Secure Persona Prediction and Data Leakage Prevention System using Python

Businesses must customize according to their target audience because this helps them better understand their customers’ needs and preferences, and tailor their products, services, and marketing strategies accordingly. By customizing their offerings to meet the specific needs and preferences of their target audience, businesses can increase the relevance of their products and services, which can help to attract more customers and improve customer satisfaction.

In order to help businesses get to know more about their target audience, we have introduced a Secure Persona Prediction. It can be used in marketing and user experience design to anticipate the needs, wants, and behaviours of specific groups of individuals or personas. It involves analyzing data and gathering insights about a group of users, such as their demographics and using that information to recommend products accordingly.

The system comprises 1 major module: User.
The user would require to register using their name, email address, phone number, age, gender, username and password. They can log in using their username and password after registration. For the prediction of clusters, the user would require to enter the data like age, gender, income, and spending scores. To view the predicted persona, they would require to enter the detected cluster number and password and accordingly, the persona will be shown. The system can also give product recommendations to the persona generated in a text file. The user can send the file to the other users in the system. The user will be able to see the received data.

The technologies used in the system involve HTML, CSS and JavaScript in the front end and Python in the back end. The database used is MySQL and the framework used is Django.

The algorithm used for creating a cluster is K-Means Clustering. It is commonly used for creating clusters based on similarities in a dataset. The algorithm used for predicting clusters is Linear Regression. Linear Regression is used for predicting a continuous output variable based on one or more input variables, assuming a linear relationship between them.

Advantages

• The system will help enable personalized experiences and recommendations for users.
• It can help marketers understand their target audience better.
• It can help create more user-friendly interfaces and experiences.
• It will help businesses to make data-driven decisions.