# An R Enthusiast Goes Pythonic!

I’ve spent so many years using and broadcasting my love for R and using Python quite minimally. Having read recently about machine learning in Python, I decided to take on a fun little ML project using Python from start to finish.

What follows below takes advantage of a neat dataset from the UCI Machine Learning Repository.  The data contain Math test performance of 649 students in 2 Portuguese schools.  What’s neat about this data set is that in addition to grades on the students’ 3 Math tests, they managed to collect a whole whack of demographic variables (and some behavioural) as well.  That lead me to the question of how well can you predict final math test performance based on demographics and behaviour alone.  In other words, who is likely to do well, and who is likely to tank?

I have to admit before I continue, I initially intended on doing this analysis in Python alone, but I actually felt lost 3 quarters of the way through and just did the whole darned thing in R.  Once I had completed the analysis in R to my liking, I then went back to my Python analysis and continued until I finished to my reasonable satisfaction.  For that reason, for each step in the analysis, I will show you the code I used in Python, the results, and then the same thing in R.  Do not treat this as a comparison of Python’s machine learning capabilities versus R per se.  Please treat this as a comparison of my understanding of how to do machine learning in Python versus R!

```#Python Code
from pandas import *
from matplotlib import *
import seaborn as sns
sns.set_style("darkgrid")
import matplotlib.pyplot as plt
%matplotlib inline # I did this in ipython notebook, this makes the graphs show up inline in the notebook.
import statsmodels.formula.api as smf
from scipy import stats
from numpy.random import uniform
from numpy import arange
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt
```

I’d like to comment on the number of import statements I found myself writing in this python script. Eleven!! Is that even normal? Note the smaller number of library statements in my R code block below:

```#R Code
library(ggplot2)
library(dplyr)
library(ggthemr)
library(caret)
ggthemr('flat') # I love ggthemr!
mat_perf = read.csv('student-mat.csv', sep = ';')
```

Now let’s do a quick plot of our target variable, scores on the students’ final math test, named ‘G3’.

```#Python Code
sns.set_palette("deep", desat=.6)
sns.set_context(context='poster', font_scale=1)
sns.set_context(rc={"figure.figsize": (8, 4)})
plt.hist(mat_perf.G3)
plt.xticks(range(0,22,2))
```

Distribution of Final Math Test Scores (“G3”)

That looks pretty pleasing to my eyes. Now let’s see the code for the same thing in R (I know, the visual theme is different. So sue me!)

```#R Code
ggplot(mat_perf) + geom_histogram(aes(x=G3), binwidth=2)
```

You’ll notice that I didn’t need to tweak any palette or font size parameters for the R plot, because I used the very fun ggthemr package. You choose the visual theme you want, declare it early on, and then all subsequent plots will share the same theme! There is a command I’ve hidden, however, modifying the figure height and width. I set the figure size using rmarkdown, otherwise I just would have sized it manually using the export menu in the plot frame in RStudio.  I think both plots look pretty nice, although I’m very partial to working with ggthemr!

Univariate estimates of variable importance for feature selection

Below, what I’ve done in both languages is to cycle through each variable in the dataset (excepting prior test scores) insert the variable name in a dictionary/list, and get a measure of importance of how predictive that variable is, alone, of the final math test score (variable G3). Of course if the variable is qualitative then I get an F score from an ANOVA, and if it’s quantitative then I get a t score from the regression.

