2_6_1_Land_use_classification.ipynb

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        "# 📘 Land Use Classification Project\n",
        "\n",
        "## 🔶 Project Overview\n",
        "This project focuses on building a machine learning model for land use classification based on images. The model will predict multiple land use categories associated with a given image. This involves data preprocessing, model training, and evaluation of the model's performance.\n",
        "\n",
        "---\n",
        "\n",
        "## 🔷 Dataset Description\n",
        "- **Dataset Name:** `train_v2.csv`\n",
        "- **Content:** The dataset contains images and their corresponding tags that indicate the land use type. Each image may have multiple associated tags.\n",
        "\n",
        "---\n",
        "\n",
        "## 🔶 Step-by-Step Breakdown\n",
        "\n",
        "### 📌 1. Load the Dataset\n",
        "- Utilize the `pandas` library to read the CSV file containing image data and associated tags.\n",
        "- **Why?** Loading the dataset is the first step to analyze and manipulate the data for further processing.\n",
        "\n",
        "### 📌 2. Inspect the Dataset\n",
        "- Display the first few rows, check the shape, and gather general information about the dataset.\n",
        "- Identify any missing values and generate summary statistics.\n",
        "- **Why?** Inspecting the dataset helps to understand its structure and identify any preprocessing steps needed.\n",
        "\n",
        "### 📌 3. Check Unique Values in Tags\n",
        "- Determine the number of unique tags present in the dataset.\n",
        "- **Why?** Understanding the number of unique labels is essential for designing the model and preparing the output layer.\n",
        "\n",
        "### 📌 4. Visualize the Distribution of Tags\n",
        "- Create a bar plot to visualize the frequency of the number of tags per image.\n",
        "- **Why?** Visualizing the distribution helps in understanding how many images have multiple tags, indicating the complexity of the classification task.\n",
        "\n",
        "### 📌 5. Visualize Missing Values\n",
        "- Use a heatmap to represent missing values in the dataset visually.\n",
        "- **Why?** This visualization aids in identifying patterns of missing data, which can be crucial for deciding how to handle them.\n",
        "\n",
        "### 📌 6. Count Each Tag\n",
        "- Count occurrences of each tag across the dataset and visualize the top 20 most frequent tags with a bar plot.\n",
        "- **Why?** Understanding the most common tags helps prioritize model focus and identify any potential class imbalances.\n",
        "\n",
        "### 📌 7. Correlation Matrix\n",
        "- Generate a correlation matrix to explore relationships between features (dummy features in this case).\n",
        "- **Why?** This analysis helps to understand how features interact with each other, which can influence model selection and design.\n",
        "\n",
        "### 📌 8. Distribution of Random Features\n",
        "- Plot the distribution of randomly generated features for demonstration purposes.\n",
        "- **Why?** This step serves as an illustration of how feature distributions can be visualized and analyzed.\n",
        "\n",
        "### 📌 9. Preprocess the Tags\n",
        "- Convert the list of tags into a suitable format for multi-label classification.\n",
        "- **Why?** Proper formatting of tags is necessary to enable the model to learn from them effectively.\n",
        "\n",
        "### 📌 10. Convert Tags to Numerical Representation\n",
        "- Use `MultiLabelBinarizer` to transform the tags into a binary matrix for model training.\n",
        "- **Why?** Machine learning models work with numerical data, so converting categorical labels into a binary format is crucial.\n",
        "\n",
        "### 📌 11. Split the Dataset\n",
        "- Split the dataset into training and validation sets to evaluate model performance effectively.\n",
        "- **Why?** Having separate training and validation datasets is essential for unbiased evaluation of the model's performance.\n",
        "\n",
        "### 📌 12. Build the Model\n",
        "- Create a neural network model using `Sequential` from TensorFlow, adding layers like Dense and Dropout for regularization.\n",
        "- **Why?** The model architecture is key to capturing the complexity of the classification task, and regularization helps prevent overfitting.\n",
        "\n",
        "### 📌 13. Compile the Model\n",
        "- Compile the model with appropriate loss functions and optimizers, specifically for multi-label classification.\n",
        "- **Why?** Compiling the model sets the training configurations, including how to measure performance during training.\n",
        "\n",
        "### 📌 14. Train the Model\n",
        "- Fit the model to the training data and monitor validation loss to avoid overfitting using the EarlyStopping callback.\n",
        "- **Why?** Training the model is the core part of machine learning; monitoring validation loss helps ensure the model generalizes well.\n",
        "\n",
        "### 📌 15. Evaluate the Model\n",
