working on submission

Author: adamw523

projects/student_intervention/student_intervention.ipynb

@@ -1049,7 +1049,7 @@
    "source": [
     "The Support Vector Classifier was the best performing model with an F<sub>1</sub> score of 0.8462 when testing on 300 samples. Compared to a score of 0.7681 for K Nearest Neighbors and 0.7481 for Decision Trees.\n",
     "\n",
-    "The resources, cost and performanc were not a major factor for the given data. This is due to the small amount of data in this example. Although all training and prediction times were very low, training was shortest for k-nearest neighbors because it does it's computation at prediction time, then decision trees, and the SVC was the slowest. Prediction time was shortest for decision trees, then the SVC and slowest for k-nearest neighbors.\n"
+    "The resources, cost and performanc were not a major factor for the given data. This is due to the small amount of data in this example. Although all training and prediction times were very low, training was shortest for K Nearest Neighbors at 0.001 seconds for 300 samples because it does it's computation at prediction time, then decision trees at 0.003 seconds for 300 samples, and the SVC at 0.009 seconds for 300 samples was the slowest. Prediction time was shortest for decision trees, then the SVC and slowest for k-nearest neighbors.\n"
    ]
   },
   {
@@ -1058,7 +1058,7 @@
    "source": [
     "Support Vector classification works by linearly separating classes of data. A linear separator is chosen by finding the largest margin between classes. Data points that are closest to the margins on both sides of the separator are considerted the support vecotors.\n",
     "\n",
-    "When a linear separation of data cannot be acoomplished, SVM uses a kernel trick to map the data into a higher dimension. With a kernel trick applied, the data becomes linearly separable."
+    "When a linear separation of data cannot be acoomplished, SVM uses a kernel trick to map add the data into a higher dimension. With a kernel trick applied, the data becomes linearly separable."
    ]
   },
   {