diff --git a/kaggle/titanic.ipynb b/kaggle/titanic.ipynb
index 422c7c5..f0bf41d 100644
--- a/kaggle/titanic.ipynb
+++ b/kaggle/titanic.ipynb
@@ -426,6 +426,13 @@
      ],
      "prompt_number": 4
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Type 'object' is a string for pandas, which poses problems with machine learning algorithms.  If we want to use these as features, we'll need to convert these to number representations."
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -471,7 +478,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Note Age, Cabin, and Embarked are missing values.  Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked."
+      "Age, Cabin, and Embarked are missing values.  Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked."
      ]
     },
     {
@@ -643,7 +650,6 @@
       "# Plot Pclass counts\n",
       "ax1 = plt.subplot2grid((3, 2), (0, 1))\n",
       "df['Pclass'].value_counts().plot(kind='bar', title='Passenger Class Counts')\n",
-      "#plt.xticks((1, 2, 3), ('First', 'Second', 'Third'), rotation=0)\n",
       "\n",
       "# Plot Sex counts\n",
       "ax2 = plt.subplot2grid((3, 2), (1, 0))\n",
@@ -683,6 +689,13 @@
      ],
      "prompt_number": 7
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next we'll explore various features to view their impact on survival rates."
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -694,36 +707,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Plot a histogram of Pclass:"
+      "From our exploratory data analysis in the previous section, we see there are three passenger classes: First, Second, and Third class.  We'll determine which proportion of passengers survived based on their passenger class."
      ]
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Get the unique values of Pclass:\n",
-      "passenger_classes = sort(df['Pclass'].unique())\n",
-      "\n",
-      "df['Pclass'].hist(bins=len(passenger_classes))\n",
-      "plt.title('Passenger Class Histogram')\n",
-      "plt.xlabel('Passenger Class')\n",
-      "plt.ylabel('Count')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Fo68Ig2KsYDh17gAoHAUaAABAy9CDBgDIgh40jAd60AAAAIpAgYai0VeEQTFWMJw6dwAU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\nAAC0zP8HY4b11pIaXGQAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10a28bb90>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -805,10 +791,10 @@
       "# Normalize the cross tab to sum to 1:\n",
       "pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
       "\n",
-      "pclass_xt_pct.plot(kind='bar', stacked=True)\n",
-      "plt.title('Survival Rate by Passenger Classes')\n",
+      "pclass_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Passenger Classes')\n",
       "plt.xlabel('Passenger Class')\n",
-      "plt.ylabel('Survival Rate')"
+      "plt.ylabel('Survival Rate')\n",
+      "plt.xticks((0, 1, 2), ('First', 'Second', 'Third'), rotation=0)"
      ],
      "language": "python",
      "metadata": {},
@@ -816,27 +802,30 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 52,
        "text": [
-        "<matplotlib.text.Text at 0x10aaa1cd0>"
+        "([<matplotlib.axis.XTick at 0x10e60f610>,\n",
+        "  <matplotlib.axis.XTick at 0x10e621c90>,\n",
+        "  <matplotlib.axis.XTick at 0x10e6c7f50>],\n",
+        " <a list of 3 Text xticklabel objects>)"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+FzNmZr55qJFJkiTNMtONeF1b/nl5ZV0CUf6p2ewmHPVSB/RwNECzXw/zfHRM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\nnnQsfwu8sXJj1nsz8z8nH3B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JjBERn+hqZGZmZmZjzHAjXnelf+cW1gWg9K+NZQ/iUS9rgAyPBtjYl+E87x1t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\nRK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10aa7bfd0>"
+        "<matplotlib.figure.Figure at 0x10e606e10>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 52
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We can see that passenger class seems to have a significant impact on whether a passenger survived."
+      "We can see that passenger class seems to have a significant impact on whether a passenger survived.  Those in First Class the highest chance for survival."
