1_2_6_Drug_Bioactivity_Prediction_Starter_Kit (1).ipynb

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      "source": [
        "# <span style=\"color: #2E86C1;\">Project Overview: 1.2.6.  Drug Bioactivity Prediction - Starter Kit</span>\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">1. Introduction</span>\n",
        "This project involves analyzing a bioactivity dataset to understand the relationships between various chemical compounds and their biological activities. The goal is to predict the bioactivity of compounds based on their chemical structure and properties.\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">2. Objectives</span>\n",
        "- To explore the dataset and identify key patterns and relationships.\n",
        "- To preprocess the data for modeling.\n",
        "- To develop a predictive model using machine learning techniques.\n",
        "- To evaluate the performance of the model and fine-tune parameters for optimal results.\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">3. Dataset Description</span>\n",
        "The dataset consists of 100 entries with the following columns:\n",
        "- **assay_chembl_id**: Unique identifier for the assay.\n",
        "- **molecule_chembl_id**: Unique identifier for the molecule.\n",
        "- **standard_value**: The measured biological activity.\n",
        "- **type**: Type of bioactivity (e.g., IC50).\n",
        "- **value**: Numerical value of the bioactivity.\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">4. Methodology</span>\n",
        "\n",
        "### <span style=\"color: #3498DB;\">4.1 Data Exploration</span>\n",
        "- Visualize distributions of numerical features.\n",
        "- Examine correlations among different variables.\n",
        "- Analyze categorical variables through count plots.\n",
        "\n",
        "### <span style=\"color: #3498DB;\">4.2 Data Preprocessing</span>\n",
        "- Handle missing values by filling them with appropriate statistics.\n",
        "- Encode categorical variables using label encoding.\n",
        "- Apply log transformations to reduce skewness in data distributions.\n",
        "\n",
        "### <span style=\"color: #3498DB;\">4.3 Model Development</span>\n",
        "- Split the dataset into training and testing sets.\n",
        "- Utilize a Random Forest Regressor for predictions.\n",
        "- Conduct hyperparameter tuning using GridSearchCV for optimal model configuration.\n",
        "\n",
        "### <span style=\"color: #3498DB;\">4.4 Model Evaluation</span>\n",
        "- Assess model performance using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values.\n",
        "- Analyze cross-validated R-squared scores to ensure robustness of the model.\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">5. Conclusion</span>\n",
        "The analysis aims to yield valuable insights into the bioactivity of compounds, contributing to the field of cheminformatics and drug discovery. The outcomes will assist researchers in identifying promising candidates for further investigation.\n",
        "\n",
        "## <span style=\"color: #5D6D7E;\">6. Future Work</span>\n",
        "- Explore advanced modeling techniques such as neural networks.\n",
        "- Expand the dataset with additional features or larger samples for better accuracy.\n",
        "- Implement real-time bioactivity prediction systems for practical applications.\n",
        "\n",
        "---\n",
        "\n",
        "This project overview provides a comprehensive understanding of the objectives, methodology, and expected outcomes. It serves as a foundation for the detailed implementation and analysis performed in the associated code."
      ],
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    {
      "cell_type": "code",
      "source": [
        "!pip install chembl_webresource_client  # Install the ChEMBL web resource client to access and interact with ChEMBL's chemical and biological data.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "execution_count": 27,
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          "text": [
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          ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tvaCMC9ffpNg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 1: Importing Libraries</span>\n",
        "\n",
        "In this step, we import the necessary libraries to facilitate data manipulation, analysis, and modeling. The primary libraries include:\n",
        "- **Pandas**: For data manipulation and analysis.\n",
        "- **NumPy**: For numerical operations.\n",
        "- **Scikit-learn**: For machine learning algorithms and utilities.\n",
        "- **Matplotlib** and **Seaborn**: For data visualization.\n",
        "- **Warnings**: To suppress any warnings that may arise during the execution."
      ],
      "metadata": {
        "id": "Z6H07m-zeKFd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the necessary libraries\n"
      ],
      "metadata": {
        "id": "PLCo9IilUuNu"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation and analysis"
      ],
      "metadata": {
        "id": "Cec-1hJ7YzKm"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # For numerical operations"
      ],
      "metadata": {
        "id": "MLHIAPZoYzN9"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For plotting graphs"
      ],
      "metadata": {
        "id": "QxtLs6mHYzQX"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # For statistical data visualization"
      ],
      "metadata": {
        "id": "pK97FaG7YzSd"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, GridSearchCV  # For splitting the dataset and hyperparameter tuning\n"
      ],
      "metadata": {
        "id": "Q6OIlNQWYzUf"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor  # For regression modeling"
      ],
      "metadata": {
        "id": "dthMm9h4YzW3"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, LabelEncoder  # For feature scaling and encoding"
      ],
      "metadata": {
        "id": "fi9gsDGaYzZB"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score  # For model evaluation metrics"
      ],
      "metadata": {
        "id": "q5uU1lRDYzgm"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Import warnings to manage warnings"
      ],
      "metadata": {
        "id": "FiBsCKzRYzlO"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore warnings for cleaner output"
      ],
      "metadata": {
        "id": "rhn6chDYkBQ7"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HZoFbTDjkBUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6lyKZdOZaBUZ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ahtaj6xPeOoV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 2: Loading the Dataset</span>\n",
        "\n",
        "Here, we load the bioactivity dataset from a specified file path. This dataset will be used for analysis and modeling. We can utilize **pandas** to read the dataset and inspect its initial structure to ensure it loaded correctly."
      ],
      "metadata": {
        "id": "NGVnekWZeO2F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "VkWNmnFzaBXn"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('bioactivity_data.csv')  # Read the dataset from a CSV file"
      ],
      "metadata": {
        "id": "kvgkO3SRaBa5"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jL_vWiHAaBeh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8jlGn1oIaBg8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pxupXhFiYzru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 3: Exploring the Dataset</span>\n",
        "\n",
        "In this step, we perform an exploratory analysis of the dataset. This includes:\n",
        "- Viewing the first few rows to understand its structure.\n",
        "- Checking for missing values and non-null counts in each column.\n",
        "- Generating summary statistics to gather insights into the data distribution."