In the case of Python this is achieved in both cases using the ols function from scipy’s statsmodels package. In the case of R I’ve achieved this using the aov function for qualitative and the lm function for quantitative variables. The numerical outcome, as you’ll see from the graphs, is the same.

```#Python Code
test_stats = {'variable': [], 'test_type' : [], 'test_value' : []}

for col in mat_perf.columns[:-3]:
test_stats['variable'].append(col)
if mat_perf[col].dtype == 'O':
# Do ANOVA
aov = smf.ols(formula='G3 ~ C(' + col + ')', data=mat_perf, missing='drop').fit()
test_stats['test_type'].append('F Test')
test_stats['test_value'].append(round(aov.fvalue,2))
else:
# Do correlation
print col + '\n'
model = smf.ols(formula='G3 ~ ' + col, data=mat_perf, missing='drop').fit()
value = round(model.tvalues[1],2)
test_stats['test_type'].append('t Test')
test_stats['test_value'].append(value)

test_stats = DataFrame(test_stats)
test_stats.sort(columns='test_value', ascending=False, inplace=True)
```
```#R Code
test.stats = list(test.type = c(), test.value = c(), variable = c())

for (i in 1:30) {
test.stats\$variable[i] = names(mat_perf)[i]
if (is.factor(mat_perf[,i])) {
anova = summary(aov(G3 ~ mat_perf[,i], data=mat_perf))
test.stats\$test.type[i] = "F test"
test.stats\$test.value[i] = unlist(anova)[7]
}
else {
reg = summary(lm(G3 ~ mat_perf[,i], data=mat_perf))
test.stats\$test.type[i] = "t test"
test.stats\$test.value[i] = reg\$coefficients[2,3]
}

}

test.stats.df = arrange(data.frame(test.stats), desc(test.value))
test.stats.df\$variable = reorder(test.stats.df\$variable, -test.stats.df\$test.value)
```

And now for the graphs. Again you’ll see a bit more code for the Python graph vs the R graph. Perhaps someone will be able to show me code that doesn’t involve as many lines, or maybe it’s just the way things go with graphing in Python. Feel free to educate me 🙂

```#Python Code
f, (ax1, ax2) = plt.subplots(2,1, figsize=(48,18), sharex=False)
sns.set_context(context='poster', font_scale=1)
sns.barplot(x='variable', y='test_value', data=test_stats.query("test_type == 'F Test'"), hline=.1, ax=ax1, x_order=[x for x in test_stats.query("test_type == 'F Test'")['variable']])
ax1.set_ylabel('F Values')
ax1.set_xlabel('')

sns.barplot(x='variable', y='test_value', data=test_stats.query("test_type == 't Test'"), hline=.1, ax=ax2, x_order=[x for x in test_stats.query("test_type == 't Test'")['variable']])
ax2.set_ylabel('t Values')
ax2.set_xlabel('')

sns.despine(bottom=True)
```

```#R Code
ggplot(test.stats.df, aes(x=variable, y=test.value)) +
geom_bar(stat="identity") +
facet_grid(.~test.type ,  scales="free", space = "free") +
theme(axis.text.x = element_text(angle = 45, vjust=.75, size=11))
```

As you can see, the estimates that I generated in both languages were thankfully the same. My next thought was to use only those variables with a test value (F or t) of 3.0 or higher. What you’ll see below is that this led to a pretty severe decrease in predictive power compared to being liberal with feature selection.

In reality, the feature selection I use below shouldn’t be necessary at all given the size of the data set vs the number of predictors, and the statistical method that I’m using to predict grades (random forest). What’s more is that my feature selection method in fact led me to reject certain variables which I later found to be important in my expanded models! For this reason it would be nice to investigate a scalable multivariate feature selection method (I’ve been reading a bit about boruta but am skeptical about how well it scales up) to have in my tool belt. Enough blathering, and on with the model training:

Training the First Random Forest Model

```#Python code
usevars =  [x for x in test_stats.query("test_value >= 3.0 | test_value <= -3.0")['variable']]
mat_perf['randu'] = np.array([uniform(0,1) for x in range(0,mat_perf.shape[0])])

mp_X = mat_perf[usevars]
mp_X_train = mp_X[mat_perf['randu'] <= .67]
mp_X_test = mp_X[mat_perf['randu'] > .67]

mp_Y_train = mat_perf.G3[mat_perf['randu'] <= .67]
mp_Y_test = mat_perf.G3[mat_perf['randu'] > .67]

# for the training set
cat_cols = [x for x in mp_X_train.columns if mp_X_train[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_train[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_train = concat([mp_X_train, new_cols], axis=1)

# for the testing set
cat_cols = [x for x in mp_X_test.columns if mp_X_test[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_test[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_test = concat([mp_X_test, new_cols], axis=1)

mp_X_train.drop(cat_cols, inplace=True, axis=1)
mp_X_test.drop(cat_cols, inplace=True, axis=1)

rf = RandomForestRegressor(bootstrap=True,
criterion='mse', max_depth=2, max_features='auto',
min_density=None, min_samples_leaf=1, min_samples_split=2,
n_estimators=100, n_jobs=1, oob_score=True, random_state=None,
verbose=0)
rf.fit(mp_X_train, mp_Y_train)
```

