Is linear regression fit for this data

@shounakrockz47 wrote:

I am predicting the number of vehicles in 4 traffic junctions.

So, I have following columns in my dataset :

1. DateTime
2. Junction_ID
3. Number_of_vehicles

At the first glance, this problem may look like Time series regression. But, the data given seems like Linear Regression problem.

So, I have applied linear regression in the following manner :

• Used get_dummies extensively for all the columns. I used dummy variables for 31 days,24 hours, 7 days of weeks and 4 Junction Ids.

• Then applied Linear Regression model in following way :

       from sklearn.model_selection import train_test_split
  
        x_train, x_test, y_train, y_test = train_test_split(train_data,train_vehicles)
  
    clf.fit(x_train,y_train)
  
    import math
  
    pred=clf.predict(x_test)
  
    pred.shape #got result as (12030,)
  
    result = []
    for x in pred:
    result.append(math.ceil(x))
  
    from sklearn.metrics import mean_squared_error
  
    score=mean_squared_error(y_test, result)
    rmse=math.sqrt(score)
    print('RMSE is :', rmse)
  

I am getting RMSE value as 10.636853077462394

My questions are :

• Since RMSE value is on lower side , can I say this model is decent ?

• Is there any other approach which I can use on this dataset ?

• Do I need to check for colinearity ?

• How can I check if multiple variables are interrelated ?

• Should I go for non-linear regression on this dataset ?

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