw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled = pd.DataFrame(scaled_data, columns=df.columns)"
      ],
      "metadata": {
        "id": "sHueZPWjpOps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9vo2waYXWeiP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dxxJB2it5jC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Y0jXqzAi5jGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<span style=\"color:#FF6347;\">13. Save Processed Data</span>\n",
        "\n",
        "- **Objective**: Save the cleaned and processed dataset for future use or model training.\n",
        "\n",
        "- **Details**:\n",
        "  - **df.to_csv()**:\n",
        "    - **Purpose**: Writes the DataFrame to a CSV file.\n",
        "    - **Functionality**: Saves the DataFrame as a CSV file that can be used for further analysis or model training.\n",
        "    - **Syntax**:\n",
        "      ```python\n",
        "      df.to_csv('/content/titanic_test_processed.csv', index=False)\n",
        "      ```\n",
        "    - **Explanation**: Saves the processed DataFrame to a file named 'titanic_test_processed.csv' in the specified directory without including row indices."
      ],
      "metadata": {
        "id": "lTAicYhx5jYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# 13. Save the processed data to a CSV file\n",
        "# After preprocessing, it's good practice to save the processed data for future analysis."
      ],
      "metadata": {
        "id": "tHWQyiZXpPvT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_scaled.to_csv('processed_titanic_test_data.csv', index=False)"
      ],
      "metadata": {
        "id": "gF5VxJceo4xl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EricclMXmFap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ASjZjwf9mFdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POSSIBLE TASKS FROM THE DATASET"
      ],
      "metadata": {
        "id": "IJz36qrF5YTO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sGwCCaPs6kqz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color='#FF6347'>Outlier Detection and Treatment</font>\n",
        "\n",
        "- **Objective**:\n",
        "  - **Purpose**: Outliers are data points that significantly differ from other observations in a dataset. They can disproportionately impact statistical analyses and machine learning models by skewing results, inflating variances, and degrading model performance. The goal of this section is to detect and manage outliers in the 'Fare' column to ensure the data is clean and reliable for subsequent analysis.\n",
        "\n",
        "- **Details**:\n",
        "  - **Outlier Detection**:\n",
        "    - **Method Used**: The Interquartile Range (IQR) method is employed to detect outliers. This method is effective because it is not influenced by extreme values and provides a robust measure of the data’s spread.\n",
        "    - **Calculation**:\n",
        "      - **Quartiles Calculation**:\n",
        "        - **Q1 (First Quartile)**: This represents the 25th percentile of the data and is calculated as the value below which 25% of the data falls.\n",
        "        - **Q3 (Third Quartile)**: This represents the 75th percentile and is calculated as the value below which 75% of the data falls.\n",
        "      - **IQR Calculation**:\n",
        "        - **IQR (Interquartile Range)**: Calculated as Q3 - Q1, this range covers the middle 50% of the data.\n",
        "        - **Upper Limit for Outliers**: The threshold for identifying outliers is set at Q3 + 1.5 * IQR. Any data point above this threshold is considered an outlier.\n",
        "      - **Code**:\n",
        "        ```python\n",
        "        fare_q1 = df['Fare'].quantile(0.25)  # Calculate the first quartile (Q1)\n",
        "        fare_q3 = df['Fare'].quantile(0.75)  # Calculate the third quartile (Q3)\n",
        "        fare_iqr = fare_q3 - fare_q1  # Calculate the Interquartile Range (IQR)\n",
        "        fare_upper_limit = fare_q3 + 1.5 * fare_iqr  # Calculate the upper limit for outliers\n",
        "        ```\n",
        "    - **Example**:\n",
        "      - If Q1 = 10, Q3 = 30, then IQR = 30 - 10 = 20.\n",
        "      - Upper limit = Q3 + 1.5 * IQR = 30 + 1.5 * 20 = 60.\n",
        "      - Any 'Fare' value above 60 is flagged as an outlier.\n",
        "\n",
        "  - **Outlier Treatment**:\n",
        "    - **Objective**: To mitigate the impact of outliers by capping extreme values. This helps in maintaining the stability of statistical measures and model performance.\n",
        "    - **Method Used**:\n",
        "      - **Capping/Clipping**: This approach involves setting a maximum threshold for values. Values exceeding this threshold are capped at the threshold value itself, which helps reduce their disproportionate effect on the analysis.\n",
        "      - **Implementation**:\n",
        "        - **Code**:\n",
        "          ```python\n",
        "          df['Fare'] = np.where(df['Fare'] > fare_upper_limit, fare_upper_limit, df['Fare'])\n",
        "          ```\n",
        "        - **Explanation**: Using NumPy’s `np.where` function, any 'Fare' values exceeding the calculated upper limit are replaced with the upper limit value. This method ensures that extreme values do not disproportionately influence the analysis or modeling processes.\n",
        "    - **Benefits**:\n",
        "      - **Reduces Skew**: Mitigates the impact of extreme values on the statistical measures such as mean and variance.\n",
        "      - **Enhances Model Robustness**: Improves model performance by preventing outliers from dominating the training process.\n",
        "      - **Maintains Data Integrity**: By capping, the data remains representative of the majority while handling extremes appropriately.\n",
        "\n",
        "    - **Note**: This demonstration focuses solely on the 'Fare' column for simplicity. In practice, outlier detection and treatment may involve multiple columns and more sophisticated methods depending on the dataset and analysis objectives."
