From 950732c4838bcbaa2af3c338ad8d2da86fb35a17 Mon Sep 17 00:00:00 2001 From: Chris LaPollo Date: Sun, 27 Aug 2017 14:07:13 -0400 Subject: [PATCH] Reverting commits made from student repository Revert "update project 2 with finishied notebook in finding donors project folder" This reverts commit b48b3d19654722562f8d7142a336dcecd782a28d, reversing changes made to 2d30eb37a5cc18bcad15458e1ae02215f852a109. --- .../customer_segments/customer_segments.ipynb | 13 ++++++------- projects/finding_donors/finding_donors.ipynb | 16 ++++++---------- 2 files changed, 12 insertions(+), 17 deletions(-) diff --git a/projects/customer_segments/customer_segments.ipynb b/projects/customer_segments/customer_segments.ipynb index 9f84b215c8..abb9a9fb07 100755 --- a/projects/customer_segments/customer_segments.ipynb +++ b/projects/customer_segments/customer_segments.ipynb @@ -121,8 +121,7 @@ "\n", "* Fresh: 12000.2977\n", "* Milk: 5796.2\n", - "* Frozen: 3071.9\n", - "* Grocery: 7951.2\n", + "* Grocery: 3071.9\n", "* Detergents_paper: 2881.4\n", "* Delicatessen: 1524.8\n", "\n", @@ -821,21 +820,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, diff --git a/projects/finding_donors/finding_donors.ipynb b/projects/finding_donors/finding_donors.ipynb index 2bf4207674..30ba81c15e 100644 --- a/projects/finding_donors/finding_donors.ipynb +++ b/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_log_minmax_transform'` 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_raw'` 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": true + "collapsed": false }, "outputs": [ { @@ -679,6 +679,7 @@ "- 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" ] @@ -1291,26 +1292,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "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,