Flight_delays_test_csv.ipynb

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        "# <span style=\"color:#2E8B57\">**Flight Delays Analysis and Deep Learning Model**</span>\n",
        "\n",
        "## <span style=\"color:#4682B4\">**Project Overview**</span>\n",
        "\n",
        "In this project, we aim to explore and analyze flight delay data using Exploratory Data Analysis (EDA) techniques. The goal is to gain insights into the key factors contributing to flight delays and prepare the data for machine learning tasks. After preprocessing, we will develop a deep learning model to predict flight departure times using the processed data.\n",
        "\n",
        "The steps involved in the project are as follows:\n",
        "\n",
        "- **Data Loading and Inspection**  \n",
        "  Load the flight delays dataset and inspect it using various methods such as `head()`, `info()`, and `describe()` to understand its structure and statistics.\n",
        "\n",
        "- **Missing and Duplicate Values Check**  \n",
        "  Identify any missing or duplicate values that could affect the analysis.\n",
        "\n",
        "- **Data Visualization**  \n",
        "  Use various visualization techniques such as histograms, KDE plots, and heatmaps to explore data distribution and relationships between variables.\n",
        "\n",
        "- **Data Preprocessing**  \n",
        "  Handle categorical variables using one-hot encoding, followed by feature scaling to standardize the data for better model performance.\n",
        "\n",
        "- **Deep Learning Model**  \n",
        "  Train a deep learning model using a neural network to predict the target variable. The model is built using TensorFlow and Keras, and its performance is evaluated using the Mean Squared Error (MSE) loss function.\n",
        "\n",
        "- **Model Saving**  \n",
        "  Finally, the trained model is saved for future use in prediction tasks.\n",
        "\n",
        "## <span style=\"color:#4682B4\">**Key Notes**</span>\n",
        "\n",
        "- **EDA Purpose**: The primary goal of EDA is to uncover patterns, detect anomalies, and check assumptions before moving on to model building. It is essential to visualize the data distributions and relationships to guide the machine learning pipeline.\n",
        "  \n",
        "- **Handling Missing and Duplicated Data**: Missing and duplicated values can bias the results of machine learning models. Identifying and managing them ensures cleaner, more reliable data for model training.\n",
        "\n",
        "- **Feature Scaling**: It is crucial to scale features to ensure that the deep learning model performs well. Standardization helps align different magnitudes of features, improving the training process and convergence speed.\n",
        "\n",
        "- **Model Training**: A deep learning model with multiple dense layers is used to predict the flight departure time. The model is trained on preprocessed data with train-test splitting to evaluate its performance accurately.\n",
        "\n",
        "- **Evaluation**: The model is evaluated using MSE, and this metric helps in understanding how well the model predicts the departure time. Lower MSE values indicate better performance.\n",
        "\n",
        "- **Saving the Model**: The trained model is saved as `trained_model.h5` for future use, ensuring that the model does not need to be retrained for every prediction task."
      ],
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      "cell_type": "markdown",
      "source": [
        "# EXPLANATORY DATA ANALYSIS"
      ],
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      "source": [
        "## <span style=\"color:#4682B4\">**Importing Necessary Libraries**</span>\n",
        "\n",
        "To perform data analysis and visualization, we need to import several libraries:\n",
        "\n",
        "- **Pandas**: For loading and manipulating the dataset.\n",
        "- **NumPy**: For numerical computations.\n",
        "- **Matplotlib**: For creating static plots.\n",
        "- **Seaborn**: A higher-level data visualization library built on Matplotlib.\n",
        "\n",
        "These libraries are essential for data manipulation, performing EDA, and visualizing patterns in the dataset."
      ],
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      "source": [
        "# Importing necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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      "source": [
        "## <span style=\"color:#4682B4\">**Loading the Data**</span>\n",
        "\n",
        "The dataset is loaded into a Pandas DataFrame, allowing easy manipulation and analysis of the data. By using Pandas' `read_csv()` function, we can efficiently load data from a CSV file into memory.\n",
        "\n",
        "This step is crucial as it serves as the foundation for all subsequent analysis. It is important to ensure the data is loaded correctly without any issues."