After I got past the part where I constructed the training and testing sets (with “unimportant” variables filtered out) I ran into a real annoyance. I learned that categorical variables need to be converted to dummy variables before you do the modeling (where each level of the categorical variable gets its own variable containing 1s and 0s. 1 means that the level was present in that row and 0 means that the level was not present in that row; so called “one-hot encoding”). I suppose you could argue that this puts less computational demand on the modeling procedures, but when you’re dealing with tree based ensembles I think this is a drawback. Let’s say you have a categorical variable with 5 features, “a” through “e”. It just so happens that when you compare a split on that categorical variable where “abc” is on one side and “de” is on the other side, there is a very significant difference in the dependent variable. How is one-hot encoding going to capture that? And then, your dataset which had a certain number of columns now has 5 additional columns due to the encoding. “Blah” I say!

Anyway, as you can see above, I used the get_dummies function in order to do the one-hot encoding. Also, you’ll see that I’ve assigned two thirds of the data to the training set and one third to the testing set. Now let’s see the same steps in R:

```#R Code
keep.vars = match(filter(test.stats.df, abs(test.value) >= 3)\$variable, names(mat_perf))
ctrl = trainControl(method="repeatedcv", number=10, selectionFunction = "oneSE")
mat_perf\$randu = runif(395)
test = mat_perf[mat_perf\$randu > .67,]
trf = train(mat_perf[mat_perf\$randu <= .67,keep.vars], mat_perf\$G3[mat_perf\$randu <= .67],
method="rf", metric="RMSE", data=mat_perf,
trControl=ctrl, importance=TRUE)
```

Wait a minute. Did I really just train a Random Forest model in R, do cross validation, and prepare a testing data set with 5 commands!?!? That was a lot easier than doing these preparations and not doing cross validation in Python! I did in fact try to figure out cross validation in sklearn, but then I was having problems accessing variable importances after. I do like the caret package 🙂 Next, let’s see how each of the models did on their testing set:

Testing the First Random Forest Model

```#Python Code
y_pred = rf.predict(mp_X_test)
sns.set_context(context='poster', font_scale=1)
first_test = DataFrame({"pred.G3.keepvars" : y_pred, "G3" : mp_Y_test})
sns.lmplot("G3", "pred.G3.keepvars", first_test, size=7, aspect=1.5)
print 'r squared value of', stats.pearsonr(mp_Y_test, y_pred)[0]**2
print 'RMSE of', sqrt(mean_squared_error(mp_Y_test, y_pred))
```

R^2 value of 0.104940038879
RMSE of 4.66552400292

Here, as in all cases when making a prediction using sklearn, I use the predict method to generate the predicted values from the model using the testing set and then plot the prediction (“pred.G3.keepvars”) vs the actual values (“G3”) using the lmplot function. I like the syntax that the lmplot function from the seaborn package uses as it is simple and familiar to me from the R world (where the arguments consist of “X variable, Y Variable, dataset name, other aesthetic arguments). As you can see from the graph above and from the R^2 value, this model kind of sucks. Another thing I like here is the quality of the graph that seaborn outputs. It’s nice! It looks pretty modern, the text is very readable, and nothing looks edgy or pixelated in the plot. Okay, now let’s look at the code and output in R, using the same predictors.

```#R Code
test\$pred.G3.keepvars = predict(trf, test, "raw")
cor.test(test\$G3, test\$pred.G3.keepvars)\$estimate[[1]]^2
summary(lm(test\$G3 ~ test\$pred.G3.keepvars))\$sigma
ggplot(test, aes(x=G3, y=pred.G3.keepvars)) + geom_point() + stat_smooth(method="lm") + scale_y_continuous(breaks=seq(0,20,4), limits=c(0,20))
```

R^2 value of 0.198648
RMSE of 4.148194

Well, it looks like this model sucks a bit less than the Python one. Quality-wise, the plot looks super nice (thanks again, ggplot2 and ggthemr!) although by default the alpha parameter is not set to account for overplotting. The docs page for ggplot2 suggests setting alpha=.05, but for this particular data set, setting it to .5 seems to be better.

