From 44a555ac2d89fc94b53597d946980a86362e731c Mon Sep 17 00:00:00 2001
From: "Carlos A. Iglesias" <cif2cif@gmail.com>
Date: Mon, 2 Jun 2025 16:09:55 +0300
Subject: [PATCH] Update 2_3_1_Advanced_Visualisation.ipynb

Changed image path
---
 ml1/2_3_1_Advanced_Visualisation.ipynb | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/ml1/2_3_1_Advanced_Visualisation.ipynb b/ml1/2_3_1_Advanced_Visualisation.ipynb
index ba80af5..50776e1 100644
--- a/ml1/2_3_1_Advanced_Visualisation.ipynb
+++ b/ml1/2_3_1_Advanced_Visualisation.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
+    "![](./images/EscUpmPolit_p.gif \"UPM\")"
    ]
   },
   {
@@ -52,11 +52,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the previous notebook we developed plots with the [matplotlib](http://matplotlib.org/) plotting library.\n",
+    In the previous notebook, we developed plots with the [matplotlib](http://matplotlib.org/) plotting library.\n",
     "\n",
     "This notebook introduces another plotting library, [**seaborn**](https://stanford.edu/~mwaskom/software/seaborn/),  which provides advanced facilities for data visualization.\n",
     "\n",
-    "*Seaborn* is a library for making attractive and informative statistical graphics in Python. It is built on top of *matplotlib* and tightly integrated with the *PyData* stack, including support for *numpy* and *pandas* data structures and statistical routines from *scipy* and *statsmodels*.\n",
+    "*Seaborn* is a library that makes attractive and informative statistical graphics in Python. It is built on top of *matplotlib* and tightly integrated with the *PyData* stack, including support for *numpy* and *pandas* data structures and statistical routines from *scipy* and *statsmodels*.\n",
     "\n",
     "*Seaborn* requires its input to be *DataFrames* (a structure created with the library *pandas*)."
    ]
@@ -197,9 +197,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A very common way to use this plot colors the observations by a separate categorical variable. For example, the iris dataset has four measurements for each of the three different species of iris flowers.\n",
+    "A widespread way to use this plot colors the observations by a separate categorical variable. For example, the iris dataset has four measurements for each of the three different species of iris flowers.\n",
     "\n",
-    "We are going to color each class, so that we can easily identify **clustering** and **linear relationships**."
+    "We are going to color each class, so we can easily identify **clustering** and **linear relationships**."
    ]
   },
   {
@@ -220,7 +220,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By default every numeric column in the dataset is used, but you can focus on particular relationships if you want."
+    "By default, every numeric column in the dataset is used, but you can focus on particular relationships if you want."
    ]
   },
   {
@@ -321,7 +321,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# One way we can extend this plot is adding a layer of individual points on top of\n",
+    "# One way we can extend this plot is by adding a layer of individual points on top of\n",
     "# it through Seaborn's striplot\n",
     "# \n",
     "# We'll use jitter=True so that all the points don't fall in single vertical lines\n",
@@ -347,7 +347,7 @@
    "outputs": [],
    "source": [
     "# A violin plot combines the benefits of the previous two plots and simplifies them\n",
-    "# Denser regions of the data are fatter, and sparser thiner in a violin plot\n",
+    "# Denser regions of the data are fatter, and sparser thinner in a violin plot\n",
     "sns.violinplot(x=\"species\", y=\"petal length (cm)\", data=iris_df, size=6)"
    ]
   },
@@ -389,10 +389,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Depending on the data, we can choose which visualisation suits better. the following [diagram](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/) guides this selection.\n",
+    "Depending on the data, we can choose which visualisation suits us better. the following [diagram](http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/) guides this selection.\n",
     "\n",
     "\n",
-    "![](files/images/data-chart-type.png \"Graphs\")"
+    "![](./images/data-chart-type.png \"Graphs\")"
    ]
   },
   {
@@ -421,7 +421,7 @@
    "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."
    ]