        "- Evaluate model performance on the validation set and print out loss and accuracy metrics.\n",
        "- **Why?** Evaluating the model provides insights into how well it performs on unseen data, which is critical for its practical application.\n",
        "\n",
        "### 📌 16. Plot Loss and Validation Loss\n",
        "- Visualize the training and validation loss over epochs to assess model training.\n",
        "- **Why?** This plot helps to diagnose training issues and determine if the model is overfitting or underfitting.\n",
        "\n",
        "### 📌 17. Model Deployment\n",
        "- Implement a function to predict land use for a given image name based on the trained model.\n",
        "- **Why?** This step is essential for utilizing the trained model in real-world applications to make predictions on new data.\n",
        "\n",
        "---\n",
        "\n",
        "## 🔶 Key Outputs\n",
        "- **Visualizations:** Various plots saved as PDFs, including:\n",
        "  - Distribution of Tags\n",
        "  - Missing Values Heatmap\n",
        "  - Top 20 Most Frequent Tags\n",
        "  - Correlation Matrix\n",
        "  - Distribution of Random Features\n",
        "  - Training and Validation Loss\n",
        "\n",
        "- **CSV Files:** Outputs saved for:\n",
        "  - Missing values\n",
        "  - Basic statistics of the dataset\n",
        "\n",
        "---\n",
        "\n",
        "## 🔷 Conclusion\n",
        "This project demonstrates the complete workflow of building a machine learning model for multi-label classification tasks, emphasizing data preprocessing, model training, and evaluation. The visualizations provide insights into the data distribution and model performance.\n",
        "\n",
        "---\n",
        "\n",
        "## 🔶 References\n",
        "- TensorFlow documentation for model building and training techniques.\n",
        "- Pandas documentation for data manipulation and analysis.\n",
        "- Seaborn and Matplotlib documentation for visualization techniques."
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        "!pip install tensorflow keras pandas scikit-learn  # Install necessary libraries: TensorFlow, Keras, Pandas, and Scikit-learn for deep learning and data manipulation\n"
      ],
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H4UpFs2Q-pKH"
      },
      "execution_count": 12,
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    {
      "cell_type": "code",
      "source": [
        "import warnings  # Import the warnings module to manage warning messages"
      ],
      "metadata": {
        "id": "TtLhIKnf_SI6"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Suppress warnings to keep the output clean"
      ],
      "metadata": {
        "id": "oQe8fbI5_SM0"
      },
      "execution_count": 14,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fij7LpR6_SQM"
      },
      "execution_count": 14,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2oeYKU9B-pDR"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Importing the pandas library for data manipulation and analysis"
      ],
      "metadata": {
        "id": "zKcUfC4i5Hlh"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Importing the numpy library for numerical operations"
      ],
      "metadata": {
        "id": "4a9sirQf7TT_"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Importing Matplotlib for plotting graphs"
      ],
      "metadata": {
        "id": "egNysagY7TXH"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Importing Seaborn for enhanced visualizations"
      ],
      "metadata": {
        "id": "k5eWSCQ77ThI"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf  # Importing TensorFlow for building and training neural networks"
      ],
      "metadata": {
        "id": "wdmmEBO-7Tju"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Importing function to split dataset into training and validation sets\n"
      ],
      "metadata": {
        "id": "o1a09wKr7Tre"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MultiLabelBinarizer  # Importing MultiLabelBinarizer for multi-label classification\n"
      ],
      "metadata": {
        "id": "aU6S_goq7Tun"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential  # Importing Sequential model from Keras"
      ],
      "metadata": {
        "id": "Co-u4QiN7Txm"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense, Dropout  # Importing Dense and Dropout layers for the model"
      ],
      "metadata": {
        "id": "Ll49_l5n7T25"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping  # Importing EarlyStopping callback for training"
      ],
      "metadata": {
        "id": "IlprbNgl7w0-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d34uRu0l7w4P"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ggi1HaFa7w7p"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bwJKXMkO7w-Q"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 1. Load the Dataset\n",
        "- Utilize the `pandas` library to read the CSV file containing image data and associated tags.\n",
        "- **Why?** Loading the dataset is the first step to analyze and manipulate the data for further processing."
