Management Information Systems

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https://hdl.handle.net/20.500.11888/10131

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Browsing Management Information Systems by Author "Abu baker, Maher"

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    Al Najah tours
    (2019) Nassar, Duha; Haj ali, Raneen; Abu baker, Maher
    Create a website for Al Najah tours, which organize weekly and monthly trips. The site includes online booking and reports on the amount of booking and sales per trip. It is also linked to Google Analytics to get statistics about people visiting this site
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    Assist The Management Of Tulkarem Municipality In Electricity Of Prepaid Bills
    (2019) Nassar, Duha; Haj ali, Raneen; Abu baker, Maher
    Data analysis in Tulkarem municipality for electricity consumption in a main challenge ,in order to support the management specifically planning at strategic at a tactical level to mine the data of electricity consumption particularly because data related consume prepaid electricity bills in order to know behavior of consumers in their consumption according to different time of period, areas and tariff price In new classification methods of problem-related to electricity quantities charges by python language . We used python language to prepare the data in a way that would enable us to perform the analysis of the data, and then we collected the three excel files that were supplied to us properly in one file, and then the classification of consumers into three categories according to the quantities of electricity consumption, and then we assembled and visualization the data in the form that answers the most important questions in addition to providing the necessary information that helps management to make the right decisions, and finally we have made a prediction of the classification of consumers according to the classification we have previously by using seven methods in the process of prediction and calculation of accuracy ratio in Each method. Through the process of analysis on these data ,we came out with many results, including 1. Quantities of electricity consumption by region, tariff rate, time and consumer classification. 2. The number of consumers according to their categories, tariff rate and time. 3. Count of invoices by regions, tariff rate, time and consumer categories. 4. Predicting the consumer category according to tariff, time and region.