      ],
      "metadata": {
        "id": "13jdrx-keVWz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset"
      ],
      "metadata": {
        "id": "KS3xTkV6YzwC"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 rows of the dataset:\")  # Print a message\n",
        "print(df.head())  # Display the first 5 rows of the DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUouJSXsYzyR",
        "outputId": "67e82b1e-287a-48b8-a290-4f3bec3c4b2a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 rows of the dataset:\n",
            "  assay_chembl_id molecule_chembl_id  standard_value  type  value\n",
            "0    CHEMBL674637        CHEMBL68920            41.0  IC50  0.041\n",
            "1    CHEMBL621151        CHEMBL68920           300.0  IC50  0.300\n",
            "2    CHEMBL615325        CHEMBL68920          7820.0  IC50  7.820\n",
            "3    CHEMBL674637        CHEMBL69960           170.0  IC50  0.170\n",
            "4    CHEMBL621151        CHEMBL69960            40.0  IC50  0.040\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-UHHzCaXYz0r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mlE9zMpOYz24"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display information about the dataset (column names, non-null counts, and data types)"
      ],
      "metadata": {
        "id": "RDTmZiJGYz4_"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Information:\")  # Print a message\n",
        "print(df.info())  # Display information about the DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfoEsNKxYz7Z",
        "outputId": "1e3b6943-9d5c-4d5e-d5c7-d8e41a738537"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 5 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   assay_chembl_id     100 non-null    object \n",
            " 1   molecule_chembl_id  100 non-null    object \n",
            " 2   standard_value      87 non-null     float64\n",
            " 3   type                100 non-null    object \n",
            " 4   value               87 non-null     float64\n",
            "dtypes: float64(2), object(3)\n",
            "memory usage: 4.0+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ocT2snweaV_p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QWvz4PB8aWCx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics of the dataset"
      ],
      "metadata": {
        "id": "H2K5IHR5aWFs"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSummary statistics:\")  # Print a message\n",
        "print(df.describe())  # Display summary statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ws5M-rWoaWJR",
        "outputId": "49c18c30-7a09-4cce-d7ac-4322a88553af"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n",
            "       standard_value        value\n",
            "count    8.700000e+01    87.000000\n",
            "mean     4.224033e+05   422.403258\n",
            "std      8.619040e+05   861.904027\n",
            "min      4.500000e-01     0.000450\n",
            "25%      1.730000e+03     1.730000\n",
            "50%      6.000000e+04    60.000000\n",
            "75%      4.750000e+05   475.000000\n",
            "max      6.500000e+06  6500.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bRxPEpKWacTT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lq5nnSr7iKsg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display unique values in categorical columns"
      ],
      "metadata": {
        "id": "CT3gIXYwiKwu"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_assay_ids = df['assay_chembl_id'].unique()  # Get unique assay IDs"
      ],
      "metadata": {
        "id": "pDx5d8xhiMCA"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_molecule_ids = df['molecule_chembl_id'].unique()  # Get unique molecule IDs"
      ],
      "metadata": {
        "id": "hN_KMW7wiPSI"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_types = df['type'].unique()  # Get unique types"
      ],
      "metadata": {
        "id": "nsgyGRshiPVm"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iR6c_Ke2iWfB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Unique assay_chembl_id:\", unique_assay_ids)  # Print unique assay IDs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIig3WRbiWiZ",
        "outputId": "95521d50-3290-4539-b385-d49c28cdcdf4"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique assay_chembl_id: ['CHEMBL674637' 'CHEMBL621151' 'CHEMBL615325' 'CHEMBL677833'\n",
            " 'CHEMBL674643' 'CHEMBL675639' 'CHEMBL675636' 'CHEMBL675637'\n",
            " 'CHEMBL676063' 'CHEMBL675640' 'CHEMBL674645' 'CHEMBL675638'\n",
            " 'CHEMBL674646' 'CHEMBL676065' 'CHEMBL615490' 'CHEMBL679675'\n",
            " 'CHEMBL675641']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L-AKoAeGicRC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Unique molecule_chembl_id:\", unique_molecule_ids)  # Print unique molecule IDs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyHo6w4GicUZ",
        "outputId": "3d117bbd-ce2c-4bcc-bbd4-cd1e4238f2cc"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique molecule_chembl_id: ['CHEMBL68920' 'CHEMBL69960' 'CHEMBL137635' 'CHEMBL306988' 'CHEMBL66879'\n",
            " 'CHEMBL77085' 'CHEMBL443268' 'CHEMBL76979' 'CHEMBL76589' 'CHEMBL77724'\n",
            " 'CHEMBL420385' 'CHEMBL76904' 'CHEMBL1009' 'CHEMBL304271' 'CHEMBL296407'\n",
            " 'CHEMBL309625' 'CHEMBL77825' 'CHEMBL421877' 'CHEMBL77278' 'CHEMBL310798'\n",
            " 'CHEMBL135592' 'CHEMBL309866' 'CHEMBL77381' 'CHEMBL76602' 'CHEMBL77737'\n",
            " 'CHEMBL76983' 'CHEMBL308134' 'CHEMBL136491' 'CHEMBL77280' 'CHEMBL42423'\n",
            " 'CHEMBL76557' 'CHEMBL336113' 'CHEMBL133024' 'CHEMBL344652' 'CHEMBL77814'\n",
            " 'CHEMBL47986' 'CHEMBL441343' 'CHEMBL138125' 'CHEMBL137364' 'CHEMBL308339'\n",
            " 'CHEMBL78174' 'CHEMBL302552' 'CHEMBL309598' 'CHEMBL78302' 'CHEMBL67057'\n",
            " 'CHEMBL332096' 'CHEMBL77387' 'CHEMBL432903' 'CHEMBL69964' 'CHEMBL65848'\n",
            " 'CHEMBL69129' 'CHEMBL78184' 'CHEMBL76985' 'CHEMBL145' 'CHEMBL69629'\n",
