completed answer to question 6

Author: juanerolon

finding_donors.ipynb

@@ -1519,21 +1519,21 @@
     "So we are also looking for MAXIMAL values of GT when possible\n",
     "\n",
     "** 1. Capital loss:**\n",
-    "This feature contains 3 subfeatures (capital gain amounts of 10-20K, 20-30K and 90-100K) with majortiy '>50K' class and clearly discriminated from the rest of the subfeatures. Among all features, capital gain fulfills all of our simplified feature importance criteria.\n",
+    "This feature contains 3 subfeatures (capital gain amounts of 10-20K, 20-30K and 90-100K) with majority '>50K' class and clearly discriminated from the rest of the subfeatures. Among all features, capital gain fulfills all of our simplified feature importance criteria.\n",
     "\n",
     "** 2. Capital gain:**\n",
-    "This feature contains 2 subfeatures (capital loss amounts of 1.5-2K, 2.5-3K) with majortiy '>50K' class and 1 subfeature (2-2.5K) with R > 50%. These attribute groups are clearly discriminated from the rest of the subfeatures. Capital loss fulfills all of our simplified feature importance criteria.\n",
+    "This feature contains 2 subfeatures (capital loss amounts of 1.5-2K, 2.5-3K) with majority '>50K' class and 1 subfeature (2-2.5K) with R > 50%. These attribute groups are clearly discriminated from the rest of the subfeatures. Capital loss fulfills all of our simplified feature importance criteria.\n",
     "\n",
     "** 3. Education Level:**\n",
-    "It contains subfeatures \"Masters\", \"Doctorate\" and \"Prof. School\" with majortiy '>50K' class, and \"Bachelors\", with a high percentage of \">50K\" earners in that group R > 0.5.  Althought these groups discriminate between the two income classes, the '>50K' (green) disbribution spreads a bit more among the rest of the groups in comparison to capital gain and loss. \n",
+    "It contains subfeatures \"Masters\", \"Doctorate\" and \"Prof. School\" with majority '>50K' class, and \"Bachelors\", with a high percentage of \">50K\" earners in that group R > 0.5.  Althought these groups discriminate between the two income classes, the '>50K' (green) distribution spreads a bit more among the rest of the groups in comparison to capital gain and loss. \n",
     "\n",
     "** 4. Marital Status:**\n",
     "Contains the subfeature \"Married-civ-Spouse\" that is clearly discriminated from the rest of attribute groups, and which contains a significant proportion of '>50K' earners, R >0.5.\n",
     "\n",
     "** 5. Age:**\n",
     "The class distribution appears to be the envelope of a normal distribution, which is good for expectation values in relation to '>50K' class. Near the center we have subfeatures corresponding to age groups 40-50, 50-60 with a significant proportion of '>50K', R >0.5.\n",
     "\n",
-    "The rest of the features do not fulfill our \"rule of thumb\" criteria except for \"Occupation\", which contains a several \">50K\" groups wiht R>0.5, but no '>50K' majority groups. In addition, 'occupation' seems to be correlated or dependent on the education feature (possibly redundant)."
+    "The rest of the features do not fulfill our \"rule of thumb\" criteria except for \"Occupation\", which contains a several \">50K\" groups with R>0.5, but no '>50K' majority groups. In addition, 'occupation' seems to be correlated or dependent on the education feature (possibly redundant)."
    ]
   },
   {