Loans default will cause huge loss for the banks, so they pay much attention on this issue and apply various method to detect and predict default behaviours of their customers. The loan default dataset has 8 variables and 850 records, each record being loan default status for each customer. Each Applicant was rated as “Defaulted” or “Not-Defaulted”. New applicants for loan application can also be evaluated on these 8 predictor variables and classified as a default or non-default based on predictor variables.

bike-rental-count-prediction

The objective of this Case is to Prediction of bike rental count on daily basis on the environmental and seasonal settings. The details of data attributes in the dataset are as follows - instant: Record index dteday: Date season: Season (1:springer, 2:summer, 3:fall, 4:winter) yr: Year (0: 2011, 1:2012) mnth: Month (1 to 12) hr: Hour (0 to 23) holiday: weather day is holiday or not (extracted fromHoliday Schedule) weekday: Day of the week workingday: If day is neither weekend nor holiday is 1, otherwise is 0. weathersit: (extracted fromFreemeteo) 1: Clear, Few clouds, Partly cloudy, Partly cloudy 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog temp: Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale) atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_maxt_min), t_min=-16, t_max=+50 (only in hourly scale) hum: Normalized humidity. The values are divided to 100 (max) windspeed: Normalized wind speed. The values are divided to 67 (max) casual: count of casual users registered: count of registered users cnt: count of total rental bikes including both casual and registered