Customer Relationship Management is important in analyzing business performance. Predicting customer buying behavior enables the business to better address their customers and enhance service level and overall profit. This paper focuses on proposing a model that predicts future period sales in a real retail department store with low prediction error rate, and it also discovers the main sales trends over time. A model based on the Prophet algorithm is implemented and modified according to different parameters in order to lower the prediction error. The modifications consisted of the insertion of a new seasonality pattern, changes in the Fourier order of the existing and the new seasonality pattern, inclusion of the holiday data, and parameterizing its impact. The performance of the standard and modified model is evaluated in terms of the MAE (mean absolute error) and MAPE (mean absolute percentage error). The standard and the modified model were tested on a real dataset consisting of the sales between 2011-2019 in a department store of a shopping center in Albania. Implementation results show that the MAE in sales prediction for the modified model is reduced, while the MAPE in sales prediction for the modified model was measured for prediction periods. The implementation results indicate a comparable or evenbetter performance than the standard model. different

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Healthy food leads to healthy living and it is a major issue in our days. Nutri-Score is a nutrition label that can be calculated from the nutritional values of a food and helps evaluating the healthiness of it. Nevertheless, we don't always have the nutritional values of the food, so it is not always easy identifying this label. In the same way, it is not easy finding the healthier option to a favorite food. In this paper an end-to end deep learning system is proposed to identify the Nutri-Score label and recommend similar but healthier recipes based on food images. A new dataset of images is extracted from the Recipe 1M and labeled with the Nutri-Score value calculated for each image. Pretrained models Resnet50, Resnet101, EfficientNetB2 and DensNet121 are tuned based on this dataset. The embeddings from the last convolutional layer of the input image are used to find its most similar neighbor based on KNN algorithm. The proposed system suggests recipes with the lowest Nutri-Score similar to the inputted image. Implementations show that the Resnet50 provides highest prediction accuracy.

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