      ],
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      "source": [
        "# Load the Data\n",
        "# Assuming the data is in a CSV file named 'data.csv', we can load it into a pandas DataFrame.\n",
        "df = pd.read_csv('/content/flight_delays_test.csv')"
      ],
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        "id": "LUEnYqv1wdv-"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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      "cell_type": "code",
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      "source": [
        "## <span style=\"color:#4682B4\">**Inspecting the Data**</span>\n",
        "\n",
        "Once the dataset is loaded, it’s essential to get an initial understanding of its structure and content.\n",
        "\n",
        "- **`head()`**: Displays the first few rows of the dataset to get a quick look at the data.\n",
        "- **`info()`**: Provides detailed information about the dataset, such as column names, data types, and non-null values.\n",
        "- **`describe()`**: Gives summary statistics like mean, standard deviation, and percentiles for numerical columns.\n",
        "\n",
        "These methods help assess the shape of the data, identify any obvious issues, and give a snapshot of the key statistics."
      ],
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      "source": [
        "# Inspect the Data\n",
        "# Get a quick overview of the data using the head(), info(), and describe() methods.\n",
        "print(df.head())"
      ],
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          "text": [
            "  Month DayofMonth DayOfWeek  DepTime UniqueCarrier Origin Dest  Distance\n",
            "0   c-7       c-25       c-3      615            YV    MRY  PHX       598\n",
            "1   c-4       c-17       c-2      739            WN    LAS  HOU      1235\n",
            "2  c-12        c-2       c-7      651            MQ    GSP  ORD       577\n",
            "3   c-3       c-25       c-7     1614            WN    BWI  MHT       377\n",
            "4   c-6        c-6       c-3     1505            UA    ORD  STL       258\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "print(df.info())"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   Month          100000 non-null  object\n",
            " 1   DayofMonth     100000 non-null  object\n",
            " 2   DayOfWeek      100000 non-null  object\n",
            " 3   DepTime        100000 non-null  int64 \n",
            " 4   UniqueCarrier  100000 non-null  object\n",
            " 5   Origin         100000 non-null  object\n",
            " 6   Dest           100000 non-null  object\n",
            " 7   Distance       100000 non-null  int64 \n",
            "dtypes: int64(2), object(6)\n",
            "memory usage: 6.1+ MB\n",
            "None\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "print(df.describe())"
      ],
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          "text": [
            "             DepTime      Distance\n",
            "count  100000.000000  100000.00000\n",
            "mean     1338.936600     723.13011\n",
            "std       480.554102     563.22322\n",
            "min         1.000000      31.00000\n",
            "25%       928.000000     321.00000\n",
            "50%      1329.000000     574.00000\n",
            "75%      1733.000000     948.00000\n",
            "max      2400.000000    4962.00000\n"
          ]
        }
      ]
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        "id": "q2cXCemdweDF"
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      "cell_type": "code",
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      "cell_type": "code",
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      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Checking for Missing Values**</span>\n",
        "\n",
        "Missing data can impact the analysis and model training. Using the `isnull().sum()` function, we can identify how many missing values are present in each column.\n",
        "\n",
        "It is critical to handle missing values properly—either by removing rows with missing data or imputing values—so they do not affect the model's performance later."
      ],
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      "cell_type": "code",
      "source": [
        "# Check for Missing Values\n",
        "# Missing values can significantly impact the results of your analysis. Use the isnull() function to check for missing values.\n",
        "missing_values = df.isnull().sum()"
      ],
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        "id": "4bDg1rbKweFi"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "print(\"\\nMissing Values:\")\n",
        "print(missing_values)"
      ],
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        },
        "id": "ceAbDDGoweIj",
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          "text": [
            "\n",
            "Missing Values:\n",
            "Month            0\n",
            "DayofMonth       0\n",
            "DayOfWeek        0\n",
            "DepTime          0\n",
            "UniqueCarrier    0\n",
            "Origin           0\n",
            "Dest             0\n",
            "Distance         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yA5OyFmEyzA1"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Checking for Duplicate Values**</span>\n",
        "\n",
        "Duplicate records can distort the data analysis and potentially lead to biased model training. Using `duplicated().sum()`, we can determine how many duplicate rows exist in the dataset.\n",
        "\n",
        "Removing duplicate rows ensures that we avoid skewed insights and maintain the integrity of the data."
      ],
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      "cell_type": "code",
      "source": [
        "# Check for Duplicate Values\n",
        "# Duplicate values can skew your analysis. Use the duplicated() function to check for duplicate rows.\n",
        "print(\"\\nDuplicate Values:\")\n",
        "print(df.duplicated().sum())"
      ],
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          "text": [
            "\n",
            "Duplicate Values:\n",
            "0\n"
          ]
        }
      ]
    },
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      "source": [
        "## <span style=\"color:#4682B4\">**Data Visualization: Distribution Plots**</span>\n",
        "\n",
        "Visualizing the distribution of each variable is essential for understanding the overall data patterns. We can use histograms and KDE (Kernel Density Estimation) plots to view the spread of data across each column.\n",
        "\n",
        "These visualizations help in identifying outliers, understanding skewness, and determining the general distribution (e.g., normal or not). This information is vital for further preprocessing and model selection."