Finally for this section, let’s look at the variable importances generated for each training model:

```#Python Code
importances = DataFrame({'cols':mp_X_train.columns, 'imps':rf.feature_importances_})
print importances.sort(['imps'], ascending=False)

cols      imps
3        failures  0.641898
0            Medu  0.064586
10          sex_F  0.043548
19  Mjob_services  0.038347
11          sex_M  0.036798
16   Mjob_at_home  0.036609
2             age  0.032722
1            Fedu  0.029266
15   internet_yes  0.016545
6     romantic_no  0.013024
7    romantic_yes  0.011134
5      higher_yes  0.010598
14    internet_no  0.007603
4       higher_no  0.007431
12        paid_no  0.002508
20   Mjob_teacher  0.002476
13       paid_yes  0.002006
18     Mjob_other  0.001654
17    Mjob_health  0.000515
```
```#R Code
varImp(trf)

## rf variable importance
##
##          Overall
## failures 100.000
## romantic  49.247
## higher    27.066
## age       17.799
## Medu      14.941
## internet  12.655
## sex        8.012
## Fedu       7.536
## Mjob       5.883
## paid       1.563
```

My first observation is that it was obviously easier for me to get the variable importances in R than it was in Python. Next, you’ll certainly see the symptom of the dummy coding I had to do for the categorical variables. That’s no fun, but we’ll survive through this example analysis, right? Now let’s look which variables made it to the top:

Whereas failures, mother’s education level, sex and mother’s job made it to the top of the list for the Python model, the top 4 were different apart from failures in the R model.

With the understanding that the variable selection method that I used was inappropriate, let’s move on to training a Random Forest model using all predictors except the prior 2 test scores. Since I’ve already commented above on my thoughts about the various steps in the process, I’ll comment only on the differences in results in the remaining sections.

Training and Testing the Second Random Forest Model

```#Python Code

#aav = almost all variables
mp_X_aav = mat_perf[mat_perf.columns[0:30]]
mp_X_train_aav = mp_X_aav[mat_perf['randu'] <= .67]
mp_X_test_aav = mp_X_aav[mat_perf['randu'] > .67]

# for the training set
cat_cols = [x for x in mp_X_train_aav.columns if mp_X_train_aav[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_train_aav[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_train_aav = concat([mp_X_train_aav, new_cols], axis=1)

# for the testing set
cat_cols = [x for x in mp_X_test_aav.columns if mp_X_test_aav[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_test_aav[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_test_aav = concat([mp_X_test_aav, new_cols], axis=1)

mp_X_train_aav.drop(cat_cols, inplace=True, axis=1)
mp_X_test_aav.drop(cat_cols, inplace=True, axis=1)

rf_aav = RandomForestRegressor(bootstrap=True,
criterion='mse', max_depth=2, max_features='auto',
min_density=None, min_samples_leaf=1, min_samples_split=2,
n_estimators=100, n_jobs=1, oob_score=True, random_state=None,
verbose=0)
rf_aav.fit(mp_X_train_aav, mp_Y_train)

y_pred_aav = rf_aav.predict(mp_X_test_aav)
second_test = DataFrame({"pred.G3.almostallvars" : y_pred_aav, "G3" : mp_Y_test})
sns.lmplot("G3", "pred.G3.almostallvars", second_test, size=7, aspect=1.5)
print 'r squared value of', stats.pearsonr(mp_Y_test, y_pred_aav)[0]**2
print 'RMSE of', sqrt(mean_squared_error(mp_Y_test, y_pred_aav))
```

R^2 value of 0.226587731888
RMSE of 4.3338674965

Compared to the first Python model, the R^2 on this one is more than doubly higher (the first R^2 was .10494) and the RMSE is 7.1% lower (the first was 4.6666254). The predicted vs actual plot confirms that the predictions still don’t look fantastic compared to the actuals, which is probably the main reason why the RMSE hasn’t decreased by so much. Now to the R code using the same predictors:

```#R code
trf2 = train(mat_perf[mat_perf\$randu <= .67,1:30], mat_perf\$G3[mat_perf\$randu <= .67],
method="rf", metric="RMSE", data=mat_perf,
trControl=ctrl, importance=TRUE)
test\$pred.g3.almostallvars = predict(trf2, test, "raw")
cor.test(test\$G3, test\$pred.g3.almostallvars)\$estimate[[1]]^2
summary(lm(test\$G3 ~ test\$pred.g3.almostallvars))\$sigma
ggplot(test, aes(x=G3, y=pred.g3.almostallvars)) + geom_point() +
stat_smooth() + scale_y_continuous(breaks=seq(0,20,4), limits=c(0,20))
```