      ],
      "metadata": {
        "id": "riS1OXRa6k_K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outlier Detection and Treatment (for demonstration purposes only)"
      ],
      "metadata": {
        "id": "Ey74a3vTnsOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier Detection and Treatment (for demonstration purposes only)\n",
        "# Outliers, or extreme values in a dataset, can significantly affect statistical analyses and machine learning models. In this task, we focus on outlier detection and treatment specifically for the 'Fare' column.\n"
      ],
      "metadata": {
        "id": "4OEv5KmEmFfl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier Detection:\n",
        "# To identify outliers in the 'Fare' column, we use the Interquartile Range (IQR) method. The IQR is calculated as the difference between the third quartile (Q3) and the first quartile (Q1). Any data point outside the range [Q1 - 1.5 * IQR, Q3 + 1.5 * IQR] is considered an outlier.\n"
      ],
      "metadata": {
        "id": "aBj7KXsomFho"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fare_q1 = df['Fare'].quantile(0.25)  # Calculate the first quartile (Q1)"
      ],
      "metadata": {
        "id": "N4RNA_c5mFj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fare_q3 = df['Fare'].quantile(0.75)  # Calculate the third quartile (Q3)"
      ],
      "metadata": {
        "id": "Z4fxwAFZmFoz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fare_iqr = fare_q3 - fare_q1  # Calculate the Interquartile Range (IQR)"
      ],
      "metadata": {
        "id": "xkXZPkTGou6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fare_upper_limit = fare_q3 + 1.5 * fare_iqr  # Calculate the upper limit for outliers"
      ],
      "metadata": {
        "id": "VpyKj70Kou-C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uC-ARBKEovB_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Outlier Treatment:\n",
        "# Once outliers are identified, we need to decide how to handle them. In this demonstration, we choose a simple approach known as capping or clipping, where we replace any outlier value above the upper limit with the upper limit value itself. This ensures that extreme values do not disproportionately influence our analysis.\n"
      ],
      "metadata": {
        "id": "s9gdlX3tovEs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'] = np.where(df['Fare'] > fare_upper_limit, fare_upper_limit, df['Fare'])"
      ],
      "metadata": {
        "id": "mxaOJSL8ovHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gxBuGuKUovJ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Using NumPy's 'np.where' function, we replace values in the 'Fare' column that exceed the upper limit with the upper limit value, effectively capping the outliers.\n",
        "\n",
        "# Note: This demonstration focuses only on the 'Fare' column for simplicity. In practice, outlier detection and treatment may involve more comprehensive methods and consideration of multiple columns.\n"
      ],
      "metadata": {
        "id": "lJydfH4rovLy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XsATec2uppGV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ibwGFbUxppI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KN5laD2Z6zly"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <font color='#FF6347'>Feature Selection</font>\n",
        "\n",
        "- **Objective**:\n",
        "  - **Purpose**: Feature selection aims to improve model performance and interpretability by removing irrelevant or redundant features. It helps in reducing dimensionality, enhancing computational efficiency, and avoiding overfitting by focusing on features that provide significant predictive power.\n",
        "\n",
        "- **Details**:\n",
        "  - **Drop Columns**:\n",
        "    - **Rationale**:\n",
        "      - **'Name' Column**:\n",
        "        - **Description**: This column contains textual information about passenger names. While it uniquely identifies individuals, it does not contribute to the predictive modeling of outcomes such as survival.\n",
        "        - **Reason for Removal**: Names are non-numeric and do not have predictive value in the context of the analysis. Removing this column simplifies the dataset and avoids potential noise.\n",
        "      - **'Ticket' Column**:\n",
        "        - **Description**: Contains unique ticket identifiers for each passenger. This column serves to uniquely identify tickets but does not provide useful information for predicting outcomes.\n",
        "        - **Reason for Removal**: Since each ticket is unique and does not contribute to understanding patterns or outcomes, it is dropped to reduce complexity.\n",
        "      - **'PassengerId' Column**:\n",
        "        - **Description**: A unique identifier for each passenger in the dataset. This column ensures that each record is unique but does not offer meaningful predictive information.\n",
        "        - **Reason for Removal**: Including 'PassengerId' can lead to misleading results as it does not contribute to the analysis of passenger characteristics or outcomes. Removing it avoids unnecessary data.\n",
        "    - **Implementation**:\n",
        "      - **Code**:\n",
