## 4 Data Sets and Experimental Results  
A real data set obtained from a prominent Polish mobile telecommunication provider was utilized for our examination. The data were collected with strict adherence to privacy laws and industry best practices. In order to respect contractual limitations, we cannot disclose the company's identity. However, we can confirm that the initial raw data set comprised three tables detailing the usage patterns of 15,000 randomly selected clients over a year on a monthly basis from a specific date. Within this time frame, certain clients terminated their services. To identify patterns over a corresponding duration, the data set was constrained to clients who utilized the services for a minimum of three quarters within the period under analysis. The consolidated file, presented as a single table, includes the subsequent information: customers with a minimum service usage of 9 months (three quarters) and information on service utilization and expenses (subscription and invoice amounts). Within this context, the final month assessed is denoted with the number 0, the subsequent one with 1, and the initial one with 8. For clients who terminated their services, the last complete month prior to their resignation month was scrutinized. The data set contains entries with missing values in certain fields (e.g., date of birth, date CLIR, and date query), primarily due to data unavailability or the non-occurrence of these events for the individuals. Attributes stored in the data refer to the following: gender, post code, district, suspension period, date of first SIM card activation, date of birth, date of last promotion, date of calling line identification restriction (date CLIR), date of the customer's last request to cancel a contract (date query), number of days between a deactivation inquiry and the last promotion date, and information such as monthly and quarterly calculated invoice amounts, number of outgoing sms, mms and calls, plus call time. The target variable, called churn, is binary, and it contains information about whether or not the user has churned mobile services. Data pre-processing included the following steps:  
* Two attributes were removed: \"date query\" and \"date CLIR\". The \"date query\" attribute was removed due to its high correlation with the target variable, as it is the date of the customer's query to stay with a mobile service. The \"date CLIR\" attribute was removed due to lots of missing values. * Missing values in the other attributes were replaced with 0. Instead of dropping useful entries in these columns, we used this indicator. This gave the neural network extra information about which entries had no value in the columns. * For all categorical attributes, such as post code, gender, and district, a binary encoder was used. Binary encoding encodes data in fewer dimensions than one-hot encoding. Binary encoding first arbitrarily replaces the categories with ordinal numbers, after which the resulting numbers are converted into binary code. * Finally, all variables are standardized. The meticulous data preparation steps undertaken in this study have laid a strong foundation for the development of robust churn prediction models. The decision to focus on customers with at least nine months of service usage ensures that the model targets a segment of the customer base that is sufficiently mature to exhibit churn behavior, making the predictions more relevant and actionable. The removal of highly correlated and incomplete attributes, while seemingly trivial, plays a critical role in enhancing the model's predictive power by eliminating noise and reducing over-fitting. Additionally, the strategy of replacing missing values with zeros, rather than excluding them, preserves the completeness of the data set, which is particularly important in machine learning applications where the quantity of data directly impacts the model's learning capacity. The use of binary encoding for categorical variables is another notable choice.