diff --git a/Mission213Solution.ipynb b/Mission213Solution.ipynb
index a5968e6..85df62e 100644
--- a/Mission213Solution.ipynb
+++ b/Mission213Solution.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -170,9 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -210,9 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -270,17 +264,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Error metric\n",
+    "## Error Metric\n",
     "\n",
-    "The mean squared error metric makes the most sense to evaluate our error.  MSE works on continuous numeric data, which fits our data quite well."
+    "The mean squared error metric makes the most sense to evaluate our error. MSE works on continuous numeric data, which fits our data quite well."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "train = bike_rentals.sample(frac=.8)"
@@ -289,9 +281,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "test = bike_rentals.loc[~bike_rentals.index.isin(train.index)]"
@@ -300,9 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -332,9 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -360,15 +346,13 @@
    "source": [
     "## Error\n",
     "\n",
-    "The error is very high, which may be due to the fact that the data has a few extremely high rental counts, but otherwise mostly low counts.  Larger errors are penalized more with MSE, which leads to a higher total error."
+    "The error is very high, which may be due to the fact that the data has a few extremely high rental counts but otherwise mostly low counts. Larger errors are penalized more with MSE, which leads to a higher total error."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -395,9 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -419,9 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -448,7 +428,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Decision tree error\n",
+    "## Decision Tree Error\n",
     "\n",
     "By taking the nonlinear predictors into account, the decision tree regressor appears to have much higher accuracy than linear regression."
    ]
@@ -456,9 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -485,9 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -510,7 +486,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Random forest error\n",
+    "## Random Forest Error\n",
     "\n",
     "By removing some of the sources of overfitting, the random forest accuracy is improved over the decision tree accuracy."
    ]
@@ -532,7 +508,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,