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To add to the number of methods you can use to convert your regression problem into a classification problem, you can use discretised percentiles to define categories instead of numerical values. For example, from this you can then predict if the price is in the top 10th (20th, 30th, etc.) percentile. These values you can easily find out using Python's numpy....


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First of all as your target value is continuous so it's a regression problem. If the correlation doesn't give desired value it means there exist no linear relationship between variable and target variable you should look into mutual information. Also, while interpreting a relationship, one should be careful to not confound correlation and causality, because ...


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The task you are converting is not actually classification per se, it's ordinal classification. I am pointing this out cause there are implementations which specialise on this matter. Moreover, the task you are asking is how to properly bin the values. For that, you can refer to binning as a pre-processing step. I am sure if you search for "binning ...


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Since your output is one dimensional, clustering the output is equivalent to fixing thresholds. The best you can do is use field knowledge to distinguish the different classes. You can also plot the histogram of the log of your price and see if there is a mixture of gaussians and try to seperate them into classes.


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Use whatever conveys the message more clearly. This usually means to use percentages only for absolute metrics and use %-points for metrics which are presented as percentages (e.g. "retention rate increased by 5%-points" in your example). From my experience you can expect almost everybody to know the difference. The only exception would be highly ...


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(0.0053726552728412666, 0.9415686658133314, 1), that 0.005 represents the test statistic Yes, 0.94 represents the P-value - Yes. and 1 the degrees of freedom? No. And 1,2,3 and 4 are ordinary serial numbers and not, degree of freedom. At 10% level of confidence, you need at least 2.71(value of Chi-square). You have computed the chi-square = 2.576 and ...


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The terms standardization and normalization are often used interchangeably. However, strictly speaking they do refer to distinct feature transformations. Normalization Normalization, also called feature scaling usually means scaling the data between 0 and 1. There are many approaches that can be used to achieve this. One common way is by $x' = \frac{x - x_{...


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