Update Mission165Solutions.ipynb

Mission165Solutions.ipynb

@@ -525,7 +525,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## How many rows in the data set?"
+    "## How many rows are in the dataset?"
    ]
   },
   {
@@ -632,9 +632,9 @@
     "collapsed": true
    },
    "source": [
-    "### Observation 1: By default -- 31 numeric columns and 21 string columns.\n",
+    "### Observation 1: By default — 31 numeric columns and 21 string columns.\n",
     "\n",
-    "### Observation 2: It seems like one column in particular (the `id` column) is being cast to int64 in the last 2 chunks but not in the earlier chunks. Since the `id` column won't be useful for analysis, visualization, or predictive modelling let's ignore this column.\n",
+    "### Observation 2: It seems like one column in particular (the `id` column) is being cast to int64 in the last 2 chunks but not in the earlier chunks. Since the `id` column won't be useful for analysis, visualization, or predictive modeling, let's ignore this column.\n",
     "\n",
     "## How many unique values are there in each string column? How many of the string columns contain values that are less than 50% unique?"
    ]
@@ -797,7 +797,7 @@
    "source": [
     "## Optimizing String Columns\n",
     "\n",
-    "### Determine which string columns you can convert to a numeric type if you clean them. Let's focus on columns that would actually be useful for analysis and modelling."
+    "### Determine which string columns you can convert to a numeric type if you clean them. Let's focus on columns that would actually be useful for analysis and modeling."
    ]
   },
   {
@@ -1108,7 +1108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Convert to category"
+    "### Convert to category."
    ]
   },
   {
@@ -1613,7 +1613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,