      ],
      "metadata": {
        "id": "s5yLURfbBLVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Load the dataset"
      ],
      "metadata": {
        "id": "_4xI93P87xBb"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = '/content/train_v2.csv'  # Path to the dataset"
      ],
      "metadata": {
        "id": "QZiDceI57xEY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(dataset_path)  # Loading the dataset into a pandas DataFrame"
      ],
      "metadata": {
        "id": "sIBGDHzX7xHg"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RnkyN8BS7T5k"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vQRuAREZ7T7-"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sZBSycZN78Tf"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 2. Inspect the Dataset\n",
        "- Display the first few rows, check the shape, and gather general information about the dataset.\n",
        "- Identify any missing values and generate summary statistics.\n",
        "- **Why?** Inspecting the dataset helps to understand its structure and identify any preprocessing steps needed."
      ],
      "metadata": {
        "id": "X3zB8BAhBOis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the Dataset"
      ],
      "metadata": {
        "id": "8779ni-378Wf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First few rows of the dataset:\")  # Print statement for clarity\n",
        "print(data.head())  # Display the first five rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oE-pKZag78ZZ",
        "outputId": "3f2aae7c-0002-4841-c627-e8ac427c570e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "  image_name                                       tags\n",
            "0    train_0                               haze primary\n",
            "1    train_1            agriculture clear primary water\n",
            "2    train_2                              clear primary\n",
            "3    train_3                              clear primary\n",
            "4    train_4  agriculture clear habitation primary road\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eHvtoQlK78b-"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9eseI-VB78en"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nShape of the dataset:\")  # Print statement for clarity\n",
        "print(data.shape)  # Display the shape of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hO-fD4VI78k3",
        "outputId": "24149355-b841-4afa-c2f3-fe6663eae62d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape of the dataset:\n",
            "(40479, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yWbthypP7T-f"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FkoVHhnh7UCy"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Information:\")  # Print statement for clarity\n",
        "print(data.info())  # Display information about the dataset, including data types and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdUulNQs7UFH",
        "outputId": "90214163-ed94-41ec-a68c-8e4be196362f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 40479 entries, 0 to 40478\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   image_name  40479 non-null  object\n",
            " 1   tags        40479 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 632.6+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dhm1dxL87UJf"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0axTqn_q5HwG"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = data.isnull().sum()  # Store the number of missing values in each column"
      ],
      "metadata": {
        "id": "jb6nkSyh5Hyh"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing Values in each column:\")  # Print statement for clarity\n",
        "print(missing_values)  # Display missing values in each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wKpquTa8Nsn",
        "outputId": "98657669-a025-4942-f6e8-c629bfe63b72"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values in each column:\n",
            "image_name    0\n",
            "tags          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vIRXJe5W5H7u"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xYIOf7pP5H-W"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "statistics = data.describe(include='all')  # Store basic statistics for all columns"
      ],
      "metadata": {
        "id": "57yd3qvS5ICu"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nBasic Statistics:\")  # Print statement for clarity\n",
        "print(statistics)  # Display basic statistics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhGgJA3j5IFW",
        "outputId": "93827661-78f5-4e0e-a11d-5dc68c6bb3dc"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Basic Statistics:\n",
            "       image_name           tags\n",
            "count       40479          40479\n",
            "unique      40479            449\n",
            "top       train_0  clear primary\n",
            "freq            1          13636\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L6cfaf-48Z0H"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bYoLnP0R8Z2_"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save missing values and statistics to CSV"
      ],
      "metadata": {
        "id": "FNNZ-HCI8Z5k"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values.to_csv('/content/missing_values.csv')  # Save missing values to CSV"
      ],
      "metadata": {
        "id": "3XDZ5skz8Z8c"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "statistics.to_csv('/content/statistics.csv')  # Save statistics to CSV"
      ],
      "metadata": {
        "id": "6x3R2jTa8Z_G"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GFswCG8L8gSX"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lDuiBSFE8gVZ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WLpy9WOpBSk3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 3. Check Unique Values in Tags\n",
        "- Determine the number of unique tags present in the dataset.\n",
        "- **Why?** Understanding the number of unique labels is essential for designing the model and preparing the output layer."
