diff --git a/learning_curve.py b/learning_curve.py
index 2364f2c..a8e9f31 100644
--- a/learning_curve.py
+++ b/learning_curve.py
@@ -7,8 +7,8 @@
 from sklearn.linear_model import LogisticRegression
 
 data = load_digits()
-print data.DESCR
-num_trials = 10
+# print data.DESCR
+num_trials = 100
 train_percentages = range(5,95,5)
 test_accuracies = numpy.zeros(len(train_percentages))
 
@@ -17,10 +17,18 @@
 # You should repeat each training percentage num_trials times to smooth out variability
 # for consistency with the previous example use model = LogisticRegression(C=10**-10) for your learner
 
-# TODO: your code here
+for i in range(len(train_percentages)):
+	trial_accuracies = numpy.zeros(num_trials)
+	for trial in range(num_trials):
+		X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, train_size=train_percentages[i])
+		model = LogisticRegression(C=10**-3)
+		model.fit(X_train, y_train)
+		trial_accuracies[trial] = model.score(X_test,y_test)
+	test_accuracies[i] = sum(trial_accuracies) / num_trials
+
 
 fig = plt.figure()
 plt.plot(train_percentages, test_accuracies)
 plt.xlabel('Percentage of Data Used for Training')
 plt.ylabel('Accuracy on Test Set')
-plt.show()
+plt.show()
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diff --git a/questions.txt b/questions.txt
new file mode 100644
index 0000000..35d0e94
--- /dev/null
+++ b/questions.txt
@@ -0,0 +1,11 @@
+1. What is the general trend in the curve?
+The accuracy of the test generally increases as the percentage of the data used to train the machine increases.
+
+2. Are there parts of the curve that appear to be noisier than others?  Why?
+The curve seems to be consistently noisy at 10 trials.
+
+3. How many trials do you need to get a smooth curve?
+The curve started to smooth out at 2000 trials per percentage.
+
+4. Try different values for C (by changing LogisticRegression(C=10**-10)).  What happens?  If you want to know why this happens, see this Wikipedia page as well as the documentation for LogisticRegression in scikit-learn.
+As C gets larger, the noise in the curve decreases.
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