# what steps to take to get better performance

I have a data frame like this :

dictt = {'country_cd': ['SE', 'IL', 'PL'],'timezone':['Euro/Stockho','Asia/Jerusalem','Euro/Warsaw'] , 'datetime':['8/10/2017 19:15', '8/10/2017 2:15', '8/10/2016 10:15'],
'x':[1, 73, 248], 'y':[0, 48, 144], 'z':[0, 5,30], 'm':[0,6,40], 'response':['ok', 'cacel', 'suspend'] }
print(pd.DataFrame(dictt))


And I want to do multi-class classification over this(I have nearly 450000 observations).

The accuracy I got so far is around 56% which is not good, so I am asking if you guys have any idea what can I do to improve the performance:

so far I have done this:

1.country_cd have 85 unique nominal data, so I used dummy version of them (categorical to numeric)

1. time zone I split the data (continent/country) the continent is separated from the country. like the previous feature I again got the dummy of that feature

2. datetime I split the year and month, and created two different feature(I could not see any important correlation between month and response).

4.The other 4 features that I named x y z m I dont know what they are stands for(imagine you can not get the business behind them). in these 4 features, the sample presented in the dictt above is (min, mean, max) of each feature(just for getting an idea of how the distribution of the numbers is). for the numerical features, I applied standardization in sklearn.

1. null value is available in the country_sd only 100 rows which I filled them with Mod.

and I did labelencoding in the response feature having 4 different values(0,1,2,3).

I tried different approaches: MLP, LogisticRegression, Knearestneighbor ... but all the accuracy is around 55%.

I was wondering if someone has any idea I can apply and improve the model. (overal with all pd.dummied I did over the categorical features I came up with 220 features).

• What hyperparameters are you searching over? It could also just be the case that there is no strong relationship between your measured features and the outcome Oct 23 '19 at 4:53
• @DemetriPananos Thank you so much for your response. Yea that might be the case that there is no strong relationship between response and the independent variables. I am looking for an idea in case I can improve this model without adding extra features. I am also interested in learning if I have missed something to apply in case of the steps of preprocessing on the data before feeding to the model. Oct 23 '19 at 14:46

• Thank you so much for your response. Actually there are four more features x y z ...` which I do not know what they are. Actually I have to solve this without knowing the business behind the data. I was wondering if I am missing something in which can help to get better performance. Oct 23 '19 at 14:47