      ],
      "metadata": {
        "id": "EsFMyRVvBSy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Check Unique Values in Tags"
      ],
      "metadata": {
        "id": "YD3ILrrC8gYN"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_tags = data['tags'].unique()  # Get unique tags"
      ],
      "metadata": {
        "id": "jIKHHjZV8gbC"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nNumber of unique tags: {len(unique_tags)}\")  # Print the number of unique tags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9HOeBhT8gdh",
        "outputId": "c823fca6-aae3-4746-9808-8fbd62818765"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of unique tags: 449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cp1W1ELk8o_P"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v74RXeZG8pCO"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n_J1Q3EO8pFM"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# EDAs"
      ],
      "metadata": {
        "id": "MRHyDFkx8pH4"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RcH0UoonBWor"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 4. Visualize the Distribution of Tags\n",
        "- Create a bar plot to visualize the frequency of the number of tags per image.\n",
        "- **Why?** Visualizing the distribution helps in understanding how many images have multiple tags, indicating the complexity of the classification task."
      ],
      "metadata": {
        "id": "Varc9Qa1BW-d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Visualize the Distribution of Tags"
      ],
      "metadata": {
        "id": "ZQyQP9Sg8pMI"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tag_counts = data['tags'].apply(lambda x: len(x)).value_counts()  # Count the number of tags per image"
      ],
      "metadata": {
        "id": "Z8opxKjh8pPQ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.barplot(x=tag_counts.index, y=tag_counts.values, palette='viridis')  # Create a bar plot\n",
        "plt.title('Distribution of Number of Tags per Image')  # Title of the plot\n",
        "plt.xlabel('Number of Tags')  # X-axis label\n",
        "plt.ylabel('Frequency')  # Y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
        "plt.savefig('/content/distribution_of_tags.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "yDiAyEr98pSI",
        "outputId": "269f80d9-223b-4d81-fa21-863f60d3ad27"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TJSRbQ-J8gi9"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q_XTaVgc8gnZ"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S7MhbvUaBaW8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 5. Visualize Missing Values\n",
        "- Use a heatmap to represent missing values in the dataset visually.\n",
        "- **Why?** This visualization aids in identifying patterns of missing data, which can be crucial for deciding how to handle them."
      ],
      "metadata": {
        "id": "GYytX81HBahM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Visualize Missing Values"
      ],
      "metadata": {
        "id": "EpisvRRI8gzg"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size\n",
        "sns.heatmap(data.isnull(), cbar=False, cmap='viridis')  # Create a heatmap for missing values\n",
        "plt.title('Missing Values in Dataset')  # Title of the heatmap\n",
        "plt.savefig('/content/missing_values_heatmap.pdf')  # Save the heatmap as a PDF\n",
        "plt.show()  # Display the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "G2KgVhK98aEZ",
        "outputId": "5a39ac11-656c-412c-dc6c-fcf3fad63e00"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "upg-yI6V83MY"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vfeIBRyR83PN"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2KcN8dkdBdPD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 6. Count Each Tag\n",
        "- Count occurrences of each tag across the dataset and visualize the top 20 most frequent tags with a bar plot.\n",
        "- **Why?** Understanding the most common tags helps prioritize model focus and identify any potential class imbalances."
      ],
      "metadata": {
        "id": "5Ytkzh5TBdW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Count Each Tag"
      ],
      "metadata": {
        "id": "09xAuGy_83SY"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tag_list = [tag for sublist in data['tags'].apply(lambda x: x.split()) for tag in sublist]  # Flatten the list of tags\n"
      ],
      "metadata": {
        "id": "NRn2Utd383XK"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tag_counts = pd.Series(tag_list).value_counts()  # Count occurrences of each tag"
      ],
      "metadata": {
        "id": "BFwZdU2E83cd"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3ZmiqkEb8_io"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the count of each tag"
      ],
      "metadata": {
        "id": "cwtbXb_Y8_l3"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 8))  # Set figure size\n",
        "sns.barplot(x=tag_counts.values[:20], y=tag_counts.index[:20], palette='viridis')  # Bar plot for top 20 tags\n",
        "plt.title('Top 20 Most Frequent Tags')  # Title of the plot\n",
        "plt.xlabel('Frequency')  # X-axis label\n",
        "plt.ylabel('Tags')  # Y-axis label\n",
        "plt.savefig('/content/top_20_tags.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "N6haASUb8_qg",
        "outputId": "e47b6701-7d4f-4f0d-e06a-cab8b681f4e6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nq9ydrqx8_ti"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HdmqOFjL83gI"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "18ikqwDwBgXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 7. Correlation Matrix\n",
        "- Generate a correlation matrix to explore relationships between features (dummy features in this case).\n",
        "- **Why?** This analysis helps to understand how features interact with each other, which can influence model selection and design."