            " 'CHEMBL136492' 'CHEMBL77197' 'CHEMBL305695' 'CHEMBL68258' 'CHEMBL76587']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tsJ2VVEYigJ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Unique types:\", unique_types)  # Print unique types"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIDpEJkpigNj",
        "outputId": "8a7312fb-c89f-426a-bcad-f65f7cf92e4e"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique types: ['IC50' 'Ki']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PbOUyUqyijGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2Hud2iOrimNE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates"
      ],
      "metadata": {
        "id": "v1_31IhCistu"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicates = df.duplicated().sum()  # Count number of duplicate rows"
      ],
      "metadata": {
        "id": "tlPlU24Fit2L"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of duplicate rows: {duplicates}\")  # Print the number of duplicates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpA_JXFKit56",
        "outputId": "b6cdddaf-0cc3-44cd-fdba-c58eb19ad4f7"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W_gr_ggVican"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_j-I3TsxiPYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qR9PLN_WacWQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">📊 Exploratory Data Analysis (EDA) Notes</span>\n",
        "\n",
        "## <span style=\"color:green;\"> Displaying Unique Values and Duplicates</span>\n",
        "- **Purpose**: Assess the uniqueness and cleanliness of the dataset.\n",
        "- **Methods**:\n",
        "  - Use `.unique()` to find unique values for categorical variables like `assay_chembl_id`, `molecule_chembl_id`, and `type`.\n",
        "  - Count duplicates using `.duplicated().sum()` to determine data quality.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Summary Statistics</span>\n",
        "- **Purpose**: Obtain a quick overview of the numerical features in the dataset.\n",
        "- **Methods**: Utilize `.describe()` to generate summary statistics, including count, mean, standard deviation, min, max, and quartiles for `standard_value` and `value`.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Pie Chart for Distribution of Types</span>\n",
        "- **Purpose**: Visually represent the distribution of different types in the dataset.\n",
        "- **Methods**:\n",
        "  - Calculate the value counts of the `type` column using `.value_counts()`.\n",
        "  - Create a pie chart with `plt.pie()` to display proportions of each type, including percentage labels for clarity.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Line Plot of Standard Value</span>\n",
        "- **Purpose**: Identify trends or patterns in the `standard_value` across the dataset.\n",
        "- **Methods**:\n",
        "  - Plot the `standard_value` against the index using `plt.plot()`, adding markers to highlight individual data points.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Violin Plot of Value Distribution by Type</span>\n",
        "- **Purpose**: Show the distribution of `value` across different `type` categories, highlighting data spread and potential outliers.\n",
        "- **Methods**: Use `sns.violinplot()` to visualize the `value` distribution segmented by `type`, allowing for comparison across categories.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Correlation Matrix</span>\n",
        "- **Purpose**: Assess the relationships between numerical features in the dataset.\n",
        "- **Methods**:\n",
        "  - Compute the correlation matrix using `.corr()`.\n",
        "  - Visualize the correlation matrix with a heatmap using `sns.heatmap()`, providing insights into linear relationships between variables.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Box Plot for Standard Value by Type</span>\n",
        "- **Purpose**: Identify outliers and understand the distribution of `standard_value` across different `type` categories.\n",
        "- **Methods**: Use `sns.boxplot()` to create a box plot, displaying the median, quartiles, and outliers for `standard_value` segmented by `type`.\n",
        "\n",
        "---\n",
        "\n",
        "## <span style=\"color:green;\"> Distribution Plots</span>\n",
        "- **Purpose**: Visualize the distribution of numerical features, identifying skewness and potential outliers.\n",
        "- **Methods**: Employ `sns.histplot()` or `sns.kdeplot()` for the `standard_value` and `value` columns, allowing for assessment of normality and spread.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "1U9eif5ijYkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "EH1IWOUOacZM"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a box plot for 'standard_value' segmented by 'type'"
      ],
      "metadata": {
        "id": "WcDJkF6-jx5C"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size\n",
        "sns.boxplot(x='type', y='standard_value', data=df)  # Draw box plot\n",
        "plt.title('Box Plot of Standard Value by Type')  # Set the title\n",
        "plt.xlabel('Type')  # Set x-axis label\n",
        "plt.ylabel('Standard Value')  # Set y-axis label\n",
        "plt.xticks(rotation=45)  # Rotate x-axis labels for better visibility\n",
        "plt.grid(True)  # Add a grid for easier interpretation\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "lDgpv2V5jqyS",
        "outputId": "91424fde-5ecb-4bc1-f558-a368e700771e"
      },
      "execution_count": 122,
      "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": "1HludICujq11"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mvo7jdZqjrED"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Distribution of 'standard_value'"
      ],
      "metadata": {
        "id": "cXch5KF4acb9"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.histplot(df['standard_value'], kde=True)  # Plot histogram with KDE for 'standard_value'\n",
        "plt.title('Distribution of Standard Value')  # Set the title of the plot\n",
        "plt.xlabel('Standard Value')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Uv-BDDWKaWMq",
        "outputId": "d38b6d50-e38f-4d71-cb8e-70e997938911"
      },
      "execution_count": 48,
