FIX: few links (#98)

notebooks/introduction/3_model_clogit.ipynb

@@ -52,13 +52,13 @@
    "source": [
     "## Example 1: SwissMetro\n",
     "\n",
-    "The choice-learn package offers a high level API to conceive and estimate discrete choice models. Several models are ready to be used, you can check the list [here](../README.md). If you want to create your own model or another one that is not in the list, the lower level API can help you. Check the notebook [here](./4_model_customization.ipynb).\n",
+    "The choice-learn package offers a high level API to conceive and estimate discrete choice models. Several models are ready to be used, you can check the list [here](../../README.md). If you want to create your own model or another one that is not in the list, the lower level API can help you. Check the notebook [here](./4_model_customization.ipynb).\n",
     "\n",
     "Let's begin this tutorial with the estimation of a Conditional Logit Model on the SwissMetro dataset[3].\n",
     "It follows the specifications described in [PyLogit](https://github.com/timothyb0912/pylogit/blob/master/examples/notebooks/Main%20PyLogit%20Example.ipynb) and [Biogeme](https://github.com/mncosta/biogeme_tutorial/blob/master/6.1-MultinomialLogitAndProbitModels/01-biogeme-basics.ipynb).\n",
     "\n",
     "\n",
-    "First, we download our data as a ChoiceDataset. See the [data management tutorial](./choice_learn_introduction_data.ipynb) first if needed."
+    "First, we download our data as a ChoiceDataset. See the [data management tutorial](./2_data_handling.ipynb) first if needed."
    ]
   },
   {
@@ -1173,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,