added documentation

Author: pwrose

README.md

@@ -5,11 +5,11 @@ This repository is an adjunct to the "Ten Simple Rules for Reproducible Research
 The example notebooks demonstrate some of rules. 
 
 ## Example 1
-This example demonstrates a 3-step workflow for predicting the protein fold type using a Machine Learning approach.
+This example demonstrates a 4-step workflow for predicting the protein fold type using a Machine Learning approach.
 
-You can launch the top level notebook directly in your web browser: [example1/0-Workflow.ipynb](https://mybinder.org/v2/gh/jupyter-guide/ten-rules-jupyter/master?filepath=example1%2F0-Workflow.ipynb).
+You can launch the top level notebook directly in your web browser: [0-Workflow.ipynb](https://mybinder.org/v2/gh/jupyter-guide/ten-rules-jupyter/master?filepath=example1%2F0-Workflow.ipynb).
 
-Then follow the steps in the notebook to run the 3 steps of the workflow.
+Then follow the steps in the notebook to run the 4 steps of the workflow.
 
 ## Example 2
 

example1/0-Workflow.ipynb

@@ -158,12 +158,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "[3-FitModel.ipynb](./3-FitModel.ipynb)"
+    "[4-Predict.ipynb](./4-Predict.ipynb)"
    ]
   },
   {
@@ -186,13 +184,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
       "CPython 3.6.3\n",
       "IPython 6.3.1\n",
       "\n",
@@ -208,23 +208,14 @@
       "machine    : x86_64\n",
       "processor  : i386\n",
       "CPU cores  : 4\n",
-      "interpreter: 64bit\n",
-      "Git hash   : 13806bae2b84b9732346ca9b09b58c05b5a2d641\n",
-      "Git repo   : https://github.com/pwrose/ten-rules-jupyter.git\n"
+      "interpreter: 64bit\n"
      ]
     }
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -v -m -p gensim,matplotlib,numpy,pandas,sklearn -r -g"
+    "%watermark -v -m -p gensim,matplotlib,numpy,pandas,sklearn"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

example1/3-FitModel.ipynb

@@ -16,8 +16,6 @@
     "import pandas as pd\n",
     "import mlutils\n",
     "from sklearn import svm, metrics, preprocessing\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.externals import joblib"
    ]
@@ -343,6 +341,21 @@
    "source": [
     "joblib.dump(classifier, \"./classifier\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Step\n",
+    "After you saved the classifier model here, go back to the [0-Workflow.ipynb](./0-Workflow.ipynb) to run the next step of the analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {