Data Mining For Improved Customer Relationship Management

A data mining system that measures each customer's interest in products from past orders. It calculates an interest percentage per product so the sales team can offer customers what they are most likely to buy.

Data is collected from the source and stored in a database. It is pre-processed: cleaned, integrated and transformed. A mining algorithm (classification, clustering or association rules) finds patterns. Results are shown as charts and reports that help the user make decisions.

It finds frequent itemsets in transaction data by building larger itemsets only from smaller ones that are already frequent, and then derives association rules with support and confidence above set thresholds.

It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.

All queries use prepared statements with bound parameters, so user input is never joined into the SQL text. Inputs are also validated on the server.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.