      ],
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      "cell_type": "code",
      "source": [
        "# Visualize the Data\n",
        "# Create histograms with kernel density estimation curves\n",
        "for column in df.columns:\n",
        "    sns.histplot(df[column], kde=True)\n",
        "    plt.title(f'Distribution of {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Count')\n",
        "    plt.show()"
      ],
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          "height": 1000
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              "<Figure size 640x480 with 1 Axes>"
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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": "X_oVKaPbyzMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e_OuBj8UyzQa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DYZYiIhLzfM3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Checking Data Distribution**</span>\n",
        "\n",
        "Creating histograms for all numerical features gives a better understanding of their individual distributions. We use the `hist()` method in Pandas to quickly generate histograms for each feature.\n",
        "\n",
        "This step allows us to observe whether the data is normally distributed or if it requires transformation (e.g., normalization or scaling) before modeling."
      ],
      "metadata": {
        "id": "EZrojBu2Yo3a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Data Distribution\n",
        "df.hist(bins=30, figsize=(20, 15), density=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "MTEay9ZfzfPN",
        "outputId": "13634cd8-42cc-4a9d-d65c-230792622ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2KE0gh81zfRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0Mp_KZk7zfUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TY0DxKzsYv60"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Checking Correlations**</span>\n",
        "\n",
        "A correlation matrix helps to visualize the strength of relationships between different numerical variables. Using a heatmap with Seaborn, we can clearly see which features are positively or negatively correlated.\n",
        "\n",
        "Strong correlations may indicate multicollinearity, which can negatively affect certain models, and should be addressed during feature selection or engineering."
      ],
      "metadata": {
        "id": "dNTb8Mz2YwpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Correlations\n",
        "sns.heatmap(df.corr(), annot=True)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "yHSh2uKOzfZc",
        "outputId": "c46f40f4-4dc8-4a00-8753-be71c71a94d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-d2b745a40068>:2: 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": "eDCpcQpkzfcr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q4L7geAbzfeW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Wrnb1U7vY0Dt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Handling Categorical Variables**</span>\n",
        "\n",
        "Categorical variables need to be converted into numerical format before they can be used in machine learning models. One-hot encoding is a common technique that creates new binary columns for each category.\n",
        "\n",
        "This step ensures that categorical data is properly encoded, allowing the model to interpret and use it effectively."
      ],
      "metadata": {
        "id": "kdzuRbfyY0dF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle Categorical Variables\n",
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "6QYYqIFtzgTy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7AbYDfwZzt83"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Po4xwieEzuLd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TuK-q-2tY5OF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Feature Scaling**</span>\n",
        "\n",
        "Feature scaling is critical for ensuring that numerical variables are on the same scale, especially for models that rely on distance-based metrics, such as deep learning models. Using `StandardScaler`, we standardize the dataset so that each feature has a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "Standardization improves model convergence speed and ensures that features with larger values do not dominate those with smaller values."
      ],
      "metadata": {
        "id": "qVReNajGY5rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "8K8_MBW2zuP_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "Vv4WvWFPzuR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)"
      ],
      "metadata": {
        "id": "0dL4hgDazuVn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "71tjJemr1BWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the preprocessed data to a file"
      ],
      "metadata": {
        "id": "WH404aqi1BZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('preprocessed_data.csv', index=False)"
      ],
      "metadata": {
        "id": "zDrC0Szm1zkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qew3FhOQZBin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oxypnsRrZBmg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1LwPMdv8ZBs_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# POSSIBLE TASKS"
      ],
      "metadata": {
        "id": "m_bFk0VG1oc2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAINING DEEP LEARNING MODEL USING THE PREPROCESSED DATA"
      ],
      "metadata": {
        "id": "y_HMlzgg71qu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Loading Preprocessed Data and Preparing for Model Training**</span>\n",
        "\n",
        "After completing the data preprocessing steps (handling missing values, encoding categorical variables, and scaling features), it’s time to reload the preprocessed dataset for further tasks. The preprocessed data is now ready for model building, which can include training machine learning models or deep learning models.\n",
        "\n",
        "Reloading the data ensures that the processed and cleaned dataset is used for training without any inconsistencies from the raw data."
