I have a dataset, and i have to predict the flow of users at a certain city given some information like the day of the week, the month, the distance of the city of origin ecc..

First i decided to plot the heatmap of correlation, to see if there are correlations between the features, and this is the result:

As we can see there's no much correlations between the features.

I have done Linear regression obtaining very bad results (R^2 = 0.1).

I have done Lasso Regression in order to drop the bad features but the best result for Lasso is given by lambda=0, so the best result is using all the features.

My question is, is it possibile that the dataset is very bad and it's not a problem of linear regression tool? Are there other techniques in order to understand if there is a better model? I'm trying to understand why Linear Regression performs so bad.

OK i plotted the features with respect to the label and i think that the problem is the dataset. The plot with the green X are the features i decided to drop, obtaining an average training error of 4200 (against the 22000 of before). Honestly i don't know what to do now.

I have a dataset, and i have to predict the flow of users at a certain city given some information like the day of the week, the month, the distance of the city of origin ecc..

First i decided to plot the heatmap of correlation, to see if there are correlations between the features, and this is the result:

As we can see there's no much correlations between the features.

I have done Linear regression obtaining very bad results (R^2 = 0.1).

I have done Lasso Regression in order to drop the bad features but the best result for Lasso is given by lambda=0, so the best result is using all the features.

My question is, is it possibile that the dataset is very bad and it's not a problem of linear regression tool? Are there other techniques in order to understand if there is a better model? I'm trying to understand why Linear Regression performs so bad.

I have a dataset, and i have to predict the flow of users at a certain city given some information like the day of the week, the month, the distance of the city of origin ecc..

First i decided to plot the heatmap of correlation, to see if there are correlations between the features, and this is the result:

As we can see there's no much correlations between the features.

I have done Linear regression obtaining very bad results (R^2 = 0.1).

I have done Lasso Regression in order to drop the bad features but the best result for Lasso is given by lambda=0, so the best result is using all the features.

My question is, is it possibile that the dataset is very bad and it's not a problem of linear regression tool? Are there other techniques in order to understand if there is a better model? I'm trying to understand why Linear Regression performs so bad.

OK i plotted the features with respect to the label and i think that the problem is the dataset. The plot with the green X are the features i decided to drop, obtaining an average training error of 4200 (against the 22000 of before). Honestly i don't know what to do now.

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