diff --git a/ml1/2_5_1_kNN_Model.ipynb b/ml1/2_5_1_kNN_Model.ipynb
index 15eeefd..7dbb048 100644
--- a/ml1/2_5_1_kNN_Model.ipynb
+++ b/ml1/2_5_1_kNN_Model.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -55,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to learn how to train a model, make predictions with that model and evaluate these predictions.\n",
+    "The goal of this notebook is to learn how to train a model, make predictions with that model, and evaluate these predictions.\n",
     "\n",
     "The notebook uses the [kNN (k nearest neighbors) algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)."
    ]
@@ -212,14 +212,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Precision, recall and f-score"
+    "### Precision, recall, and f-score"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
+    "For evaluating classification algorithms, we usually calculate three metrics: precision, recall, and F1-score\n",
     "\n",
     "* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
     "* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
@@ -246,7 +246,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another useful metric is the confusion matrix"
+    "Another useful metric is the confusion matrix."
    ]
   },
   {
@@ -262,7 +262,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see we classify well all the 'setosa' and 'versicolor' samples. "
+    "We classify all the 'setosa' and 'versicolor' samples well. "
    ]
   },
   {
@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
+    "To avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**."
    ]
   },
   {
@@ -298,7 +298,7 @@
     "# create a k-fold cross validation iterator of k=10 folds\n",
     "cv = KFold(10, shuffle=True, random_state=33)\n",
     "\n",
-    "# by default the score used is the one returned by score method of the estimator (accuracy)\n",
+    "# by default the score used is the one returned by the score method of the estimator (accuracy)\n",
     "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
     "print(scores)"
    ]
@@ -307,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
+    "We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure."
    ]
   },
   {
@@ -340,7 +340,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are going to tune the algorithm, and calculate which is the best value for the k hyperparameter."
+    "We will tune the algorithm and calculate the best value for the k hyperparameter."
    ]
   },
   {
@@ -365,7 +365,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result is very dependent of the input data. Execute again the train_test_split and test again how the result changes with k."
+    "The result is very dependent on the input data. Execute the train_test_split again and test how the result changes with k."
    ]
   },
   {
@@ -387,7 +387,7 @@
    "metadata": {},
    "source": [
     "## Licence\n",
-    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
+    "The notebook is freely licensed under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
     "\n",
     "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
    ]