{"id":46673,"date":"2021-11-12T00:00:00","date_gmt":"2021-11-12T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/exploring-customers-personalities-using-python\/"},"modified":"2025-11-13T12:55:39","modified_gmt":"2025-11-13T20:55:39","slug":"exploring-customers-personalities-using-python","status":"publish","type":"post","link":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/en\/blog\/exploring-customers-personalities-using-python\/","title":{"rendered":"Exploring customers’ personalities using Python"},"content":{"rendered":"

In this tutorial, we will be exploring a dataset containing customers’ information such as Marital Status, Education, Income, etc. Getting an insight into the kinds of customers a firm deals with helps to provide better and customized services. Therefore, customer analysis and segmentation play a crucial role in a business.<\/p>\n

The outline for this tutorial is as follows:<\/p>\n

    \n
  1. About the Dataset<\/li>\n
  2. Importing the necessary libraries<\/li>\n
  3. Exploratory Data Analysis<\/li>\n
  4. Plotting the Correlation<\/li>\n
  5. Encoding the data<\/li>\n
  6. Scaling the data<\/li>\n
  7. Dimensionality reduction<\/li>\n
  8. CLustering<\/li>\n
  9. Visualizing the clusters<\/li>\n
  10. Observation<\/li>\n
  11. Conclusion<\/li>\n<\/ol>\n

    1. About the Dataset<\/h2>\n

    The dataset used for this tutorial is publicly available on Kaggle<\/a>. The total number of instances (or rows) are 2240 whereas the total number of attributes (or columns) is 29. As mentioned above, each attribute corresponds to a person trait important for customer classification such as Marital Status, Income, Customer ID, etc.<\/p>\n

    Go ahead and download the dataset! We will next be importing libraries to begin our analysis.<\/p>\n

    2. Importing the necessary libraries<\/h2>\n
    \n
    import numpy as np\nimport pandas as pd\nimport datetime\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\nimport seaborn as sns\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import AgglomerativeClustering\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import metrics\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.decomposition import PCA<\/code><\/pre>\n<\/div>\n
    In case you run into a package installation error, you can install it by typing pip install package-name<\/code> in the command line. Alternatively, if you’re using a conda virtual environment, you can type conda install package-name<\/code>.<\/p>\n
    Loading our dataset and Exploratory Data Analysis (EDA)<\/h2>\n
    Let’s go ahead and load our CSV file.<\/p>\n
    Using GridDB<\/h3>\n
    To store large amounts of data, a CSV file can be cumbersome and chaotic. GridDB<\/a> serves as a great alternative as it is open-source and a highly scalable database. It is optimized for IoT and Big Data so that you can store your time-series data effectively. It utilizes an in-memory data architecture along with parallel processing for higher performance. You can Download GridDB<\/a> from their official website.<\/p>\n
    By using the GridDB-Python<\/a> client, we can load our data directly into the Python environment as a dataframe.<\/p>\n
    \n
    import griddb_python as griddb\nimport pandas as pd\n\nsql_statement = ('SELECT * FROM marketing_campaign')\nmovie_review_test = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n
    Note that the cont<\/code> variable has the container information where our data is stored. Replace the marketing_campaign<\/code> with the name of your container. You can also refer to the tutorial on reading and writing to GridDB<\/a> for more information.<\/p>\n
    Using Pandas<\/h3>\n
    If you do not have GridDB, you can load the file using Pandas.<\/p>\n
    \n
    data = pd.read_csv('marketing_campaign.csv', sep=\"t\")<\/code><\/pre>\n<\/div>\n
    We’ll display the first five rows using the head<\/code> command to get a gist of our data.<\/p>\n
    \n
    data.head()<\/code><\/pre>\n<\/div>\n
    \n
    data.head()<\/code><\/pre>\n<\/div>\n
    \n