added note on yelp dataset to notebook

notebooks/07_feature_engineering.ipynb

@@ -22,6 +22,7 @@
     "import os\n",
     "from pprint import pprint\n",
     "from sys import getsizeof\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -33,9 +34,8 @@
     "from sklearn import linear_model\n",
     "from sklearn.cluster import DBSCAN, KMeans\n",
     "from sklearn.datasets import make_blobs\n",
-    "from sklearn.feature_extraction import text\n",
-    "from sklearn.metrics import pairwise_distances_argmin\n",
-    "from sklearn.feature_extraction import FeatureHasher"
+    "from sklearn.feature_extraction import FeatureHasher, text\n",
+    "from sklearn.metrics import pairwise_distances_argmin"
    ]
   },
   {
@@ -2347,6 +2347,15 @@
     "In the following, we will look at different representations of text and feature extraction methods for text. We will make use of the Yelp Dataset (https://www.kaggle.com/yelp-dataset/yelp-dataset): \"This dataset is a subset of Yelp's businesses, reviews, and user data. It was originally put together for the Yelp Dataset Challenge which is a chance for students to conduct research or analysis on Yelp's data and share their discoveries. In the most recent dataset you'll find information about businesses across 8 metropolitan areas in the USA and Canada.\" This example is adapted from the FeatEng book."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "78930164-4e89-426f-b153-efdc4a1cf7ef",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Note:</b> As the dataset is simply too large to store it on gitlab, please download the dataset directly using the link above, unzip it and store it in the data directory..</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 90,
@@ -3423,8 +3432,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "feature_hasher = FeatureHasher(n_features=m, input_type='string')\n",
-    "hashed = feature_hasher.transform(reviews['business_id'])"
+    "feature_hasher = FeatureHasher(n_features=m, input_type=\"string\")\n",
+    "hashed = feature_hasher.transform(reviews[\"business_id\"])"
    ]
   },
   {
@@ -3444,8 +3453,8 @@
    ],
    "source": [
     "# We can see how this will make a difference in the future by looking at the size of each\n",
-    "print('Our pandas Series, in bytes: ', getsizeof(reviews['business_id']))\n",
-    "print('Our hashed numpy array, in bytes: ', getsizeof(hashed))"
+    "print(\"Our pandas Series, in bytes: \", getsizeof(reviews[\"business_id\"]))\n",
+    "print(\"Our hashed numpy array, in bytes: \", getsizeof(hashed))"
    ]
   },
   {
@@ -3486,8 +3495,8 @@
     }
    ],
    "source": [
-    "# compare oriignal data to hashed data \n",
-    "reviews['business_id'].unique().tolist()[0:5]\n",
+    "# compare oriignal data to hashed data\n",
+    "reviews[\"business_id\"].unique().tolist()[0:5]\n",
     "hashed.toarray()"
    ]
   },