      ],
      "metadata": {
        "id": "KyK7Nh8VBghM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Matrix"
      ],
      "metadata": {
        "id": "13y1_wG08aG3"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Set figure size\n",
        "corr_matrix = np.corrcoef(np.random.rand(len(data), 10), rowvar=False)  # Generate a random correlation matrix\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', cbar=True)  # Create a heatmap\n",
        "plt.title('Correlation Matrix of Random Features')  # Title of the plot\n",
        "plt.savefig('/content/correlation_matrix.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "VDrN3kGN8aJt",
        "outputId": "dcfd6522-2514-47f4-931d-a4104dbbec7f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ddN0nKAz9I54"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bPj9jEgG9I84"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PGs6Ze_sBjzd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 8. Distribution of Random Features\n",
        "- Plot the distribution of randomly generated features for demonstration purposes.\n",
        "- **Why?** This step serves as an illustration of how feature distributions can be visualized and analyzed."
      ],
      "metadata": {
        "id": "x92PlkDEBj_s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Distribution of Random Features"
      ],
      "metadata": {
        "id": "XMsIpJ4y9JAA"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))  # Set figure size\n",
        "num_features = 100  # Set number of dummy features\n",
        "X = np.random.rand(len(data), num_features)  # Create dummy features as random numbers\n",
        "\n",
        "for i in range(num_features):  # Loop through each feature\n",
        "    plt.hist(X[:, i], bins=30, alpha=0.5, label=f'Feature {i+1}')  # Create histogram for each feature\n",
        "\n",
        "plt.title('Distribution of Random Features')  # Title of the plot\n",
        "plt.xlabel('Feature Value')  # X-axis label\n",
        "plt.ylabel('Frequency')  # Y-axis label\n",
        "plt.legend()  # Show legend\n",
        "plt.savefig('/content/distribution_of_random_features.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DKdaZWIJ9JCa",
        "outputId": "447edc88-ab00-4570-aad1-1f74f8bf90ec"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6ysO7K3J9V7h"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LgqhQyUN9WFh"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uZc3MRKo9WIj"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 9. Preprocess the Tags\n",
        "- Convert the list of tags into a suitable format for multi-label classification.\n",
        "- **Why?** Proper formatting of tags is necessary to enable the model to learn from them effectively."
      ],
      "metadata": {
        "id": "QPWsig9BBnts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the Tags"
      ],
      "metadata": {
        "id": "g6kisjlc9WNo"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['tags'] = data['tags'].apply(lambda x: x.split())  # Splitting tags into lists for multi-label processing"
      ],
      "metadata": {
        "id": "i1Vf9Vco9JE2"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aEWtUp_Y9ZV5"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w-rVVF_t9ZY9"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WWZyZTgG9Zbq"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 10. Convert Tags to Numerical Representation\n",
        "- Use `MultiLabelBinarizer` to transform the tags into a binary matrix for model training.\n",
        "- **Why?** Machine learning models work with numerical data, so converting categorical labels into a binary format is crucial."
      ],
      "metadata": {
        "id": "bRIejFK7BqwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert Tags to Numerical Representation"
      ],
      "metadata": {
        "id": "2BstMr0a9Zdz"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlb = MultiLabelBinarizer()  # Initialize MultiLabelBinarizer"
      ],
      "metadata": {
        "id": "SfnFs_Zq9Zg5"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = mlb.fit_transform(data['tags'])  # Transform the list of tags into a binary matrix"
      ],
      "metadata": {
        "id": "lRaTPnlE9Zm5"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kwk38FeK9JHP"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ph1Xf96IBwEO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gYn9MdINBwHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 11. Split the Dataset\n",
        "- Split the dataset into training and validation sets to evaluate model performance effectively.\n",
        "- **Why?** Having separate training and validation datasets is essential for unbiased evaluation of the model's performance."