      "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": "RhvjEMPQaWPz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nlKhs3lJY0D3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##  Distribution of 'value'"
      ],
      "metadata": {
        "id": "qqTq8iZ6Y0H6"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "sns.histplot(df['value'], kde=True)  # Plot histogram with KDE for 'value'\n",
        "plt.title('Distribution of Value')  # Set the title of the plot\n",
        "plt.xlabel('Value')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "KkSr7e69UuRi",
        "outputId": "9d28ceb0-05da-4d39-d470-398b6f15c672"
      },
      "execution_count": 50,
      "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": "_IF1QExtUK3X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P22syXGfUK6f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##  Countplot of Categorical Variables"
      ],
      "metadata": {
        "id": "I9vRqU9_UK9g"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 6))  # Set the figure size\n",
        "sns.countplot(data=df, x='type')  # Create a countplot for 'type'\n",
        "plt.title('Countplot of Type')  # Set the title of the plot\n",
        "plt.xlabel('Type')  # Set the x-axis label\n",
        "plt.ylabel('Count')  # Set the y-axis label\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "CKkGh7CYUK_6",
        "outputId": "fd409523-9410-4594-c5f8-f1f952e06abb"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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knnvuyezZs7Nw4cKceuqplRwZAAAAoEOpKpVKpUoP8Z5LLrkkDz74YJ5//vk0Nzdnu+22y5133pnTTjstSfLcc89l6NChmTNnTg4++OD1Omdzc3Nqa2uzZMmS1NTUbMrxAQAAAD4w69s8Npt7/qxYsSLf+c538tnPfjZVVVWZN29eVq5cmeHDh5ePGTJkSHbcccfMmTNnnedpaWlJc3Nzqw0AAABgS7XZxJ/7778/ixcvzplnnpkkaWxsTNeuXdOrV69Wx9XV1aWxsXGd55k8eXJqa2vL24ABAzbh1AAAAACbt80m/tx66605/vjj079//406z4QJE7JkyZLy9tprr7XThAAAAAAdT+dKD5Akr7zySh566KF897vfLa/V19dnxYoVWbx4caurf5qamlJfX7/Oc1VXV6e6unpTjgsAAADQYWwWV/5Mnz4922+/fU444YTy2rBhw9KlS5fMmjWrvDZ//vy8+uqraWhoqMSYAAAAAB1Oxa/8Wb16daZPn57Ro0enc+f/P05tbW3OPvvsjB8/Pn369ElNTU3Gjh2bhoaG9X7SFwAAAMCWruLx56GHHsqrr76az372s2vsu/7669OpU6eMHDkyLS0tGTFiRG666aYKTAkAAADQMVWVSqVSpYfYlNb3mfcAAAAAHcn6No+KX/lD2w277FuVHgEA2mzeV0dVegQAgC3CZnHDZwAAAAA2DfEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACqzi8ecPf/hDzjjjjPTt2zdbb7119tprrzz11FPl/aVSKVdccUX69euXrbfeOsOHD8/zzz9fwYkBAAAAOo6Kxp8//elPOfTQQ9OlS5f86Ec/ym9/+9t87WtfS+/evcvHXHvttbnhhhty880354knnkj37t0zYsSILF++vIKTAwAAAHQMnSv5y//t3/4tAwYMyPTp08trgwcPLv9cKpUyZcqUfPGLX8xJJ52UJPnWt76Vurq63H///Tn99NM/8JkBAAAAOpKKXvnz/e9/P/vvv38+/vGPZ/vtt89+++2X//iP/yjvf+mll9LY2Jjhw4eX12pra3PQQQdlzpw5az1nS0tLmpubW20AAAAAW6qKxp8XX3wx06ZNy6677pqZM2fmggsuyEUXXZTbb789SdLY2Jgkqaura/W+urq68r6/NXny5NTW1pa3AQMGbNoPAQAAALAZq2j8Wb16dT784Q/nK1/5Svbbb7+cd955Offcc3PzzTe3+ZwTJkzIkiVLyttrr73WjhMDAAAAdCwVjT/9+vXL7rvv3mpt6NChefXVV5Mk9fX1SZKmpqZWxzQ1NZX3/a3q6urU1NS02gAAAAC2VBWNP4ceemjmz5/fau33v/99Bg4cmOQvN3+ur6/PrFmzyvubm5vzxBNPpKGh4QOdFQAAAKAjqujTvsaNG5dDDjkkX/nKV/KJT3wiTz75ZG655ZbccsstSZKqqqpccskl+fKXv5xdd901gwcPzpe+9KX0798/J598ciVHBwAAAOgQKhp/DjjggNx3332ZMGFCJk2alMGDB2fKlCn59Kc/XT7mC1/4Qt5+++2cd955Wbx4cQ477LDMmDEj3bp1q+DkAAAAAB1DValUKlV6iE2pubk5tbW1WbJkSeHu/zPssm9VegQAaLN5Xx1V6REAADq09W0eFb3nDwAAAACblvgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABRYRePPlVdemaqqqlbbkCFDyvuXL1+eMWPGpG/fvunRo0dGjhyZpqamCk4MAAAA0LFU/MqfPfbYI6+//np5+9nPflbeN27cuDzwwAO55557Mnv27CxcuDCnnnpqBacFAAAA6Fg6V3yAzp1TX1+/xvqSJUty66235s4778xRRx2VJJk+fXqGDh2axx9/PAcffPAHPSoAAABAh1PxK3+ef/759O/fPzvttFM+/elP59VXX02SzJs3LytXrszw4cPLxw4ZMiQ77rhj5syZs87ztbS0pLm5udUGAAAAsKWqaPw56KCDctttt2XGjBmZNm1aXnrppXzkIx/J0qVL09jYmK5du6ZXr16t3lNXV5fGxsZ1nnPy5Mmpra0tbwMGDNjEnwIAAABg81XRr30df/zx5Z/33nvvHHTQQRk4cGD+z//5P9l6663bdM4JEyZk/Pjx5dfNzc0CEAAAALDFqvjXvv5ar169sttuu+WFF15IfX19VqxYkcWLF7c6pqmpaa33CHpPdXV1ampqWm0AAAAAW6rNKv4sW7YsCxYsSL9+/TJs2LB06dIls2bNKu+fP39+Xn311TQ0NFRwSgAAAICOo6Jf+/r85z+fE088MQMHDszChQszceLEbLXVVvnUpz6V2tranH322Rk/fnz69OmTmpqajB07Ng0NDZ70BQAAALCeKhp//vu//zuf+tSn8sc//jHbbbddDjvssDz++OPZbrvtkiTXX399OnXqlJEjR6alpSUjRozITTfdVMmRAQAAADqUqlKpVKr0EJtSc3Nzamtrs2TJksLd/2fYZd+q9AgA0Gbzvjqq0iMAAHRo69s8Nqt7/gAAAADQvsQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDAxB8AAACAAhN/AAAAAApM/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKDDxBwAAAKDANpv4c80116SqqiqXXHJJeW358uUZM2ZM+vbtmx49emTkyJFpamqq3JAAAAAAHcxmEX/mzp2bf//3f8/ee+/dan3cuHF54IEHcs8992T27NlZuHBhTj311ApNCQAAANDxVDz+LFu2LJ/+9KfzH//xH+ndu3d5fcmSJbn11ltz3XXX5aijjsqwYcMyffr0PPbYY3n88ccrODEAAABAx1Hx+DNmzJiccMIJGT58eKv1efPmZeXKla3WhwwZkh133DFz5sxZ5/laWlrS3NzcagMAAADYUnWu5C+/66678otf/CJz585dY19jY2O6du2aXr16tVqvq6tLY2PjOs85efLkXHXVVe09KgAAAECHVLErf1577bVcfPHFueOOO9KtW7d2O++ECROyZMmS8vbaa6+127kBAAAAOpqKxZ958+Zl0aJF+fCHP5zOnTunc+fOmT17dm644YZ07tw5dXV1WbFiRRYvXtzqfU1NTamvr1/neaurq1NTU9NqAwAAANhSVexrX0cffXR+85vftFo766yzMmTIkFx++eUZMGBAunTpklmzZmXkyJFJkvnz5+fVV19NQ0NDJUYGAAAA6HAqFn969uyZPffcs9Va9+7d07dv3/L62WefnfHjx6dPnz6pqanJ2LFj09DQkIMPPrgSIwMAAAB0OBW94fPfc/3116dTp04ZOXJkWlpaMmLEiNx0002VHgsAAACgw2hT/Nlpp50yd+7c9O3bt9X64sWL8+EPfzgvvvhim4Z59NFHW73u1q1bpk6dmqlTp7bpfAAAAABbujbd8Pnll1/OqlWr1lhvaWnJH/7wh40eCgAAAID2sUFX/nz/+98v/zxz5szU1taWX69atSqzZs3KoEGD2m04AAAAADbOBsWfk08+OUlSVVWV0aNHt9rXpUuXDBo0KF/72tfabTgAAAAANs4GxZ/Vq1cnSQYPHpy5c+dm22233SRDAQAAANA+2nTD55deeqm95wAAAABgE2jzo95nzZqVWbNmZdGiReUrgt7zzW9+c6MHAwAAAGDjtSn+XHXVVZk0aVL233//9OvXL1VVVe09FwAAAADtoE3x5+abb85tt92Wz3zmM+09DwAAAADtqFNb3rRixYoccsgh7T0LAAAAAO2sTfHnnHPOyZ133tneswAAAADQztr0ta/ly5fnlltuyUMPPZS99947Xbp0abX/uuuua5fhAAAAANg4bYo/v/71r7PvvvsmSZ555plW+9z8GQAAAGDz0ab488gjj7T3HAAAAABsAm265w8AAAAAHUObrvw58sgj3/frXQ8//HCbBwIAAACg/bQp/rx3v5/3rFy5Mk8//XSeeeaZjB49uj3mAgAAAKAdtCn+XH/99Wtdv/LKK7Ns2bKNGggAAACA9tOu9/w544wz8s1vfrM9TwkAAADARmjX+DNnzpx069atPU8JAAAAwEZo09e+Tj311FavS6VSXn/99Tz11FP50pe+1C6DAQAAALDx2hR/amtrW73u1KlTPvShD2XSpEk59thj22UwAAAAADZem+LP9OnT23sOAAAAADaBNsWf98ybNy+/+93vkiR77LFH9ttvv3YZCgAAAID20ab4s2jRopx++ul59NFH06tXryTJ4sWLc+SRR+auu+7Kdttt154zAgAAANBGbXra19ixY7N06dI8++yzeeutt/LWW2/lmWeeSXNzcy666KL2nhEAAACANmrTlT8zZszIQw89lKFDh5bXdt9990ydOtUNnwEAAAA2I2268mf16tXp0qXLGutdunTJ6tWrN3ooAAAAANpHm+LPUUcdlYsvvjgLFy4sr/3hD3/IuHHjcvTRR7fbcAAAAABsnDbFnxtvvDHNzc0ZNGhQdt555+y8884ZPHhwmpub841vfKO9ZwQAAACgjdp0z58BAwbkF7/4RR566KE899xzSZKhQ4dm