CODEWITHSUNDEEP
pythonmachine-learningregressionpredictiongraphlab
user1584253
user1584253

Reputation: 1005

Predict number of rows using Machine Learnnig

I have the bank data of around 4 years of different branches. I am trying to predict number of rows in daily and hourly level. I have issue_datetime (year, month, day, hour) as important features. I applied different regression techniques (linear, decision trees, random forest, xgb) using graph lab but could not get better accuracy. I was also thinking to set the threshold based on past data like taking the mean of counts in daily, monthly level after removing outliers and set that as a threshold. What is the best approach?

Upvotes: 1

Views: 241

Answers (1)

Him
Him

Reputation: 5551

Since you have 1d time series data, it should be relatively easy to graph your data and look for interesting patterns.

Once you establish that there are some non-stationary aspects to your data, the class of models you are probably wanting to check out first are auto-regressive models, possibly with seasonal additions. ARIMA models are pretty standard for time-series data. http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/

Upvotes: 1

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