# On Predicting Ticket Reopening for Improving Customer Service in 5G Fiber Optic Networks
## 2 Data Collection, Data Preparation and Analytical Base Table
As a result, the following lessons can be learned from the data preparation activity carried out in this section:  
* no relevant linear correlations emerge between the Repeated ticket variable and QoS parameters;
* the variables exhibiting primary nonlinear correlations with the Repeated ticket variable are the percentage of 3G data availability for backup and the Alarm tag;
* the variables exhibiting secondary nonlinear correlations with the Repeated ticket variable are SAC, signal-to-noise ratio, and attenuation in downstream;
* the datasets related to the VULA and SLU technologies are very much correlated with each other due to the 0.175 entanglement coefficient computed between the two dendrograms in Figures 1 and 2. Hence, the _reduced_ dataset \(X_{red}\) we are going to resort to hereinafter can be regarded as a \(p\times n\times t\) matrix reporting the following \(p=15\) network QoS parameters for the same group of \(m\) users at time \(t\): (i) SAC, (ii) signal-to-noise ratio (downstream), (iii) attenuation (downstream), (iv) constant bitrate (downstream), (v) downstream maximum rate, (vi) Ethernet bytes (LAN data), (vii) percentage of Ethernet bytes (LAN), (viii) percentage of WiFi primary data Ethernet bytes (LAN), (ix) attenuation (upstream), (x) current bitrate (upstream), (xi) upstream maximum rate, (xii) power (upstream), (xiii) UPSO (upstream power back-off) loop length (dB), (xiv) ticket close code, and (xv) repeated ticket. The ticket close code has not been mentioned so far but was already also present in the original dataset \(X\): it testifies that the network operator has acknowledged the causes that are attributable to the variations in the QoS parameters for which the user is complaining. The ticket close code variable may take one of the following values: (a) activity on the OCA protocol, (b) existence of a known problem that is pending for resolution, (c) line/device problem needing for device reboot, (d) line/device problem needing for device reset, (e) unexploited link/GNP (Geographic Number Portability), (f) LNI-minimum bitrate, (g) maximum obtainable performance, (h) no trouble found, (i) OLO2 OLO problem--OLO2 OLO is the Italian platform allowing the migration of access lines between different network operators insisting on the same network infrastructure--, (j) performance degradation resulting from monitoring, (k) macroproblem due to the network infrastructure, (l) known network outage, (m) network outage detected as a result of monitoring, (n) access degradation of the network infrastructure resulting from monitoring, (o) access network degradation relative to the backbone provider, (p) access network degradation relative to the network operator, and (q) wrong assignment of the network service to a user. For the sake of completeness, in Table 1, we show the relative frequency of each ticket close code in the considered dataset. We now present our predictive model. In more detail, we are going to address the following classification problem: _is a user at risk of reopening a ticket that was previously closed even though the related technical issue (in terms of perceived QoS) was still not solved?_