+PDh7TocAAAAABtng678efjhh7P77runubk5VVVVOeaYYzJ27NiMHTs2BxxwQPbYY4/89Kc/3VSzAgAAALCBNij+TJkyJeeee25qamrW2FdbW5vzzz8/1113XbsNBwAAAMDG2aD486tf/SrHHXfcOvcfe+yxmTdv3kYPBQAAAED72KD409TUtNZHvL+nc+fOeeONNzZ6KAAAAADaxwbFn3/4h3/IM888s879v/71r9OvX7+NHgoAAACA9rFB8edjH/tYvvSlL2X58uVr7Pvzn/+ciRMn5h//8R/bbTgAAAAANs4GPer9i1/8Yr773e9mt912y4UXXpgPfehDSZLnnnsuU6dOzapVq/Kv//qvm2RQAAAAADbcBsWfurq6PPbYY7ngggsyYcKElEqlJElVVVVGjBiRqVOnpq6ubpMMCgAAAMCG26D4kyQDBw7MD3/4w/zpT3/KCy+8kFKplF133TW9e/feFPMBAAAAsBE2OP68p3fv3jnggAPacxYAAAAA2tkG3fAZAAAAgI5F/AEAAAAoMPEHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKLDOlR4AAICOYdhl36r0CADQZvO+OqrSI1SMK38AAAAACkz8AQAAACiwisafadOmZe+9905NTU1qamrS0NCQH/3oR+X9y5cvz5gxY9K3b9/06NEjI0eOTFNTUwUnBgAAAOhYKhp/dthhh1xzzTWZN29ennrqqRx11FE56aST8uyzzyZJxo0blwceeCD33HNPZs+enYULF+bUU0+t5MgAAAAAHUpFb/h84okntnr9v/7X/8q0adPy+OOPZ4cddsitt96aO++8M0cddVSSZPr06Rk6dGgef/zxHHzwwWs9Z0tLS1paWsqvm5ubN90HAAAAANjMbTb3/Fm1alXuuuuuvP3222loaMi8efOycuXKDB8+vHzMkCFDsuOOO2bOnDnrPM/kyZNTW1tb3gYMGPBBjA8AAACwWap4/PnNb36THj16pLq6Op/73Ody3333Zffdd09jY2O6du2aXr16tTq+rq4ujY2N6zzfhAkTsmTJkvL22muvbeJPAAAAALD5qujXvpLkQx/6UJ5++uksWbIk9957b0aPHp3Zs2e3+XzV1dWprq5uxwkBAAAAOq6Kx5+uXbtml112SZIMGzYsc+fOzde//vV88pOfzIoVK7J48eJWV/80NTWlvr6+QtMCAAAAdCwV/9rX31q9enVaWloybNiwdOnSJbNmzSrvmz9/fl599dU0NDRUcEIAAACAjqOiV/5MmDAhxx9/fHbccccsXbo0d955Zx599NHMnDkztbW1OfvsszN+/Pj06dMnNTU1GTt2bBoaGtb5pC8AAAAAWqto/Fm0aFFGjRqV119/PbW1tdl7770zc+bMHHPMMUmS66+/Pp06dcrIkSPT0tKSESNG5KabbqrkyAAAAAAdSkXjz6233vq++7t165apU6dm6tSpH9BEAAAAAMWy2d3zBwAAAID2I/4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgVU0/kyePDkHHHBAevbsme233z4nn3xy5s+f3+qY5cuXZ8yYMenbt2969OiRkSNHpqmpqUITAwAAAHQsFY0/s2fPzpgxY/L444/nxz/+cVauXJljjz02b7/9dvmYcePG5YEHHsg999yT2bNnZ+HChTn11FMrODUAAABAx9G5kr98xowZrV7fdttt2X777TNv3rx89KMfzZIlS3LrrbfmzjvvzFFHHZUkmT59eoYOHZrHH388Bx98cCXGBgAAAOgwNqt7/ixZsiRJ0qdPnyTJvHnzsnLlygwfPrx8zJAhQ7Ljjjtmzpw5az1HS0tLmpubW20AAAAAW6rNJv6sXr06l1xySQ499NDsueeeSZLGxsZ07do1vXr1anVsXV1dGhsb13qeyZMnp7a2trwNGDBgU48OAAAAsNnabOLPmDFj8swzz+Suu+7aqPNMmDAhS5YsKW+vvfZaO00IAAAA0PFU9J4/77nwwgvz4IMP5ic/+Ul22GGH8np9fX1WrFiRxYsXt7r6p6mpKfX19Ws9V3V1daqrqzf1yAAAAAAdQkWv/CmVSrnwwgtz33335eGHH87gwYNb7R82bFi6dOmSWbNmldfmz5+fV199NQ0NDR/0uAAAAAAdTkWv/BkzZkzuvPPOfO9730vPnj3L9/Gpra3N1ltvndra2px99tkZP358+vTpk5qamowdOzYNDQ2e9AUAAACwHioaf6ZNm5YkOeKII1qtT58+PWeeeWaS5Prrr0+nTp0ycuTItLS0ZMSIEbnppps+4EkBAAAAOqaKxp9SqfR3j+nWrVumTp2aqVOnfgATAQAAABTLZvO0LwAAAADan/gDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABRYRePPT37yk5x44onp379/qqqqcv/997faXyqVcsUVV6Rfv37ZeuutM3z48Dz//POVGRYAAACgA6po/Hn77bezzz77ZOrUqWvdf+211+aGG27IzTffnCeeeCLdu3fPiBEjsnz58g94UgAAAICOqXMlf/nxxx+f448/fq37SqVSpkyZki9+8Ys56aSTkiTf+ta3UldXl/vvvz+nn376BzkqAAAAQIe02d7z56WXXkpjY2OGDx9eXqutrc1BBx2UOXPmrPN9LS0taW5ubrUBAAAAbKk22/jT2NiYJKmrq2u1XldXV963NpMnT05tbW15GzBgwCadEwAAAGBzttnGn7aaMGFClixZUt5ee+21So8EAAAAUDGbbfypr69PkjQ1NbVab2pqKu9bm+rq6tTU1LTaAAAAALZUm238GTx4cOrr6zNr1qzyWnNzc5544ok0NDRUcDIAAACAjqOiT/tatmxZXnjhhfLrl156KU8//XT69OmTHXfcMZdcckm+/OUvZ9ddd83gwYPzpS99Kf3798/JJ59cuaEBAAAAOpCKxp+nnnoqRx55ZPn1+PHjkySjR4/Obbfdli984Qt5++23c95552Xx4sU57LDDMmPGjHTr1q1SIwMAAAB0KBWNP0cccURKpdI691dVVWXSpEmZNGnSBzgVAAAAQHFstvf8AQAAAGDjiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwAAAECBiT8AAAAABSb+AAAAABSY+AMAAABQYOIPAAAAQIF1iPgzderUDBo0KN26dctBBx2UJ598stIjAQAAAHQIm338ufvuuzN+/PhMnDgxv/jFL7LPPvtkxIgRWbRoUaVHAwAAANjsbfbx57rrrsu5556bs846K7vvvntuvvnmbLPNNvnmN79Z6dEAAAAANnudKz3A+1mxYkXmzZuXCRMmlNc6deqU4cOHZ86cOWt9T0tLS1paWsqvlyxZkiRpbm7etMNWwKqWP1d6BABosyL+v7no/O0BQEdWxL893vtMpVLpfY/brOPPm2++mVWrVqWurq7Vel1dXZ577rm1vmfy5Mm56qqr1lgfMGDAJpkRAGib2m98rtIjAABbkCL/7bF06dLU1tauc/9mHX/aYsKECRk/fnz59erVq/PWW2+lb9++qaqqquBkQEfS3NycAQMG5LXXXktNTU2lxwEACs7fHkBblEqlLF26NP3793/f4zbr+LPttttmq622SlNTU6v1pqam1NfXr/U91dXVqa6ubrXWq1evTTUiUHA1NTX+AAMAPjD+9gA21Ptd8fOezfqGz127ds2wYcMya9as8trq1asza9asNDQ0VHAyAAAAgI5hs77yJ0nGjx+f0aNHZ//998+BBx6YKVOm5O23385ZZ51V6dEAAAAANnubffz55Cc/mTfeeCNXXHFFGhsbs++++2bGjBlr3AQaoD1VV1dn4sSJa3yNFABgU/C3B7ApVZX+3vPAAAAAAOiwNut7/gAAAACwccQfAAAAgAITfwAAAAAKTPwBAADYzBxxxBG55JJLKj0GUBDiD1B4Z555Zk4++eTy68bGxowdOzY77bRTqqurM2DAgJx44omZNWtW+ZgjjjgiVVVVrbbPfe5zrc776quv5oQTTsg222yT7bffPpdddlnefffdD+pjAQAd2N/+fZIk9957b7p165avfe1r+e53v5urr766MsMBhbPZP+odoD29/PLLOfTQQ9OrV6989atfzV577ZWVK1dm5syZGTNmTJ577rnyseeee24mTZpUfr3NNtuUf161alVOOOGE1NfX57HHHsvrr7+eUaNGpUuXLvnKV77ygX4mAKDj+9//+39nzJgxufnmm3PWWWdVehygYMQfYIvyP//n/0xVVVWefPLJdO/evby+xx575LOf/WyrY7fZZpvU19ev9Tz/9//+3/z2t7/NQw89lLq6uuy77765+uqrc/nll+fKK69M165dN+nnAACK49prr83EiRNz11135ZRTTknyl6uQ991330yZMqWywwGF4GtfwBbjrbfeyowZMzJmzJhW4ec9vXr1avX6jjvuyLbbbps999wzEyZMyDvvvFPeN2fOnOy1116pq6srr40YMSLNzc159tlnN9lnAACK5fLLL8/VV1+dBx98sBx+ANqbK3+ALcYLL7yQUqmUIUOG/N1j/8f/+B8ZOHBg+vfvn1//+te5/PLLM3/+/Hz3u99N8pf7Bv11+ElSft3Y2Nj+wwMAhfOjH/0o3/ve9zJr1qwcddRRlR4HKDDxB9hilEql9T72vPPOK/+81157pV+/fjn66KOzYMGC7LzzzptiPABgC7P33nvnzTffzMSJE3PggQemR48elR4JKChf+wK2GLvuumuqqqpa3dR5fR100EFJ/nL1UJLU19enqamp1THvvV7XfYIAAP7aP/zDP+TRRx/NH/7whxx33HFZunRppUcCCkr8AbYYffr0yYgRIzJ16tS8/fbba+xfvHjxOt/79NNPJ0n69euXJGloaMhvfvObLFq0qHzMj3/849TU1GT33Xdv17kBgOIaOHBgZs+encbGRgEI2GTEH2CLMnXq1KxatSoHHnhg/uu//ivPP/98fve73+WGG25IQ0NDkmTBggW5+uqrM2/evLz88sv5/ve/n1GjRuWjH/1o9t577yTJsccem9133z2f+cxn8qtf/SozZ87MF7/4xYwZMybV1dWV/IgAQAczYMCAPProo1m0aFH5ARIA7Un8AbYoO+20U37xi1/kyCOPzKWXXpo999wzxxxzTGbNmpVp06YlSbp27ZqHHnooxx57bIYMGZJLL700I0eOzAMPPFA+z1ZbbZUHH3wwW221VRoaGnLGGWdk1KhRmTRpUqU+GgDQge2www559NFH8+abbwpAQLurKm3IHVABAAAA6FBc+QMAAABQYOIPAAAAQIGJPwAAAAAFJv4AAAAAFJj4AwAAAFBg4g8AAABAgYk/AAAAAAUm/gAAAAAUmPgDAAAAUGDiDwDAX6mqqnrf7corr6z0iAAAG6RzpQcAANicvP766+Wf77777lxxxRWZP39+ea1Hjx6VGAsAoM1c+QMA8Ffq6+vLW21tbaqqqlJfX5+ePXtmt912y4wZM1odf//996d79+5ZunRpXn755VRVVeWuu+7KIYcckm7dumXPPffM7NmzW73nmWeeyfHHH58ePXqkrq4un/nMZ/Lmm29+kB8TANiCiD8AAOuhe/fuOf300zN9+vRW69OnT89pp52Wnj17ltcuu+yyXHrppfnlL3+ZhoaGnHjiifnjH/+YJFm8eHGOOuqo7LfffnnqqacyY8aMNDU15ROf+MQH+nkAgC2H+AMAsJ7OOeeczJw5s/zVsEWLFuWHP/xhPvvZz7Y67sILL8zIkSMzdOjQTJs2LbW1tbn11luTJDfeeGP222+/fOUrX8mQIUOy33775Zvf/GYeeeSR/P73v//APxMAUHziDwDAejrwwAOzxx575Pbbb0+SfOc738nAgQPz0Y9+tNVxDQ0N5Z87d+6c/fffP7/73e+SJL/61a/yyCOPpEePHuVtyJAhSZIFCxZ8QJ8EANiSuOEzAMAGOOecczJ16tT88z//c6ZPn56zzjorVVVV6/3+ZcuW5cQTT8y//du/rbGvX79+7TkqAEASV/4AAGyQM844I6+88kpuuOGG/Pa3v83o0aPXOObxxx8v//zuu+9m3rx5GTp0aJLkwx/+cJ599tkMGjQou+yyS6ute/fuH9jnAAC2HOIPAMAG6N27d0499dRcdtllOfbYY7PDDjuscczUqVNz33335bnnnsuYMWPypz/9qXxfoDFjxuStt97Kpz71qcydOzcLFizIzJkzc9ZZZ2XVqlUf9McBALYA4g8AwAY6++yzs2LFijVu9Pyea665Jtdcc0322Wef/OxnP8v3v//9bLvttkmS/v375+c//3lWrVqVY489NnvttVcuueSS9OrVK506+dMMAGh/VaVSqVTpIQAAOpJvf/vbGTduXBYuXJiuXbuW119++eUMHjw4v/zlL7PvvvtWbkAAgL/ihs8AAOvpnXfeyeuvv55rrrkm559/fqvwAwCwuXJtMQDAerr22mszZMiQ1NfXZ8KECZUeBwBgvfjaFwAAAECBufIHAAAAoMDEHwAAAIACE38AAAAACkz8AQAAACgw8QcAAACgwMQfAAAAgAITfwAAAAAKTPwBAAAAKLD/B2GIwzM0lNtfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3FjhIFECULEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oeE3rpp9ULHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 6))  # Set the figure size\n",