R^2 value of 0.3262093
RMSE of 3.8037318

Compared to the first R model, the R^2 on this one is approximately 1.64 times higher (the first R^2 was .19865) and the RMSE is 8.3% lower (the first was 4.148194). Although this particular model is indeed doing better at predicting values in the test set than the one built in Python using the same variables, I would still hesitate to assume that the process is inherently better for this data set. Due to the randomness inherent in Random Forests, one run of the training could be lucky enough to give results like the above, whereas other times the results might even be slightly worse than what I managed to get in Python. I confirmed this, and in fact most additional runs of this model in R seemed to result in an R^2 of around .20 and an RMSE of around 4.2.

Again, let’s look at the variable importances generated for each training model:

```#Python Code
importances_aav = DataFrame({'cols':mp_X_train_aav.columns, 'imps':rf_aav.feature_importances_})
print importances_aav.sort(['imps'], ascending=False)

cols      imps
5            failures  0.629985
12           absences  0.057430
1                Medu  0.037081
41      schoolsup_yes  0.036830
0                 age  0.029672
23       Mjob_at_home  0.029642
16              sex_M  0.026949
15              sex_F  0.026052
40       schoolsup_no  0.019097
26      Mjob_services  0.016354
55       romantic_yes  0.014043
51         higher_yes  0.012367
2                Fedu  0.011016
39     guardian_other  0.010715
37    guardian_father  0.006785
8               goout  0.006040
11             health  0.005051
54        romantic_no  0.004113
7            freetime  0.003702
3          traveltime  0.003341
```
```#R Code
varImp(trf2)

## rf variable importance
##
##   only 20 most important variables shown (out of 30)
##
##            Overall
## absences    100.00
## failures     70.49
## schoolsup    47.01
## romantic     32.20
## Pstatus      27.39
## goout        26.32
## higher       25.76
## reason       24.02
## guardian     22.32
## Fedu         20.38
## school       20.07
## traveltime   20.02
## studytime    18.73
## health       18.21
## Mjob         17.29
## paid         15.67
## Dalc         14.93
## activities   13.67
## freetime     12.11
```

Now in both cases we’re seeing that absences and failures are considered as the top 2 most important variables for predicting final math exam grade. It makes sense to me, but frankly is a little sad that the two most important variables are so negative 😦 On to to the third Random Forest model, containing everything from the second with the addition of the students’ marks on their second math exam!

Training and Testing the Third Random Forest Model

```#Python Code

#allv = all variables (except G1)
allvars = range(0,30)
allvars.append(31)
mp_X_allv = mat_perf[mat_perf.columns[allvars]]
mp_X_train_allv = mp_X_allv[mat_perf['randu'] <= .67]
mp_X_test_allv = mp_X_allv[mat_perf['randu'] > .67]

# for the training set
cat_cols = [x for x in mp_X_train_allv.columns if mp_X_train_allv[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_train_allv[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_train_allv = concat([mp_X_train_allv, new_cols], axis=1)

# for the testing set
cat_cols = [x for x in mp_X_test_allv.columns if mp_X_test_allv[x].dtype == "O"]
for col in cat_cols:
new_cols = get_dummies(mp_X_test_allv[col])
new_cols.columns = col + '_' + new_cols.columns
mp_X_test_allv = concat([mp_X_test_allv, new_cols], axis=1)

mp_X_train_allv.drop(cat_cols, inplace=True, axis=1)
mp_X_test_allv.drop(cat_cols, inplace=True, axis=1)

rf_allv = RandomForestRegressor(bootstrap=True,
criterion='mse', max_depth=2, max_features='auto',
min_density=None, min_samples_leaf=1, min_samples_split=2,
n_estimators=100, n_jobs=1, oob_score=True, random_state=None,
verbose=0)
rf_allv.fit(mp_X_train_allv, mp_Y_train)

y_pred_allv = rf_allv.predict(mp_X_test_allv)
third_test = DataFrame({"pred.G3.plusG2" : y_pred_allv, "G3" : mp_Y_test})
sns.lmplot("G3", "pred.G3.plusG2", third_test, size=7, aspect=1.5)
print 'r squared value of', stats.pearsonr(mp_Y_test, y_pred_allv)[0]**2
print 'RMSE of', sqrt(mean_squared_error(mp_Y_test, y_pred_allv))
```

