fixed text README

README.es.md

@@ -5,12 +5,12 @@
 - Utiliza los datos que has analizado en el proyecto anterior.
 - Continúa con el desarrollo para buscar un modelo que se adapte mejor.
 
-## 🌱  Cómo iniciar este proyecto
+## 🌱 Cómo iniciar este proyecto
 
 Sigue las siguientes instrucciones:
 
-1. Crea un nuevo repositorio basado en el [proyecto de Machine Learing](https://github.com/4GeeksAcademy/machine-learning-python-template/generate) [haciendo clic aquí](https://github.com/4GeeksAcademy/machine-learning-python-template).
-2. Abre el repositorio creado recientemente en Codespace usando la [extensión del botón de Codespace](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace-for-a-repository).
+1. Crea un nuevo repositorio basado en el [proyecto de Machine Learning](https://github.com/4GeeksAcademy/machine-learning-python-template) [haciendo clic aquí](https://github.com/4GeeksAcademy/machine-learning-python-template/generate).
+2. Abre el repositorio creado recientemente en Codespace usando la [extensión del botón de Codespace](https://docs.github.com/es/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace-for-a-repository).
 3. Una vez que el VSCode del Codespace haya terminado de abrirse, comienza tu proyecto siguiendo las instrucciones a continuación.
 
 ## 🚛 Cómo entregar este proyecto
@@ -41,4 +41,4 @@ Una forma de optimizar y mejorar los resultados cuando usamos árboles de decisi
 
 Almacena el modelo en la carpeta correspondiente.
 
-> NOTA: Solución: https://github.com/4GeeksAcademy/random-forest-project-tutorial/blob/main/solution.ipynb
+> Nota: También incorporamos muestras de solución en `./solution.ipynb` que te sugerimos honestamente que solo uses si estás atascado por más de 30 minutos o si ya has terminado y quieres compararlo con tu enfoque.

README.md

@@ -5,17 +5,17 @@
 - Use the data you have analyzed in the previous project.
 - Continue with the development to find a model that fits better.
 
-## 🌱  How to start this project
+## 🌱 How to start this project
 
 Follow the instructions below:
 
-1. Create a new repository based on [machine learning project](https://github.com/4GeeksAcademy/machine-learning-python-template/generate) by [clicking here](https://github.com/4GeeksAcademy/machine-learning-python-template).
+1. Create a new repository based on [machine learning project](https://github.com/4GeeksAcademy/machine-learning-python-template) by [clicking here](https://github.com/4GeeksAcademy/machine-learning-python-template/generate).
 2. Open the newly created repository in Codespace using the [Codespace button extension](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace-for-a-repository).
 3. Once the Codespace VSCode has finished opening, start your project by following the instructions below.
 
 ## 🚛 How to deliver this project
 
-Once you have finished solving the exercises, be sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link.
+Once you have finished solving the exercises, be sure to commit your changes, push them to your repository, and go to 4Geeks.com to upload the repository link.
 
 ## 📝 Instructions
 
@@ -25,7 +25,7 @@ In the previous project we saw how we could use a decision tree to predict data
 
 As we have studied, a random forest is a grouping of trees generated with random portions of the data and with random criteria. This view would allow us to improve the effectiveness of the model when an individual tree is not sufficient.
 
-In this project you will focus on this idea by training the dataset to improve the $accuracy$.
+In this project, you will focus on this idea by training the dataset to improve the $accuracy$.
 
 Remember that the previous project can be found [here](https://github.com/4GeeksAcademy/decision-tree-project-tutorial).
 
@@ -35,10 +35,10 @@ Load the processed dataset from the previous project (split into training and te
 
 ### Step 2: Build a random forest
 
-One way to optimize and improve the results when using decision trees is to generate a random forest with enough trees so that there is the necessary variety to enrich the prediction. Train it and analyze its results. Try modifying the two hyperparameters that define the tree with different values and analyze their impact on the final accuracy and plot the conclusions.
+One way to optimize and improve the results when using decision trees is to generate a random forest with enough trees so that there is the necessary variety to enrich the prediction. Train it and analyze its results. Try modifying the two hyperparameters that define the tree with different values, analyzing their impact on the final accuracy, and plotting the conclusions.
 
 ### Step 3: Save the model
 
 Store the model in the corresponding folder.
 
-> NOTE: Solution: https://github.com/4GeeksAcademy/random-forest-project-tutorial/blob/main/solution.ipynb
+> Note: We also incorporated the solution samples on `./solution.ipynb` that we strongly suggest you only use if you are stuck for more than 30 min or if you have already finished and want to compare it with your approach.

learn.json

@@ -9,5 +9,5 @@
 	"syntax": "python",
 	"duration" : 2,
     "projectType": "project",
-	"description" : "Use Random Forest algorithm to predict a marketing campaign success by predicting the campaign impressions"
+	"description" : "Use the Random Forest algorithm to predict diabetes in the given dataset"
 }