From 7cc503ea2c787d46bd92f0d49f68ac83d0962086 Mon Sep 17 00:00:00 2001 From: Adarsh Nair Date: Tue, 22 Aug 2017 09:50:12 -0700 Subject: [PATCH 1/3] typo --- projects/finding_donors/finding_donors.ipynb | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/projects/finding_donors/finding_donors.ipynb b/projects/finding_donors/finding_donors.ipynb index 770cbff562..1dbecd6257 100644 --- a/projects/finding_donors/finding_donors.ipynb +++ b/projects/finding_donors/finding_donors.ipynb @@ -237,7 +237,7 @@ "| 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`." ] @@ -353,9 +353,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "'''\n", @@ -836,21 +834,21 @@ ], "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, From a1cbaf36520e1926a0cce66ab4ac6a5cf137d79e Mon Sep 17 00:00:00 2001 From: Adarsh Nair Date: Wed, 23 Aug 2017 12:17:08 -0700 Subject: [PATCH 2/3] typo fixed --- projects/customer_segments/customer_segments.ipynb | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/projects/customer_segments/customer_segments.ipynb b/projects/customer_segments/customer_segments.ipynb index abb9a9fb07..9f84b215c8 100755 --- a/projects/customer_segments/customer_segments.ipynb +++ b/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, From 9fdeb5401039b5e0cb08048e236e1e661de673fd Mon Sep 17 00:00:00 2001 From: Adarsh Nair Date: Wed, 23 Aug 2017 14:27:27 -0700 Subject: [PATCH 3/3] sgd removal --- projects/finding_donors/finding_donors.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/projects/finding_donors/finding_donors.ipynb b/projects/finding_donors/finding_donors.ipynb index 1dbecd6257..0a38c23c14 100644 --- a/projects/finding_donors/finding_donors.ipynb +++ b/projects/finding_donors/finding_donors.ipynb @@ -353,7 +353,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "'''\n", @@ -387,7 +389,6 @@ "- 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" ]