R^2 value of 0.836089929903
RMSE of 2.11895794845

Obviously we have added a highly predictive piece of information here by adding the grades from their second math exam (variable name was “G2”). I was reluctant to add this variable at first because when you predict test marks with previous test marks then it prevents the model from being useful much earlier on in the year when these tests have not been administered. However, I did want to see what the model would look like when I included it anyway! Now let’s see how predictive these variables were when I put them into a model in R:

```#R Code
trf3 = train(mat_perf[mat_perf\$randu <= .67,c(1:30,32)], mat_perf\$G3[mat_perf\$randu <= .67],
method="rf", metric="RMSE", data=mat_perf,
trControl=ctrl, importance=TRUE)
test\$pred.g3.plusG2 = predict(trf3, test, "raw")
cor.test(test\$G3, test\$pred.g3.plusG2)\$estimate[[1]]^2
summary(lm(test\$G3 ~ test\$pred.g3.plusG2))\$sigma
ggplot(test, aes(x=G3, y=pred.g3.plusG2)) + geom_point() +
stat_smooth(method="lm") + scale_y_continuous(breaks=seq(0,20,4), limits=c(0,20))
```

R^2 value of 0.9170506
RMSE of 1.3346087

Well, it appears that yet again we have a case where the R model has fared better than the Python model. I find it notable that when you look at the scatterplot for the Python model you can see what look like steps in the points as you scan your eyes from the bottom-left part of the trend line to the top-right part. It appears that the Random Forest model in R has benefitted from the tuning process and as a result the distribution of the residuals are more homoscedastic and also obviously closer to the regression line than the Python model. I still wonder how much more similar these results would be if I had carried out the Python analysis by tuning while cross validating like I did in R!

For the last time, let’s look at the variable importances generated for each training model:

```#Python Code
importances_allv = DataFrame({'cols':mp_X_train_allv.columns, 'imps':rf_allv.feature_importances_})
print importances_allv.sort(['imps'], ascending=False)

cols      imps
13                 G2  0.924166
12           absences  0.075834
14          school_GP  0.000000
25        Mjob_health  0.000000
24       Mjob_at_home  0.000000
23          Pstatus_T  0.000000
22          Pstatus_A  0.000000
21        famsize_LE3  0.000000
20        famsize_GT3  0.000000
17              sex_M  0.000000
16              sex_F  0.000000
15          school_MS  0.000000
56       romantic_yes  0.000000
27      Mjob_services  0.000000
11             health  0.000000
10               Walc  0.000000
9                Dalc  0.000000
8               goout  0.000000
```
```#R Code
varImp(trf3)

## rf variable importance
##
##   only 20 most important variables shown (out of 31)
##
##            Overall
## G2         100.000
## absences    33.092
## failures     9.702
## age          8.467
## paid         7.591
## schoolsup    7.385
## Pstatus      6.604
## studytime    5.963
## famrel       5.719
## reason       5.630
## guardian     5.278
## Mjob         5.163
## school       4.905
## activities   4.532
## romantic     4.336
## famsup       4.335
## traveltime   4.173
## Medu         3.540
## Walc         3.278
## higher       3.246
```

Now this is VERY telling, and gives me insight as to why the scatterplot from the Python model had that staircase quality to it. The R model is taking into account way more variables than the Python model. G2 obviously takes the cake in both models, but I suppose it overshadowed everything else by so much in the Python model, that for some reason it just didn’t find any use for any other variable than absences.