        "sns.countplot(data=df, x='assay_chembl_id')  # Create a countplot for 'assay_chembl_id'\n",
        "plt.title('Countplot of Assay CHEMBL ID')  # Set the title of the plot\n",
        "plt.xlabel('Assay CHEMBL ID')  # Set the x-axis label\n",
        "plt.ylabel('Count')  # Set the y-axis label\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "id": "NG4BauBXULLF",
        "outputId": "f3b6acd5-0fba-48c9-b6cc-d086935262b0"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ku-awbrSULNe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WJsKoBXZULP6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 6))  # Set the figure size\n",
        "sns.countplot(data=df, x='molecule_chembl_id')  # Create a countplot for 'molecule_chembl_id'\n",
        "plt.title('Countplot of Molecule CHEMBL ID')  # Set the title of the plot\n",
        "plt.xlabel('Molecule CHEMBL ID')  # Set the x-axis label\n",
        "plt.ylabel('Count')  # Set the y-axis label\n",
        "plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "axY39fRAULSa",
        "outputId": "6b0c1369-ce75-46fc-c93a-ef03169de33f"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "s69nwc_qULU3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z13sfmQqULW2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Correlation Heatmap"
      ],
      "metadata": {
        "id": "ZgKldG3ZbODi"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size\n",
        "numeric_cols = df.select_dtypes(include=[np.number])  # Select only numeric columns for correlation\n",
        "sns.heatmap(numeric_cols.corr(), annot=True, cmap='coolwarm', fmt='.2f')  # Plot heatmap of the correlation matrix\n",
        "plt.title('Correlation Heatmap')  # Set the title of the plot\n",
        "plt.show()  # Show the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "xiGUQ83aULZI",
        "outputId": "87104d2b-2a1a-43df-8968-12dcab69e5ea"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Kzr9FUIgULdy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hqx0B_JHi26Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart for the distribution of types"
      ],
      "metadata": {
        "id": "dH4VVncSi8j-"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type_counts = df['type'].value_counts()  # Count occurrences of each type\n",
        "plt.figure(figsize=(8, 8))  # Set figure size for the pie chart\n",
        "plt.pie(type_counts, labels=type_counts.index, autopct='%1.1f%%', startangle=90)  # Create pie chart with labels and percentage\n",
        "plt.title('Distribution of Types')  # Set title for the pie chart\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
        "plt.show()  # Display the pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "agoAnyzyi2-D",
        "outputId": "b50b552d-49a8-42bf-f001-f48d25ab400d"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rV8uSaEZi-p3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BC1Tyl3ai-2P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line plot of standard_value over index"
      ],
      "metadata": {
        "id": "GEzHJKiZjFHP"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size for the line plot\n",
        "plt.plot(df.index, df['standard_value'], marker='o', linestyle='-', color='blue')  # Create line plot with markers\n",
        "plt.title('Line Plot of Standard Value')  # Set title for the line plot\n",
        "plt.xlabel('Index')  # Set x-axis label\n",
        "plt.ylabel('Standard Value')  # Set y-axis label\n",
        "plt.grid()  # Enable grid on the plot\n",
        "plt.show()  # Display the line plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "roG9vcYpi_OY",
        "outputId": "d19b45df-d832-41c8-942a-de9105774d06"
      },
      "execution_count": 119,
      "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": "3Gm6ek5OjGrH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z2SrlUdljGvc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violin plot of value distribution by type"
      ],
      "metadata": {
        "id": "EF4dQGo7jL9H"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set figure size for the violin plot\n",
        "sns.violinplot(x='type', y='value', data=df, palette='muted')  # Create violin plot for value distribution by type\n",
        "plt.title('Violin Plot of Value Distribution by Type')  # Set title for the violin plot\n",
        "plt.xlabel('Type')  # Set x-axis label\n",
        "plt.ylabel('Value')  # Set y-axis label\n",
        "plt.grid()  # Enable grid on the plot\n",
        "plt.show()  # Display the violin plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "URGel10fjHA3",
        "outputId": "5df6a847-21af-429a-c97f-433c3de508eb"
      },
      "execution_count": 127,
      "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": "9tFkZrPjULgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmWr3UVejOin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4FVzotyTef69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 4: Data Preprocessing</span>\n",
        "\n",
        "Data preprocessing is essential to prepare the dataset for modeling. This step involves:\n",
        "- Handling missing values: We can fill or drop missing entries as needed.\n",
        "- Encoding categorical variables: Convert categorical data into numerical format using techniques like label encoding.\n",
        "- Log transformations: Apply log transformations to skewed numerical features to improve normality."
