diff --git a/Jeff spam homework.ipynb b/Jeff spam homework.ipynb
new file mode 100644
index 0000000..94acdf5
--- /dev/null
+++ b/Jeff spam homework.ipynb	
@@ -0,0 +1,472 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sklearn\n",
+    "from sklearn.cross_validation import train_test_split \n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.naive_bayes import MultinomialNB as mnb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"spambase/spambase.data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
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+       "      <th>0.42</th>\n",
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+       "      <th>0.43</th>\n",
+       "      <th>0.44</th>\n",
+       "      <th>3.756</th>\n",
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+       "      <th>1</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <td>0.25</td>\n",
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+       "      <td>0.184</td>\n",
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+       "      <td>485</td>\n",
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+       "      <td>1</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0  0.64  0.64.1  0.1  0.32   0.2   0.3   0.4   0.5   0.6 ...  0.40  \\\n",
+       "0  0.21  0.28    0.50    0     0  0.28  0.21  0.07  0.00  0.94 ...  0.00   \n",
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+       "1  0.143     0  0.276  0.184  0.010  9.821  485  2259  1  \n",
+       "\n",
+       "[2 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "spam_train, spam_test = train_test_split(df, test_size=.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>0.64</th>\n",
+       "      <th>0.64.1</th>\n",
+       "      <th>0.1</th>\n",
+       "      <th>0.32</th>\n",
+       "      <th>0.2</th>\n",
+       "      <th>0.3</th>\n",
+       "      <th>0.4</th>\n",
+       "      <th>0.5</th>\n",
+       "      <th>0.6</th>\n",
+       "      <th>...</th>\n",
+       "      <th>0.40</th>\n",
+       "      <th>0.41</th>\n",
+       "      <th>0.42</th>\n",
+       "      <th>0.778</th>\n",
+       "      <th>0.43</th>\n",
+       "      <th>0.44</th>\n",
+       "      <th>3.756</th>\n",
+       "      <th>61</th>\n",
+       "      <th>278</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2215</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>1.408</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3310</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      0  0.64  0.64.1  0.1  0.32  0.2  0.3  0.4  0.5  0.6 ...   0.40  0.41  \\\n",
+       "2215  0     0       0    0     0    0    0    0    0    0 ...  1.408     0   \n",
+       "3310  0     0       0    0     0    0    0    0    0    0 ...  0.000     0   \n",
+       "\n",
+       "      0.42  0.778  0.43  0.44  3.756  61  278  1  \n",
+       "2215     0      0     0     0    2.6   6   13  0  \n",
+       "3310     0      0     0     0    1.4   3    7  0  \n",
+       "\n",
+       "[2 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spam_train.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>0.64</th>\n",
+       "      <th>0.64.1</th>\n",
+       "      <th>0.1</th>\n",
+       "      <th>0.32</th>\n",
+       "      <th>0.2</th>\n",
+       "      <th>0.3</th>\n",
+       "      <th>0.4</th>\n",
+       "      <th>0.5</th>\n",
+       "      <th>0.6</th>\n",
+       "      <th>...</th>\n",
+       "      <th>0.40</th>\n",
+       "      <th>0.41</th>\n",
+       "      <th>0.42</th>\n",
+       "      <th>0.778</th>\n",
+       "      <th>0.43</th>\n",
+       "      <th>0.44</th>\n",
+       "      <th>3.756</th>\n",
+       "      <th>61</th>\n",
+       "      <th>278</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>871</th>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.599</td>\n",
+       "      <td>0.148</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>4.947</td>\n",
+       "      <td>102</td>\n",
+       "      <td>564</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1074</th>\n",
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+       "      <td>0.55</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0.165</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0.000</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>16.826</td>\n",
+       "      <td>148</td>\n",
+       "      <td>387</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         0  0.64  0.64.1  0.1  0.32   0.2  0.3   0.4   0.5   0.6 ...  0.40  \\\n",
+       "871   0.45  0.00    0.67    0     0  0.67  0.0  0.67  0.22  0.22 ...     0   \n",
+       "1074  0.00  0.55    0.55    0     0  0.55  2.2  0.00  0.00  0.55 ...     0   \n",
+       "\n",
+       "       0.41  0.42  0.778   0.43   0.44   3.756   61  278  1  \n",
+       "871   0.111     0  1.599  0.148  0.000   4.947  102  564  1  \n",
+       "1074  0.165     0  0.496  0.000  0.082  16.826  148  387  1  \n",
+       "\n",
+       "[2 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spam_test.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "training_run = mnb()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_run.fit(spam_train.iloc[:, :57], spam_train.iloc[:, -1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.79619565217391308"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_run.score(spam_test.iloc[:, :57], spam_test.iloc[:, -1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spam detector is coming back with 80% accuracy score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
index 3a8b6f2..0821c82 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
-# Classifying spam
+# Run Jeff spam homework.ipynb using Ipython Notebook
+
+## Classifying spam
 
 ## Description