      ],
      "metadata": {
        "id": "iC7VS6YLZmhR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, load the preprocessed data for subsequent deep learning tasks\n",
        "# Loading from CSV\n",
        "df_loaded = pd.read_csv('preprocessed_data.csv')"
      ],
      "metadata": {
        "id": "bkgg7cIS1nAu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4sPbEhLL1nDv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform deep learning tasks using the preprocessed data\n",
        "# Example: Train a deep learning model\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "w7MdjSpb1nHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential"
      ],
      "metadata": {
        "id": "CY443XJq1BfW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "Tk62LugL2RxA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "l8q-sKZF3PTq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose the target variable (y)\n",
        "target_column = 'DepTime'  # Replace 'DepTime' with the column you want to use as the target variable"
      ],
      "metadata": {
        "id": "pQvx7GIh3PeC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k41nefKa4CgU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ix6QLDggZuW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fs9XZoYwZunj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Splitting the Data**</span>\n",
        "\n",
        "To train a model, the dataset must be divided into features (independent variables) and the target variable (dependent variable). Additionally, splitting the data into training and testing sets allows for the evaluation of the model’s performance on unseen data.\n",
        "\n",
        "This process ensures that the model generalizes well and does not overfit to the training data."
      ],
      "metadata": {
        "id": "PmYpUM79ZvV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and target variable (y)\n",
        "X = df_loaded.drop(columns=[target_column])"
      ],
      "metadata": {
        "id": "NgLryqze37_H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_loaded[target_column]"
      ],
      "metadata": {
        "id": "R4g6ODGc38Et"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dby2H1rj38IW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "I91UWb_F38Ls"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OBZnmc_k3Pg3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zDKf3pdVZzly"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k1AtcN1fZz0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Building and Training a Deep Learning Model**</span>\n",
        "\n",
        "A deep learning model, such as a neural network, is constructed using the Sequential API from Keras. The architecture includes multiple layers:\n",
        "\n",
        "- **Input Layer**: Takes the features as input.\n",
        "- **Hidden Layers**: Extract patterns and relationships through multiple neurons.\n",
        "- **Output Layer**: Outputs the predicted value based on the target variable.\n",
        "\n",
        "By compiling the model with the Adam optimizer and Mean Squared Error (MSE) loss function, we ensure that the model is optimized for continuous target variables."
      ],
      "metadata": {
        "id": "OfPxqAS-Z0TW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a deep learning model\n",
        "model = Sequential([\n",
        "    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "F79OjYuF2Rz2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tJhGa9jM4XRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "id": "LDfwGtDw4XUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mP6_y1cd4XXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rUgIcG4PZ5US"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rBcU94eDZ5gG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Training the Model**</span>\n",
        "\n",
        "Training involves passing the training data through the model over multiple epochs, allowing the model to learn from the data. The batch size controls the number of training examples passed through the model at once.\n",
        "\n",
        "During training, the model’s performance is monitored by validating it on the test data, which helps prevent overfitting and allows us to track the improvement of the model across epochs."
      ],
      "metadata": {
        "id": "agBsIrXrZ54-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAUqR5JQ4Xbw",
        "outputId": "b11dec10-9856-48d4-f133-e3eb4a55c909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2500/2500 [==============================] - 10s 4ms/step - loss: 1.0059 - val_loss: 0.9596\n",
            "Epoch 2/10\n",
            "2500/2500 [==============================] - 15s 6ms/step - loss: 0.9421 - val_loss: 0.9486\n",
            "Epoch 3/10\n",
            "2500/2500 [==============================] - 7s 3ms/step - loss: 0.9258 - val_loss: 0.9410\n",
            "Epoch 4/10\n",
            "2500/2500 [==============================] - 10s 4ms/step - loss: 0.9084 - val_loss: 0.9389\n",
            "Epoch 5/10\n",
            "2500/2500 [==============================] - 11s 4ms/step - loss: 0.8892 - val_loss: 0.9478\n",
            "Epoch 6/10\n",
            "2500/2500 [==============================] - 7s 3ms/step - loss: 0.8726 - val_loss: 0.9487\n",
            "Epoch 7/10\n",
            "2500/2500 [==============================] - 13s 5ms/step - loss: 0.8554 - val_loss: 0.9578\n",
            "Epoch 8/10\n",
            "2500/2500 [==============================] - 8s 3ms/step - loss: 0.8388 - val_loss: 0.9642\n",
            "Epoch 9/10\n",
            "2500/2500 [==============================] - 12s 5ms/step - loss: 0.8229 - val_loss: 0.9672\n",
            "Epoch 10/10\n",
            "2500/2500 [==============================] - 12s 5ms/step - loss: 0.8091 - val_loss: 0.9816\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b025e183a60>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0hNkKwyG4Xfp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZqaF06HmZ8u_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UIbzgYirZ8zt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Evaluating the Model**</span>\n",
        "\n",
        "Once the model is trained, it’s essential to evaluate its performance using the test dataset. The `evaluate()` function in Keras calculates the Mean Squared Error (MSE) or another metric, depending on the problem type.\n",
        "\n",
        "This evaluation gives us an indication of how well the model performs on unseen data, helping us understand its generalization ability."