        "        ```python\n",
        "        if 'Name' in df.columns:\n",
        "            df.drop(columns=['Name'], inplace=True)\n",
        "\n",
        "        if 'Ticket' in df.columns:\n",
        "            df.drop(columns=['Ticket'], inplace=True)\n",
        "\n",
        "        if 'PassengerId' in df.columns:\n",
        "            df.drop(columns=['PassengerId'], inplace=True)\n",
        "        ```\n",
        "      - **Explanation**: The code checks for the existence of these columns and removes them if they are present. This ensures that only relevant features remain in the dataset.\n",
        "    - **Benefits**:\n",
        "      - **Improved Model Performance**: By focusing on significant features, the model’s performance is enhanced, as it learns from the most relevant predictors.\n",
        "      - **Simplified Model**: Reduces the complexity of the model, making it easier to understand and interpret.\n",
        "      - **Reduced Risk of Overfitting**: By eliminating irrelevant features, the model is less likely to overfit to the noise in the data.\n",
        "      - **Increased Efficiency**: Streamlines computations, making data processing and model training faster and more efficient.\n",
        "\n",
        "    - **Note**: Feature selection is an iterative process and may involve more steps, including advanced techniques like Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), or correlation analysis, depending on the dataset and modeling goals."
      ],
      "metadata": {
        "id": "5pmrpcXF6z21"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection (for demonstration purposes only)"
      ],
      "metadata": {
        "id": "0c-pAXYK3dch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Selection (for demonstration purposes only)\n",
        "# Let's drop 'Name' column if it exists, as it's unlikely to have significant impact on prediction"
      ],
      "metadata": {
        "id": "OIgMQVehppLZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if 'Name' in df.columns:\n",
        "    df.drop(columns=['Name'], inplace=True)"
      ],
      "metadata": {
        "id": "Je4uLamK3igW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p8Fj9nVC3ijB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avoid Error: Drop the 'Ticket' column to avoid unnecessary complexity"
      ],
      "metadata": {
        "id": "BeVzcV8i3ilu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if 'Ticket' in df.columns:\n",
        "    df.drop(columns=['Ticket'], inplace=True)"
      ],
      "metadata": {
        "id": "nnxC94-e3ioU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2NJvUXzr3vH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avoid Error: Ensure 'PassengerId' is not included in modeling"
      ],
      "metadata": {
        "id": "wL7Caup93vK8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if 'PassengerId' in df.columns:\n",
        "    df.drop(columns=['PassengerId'], inplace=True)"
      ],
      "metadata": {
        "id": "UFwG0qpI3vN4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iV59EJe23vSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the updated dataframe"
      ],
      "metadata": {
        "id": "LSSNhILG3vVT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Updated DataFrame:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cULTW8654DMr",
        "outputId": "aade9788-c44b-4ea6-ad68-250aa90f03a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated DataFrame:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcrdEYN94DP6",
        "outputId": "4e4996c2-a4d5-455b-abc9-9eb733815fb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Pclass   Age  SibSp  Parch     Fare  Name_Abelseth, Miss. Karen Marie  \\\n",
            "0       3  34.5      0      0   7.8292                                 0   \n",
            "1       3  47.0      1      0   7.0000                                 0   \n",
            "2       2  62.0      0      0   9.6875                                 0   \n",
            "3       3  27.0      0      0   8.6625                                 0   \n",
            "4       3  22.0      1      1  12.2875                                 0   \n",
            "\n",
            "   Name_Abelseth, Mr. Olaus Jorgensen  \\\n",
            "0                                   0   \n",
            "1                                   0   \n",
            "2                                   0   \n",
            "3                                   0   \n",
            "4                                   0   \n",
            "\n",
            "   Name_Abrahamsson, Mr. Abraham August Johannes  \\\n",
            "0                                              0   \n",
            "1                                              0   \n",
            "2                                              0   \n",
            "3                                              0   \n",
            "4                                              0   \n",
            "\n",
            "   Name_Abrahim, Mrs. Joseph (Sophie Halaut Easu)  \\\n",
            "0                                               0   \n",
            "1                                               0   \n",