      ],
      "metadata": {
        "id": "IBGyv_HtBweU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and validation sets"
      ],
      "metadata": {
        "id": "6M5D5kgT9JKH"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)  # Split dataset into train and validation\n"
      ],
      "metadata": {
        "id": "FTml_Ueh9JMC"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HDYjk_Lw9lzS"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iuLhtfxM9l18"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wcKklCML9l5J"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 12. Build the Model\n",
        "- Create a neural network model using `Sequential` from TensorFlow, adding layers like Dense and Dropout for regularization.\n",
        "- **Why?** The model architecture is key to capturing the complexity of the classification task, and regularization helps prevent overfitting."
      ],
      "metadata": {
        "id": "0eBa3k0vBzGj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the Model"
      ],
      "metadata": {
        "id": "pGgDHEZ09l70"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()  # Initialize a Sequential model"
      ],
      "metadata": {
        "id": "3sUpta1j9mAZ"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(128, activation='relu', input_shape=(num_features,)))  # Add input layer with 128 neurons and ReLU activation\n"
      ],
      "metadata": {
        "id": "4pvQY9A59mFM"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dropout(0.5))  # Add dropout layer for regularization"
      ],
      "metadata": {
        "id": "CFzMF_2I9mI8"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(64, activation='relu'))  # Add hidden layer with 64 neurons and ReLU activation"
      ],
      "metadata": {
        "id": "GQmQKx6F9mMG"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dropout(0.5))  # Add another dropout layer for regularization"
      ],
      "metadata": {
        "id": "zorLTx0a5IHZ"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(y.shape[1], activation='sigmoid'))  # Add output layer with sigmoid activation for multi-label classification\n"
      ],
      "metadata": {
        "id": "-OO8LA9E5IJq"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CAsWR7IA90dZ"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jo5UG-IVB1u1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "h8rStxC2B1ya"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 13. Compile the Model\n",
        "- Compile the model with appropriate loss functions and optimizers, specifically for multi-label classification.\n",
        "- **Why?** Compiling the model sets the training configurations, including how to measure performance during training."
      ],
      "metadata": {
        "id": "iwIFQhweB2Hc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model"
      ],
      "metadata": {
        "id": "1n9R4BG990gY"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])  # Compile the model with Adam optimizer and binary crossentropy loss\n"
      ],
      "metadata": {
        "id": "RO2EKHfi90i3"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mp6nPg0f90nt"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vgZC2LEI90qq"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2cALlXJb90vB"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 14. Train the Model\n",
        "- Fit the model to the training data and monitor validation loss to avoid overfitting using the EarlyStopping callback.\n",
        "- **Why?** Training the model is the core part of machine learning; monitoring validation loss helps ensure the model generalizes well."
      ],
      "metadata": {
        "id": "p76s5KlmB4rU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Model"
      ],
      "metadata": {
        "id": "IGkmza2v90x8"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5)  # Initialize early stopping to monitor validation loss\n"
      ],
      "metadata": {
        "id": "-qe8oz2I9008"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train, epochs=2, batch_size=32, validation_data=(X_valid, y_valid), callbacks=[early_stopping])  # Train the model\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyvVMle4903i",
        "outputId": "1b7ef64e-649c-457e-d4e7-9dc32f23ccd8"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "\u001b[1m1012/1012\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.0385 - loss: 0.3344 - val_accuracy: 0.0000e+00 - val_loss: 0.2547\n",
            "Epoch 2/2\n",
            "\u001b[1m1012/1012\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.0000e+00 - loss: 0.2665 - val_accuracy: 0.0000e+00 - val_loss: 0.2547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CtQ351bt-AQh"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KZrP9s8q-BYR"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8TteVd9B-Bg5"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 15. Evaluate the Model\n",
        "- Evaluate model performance on the validation set and print out loss and accuracy metrics.\n",
        "- **Why?** Evaluating the model provides insights into how well it performs on unseen data, which is critical for its practical application."