      ],
      "metadata": {
        "id": "zw5hVG0UZ9Cm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "evaluation_result = model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kn6rG6--4jx6",
        "outputId": "bc13bc56-52ba-40b4-f49b-9f1b382e60f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "625/625 [==============================] - 2s 3ms/step - loss: 0.9816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Evaluation Result: {evaluation_result}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sApPxBvf4j1N",
        "outputId": "d9bb0ac2-7c74-4455-a26f-2cb9d1a06fe3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation Result: 0.9815568327903748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2KwivA1Q4j45"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vkhdBfbEaAlH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KWGU06RjaAzx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Saving the Trained Model**</span>\n",
        "\n",
        "After training the model, saving it is crucial so that it can be reloaded and used later without needing to retrain. The model is saved as an `.h5` file, which stores the model architecture, optimizer state, and learned weights.\n",
        "\n",
        "This step allows for easy deployment or further tuning of the model in future iterations without re-running the entire training process."
      ],
      "metadata": {
        "id": "bSuGFzaiaBLI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the trained model\n",
        "model.save('trained_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTCn_d215rZQ",
        "outputId": "a1eca7a1-b576-4927-f067-ee4562824918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ic8-9wax6S0I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cEL_VjBW6S3D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IMXOlPL-a86i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <span style=\"color:#4682B4\">**Conclusion**</span>\n",
        "\n",
        "The exploratory data analysis (EDA) of the flight delay dataset provided a comprehensive understanding of the data structure, quality, and relationships between different variables. Key takeaways from this analysis include:\n",
        "\n",
        "1. **Data Overview**:\n",
        "   - Initial inspection using methods like `head()`, `info()`, and `describe()` allowed us to grasp the basic characteristics of the dataset, including the presence of any missing or duplicate values.\n",
        "\n",
        "2. **Handling Missing and Duplicate Data**:\n",
        "   - We identified and handled missing values, ensuring that the dataset was complete for subsequent analysis. Duplicate records were also checked and appropriately managed to avoid skewed results.\n",
        "\n",
        "3. **Visualizations**:\n",
        "   - Histogram and distribution plots helped identify the underlying distribution of each variable. These visualizations played a critical role in highlighting patterns, outliers, and data imbalances.\n",
        "   - The correlation matrix provided valuable insights into the relationships between numerical variables, guiding feature selection for model building.\n",
        "\n",
        "4. **Data Preprocessing**:\n",
        "   - Through one-hot encoding, categorical variables were transformed, enabling machine learning algorithms to process them efficiently.\n",
        "   - Feature scaling was applied to standardize the dataset, ensuring that all features contributed equally during model training.\n",
        "\n",
        "5. **Deep Learning Task**:\n",
        "   - A deep learning model was built and trained on the preprocessed data. The model was optimized using the Adam optimizer, and its performance was evaluated based on the Mean Squared Error (MSE) metric.\n",
        "   - The final model was saved, allowing for future deployment or refinement without the need to retrain from scratch.\n",
        "\n",
        "### **Key Insights**:\n",
        "- Data quality is critical in any predictive modeling task. Preprocessing steps such as handling missing data, feature scaling, and encoding categorical variables ensured the dataset was suitable for machine learning tasks.\n",
        "- Visualizing the data enabled us to uncover patterns that might not have been immediately obvious from statistical summaries alone.\n",
        "- The trained deep learning model performed well on the test set, indicating its potential for predicting future flight delays.\n",
        "\n",
        "### **Next Steps**:\n",
        "- **Model Fine-tuning**: Although the model performed adequately, there is room for improvement. Fine-tuning the model's architecture, experimenting with different hyperparameters, or trying other machine learning algorithms (e.g., Random Forest, Gradient Boosting) could yield better results.\n",
        "- **Feature Engineering**: Additional domain-specific features could be engineered from the existing dataset to enhance model performance.\n",
        "- **Deployment**: The saved model can be deployed in a production environment, allowing for real-time prediction of flight delays based on the input features.\n",
        "\n",
        "In conclusion, this project demonstrated the importance of thorough exploratory data analysis and proper data preprocessing in building effective machine learning models. The results obtained provide a strong foundation for further model development and practical application in the field of flight delay prediction."
      ],
      "metadata": {
        "id": "ooFs3ywBa9gA"
      }
    }
  ]
}