            "2                                               0   \n",
            "3                                               0   \n",
            "4                                               0   \n",
            "\n",
            "   Name_Aks, Master. Philip Frank  ...  Ticket_STON/O2. 3101270  \\\n",
            "0                               0  ...                        0   \n",
            "1                               0  ...                        0   \n",
            "2                               0  ...                        0   \n",
            "3                               0  ...                        0   \n",
            "4                               0  ...                        0   \n",
            "\n",
            "   Ticket_STON/OQ. 369943  Ticket_W./C. 14260  Ticket_W./C. 14266  \\\n",
            "0                       0                   0                   0   \n",
            "1                       0                   0                   0   \n",
            "2                       0                   0                   0   \n",
            "3                       0                   0                   0   \n",
            "4                       0                   0                   0   \n",
            "\n",
            "   Ticket_W./C. 6607  Ticket_W./C. 6608  Ticket_W.E.P. 5734  Embarked_Q  \\\n",
            "0                  0                  0                   0           1   \n",
            "1                  0                  0                   0           0   \n",
            "2                  0                  0                   0           1   \n",
            "3                  0                  0                   0           0   \n",
            "4                  0                  0                   0           0   \n",
            "\n",
            "   Embarked_S  FamilySize  \n",
            "0           0           0  \n",
            "1           1           1  \n",
            "2           0           0  \n",
            "3           1           0  \n",
            "4           1           2  \n",
            "\n",
            "[5 rows x 788 columns]\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# <font color='#FF6347'>Conclusions</font>\n",
        "\n",
        "## <font color='#4682B4'>Summary of Findings</font>\n",
        "- **Data Cleaning**: The Titanic dataset was successfully cleaned by addressing missing values, handling duplicates, and encoding categorical variables. The removal of problematic columns, such as `Cabin`, and the imputation of missing values improved the dataset's quality.\n",
        "- **Missing Values**: Numeric columns with missing values were imputed with their mean values. Columns with excessive missing values, such as `Cabin`, were removed to prevent skewed analyses.\n",
        "- **Duplicate Records**: Duplicate rows were identified and removed, ensuring the dataset's uniqueness and reducing the risk of bias in analyses.\n",
        "- **Data Visualization**: Visualizations revealed the distribution and relationships of features such as age, fare, and passenger class. This helped in understanding the data better and in identifying trends and patterns.\n",
        "- **Categorical Encoding**: Categorical variables were successfully converted into numerical format using one-hot encoding, facilitating their use in machine learning models.\n",
        "- **Feature Scaling**: Features were standardized to have the same scale, which is essential for effective modeling.\n",
        "\n",
        "## <font color='#4682B4'>Next Steps</font>\n",
        "- **Feature Engineering**: Additional features may be engineered or existing ones transformed to enhance predictive modeling. This may include creating interaction terms or polynomial features.\n",
        "- **Model Training**: The processed dataset can be used to train machine learning models to predict outcomes such as passenger survival. Potential models include Logistic Regression, Random Forest, and Support Vector Machines.\n",
        "- **Model Evaluation**: Evaluate model performance using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation should be performed to ensure the model's robustness and generalizability.\n",
        "\n",
        "## <font color='#4682B4'>Implications</font>\n",
        "- **Impact of Preprocessing**: The preprocessing steps undertaken are crucial for preparing the Titanic dataset for predictive modeling. Proper handling of missing values, duplicates, and categorical variables directly influences the performance of machine learning algorithms.\n",
        "- **Data Insights**: Insights gained from data visualization and cleaning can guide feature selection and model development, ultimately improving prediction accuracy and providing a better understanding of the dataset.\n",
        "\n",
        "## <font color='#4682B4'>Acknowledgments</font>\n",
        "- This project utilized various libraries and tools, including `pandas`, `numpy`, `matplotlib.pyplot`, and `seaborn`. Thanks are due to the creators of the Titanic dataset for providing valuable data for this analysis and modeling."
      ],
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}