Fix various typos

Author: bcopy

content/titanic/CaseStudy_Titanic-Solution.ipynb

@@ -70,7 +70,7 @@
     "| --- | --- | --- |\n",
     "| survival | Survival | 0 = No, 1 = Yes |\n",
     "| pclass | Ticket class\t| 1 = 1st, 2 = 2nd, 3 = 3rd |\n",
-    "| sex | Sex | male/femail |\t\n",
+    "| sex | Sex | male/female |\t\n",
     "| Age | Age | in years |\n",
     "| sibsp | # of siblings / spouses aboard the Titanic | |\n",
     "| parch | # of parents / children aboard the Titanic | |\n",
@@ -104,7 +104,7 @@
    "source": [
     "### Load Data\n",
     "\n",
-    "This dataset is in titanic.csv. Make sure the file is in current folder. Please download the file from [here](https://github.com/data-lessons/python-business/tree/gh-pages/data) if you haven't done so yet."
+    "This dataset is in titanic.csv. Make sure the file is in current folder."
    ]
   },
   {
@@ -934,7 +934,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Task7: Plot Perished vs. Survived Bar for Male and Femail\n",
+    "##### Task7: Plot Perished vs. Survived Bar for Male and Female\n",
     "We will use seaborn countplot() again, but set argument `hue` to 'Survived'."
    ]
   },
@@ -1727,7 +1727,7 @@
     "### Feature Engineering\n",
     "We'll create a new column FamilySize. There are 2 columns related to family size, parch indicates parent or children number, Sibsp indicates sibling and spouse number.\n",
     "\n",
-    "Take one name 'Asplund' as example, we can see that total family size is 7(Parch + SibSp + 1), and each family member has same Fare, which means the Fare is for the whole group. So family size will be an important feature to predict Fare. There're only 4 Asplunds out of 7 in the dataset becasue the dataset is only a subset of all passengers."
+    "Take one name 'Asplund' as example, we can see that total family size is 7 (Parch + SibSp + 1), and each family member has same Fare, which means the Fare is for the whole group. So family size will be an important feature to predict Fare. There're only 4 Asplunds out of 7 in the dataset becasue the dataset is only a subset of all passengers."
    ]
   },
   {
@@ -2054,17 +2054,7 @@
     "\n",
     "## Step 4: Modeling\n",
     "\n",
-    "Now we have a relatively clean dataset(Except for Cabin column which has many missing values). We can do a classification on Survived to predict whether a passenger could survive the desaster or a regression on Fare to predict ticket fare. This dataset is not a good dataset for regression. But since we don't talk about classification in this workshop we will construct a linear regression on Fare in this exercise."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Task16: Contruct a regresson on Fare\n",
-    "Construct regression model with statsmodels.\n",
-    "\n",
-    "Pick Pclass, Embarked, FamilySize as independent variables."
+    "Now we have a relatively clean dataset (except for the **Cabin** column which has many missing values). We can do a classification on Survived to predict whether a passenger could survive the disaster or a regression on Fare to predict ticket fare. This dataset is not a good dataset for regression. But since we don't talk about classification in this workshop we will construct a linear regression on Fare in this exercise."
    ]
   },
   {