## 4 Discussion and Conclusions  
This work aims to predict soccer results considering the impact of weather conditions. It is evident that environmental conditions can dramatically change the output of soccer matches. Many studies have been conducted to find out the impact of weather conditions on different aspects of the game, such as injuries, speed, etc. Both financial involvement and the number of spectators are very high for this sport, and it is therefore crucial to be able to predict the result of the match with greater accuracy. To complete the tasks addressed in this study, two case studies were set up. The first case study is to predict whether the game will result in a home team victory, an away team victory, or a draw. The second case study is to predict whether the result will be a draw or a non-draw. In order to clearly understand the importance of weather data for predicting soccer outcomes, additional analysis was performed, excluding weather data. As previously mentioned, the following features were used in this study: dates, names of teams, odds (Bet365, Bet&Win), maximum, minimum and average temperature, difference in temperature taking the mean and the range of temperature between the home team and away team, precipitation, difference in precipitation between home team and away team, maximum gusts of wind and maximum wind speed, and the difference in wind speed between the home team and the away team. Several techniques have been used to improve accuracy. SMOTE was applied in order to solve the problem related to the existing imbalance between classes. Later, RFECV was implemented to eliminate non-essential features. As shown in Table 4, eight features were used for each case study as  
Figure 8: Receiver operating characteristic for case study 2. Figure 7: Confusion matrix for case study 2.  
final features, of which seven are the same (_B365H_, _B365D_, _B365A_, _Tmed_Diff_, _Vmax_Diff_, _Trange_Home_, _Trange_Away_). After selecting and normalizing the features, the data were divided into sets for testing, training, and validation. The next step was to implement the ML methods and compare outputs. The following ML technologies were used for prediction: _RF_, _Extra-Trees learning_, _SVM_, and _KNN_. The results show that the accuracy varies between 62.1 and 65.9 for _case study 1_, and between 71.1 and 79.3 for _case study 2_. Extra-Trees was the superior method with an accuracy of 65.9% for _case study 1_, and an _SVM_ was more efficient, with an accuracy of 79.3% for _case study 2_. Moreover, with an AUC-ROC of 0.85, it can be predicted whether the game will end in a draw or not. Regarding analysis carried out without weather data, the results show that the accuracy is in the range of 49.8-53.3 for _case study 1_, and 68.3-74.9 for _case study 2_. It can be noticed that RF outperformed other methods with 53.3% accuracy for _case study 1_, and Extra-Trees with 74.9% accuracy for _case study 2_. Comparing the results of Tables 5 and 7, it can be seen that analysis, including weather data, provides a significantly higher accuracy for _case study 1_. With regard to _case study 2_, the accuracy is also higher, except KNN, which is 0.8% lower from the results executed without weather data. The reason for this can be explained with the final features involved in the analysis presented in Table 6. It can be noticed that the final features related to betting odds are the same for _case study 1_ in both circumstances (with and without weather data); however, for _case study 2_, they differ. Therefore, it can be assumed that the analysis implemented with KNN model for _case study 2_, the weather data has no effect on improving the performance accuracy, while in other cases it does. Regarding confusion matrices obtained from the analysis including weather data, it can be observed that in the first case study the higher erroneous classification was for the home-draw pair (instead of being classified as home, it was classified as draw).