How to visualize (make plot) of regression output against categorical input variable? I am doing linear regression with multiple variables. In my data I have n = 143 features and m = 13000 training examples. Some of my features are continuous (ordinal) variables (area, year, number of rooms). But I also have categorical variables (district, color, type). For now I visualized some of my feautures against predicted price. For example here is the plot of area against predicted price:


Since area is continuous ordinal variable I had no troubles visualizing the data. But now I wanted to somehow visualize dependency of my categorical variables (such as district) on predicted price.
For categorical variables I used one-hot (dummy) encoding. 
For example that kind of data:


turned to this format:


If I were using ordinal encoding for districts this way:

DistrictA - 1
DistrictB - 2
DistrictC - 3
DistrictD - 4
DistrictE - 5


I would plot this values against predicted price pretty easy by putting 1-5 to X axis and price to Y axis.

But I used dummy coding and now I do not know how can I show (visualize) dependency between price and categorical variable 'District' represented as series of zeros and ones.

How can I make a plot showing a regression line of districts against predicted price in case of using dummy coding?
One possible first step is to convert the data back to the original coding.
This is called in SQL unpivot, in R melt.

Here an R example

&gt; my.df &lt;- read.table(
+ text = "DistrictA     DistrictB    DistrictC    DistrictD     DistrictE     Price
+         1             0            0            0             0             10000
+         0             1            0            0             0             20000
+         0             0            1            0             0             30000
+         0             0            0            1             0             40000
+         0             0            0            0             1             50000"
+      , header = TRUE)
&gt; my.df
  DistrictA DistrictB DistrictC DistrictD DistrictE Price
1         1         0         0         0         0 10000
2         0         1         0         0         0 20000
3         0         0         1         0         0 30000
4         0         0         0         1         0 40000
5         0         0         0         0         1 50000

&gt; library(reshape)
&gt; subset(melt(my.df, id="Price", variable = "District"),value == 1)[,c(1,2)]
   Price  District
1  10000 DistrictA
7  20000 DistrictB
13 30000 DistrictC
19 40000 DistrictD
25 50000 DistrictE


After that you plot the Price dependent on a factor variable. You may additionally consider to order the factor based on the predicted price.

I provide no details, as you don't tagged your tool, but I would recommend additional to a scatter plot to consider a box plot and/or density plot - always combined with the prediction value from the model for each factor level.