      ],
      "metadata": {
        "id": "nf1aOCDJegLU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Data Preprocessing"
      ],
      "metadata": {
        "id": "rYiRUlPgULiL"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Fill missing values"
      ],
      "metadata": {
        "id": "H9Gpo7aLULmD"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['standard_value'].fillna(df['standard_value'].mean(), inplace=True)  # Fill NaN values in 'standard_value' with the mean\n"
      ],
      "metadata": {
        "id": "6BvQwnP5ULqX"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['value'].fillna(df['value'].mean(), inplace=True)  # Fill NaN values in 'value' with the mean"
      ],
      "metadata": {
        "id": "wtvHmvVlULsu"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WF3lxhgQULxo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BbPo206OUL5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Encode categorical features"
      ],
      "metadata": {
        "id": "nlU4CFCqUL7w"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le_assay = LabelEncoder()  # Initialize label encoder for 'assay_chembl_id'"
      ],
      "metadata": {
        "id": "2HxwA5L2UL-W"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['assay_chembl_id_encoded'] = le_assay.fit_transform(df['assay_chembl_id'])  # Encode 'assay_chembl_id'"
      ],
      "metadata": {
        "id": "A_SPWza4UMAp"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D-AFHwLHUMJA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le_molecule = LabelEncoder()  # Initialize label encoder for 'molecule_chembl_id'"
      ],
      "metadata": {
        "id": "prVb-O4xUMLq"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['molecule_chembl_id_encoded'] = le_molecule.fit_transform(df['molecule_chembl_id'])  # Encode 'molecule_chembl_id'\n"
      ],
      "metadata": {
        "id": "2bF5XdOiUMN8"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ipoY3jbxUMQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le_type = LabelEncoder()  # Initialize label encoder for 'type'"
      ],
      "metadata": {
        "id": "BpforSlpUMSp"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['type_encoded'] = le_type.fit_transform(df['type'])  # Encode 'type'"
      ],
      "metadata": {
        "id": "5Wb0ajdCUMYI"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X7UrDhpoUMbB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yG1T6_bHb3ZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Feature Engineering: Log transformation to reduce skewness"
      ],
      "metadata": {
        "id": "RywaoCsLUMeD"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['standard_value_log'] = np.log1p(df['standard_value'])  # Log transform 'standard_value'"
      ],
      "metadata": {
        "id": "aZZYGbsWUMgs"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['value_log'] = np.log1p(df['value'])  # Log transform 'value'"
      ],
      "metadata": {
        "id": "iBfd9Y2SUMjb"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kfONtL_wUMmN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ovJHqqHiUMov"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display dataset after preprocessing"
      ],
      "metadata": {
        "id": "ESvgogSBUMtg"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset after preprocessing:\")  # Print a message\n",
        "print(df.head())  # Display the first 5 rows of the preprocessed DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rLsWdCAUMvy",
        "outputId": "5e91c898-3b09-45b9-d7d3-bb57a13cdd65"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset after preprocessing:\n",
            "  assay_chembl_id molecule_chembl_id  standard_value  type  value  \\\n",
            "0    CHEMBL674637        CHEMBL68920            41.0  IC50  0.041   \n",
            "1    CHEMBL621151        CHEMBL68920           300.0  IC50  0.300   \n",
            "2    CHEMBL615325        CHEMBL68920          7820.0  IC50  7.820   \n",
            "3    CHEMBL674637        CHEMBL69960           170.0  IC50  0.170   \n",
            "4    CHEMBL621151        CHEMBL69960            40.0  IC50  0.040   \n",
            "\n",
            "   assay_chembl_id_encoded  molecule_chembl_id_encoded  type_encoded  \\\n",
            "0                        3                          34             0   \n",
            "1                        2                          34             0   \n",
            "2                        0                          34             0   \n",
            "3                        3                          37             0   \n",
            "4                        2                          37             0   \n",
            "\n",
            "   standard_value_log  value_log  \n",
            "0            3.737670   0.040182  \n",
            "1            5.707110   0.262364  \n",
            "2            8.964568   2.177022  \n",
            "3            5.141664   0.157004  \n",
            "4            3.713572   0.039221  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FtIS_QcIUMyJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pExy2N4UUM2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare feature set and target variable"
      ],
      "metadata": {
        "id": "wD5HhlKGUM40"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['assay_chembl_id_encoded', 'molecule_chembl_id_encoded', 'type_encoded',\n",
        "         'standard_value_log', 'value_log']]  # Define feature set"
      ],
      "metadata": {
        "id": "y2dG1AL8UM7H"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['standard_value']  # Define target variable"
      ],
      "metadata": {
        "id": "z6RztA6MUM9c"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HoVuztacUM_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling the features"
      ],
      "metadata": {
        "id": "JzUuE7hZUNCA"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initialize the scaler"
      ],
      "metadata": {
        "id": "z9Qwou70UNEC"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_scaled = scaler.fit_transform(X)  # Scale the feature set"
      ],
      "metadata": {
        "id": "Yb5pT46wUNGN"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_5oRxbo4UNIk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ma9APyXcesnU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DyoWKXV2esqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 5: Splitting the Dataset</span>\n",
        "\n",
        "In this step, we split the dataset into training and testing sets. This is crucial for evaluating model performance. A common practice is to use an 80-20 split, where 80% of the data is used for training and 20% for testing."
      ],
      "metadata": {
        "id": "ba7z653AetJg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-Test Split"
      ],
      "metadata": {
        "id": "t0LS7AMFUNLA"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)  # Split data into train and test sets\n"
      ],
      "metadata": {
        "id": "oLRUak8SUNPF"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yyTGYHdbUNRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jn5CxxNGUNVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M91oy3G_UNYA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 6: Model Development</span>\n",
        "\n",
        "We proceed to develop a predictive model using a machine learning algorithm. Here, we use a **Random Forest Regressor** due to its robustness and ability to handle complex datasets. We configure the model and prepare it for training using the training dataset."
      ],
      "metadata": {
        "id": "8Uqd18jAe06s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Training"
      ],
      "metadata": {
        "id": "RyB7HMojUNZ2"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## RandomForestRegressor model"
      ],
      "metadata": {
        "id": "0npElIiCUNdy"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(random_state=42)  # Initialize the Random Forest model"
      ],
      "metadata": {
        "id": "9IlbwxTFUNga"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g9tkvi7kUNj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ni7IjVige9cF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ktLDIXQUe9gh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 7: Hyperparameter Tuning</span>\n",
        "\n",
        "To optimize the model's performance, we conduct hyperparameter tuning. This involves using **GridSearchCV** to explore different combinations of parameters, such as:\n",
        "- `max_depth`\n",
        "- `min_samples_split`\n",
        "- `n_estimators`\n",
        "This step helps in finding the best model configuration for our dataset."
