change link to Rethop data in topic 6

Blueneuron · Feb 8, 2019 · 8a6509c · 8a6509c
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commit 8a6509c
Showing 1 changed file with 1 addition and 1 deletion.
diff --git a/...er_english/topic06_features_regression/topic6_feature_engineering_feature_selection.ipynb b/...er_english/topic06_features_regression/topic6_feature_engineering_feature_selection.ipynb
@@ -24,7 +24,7 @@
     "* **feature transformation**: transformation of data to improve the accuracy of the algorithm;\n",
     "* **feature selection**: removing unnecessary features.\n",
     "\n",
-    "This article will contain almost no math, but there will be a fair amount of code. Some examples will use the dataset from Renthop company, which is used in the [Two Sigma Connect: Rental Listing Inquiries Kaggle competition](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries). The file `train.json` is also kept [here](https://drive.google.com/open?id=1_lqydkMrmyNAgG4vU4wVmp6-j7tV0XI8) as `renthop_train.json.tar.gz` (so do unzip it first). In this task, you need to predict the popularity of a new rental listing, i.e. classify the listing into three classes: `['low', 'medium' , 'high']`. To evaluate the solutions, we will use the log loss metric (the smaller, the better). Those who do not have a Kaggle account, will have to register; you will also need to accept the rules of the competition in order to download the data."
+    "This article will contain almost no math, but there will be a fair amount of code. Some examples will use the dataset from Renthop company, which is used in the [Two Sigma Connect: Rental Listing Inquiries Kaggle competition](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries). The file `train.json` is also kept [here](https://drive.google.com/open?id=1_lqydkMrmyNAgG4vU4wVmp6-j7tV0XI8) as `renthop_train.json.gz` (so do unpack it first). In this task, you need to predict the popularity of a new rental listing, i.e. classify the listing into three classes: `['low', 'medium' , 'high']`. To evaluate the solutions, we will use the log loss metric (the smaller, the better). Those who do not have a Kaggle account, will have to register; you will also need to accept the rules of the competition in order to download the data."
    ]
   },
   {