Conclusion

This was fun! For all the work I did in Python, I used IPython Notebook. Being an avid RStudio user, I’m not used to web-browser based interactive coding like what IPython Notebook provides. I discovered that I enjoy it and found it useful for laying out the information that I was using to write this blog post (I also laid out the R part of this analysis in RMarkdown for that same reason). What I did not like about IPython Notebook is that when you close it/shut it down/then later reinitialize it, all of the objects that form your data and analysis are gone and all you have left are the results. You must then re-run all of your code so that your objects are resident in memory again. It would be nice to have some kind of convenience function to save everything to disk so that you can reload at a later time.

I found myself stumbling a lot trying to figure out which Python packages to use for each particular purpose and I tended to get easily frustrated. I had to keep reminding myself that it’s a learning curve to a similar extent as it was for me while I was learning R. This frustration should not be a deterrent from picking it up and learning how to do machine learning in Python. Another part of my frustration was not being able to get variable importances from my Random Forest models in Python when I was building them using cross validation and grid searches. If you have a link to share with me that shows an example of this, I’d be happy to read it.

I liked seaborn and I think if I spend more time with it then perhaps it could serve as a decent alternative to graphing in ggplot2. That being said, I’ve spent so much time using ggplot2 that sometimes I wonder if there is anything out there that rivals its flexibility and elegance!

The issue I mentioned above with categorical variables is annoying and it really makes me wonder if using a Tree based R model would intrinsically be superior due to its automatic handling of categorical variables compared with Python, where you need to one-hot encode these variables.

All in all, I hope this was as useful and educational for you as it was for me. It’s important to step outside of your comfort zone every once in a while 🙂

# Predicting Mobile Phone Prices

Recently a colleague of mine showed me a nauseating interactive scatterplot that plots mobile phones according to two dimensions of the user’s choice from a list of possible dimensions.  Although the interactive visualization was offensive to my tastes, the JSON data behind the visualization was intriguing.  It was easy enough to get the data behind it (see this link if you want an up to date copy and be sure to take out the “data=” from the start of the file! I pulled this data around noon on March 23rd.) so that I could start asking a simple question: Which of the available factors provided in the dataset were the most predictive of full mobile phone price?

I’ll present the graphs and then the predictive model first and then the code later on:

Price by OS and Brand:

Often when investigating a topic using data, we confirm things that we already knew to be true.  This is certainly the case here with price by OS and brand.  From the below boxplots we see that the bulk of iOS devices tend to be the most expensive, and that brand-wise Apple, Google, and Samsung seem to stick out.

Price by Storage Capacity, RAM, and SD Card Capacity:

Storage capacity is perhaps the least surprising to find as having such a sharply positive correlation with price. I think what is more surprising to me is that there aren’t more gradations of storage capacity in the higher range past 50 gigabytes.  I’m guessing this is because the bulk of these phones (bearing in mind roughly 90% of these phones are in fact smart phones) are catered towards lower income folks.  Can you guess which phones occupy the top-right-most position on the first graph?  If your answer involved the iPhone 6 then you’re right on two counts!

As you can see, the correlation between RAM and price is pretty linear (with phones costing \$171.54 more for each additional gigabyte of RAM) and that between SD Card capacity and price is linear past the large group of phones with 0 SD Card capacity (with phones costing \$3.64 more for each additional gigabyte of SD Card Capacity).

Price by Screen Size, Battery, and Weight:

The next factors that I think one would naturally think of when considering the price of a mobile phone are all related to how big the thing is. Smart phones these days have a lot of physical presence just by dint of their screen size alone. Add to the large screen size the batteries that are used to support such generous displays and you also get an impressive variety of weights to these phones.

In fact, for every additional inch of screen size to these phones, you can expect an additional .81504 ounces and 565.11 mAh of battery capacity. My own humble little smartphone (an HTC Desire 601) happens to be on the smaller and lighter side of the spectrum as far as screen size and weight goes (4.5 inches screen size, or 33rd percentile; 4.59 ounces or 26th percentile) but happens to have a pretty generous battery capacity all things considered (2100 mAh, or 56th percentile).

While positive correlations can be seen between Price and all these 3 factors, battery was the most correlated with Price, next to screen size and then weight.  There’s obviously a lot of variability in price when you look at the phones with the bigger screen sizes, as they probably tend to come packed with a variety of premium extra features that can be used to jack up the price.