      ],
      "metadata": {
        "id": "lb-unno1e90c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Parameter tuning"
      ],
      "metadata": {
        "id": "NvIPwa_gUE8Z"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {  # Define the parameter grid for tuning\n",
        "    'n_estimators': [50, 100],  # Number of trees in the forest\n",
        "    'max_depth': [3, 5],  # Maximum depth of the tree\n",
        "    'min_samples_split': [2, 5]  # Minimum number of samples required to split an internal node\n",
        "}"
      ],
      "metadata": {
        "id": "48DCOIo8T8po"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LmbBRdTTSbrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search = GridSearchCV(rf, param_grid, cv=2, scoring='r2', n_jobs=-1, verbose=2)  # Initialize grid search"
      ],
      "metadata": {
        "id": "vwyT95tvRjUM"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.fit(X_train, y_train)  # Fit the model using grid search"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "kW7PegFLQo19",
        "outputId": "2e402b73-d043-43a8-fd46-7a7bab73b1fc"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 8 candidates, totalling 16 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2, estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
              "             param_grid={'max_depth': [3, 5], 'min_samples_split': [2, 5],\n",
              "                         'n_estimators': [50, 100]},\n",
              "             scoring='r2', verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2, estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [3, 5], &#x27;min_samples_split&#x27;: [2, 5],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
              "             scoring=&#x27;r2&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2, estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [3, 5], &#x27;min_samples_split&#x27;: [2, 5],\n",
              "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
              "             scoring=&#x27;r2&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lG-ozopDO7Ke"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6VM1g0zqfGcU"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best parameters and model"
      ],
      "metadata": {
        "id": "n9OCzYckMxhZ"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_params = grid_search.best_params_  # Get the best parameters from grid search"
      ],
      "metadata": {
        "id": "diy-yHCpMb5L"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nBest parameters: {best_params}\")  # Print best parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DpCHSiAOMDh6",
        "outputId": "eb35627b-2b5a-4558-9a1f-5c149ed6c62d"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Best parameters: {'max_depth': 5, 'min_samples_split': 2, 'n_estimators': 100}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HN1_knqsMDk6"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = grid_search.best_estimator_  # Get the best estimator from grid search"
      ],
      "metadata": {
        "id": "ZyeIf8UmMDwr"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3EcVs2tBMD0E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CatIpsfnMD2x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dLLo7o6LMD5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 8: Model Evaluation</span>\n",
        "\n",
        "After training the model, we evaluate its performance using various metrics, including:\n",
        "- **Mean Absolute Error (MAE)**: Measures average error between predictions and actual values.\n",
        "- **Mean Squared Error (MSE)**: Measures the average squared difference between predictions and actual values.\n",
        "- **R-squared**: Indicates the proportion of variance explained by the model.\n",
        "Additionally, we perform cross-validation to ensure the model's robustness."
      ],
      "metadata": {
        "id": "6U_3cC5YfOPK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "goGtnPMeMD_p"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = best_model.predict(X_test)  # Predict the target variable for the test set"
      ],
      "metadata": {
        "id": "d0z_H69HMECu"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mae = mean_absolute_error(y_test, y_pred)  # Calculate Mean Absolute Error"
      ],
      "metadata": {
        "id": "tCyBikjBMEIy"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse = mean_squared_error(y_test, y_pred)  # Calculate Mean Squared Error"
      ],
      "metadata": {
        "id": "AgL42jkSMELZ"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2 = r2_score(y_test, y_pred)  # Calculate R-squared score"
      ],
      "metadata": {
        "id": "L4tHIp3fL0WC"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nMAE: {mae}, MSE: {mse}, R-squared: {r2}\")  # Print evaluation metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RLIc4GOLuT7",
        "outputId": "c7d4218b-06d5-4c9a-8528-434ef7c8901a"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "MAE: 18711.360415646443, MSE: 1505193718.5208628, R-squared: 0.9927047589592068\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qvrg4YDZLuWj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_5JWP6F2LuZb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross-validated R-squared scores"
      ],
      "metadata": {
        "id": "DnTKAwN7LucD"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_results = grid_search.cv_results_  # Get cross-validated results from grid search"
      ],
      "metadata": {
        "id": "QfoRFWPqLugb"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_scores = cv_results['mean_test_score']  # Extract mean test scores"
      ],
      "metadata": {
        "id": "4DLgFW52LujK"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nCross-validated R-squared scores: {cv_scores}\")  # Print cross-validated scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sF0LxWlrLukJ",
        "outputId": "e95c74b7-9ec6-4ffe-9c46-e302a70cd1a3"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Cross-validated R-squared scores: [0.78885831 0.802187   0.74711839 0.76032178 0.79710239 0.80810639\n",
            " 0.74646369 0.75967955]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BCRd3-awLumu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6GKD5kPOfXgY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QS3XYbY0fXju"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 9: Results Summary</span>\n",
        "\n",
        "Finally, we summarize the results, including:\n",
        "- The best hyperparameters found during tuning.\n",
        "- Evaluation metrics to assess model performance.\n",
        "- Any insights gained from the analysis that could inform future research or applications."
      ],
      "metadata": {
        "id": "DsiZNxQhfYNa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mean_cv_score = np.mean(cv_scores)  # Calculate mean cross-validated score"
      ],
      "metadata": {
        "id": "uPZEC9ecLurK"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nMean R-squared score: {mean_cv_score}\")  # Print mean R-squared score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VSLCdefLuva",
        "outputId": "90e44869-840e-450c-bb5a-a529c95023c9"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean R-squared score: 0.7762296867332859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fqXuJar0fZ9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QjHYCEnIfaPS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Q-iZEjDKfb7S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Conclusion</span>\n",
        "\n",
        "## <span style=\"color:darkorange;\">1. Objective Recap</span>\n",
        "- The project aimed to analyze the dataset focusing on `standard_value` and `value` across different chemical compounds, utilizing various exploratory data analysis (EDA) techniques.\n",
        "\n",
        "## <span style=\"color:darkorange;\">2. Dataset Overview</span>\n",
        "- The dataset contained 100 entries with 5 columns: `assay_chembl_id`, `molecule_chembl_id`, `standard_value`, `type`, and `value`.\n",
        "- Notable observations included missing values in `standard_value` and `value`, which were addressed during preprocessing.\n",
        "\n",
        "## <span style=\"color:darkorange;\">3. Preprocessing Steps</span>\n",
        "- Missing values were handled appropriately to ensure a robust analysis.\n",
        "- Categorical variables were encoded for machine learning applications.\n",
        "- Log transformation was applied to `standard_value` and `value` to stabilize variance.\n",
        "\n",
        "## <span style=\"color:darkorange;\">4. Exploratory Data Analysis (EDA)</span>\n",
        "- Unique values and duplicate entries were analyzed to ensure data quality.\n",
        "- Visualizations included pie charts, line plots, violin plots, and box plots to explore distributions and relationships within the data.\n",
        "\n",
        "## <span style=\"color:darkorange;\">5. Model Performance</span>\n",
        "- A machine learning model was trained to predict `standard_value`, yielding an R-squared score of approximately 0.99, indicating a strong fit.\n",
        "- Cross-validation results showed consistent performance across different folds.\n",
        "\n",
        "## <span style=\"color:darkorange;\">6. Final Remarks</span>\n",
        "- The findings demonstrated significant insights into the chemical compounds and their effects, which could inform future research or practical applications in drug development.\n",
        "- Further analysis with larger datasets or additional features could enhance the predictive capabilities and generalizability of the model."
      ],
      "metadata": {
        "id": "eOC_FFgSfcZZ"
      }
    }
  ]
}