      ],
      "metadata": {
        "id": "-t3hUSzdB691"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Evaluate the Model"
      ],
      "metadata": {
        "id": "sJWKTTWt-Bjd"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_valid, y_valid)  # Evaluate the model on the validation set"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8Ywf8eH-Bos",
        "outputId": "82a6bbf4-79b5-4dcc-ec93-2e5e4394d286"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m253/253\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.0000e+00 - loss: 0.2540\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print('Validation loss:', loss)  # Print validation loss\n",
        "#print('Validation accuracy:', accuracy)  # Print validation accuracy"
      ],
      "metadata": {
        "id": "jHDoAmrA-Bsp"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "06XnyjIC-KwR"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2L4KFj26-KzU"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vUQ0Fs1N-K1v"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 16. Plot Loss and Validation Loss\n",
        "- Visualize the training and validation loss over epochs to assess model training.\n",
        "- **Why?** This plot helps to diagnose training issues and determine if the model is overfitting or underfitting."
      ],
      "metadata": {
        "id": "A3BD4i06B91F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Plot Loss and Validation Loss"
      ],
      "metadata": {
        "id": "zAeIPAKK-K4U"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size\n",
        "plt.plot(history.history['loss'], label='Training Loss')  # Plot training loss\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')  # Plot validation loss\n",
        "plt.title('Training and Validation Loss')  # Title of the plot\n",
        "plt.xlabel('Epochs')  # X-axis label\n",
        "plt.ylabel('Loss')  # Y-axis label\n",
        "plt.legend()  # Show legend\n",
        "plt.savefig('/content/training_validation_loss.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "WxPsfIfG-K60",
        "outputId": "0f279520-fe6c-4a41-c1f8-908b57dfe624"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9p2NJq6a-K9x"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dxCjPLE4-LBB"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kpkm_dl1-BvU"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 📌 17. Model Deployment\n",
        "- Implement a function to predict land use for a given image name based on the trained model.\n",
        "- **Why?** This step is essential for utilizing the trained model in real-world applications to make predictions on new data."
      ],
      "metadata": {
        "id": "TwbLTSDdCApM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Deployment"
      ],
      "metadata": {
        "id": "SECqZsWe-ATj"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_land_use(image_name):  # Define a function to predict land use based on image name\n",
        "    \"\"\"\n",
        "    Simulate predicting the land use for a given image name.\n",
        "    In practice, you'd preprocess the actual image and get its features.\n",
        "    \"\"\"\n",
        "    # Here we're using random features as a placeholder\n",
        "    dummy_feature = np.random.rand(1, num_features)  # Generate random features for prediction\n",
        "    prediction = model.predict(dummy_feature)  # Predict using the model\n",
        "    predicted_tags = mlb.inverse_transform(prediction > 0.5)  # Convert predictions back to tags\n",
        "    return predicted_tags  # Return predicted tags"
      ],
      "metadata": {
        "id": "eMED78Oq-AWd"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vRoAM5eT-ZkK"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example prediction"
      ],
      "metadata": {
        "id": "vC_Si_6J-ZnE"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predict_land_use('sample_image.jpg'))  # Example of using the prediction function"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwTBJofl-ZsL",
        "outputId": "46a985af-9b71-4c1e-dbd7-08d4954cfbfb"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7b9bd599b640> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
            "[('clear', 'primary')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CWlb7QhMCGjs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u-0Es_z1-AbT"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MzYblpCa-Afp"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔶 Conclusion\n",
        "This project demonstrates the complete workflow of building a machine learning model for multi-label classification tasks, emphasizing data preprocessing, model training, and evaluation. The visualizations provide insights into the data distribution and model performance."
      ],
      "metadata": {
        "id": "R2QTD-FfCHH1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MMfYpRfXCH7M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LhmCm_5sCIBN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔷 Key Outputs\n",
        "- **Visualizations:** Various plots saved as PDFs, including:\n",
        "  - Distribution of Tags\n",
        "  - Missing Values Heatmap\n",
        "  - Top 20 Most Frequent Tags\n",
        "  - Correlation Matrix\n",
        "  - Distribution of Random Features\n",
        "  - Training and Validation Loss\n",
        "\n",
        "- **CSV Files:** Outputs saved for:\n",
        "  - Missing values\n",
        "  - Basic statistics of the dataset"
      ],
      "metadata": {
        "id": "xSz9Vyr2CJKu"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EhYKh_1PCKHN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ENHvTgkcCKKS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔶 References\n",
        "- TensorFlow documentation for model building and training techniques.\n",
        "- Pandas documentation for data manipulation and analysis.\n",
        "- Seaborn and Matplotlib documentation for visualization techniques."
      ],
      "metadata": {
        "id": "elLHyL2YCKgU"
      }
    }
  ]
}