Putting it all together in a model:
Finally, let’s lump all of the factors provided in the data set into a model, and see how well it performs on a testing sample. I decided on an 80/20 training/testing split, and am of course using Max Kuhn’s fabulous caret package to do the dirty work. I ran a gbm model, shown below, and managed to get an R squared of 60.4% in the training sample, so not bad.

```Stochastic Gradient Boosting

257 samples
23 predictors

No pre-processing
Resampling: Cross-Validated (10 fold)

Summary of sample sizes: 173, 173, 171, 171, 172, 171, ...

Resampling results across tuning parameters:

interaction.depth  n.trees  RMSE      Rsquared   RMSE SD   Rsquared SD
1                   50      150.1219  0.5441107  45.36781  0.1546993
1                  100      147.5400  0.5676971  46.03555  0.1528225
1                  150      146.3710  0.5803005  45.00296  0.1575795
2                   50      144.0657  0.5927624  45.46212  0.1736994
2                  100      143.7181  0.6036983  44.80662  0.1787351
2                  150      143.4850  0.6041207  45.57357  0.1760428
3                   50      148.4914  0.5729182  45.27579  0.1903465
3                  100      148.5363  0.5735842  43.41793  0.1746064
3                  150      148.8497  0.5785677  43.39338  0.1781990

Tuning parameter 'shrinkage' was held constant at a value of 0.1
RMSE was used to select the optimal model using  the smallest value.
The final values used for the model were n.trees = 150, interaction.depth = 2 and shrinkage = 0.1.```

Now let’s look at the terms that came out as the most significant in the chosen model.  Below we see some unsurprising findings! Storage, battery, weight, RAM, and whether or not the phone uses iOS as the top 5. I guess I’m surprised that screen size was not higher up in the priority list, but at least it got in 6th place!

```gbm variable importance

only 20 most important variables shown (out of 41)

Overall
att_storage      100.0000
att_battery_mah   59.7597
att_weight        46.5410
att_ram           27.5871
att_osiOS         26.9977
att_screen_size   21.1106
att_sd_card       20.1130
att_brandSamsung   9.1220```

Finally, let’s look at how our model did in the testing sample. Below I’ve shown you a plot of actual versus predicted price values. The straight line is what we would expect to see if there were a perfect correlation between the two (obviously not!!) while the smoothed line is the trend that we actually do see in the scatter plot. Considering the high R squared in the testing sample of 57% (not too far off from the training sample) it’s of course a nice confirmation of the utility of this model to see the smooth line following that perfect prediction line, but I won’t call be calling up Rogers Wireless with the magical model just yet!

In fact, before I close off this post, it would be remiss of me not to investigate a couple of cases in this final graph that look like outliers. The one on the bottom right, and the one on the top left.

The one on the bottom right happens to be a Sony Xperia Z3v Black with 32GB of storage space. What I learned from checking into this is that since the pricing data on the source website is pulled from amazon.com, sometimes instead of pulling the full regular price, it happens to pull the data on a day when a special sale or service agreement price is listed. When I pulled the data, the Xperia was listed at a price of \$29.99. Today, on April 6th, the price that you would get if you looked it up through the source website is .99! Interestingly, my model had predicted a full price of \$632.17, which was not very far off from the full price of \$599.99 that you can see if you go on the listing on amazon.com. Not bad!

Now, how about the phone that cost so much but that the model said shouldn’t? This phone was none other than the Black LG 3960 Google Nexus 4 Unlocked GSM Phone with 16GB of Storage space. The price I pulled that day was a whopping \$699.99 but the model only predicted a price of \$241.86! Considering the specs on this phone, the only features that really seem to measure up are the storage (16GB is roughly in the 85th percentile for smart phones) and the RAM (2 GB is roughly in the 93rd percentile for smart phones). Overall though, the model can’t account for any other qualities that Google might have imbued into this phone that were not measured by the source website. Hence, this is a formidable model outlier!

If you take out the Sony Xperia that I mentioned first, the Adjusted R squared value goes up from 57% to 74%, and the Residual Standard Error decreases from \$156 to \$121. That’s a lot of influence for just one outlier that we found to be based on data quality alone. Wow!

Reflecting on this exercise, the one factor that I wished were collected is processor speed.  I’m curious how much that would factor into pricing decisions, but alas this information was unavailable.

Anyway, this was fun, and I hope not too boring for you, the readers. Thanks for reading!!