{"id":46697,"date":"2022-05-05T00:00:00","date_gmt":"2022-05-05T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/building-a-linear-regression-model-for-housing-data-using-python-and-griddb\/"},"modified":"2025-11-13T12:55:56","modified_gmt":"2025-11-13T20:55:56","slug":"building-a-linear-regression-model-for-housing-data-using-python-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/building-a-linear-regression-model-for-housing-data-using-python-and-griddb\/","title":{"rendered":"Building a Linear Regression Model for Housing Data using Python and GridDB"},"content":{"rendered":"

In this tutorial, we will explore a housing dataset using Python. We will first prune the dataset as per our needs. Later, we will see how can we build a Machine Learning model to fit our dataset and make future predictions.<\/p>\n

The outline of the tutorial is as follows:<\/p>\n

    \n
  1. Pre-requisites<\/li>\n
  2. About the Dataset<\/li>\n
  3. Importing the Libraries<\/li>\n
  4. Loading the Dataset<\/li>\n
  5. Data Preprocessing<\/li>\n
  6. Data Normalization<\/li>\n
  7. Splitting the Dataset<\/li>\n
  8. Building the Model<\/li>\n
  9. Making Predictions<\/li>\n
  10. Model Evaluation<\/li>\n
  11. Conclusion<\/li>\n<\/ol>\n

    1. Prerequisites<\/h2>\n

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

      \n
    1. Pandas<\/a><\/li>\n
    2. scikit-learn<\/a><\/li>\n<\/ol>\n

      If you are using Anaconda, packages can be installed through multiple ways such as the User Interface, Command Line, or Jupyter Notebooks. The most conventional way to install a python package is via pip<\/code>. If you are using the command line or the terminal, type pip install package-name<\/code>. Another way to install a package is through conda install package-name<\/code> within the Anaconda environment.<\/p>\n

      Also, note that we will cover two methods to load our dataset in the python environment – Using Pandas<\/code> and GridDB<\/code>. For using GridDB within the python environment, the following packages are required:<\/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. About the Dataset<\/h2>\n

        We will be using a snapshot of the Melbourne Housing Dataset which has been scraped from public resources and is now available on Kaggle<\/a>. The dataset has been preprocessed to some extent and contains a total of 13580 instances. The number of attributes present in the dataset is 21. The dependent variable is the price of the property while the other 20 attributes are independent. Let us now get started on the code.<\/p>\n

        3. Importing the Libraries<\/h2>\n
        \n
        import pandas as pd\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split \nfrom sklearn.metrics import mean_absolute_error<\/code><\/pre>\n<\/div>\n
        The above cell should execute without any output if you successfully installed the libraries. In case you encounter an error, try the following :<\/p>\n
          \n
        1. Reconfirm if the installation was successful. If not, try executing pip install package-name<\/code> again. <\/li>\n
        2. Check if your system is compatible with the package version.<\/li>\n<\/ol>\n
          4. Loading the Dataset<\/h2>\n
          Using GridDB<\/h3>\n
          GridDB<\/a> is an open-source time-series database designed for handling large amounts of data. It is optimized for IoT and is highly efficient because of its in-memory architecture. Since dealing with files locally can lead to integration issues in a professional environment, using a reliable database becomes important. GridDB provides that reliability and scalability with fault tolerance.<\/p>\n
          Moreover, with GridDB’s python client<\/a>, it has become much easier to include the database and manipulate it directly within the coding environment. Learn more about the GriDB WebAPI here<\/a>.<\/p>\n
          Let us now go ahead and load our dataset.<\/p>\n
          \n
          import griddb_python as griddb\n\nsql_statement = ('SELECT * FROM melb_data')\ndataset = pd.read_sql_query(sql_statement, container)<\/code><\/pre>\n<\/div>\n
          The dataset<\/code> variable will now have the data in the form of a pandas dataframe. If you are new to GridDB, a tutorial on how to insert data in GridDB<\/a> might be helpful.<\/p>\n
          Using Pandas<\/h3>\n
          Another way to load the dataset is using the pandas directly.<\/p>\n
          \n
          dataset = pd.read_csv(\"melb_data.csv\")<\/code><\/pre>\n<\/div>\n
          5. Data Preprocessing<\/h2>\n
          Great! Now that we have our dataset, let’s see what it actually looks like –<\/p>\n
          \n
          dataset.head()<\/code><\/pre>\n<\/div>\n
          \n