update project 2 with finishied notebook in finding donors project folder

Author: Joshua Bernhard

projects/customer_segments/customer_segments.ipynb

@@ -121,7 +121,8 @@
     "\n",
     "* Fresh: 12000.2977\n",
     "* Milk: 5796.2\n",
-    "* Grocery: 3071.9\n",
+    "* Frozen: 3071.9\n",
+    "* Grocery: 7951.2\n",
     "* Detergents_paper: 2881.4\n",
     "* Delicatessen: 1524.8\n",
     "\n",
@@ -820,21 +821,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,

projects/finding_donors/finding_donors.ipynb

@@ -467,7 +467,7 @@
     "| 2 |  A  |  | 1 | 0 | 0 |\n",
     "\n",
     "Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label (\"<=50K\" and \">50K\"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:\n",
-    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.\n",
+    " - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_log_minmax_transform'` data.\n",
     " - Convert the target label `'income_raw'` to numerical entries.\n",
     "   - Set records with \"<=50K\" to `0` and records with \">50K\" to `1`."
    ]
@@ -637,7 +637,7 @@
    "cell_type": "code",
    "execution_count": 65,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [
     {
@@ -679,7 +679,6 @@
     "- Decision Trees\n",
     "- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)\n",
     "- K-Nearest Neighbors (KNeighbors)\n",
-    "- Stochastic Gradient Descent Classifier (SGDC)\n",
     "- Support Vector Machines (SVM)\n",
     "- Logistic Regression"
    ]
@@ -1292,21 +1291,26 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
+<<<<<<< HEAD
    "pygments_lexer": "ipython2",
    "version": "2.7.13"
+=======
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+>>>>>>> 9fdeb5401039b5e0cb08048e236e1e661de673fd
   }
  },
  "nbformat": 4,