diff --git a/README.es.md b/README.es.md index 13002247..514352ee 100644 --- a/README.es.md +++ b/README.es.md @@ -5,12 +5,12 @@ - Utiliza los datos que has analizado en el proyecto anterior. - Contin煤a con el desarrollo para buscar un modelo que se adapte mejor. -## 馃尡 C贸mo iniciar este proyecto +## 馃尡 C贸mo 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 aqu铆](https://github.com/4GeeksAcademy/machine-learning-python-template). -2. Abre el repositorio creado recientemente en Codespace usando la [extensi贸n del bot贸n 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 aqu铆](https://github.com/4GeeksAcademy/machine-learning-python-template/generate). +2. Abre el repositorio creado recientemente en Codespace usando la [extensi贸n del bot贸n 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 continuaci贸n. ## 馃殯 C贸mo entregar este proyecto @@ -41,4 +41,4 @@ Una forma de optimizar y mejorar los resultados cuando usamos 谩rboles de decisi Almacena el modelo en la carpeta correspondiente. -> NOTA: Soluci贸n: https://github.com/4GeeksAcademy/random-forest-project-tutorial/blob/main/solution.ipynb \ No newline at end of file +> Nota: Tambi茅n incorporamos muestras de soluci贸n en `./solution.ipynb` que te sugerimos honestamente que solo uses si est谩s atascado por m谩s de 30 minutos o si ya has terminado y quieres compararlo con tu enfoque. diff --git a/README.md b/README.md index 30903b30..6bb2f34d 100644 --- a/README.md +++ b/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. -## 馃尡 How to start this project +## 馃尡 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. ## 馃殯 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. ## 馃摑 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 \ No newline at end of file +> 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. diff --git a/learn.json b/learn.json index 2d19284e..bbf52b88 100644 --- a/learn.json +++ b/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" }