      ]
     },
     {
@@ -850,22 +839,22 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
+      "Gender might have also played a role in determining a passenger's survival rate.  We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Prepare to map Sex from a string to a number representation:"
+      "Generate a mapping of Sex from a string to a number representation:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "genders = sort(df['Sex'].unique())\n",
-      "genders_mapping = dict(zip(genders, range(0, len(genders) + 1)))\n",
+      "sexes = sort(df['Sex'].unique())\n",
+      "genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
       "genders_mapping"
      ],
      "language": "python",
@@ -874,13 +863,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 45,
        "text": [
         "{'female': 0, 'male': 1}"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
@@ -997,40 +986,6 @@
      ],
      "prompt_number": 12
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Plot a histogram of Sex_Val:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Get the unique values for gender\n",
-      "genders = sort(df['Sex_Val'].unique())\n",
-      "\n",
-      "df['Sex_Val'].hist(bins=len(genders))\n",
-      "plt.title('Gender Histogram')\n",
-      "plt.xlabel('Gender')\n",
-      "plt.ylabel('Count')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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mXesAmknXOoAGZoEmSZI0MvagSZI9aJLmxh40SZKkpTC6Ai3JKUkuTXJZkme1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\nJI2MBZokSdLIWKBJkiSNzP8HaGQZ6TcL/kUAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10a28bfd0>"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1044,10 +999,10 @@
      "input": [
       "sex_val_xt = pd.crosstab(df['Sex_Val'], df['Survived'])\n",
       "sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)\n",
-      "sex_val_xt_pct.plot(kind='bar', stacked=True)\n",
-      "plt.title('Survival Rate by Gender')\n",
+      "sex_val_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Gender')\n",
       "plt.xlabel('Gender')\n",
-      "plt.ylabel('Survival Rate')"
+      "plt.ylabel('Survival Rate')\n",
+      "plt.xticks((0, 1), ('Female', 'Male'), rotation=0)"
      ],
      "language": "python",
      "metadata": {},
@@ -1055,21 +1010,23 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 56,
        "text": [
-        "<matplotlib.text.Text at 0x10b02e1d0>"
+        "([<matplotlib.axis.XTick at 0x10e86b450>,\n",
+        "  <matplotlib.axis.XTick at 0x10e86b0d0>],\n",
+        " <a list of 2 Text xticklabel objects>)"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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SStGoOgDVhImXJElSn5h4qV6s8ZJUimbVAagmTLwkSZL6xMRL9WKNl6RSNKoO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\n+pR4CxkAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10acfd990>"
+        "<matplotlib.figure.Figure at 0x10dcca110>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 56
     },
     {
      "cell_type": "markdown",
@@ -1078,6 +1035,13 @@
       "The majority of females survived, whereas the majority of males did not."
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next we'll determine whether we can gain any insights on survival rate by looking at both Sex and Pclass."
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1089,6 +1053,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# Get the unique values of Pclass:\n",
+      "passenger_classes = sort(df['Pclass'].unique())\n",
+      "\n",
       "for p_class in passenger_classes:\n",
       "    print 'M: ', p_class, len(df[(df['Sex'] == 'male') & (df['Pclass'] == p_class)])\n",
       "    print 'F: ', p_class, len(df[(df['Sex'] == 'female') & (df['Pclass'] == p_class)])"
@@ -1111,6 +1078,74 @@
      ],
      "prompt_number": 15
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Plot survival rate by Sex and Pclass:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "females_df = df[df['Sex'] == 'female']\n",
+      "females_xt = pd.crosstab(females_df['Pclass'], df['Survived'])\n",
+      "females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
+      "females_xt_pct.plot(kind='bar', stacked=True, title='Female Survival Rate by Passenger Class')\n",
+      "plt.xlabel('Passenger Class')\n",
+      "plt.ylabel('Survival Rate')\n",
+      "plt.xticks((0, 1, 2), ('First', 'Second', 'Third'), rotation=0)\n",
+      "\n",
+      "males_df = df[df['Sex'] == 'male']\n",
+      "males_xt = pd.crosstab(males_df['Pclass'], df['Survived'])\n",
+      "males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
+      "males_xt_pct.plot(kind='bar', stacked=True, title='Male Survival Rate by Passenger Class')\n",
+      "plt.xticks((0, 1, 2), ('First', 'Second', 'Third'), rotation=0)\n",
+      "plt.xlabel('Passenger Class')\n",
+      "plt.ylabel('Survival Rate')\n",
+      "plt.xticks((0, 1, 2), ('First', 'Second', 'Third'), rotation=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 88,
+       "text": [
+        "([<matplotlib.axis.XTick at 0x110f1fb90>,\n",
+        "  <matplotlib.axis.XTick at 0x110f1fa50>,\n",
+        "  <matplotlib.axis.XTick at 0x112bba0d0>],\n",
+        " <a list of 3 Text xticklabel objects>)"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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N1AXmuTphBke9Jse8hVdVndE+/U5VnTPfcpIkSRrMIGc1/luSi5Icn2SvoUek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\nUThlptPUZgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1102a1890>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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G8CFga2ANUuZFxN+vyCDNzMzMRrpOmuu/B2wATKE8UbwJML+LMVlPKHIHYFaD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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1103af550>"
+       ]
+      }
+     ],
+     "prompt_number": 88
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The vast majority of females in First and Second class survived.  Males in First class had the highest chance for survival."
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1996,6 +2031,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "# Get the unique values for gender\n",
+      "genders = sort(df['Sex_Val'].unique())\n",
+      "\n",
       "median_ages = np.zeros((len(genders), len(passenger_classes)))\n",
       "median_ages"
      ],
@@ -2005,14 +2043,14 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 30,
+       "prompt_number": 46,
        "text": [
         "array([[ 0.,  0.,  0.],\n",
         "       [ 0.,  0.,  0.]])"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",