The tourist information offices in both Arosa and Lenzerheide staff their offices according to how many tourists they expect to be contacted by. The process of staffing the office relies on experience and laborious mapping of the next months events, season, holidays etc.
The tourist office would like a tool to facilitate and enhance the prediction of tourist requests.
A prediction model should forecast the amount of tourist requests throughout different channels such as phone, email, and front desk per month.
The data set contains the following variables: Datum Wochentag season isFerienZH Ferien Desc ZH isFerienSG Ferien Desc SG isFerienGR Ferien Desc GR Feiertag Desc isFeiertag ZH isFeiertag SG isFeiertag GR t_2m_c_avg snow_depth_cm_avg visibility_m_avg wind_speed_10m_ms_max prob_precip_1h_p_avg prob_tstorm_1h_p_avg hail_idx_avg wind_gusts_10m_ms_max wind_speed_mean_10m_24h_ms_avg t_max_2m_24h_c_max t_min_2m_24h_c_min precip_24h_mm_max fresh_snow_24h_cm_max is_sleet_1h_idx_max is_fog_1h_idx_max precip_1h_mm_sum fresh_snow_1h_cm_sum wind_speed_mean_10m_1h_ms_avg weather_symbol_1h_idx_spe number_of_events Schalter Tel Mail Total Anfragen
the variables can be grouped into the following buckets: school holidays, public holidays, season and weather.
The time range is from 01.05.2017 - 30.09.2019
|— R_Bootcamp
|—— Code (use this as working directory)
|——— RBootcamp_Assignment.Rmd
|—— DATA
|—— OUTPUT