Analyse the transactional data and Identify such factors which impacts the daily transaction volume. Also, predict the daily transaction volume for next 5 days.
Daily Transactional data for each month i.e. Jan-2017 to Oct-2017 per day transactional Data
- Jan - 41668
- Feb - 54522
- Mar - 156429
- Apr - 187696
- May - 231851
- Jun - 318340
- Jul - 385274
- Aug - 423830
- Sep - 463834
- Oct - 472852
- Data Preparation
- Synchronize the column name in each dataset
- Checked for the duplicate entries for transactions
- Checked for the 'NA' values
- Repalce the NA values witha appropriate values (e.g. Replace NA Credit_card_fee values to 0)
- Exploratory Data Analysis
- Univariate analysis
- Bi-variate analysis
- Combined the Monthly data in to one
- Performed the EDA on Merged dataset (i.e. Combined data for all the months)
- converted the data into timeseries
- Built a Time-series model
- PLotted time-series
- Checked for the white noise
- Performed ADF and KPSS test
- Predicted the next 5 Days Transaction