{"id":46686,"date":"2022-01-27T00:00:00","date_gmt":"2022-01-27T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/heart-failure-prediction-using-machine-learning-python-and-griddb\/"},"modified":"2025-11-13T12:55:49","modified_gmt":"2025-11-13T20:55:49","slug":"heart-failure-prediction-using-machine-learning-python-and-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/heart-failure-prediction-using-machine-learning-python-and-griddb\/","title":{"rendered":"Heart Failure Prediction using Machine Learning, Python, and GridDB"},"content":{"rendered":"

In this tutorial, we will explore the Heart Failure Prediction dataset which is publicly available on Kaggle. We will use GridDB to see how can we extract the data. Later, we will perform some Exploratory Data Analysis. Finally, we will build a Machine Learning Model for making future predictions. The outline of this tutorial is as follows:<\/p>\n

    \n
  1. Setting up your environment<\/li>\n
  2. Introduction to the dataset<\/li>\n
  3. Importing the necessary libraries<\/li>\n
  4. Loading the Dataset<\/li>\n
  5. Exploratory Data Analysis<\/li>\n
  6. Handling categorical variables<\/li>\n
  7. Machine Learning Model<\/li>\n
  8. Model Evaluation<\/li>\n
  9. Conclusion<\/li>\n
  10. References<\/li>\n<\/ol>\n

    You can read the Jupyter file here: https:\/\/github.com\/griddbnet\/Blogs\/blob\/main\/Heart%20Failure%20Prediction.ipynb<\/p>\n

    1. Setting up your environment<\/h2>\n

    The following tutorial is carried out in Jupyter Notebooks (Anaconda version 4.8.3) with Python version 3.8 on Windows 10 Operating system. Below mentioned packages need to be installed before the code execution:<\/p>\n

      \n
    1. Pandas<\/a><\/li>\n
    2. NumPy<\/a><\/li>\n
    3. Seaborn<\/a><\/li>\n
    4. Plotly<\/a><\/li>\n
    5. scikit-learn<\/a><\/li>\n
    6. Matplotlib<\/a><\/li>\n<\/ol>\n

      The hyperlinks will direct you to the installation. Alternatively, if you are using a command line, simply type pip install package-name<\/code>. Or in the case of Anaconda, conda install package-name<\/code> also works.<\/p>\n

      While loading the dataset, this tutorial will cover two methods – Using GridDB as well as Using Pandas. To access GridDB using Python, the following packages also need to be installed beforehand:<\/p>\n

        \n
      1. GridDB C-client<\/a><\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python Client<\/a><\/li>\n<\/ol>\n

        2. Introduction to the dataset<\/h2>\n

        Cardiovascular disease is one of the leading causes of death worldwide. Therefore, if machine learning could help predict heart failure prediction, the contribution would be significant. The dataset used in this tutorial has been developed by Davide Chicco, Giuseppe Jurman of BMC Medical Informatics and Decision Making. It has been open-sourced and can be downloaded from Kaggle<\/a>.<\/p>\n

        The data contains a total of 918 instances (or rows) with 12 attributes (or columns). Out of these 12 attributes, 5 are categorical and 7 are numerical in nature. Let’s now go ahead and import the necessary libraries.<\/p>\n

        3. Importing the necessary libraries<\/h2>\n
        \n
        import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nimport plotly.graph_objects as go\nimport plotly.express as px\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import plot_confusion_matrix<\/code><\/pre>\n<\/div>\n
        If the installation was successful, the above cell should execute just fine without any error messages or warnings. However, if you do encounter an error –<\/p>\n
          \n
        1. Recheck if the installation was successful. If not, execute pip install package-name<\/code> again. <\/li>\n
        2. Check if the version of the packages installed is compatible with your anaconda\/system version.<\/li>\n<\/ol>\n
          4. Loading the dataset<\/h2>\n
          4.1 Using GridDB<\/h3>\n
          GridDB is a scalable, in-memory, No SQL database which makes it easier for you to store large amounts of data. Using GridDB’s python-client, we can now directly load our data as a pandas dataframe into the python environment. If you are new to GridDB, a tutorial on reading and writing to GridDB<\/a> can be useful.<\/p>\n
          Assuming that you have already set up your database, we will now write the SQL query in python to load our dataset<\/p>\n
          \n
          import griddb_python as griddb\n\nsql_statement = ('SELECT * FROM heart_failure_prediction')\nheart_dataset = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n
          The cont<\/code> variable has the container information where the data is stored.<\/p>\n
          4.2 Using Pandas<\/h3>\n
          Alternatively, we can use the pandas read_csv()<\/code> function. Note that both the methods would result in the same output as both loads the data in the form of a pandas dataframe.<\/p>\n
          \n
          heart_dataset = pd.read_csv('heart.csv')<\/code><\/pre>\n<\/div>\n
          5. Exploratory Data Analysis<\/h2>\n
          Let us first determine the shape of our dataset i.e the number of rows and number of columns<\/p>\n
          \n
          heart_dataset.shape<\/code><\/pre>\n<\/div>\n
          (918, 12)\n<\/code><\/pre>\n
          We will now display the first five rows of our data using the pandas head<\/code> function to get a gist of how our data looks like.<\/p>\n
          \n
          heart_dataset.head()<\/code><\/pre>\n<\/div>\n
          \n