From fcc889508ab63a679b5cbd231478b25cff48d8fc Mon Sep 17 00:00:00 2001 From: Christian Cleberg Date: Sat, 2 Dec 2023 11:34:20 -0600 Subject: fix: update domains --- blog/2020-07-26-business-analysis.org | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) (limited to 'blog/2020-07-26-business-analysis.org') diff --git a/blog/2020-07-26-business-analysis.org b/blog/2020-07-26-business-analysis.org index 999b976..4339eee 100644 --- a/blog/2020-07-26-business-analysis.org +++ b/blog/2020-07-26-business-analysis.org @@ -48,7 +48,7 @@ map_LNK #+END_SRC #+CAPTION: Blank Map -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/01_blank_map-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/01_blank_map-min.png]] Now that we have defined our city and created the map, we need to go get the business data. The Foursquare API will limit the results to 100 per API call, so @@ -191,7 +191,7 @@ nearby_venues #+END_SRC #+CAPTION: Clean Data -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/02_clean_data-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/02_clean_data-min.png]] * Visualize the Data @@ -217,7 +217,7 @@ for lat, lng, name, categories in zip(nearby_venues['lat'], nearby_venues['lng'] map_LNK #+END_SRC -![Initial data map](https://img.0x4b1d.org/blog/20200726-ibm-data-science/03_data_map-min.png "Initial data map") +![Initial data map](https://img.cleberg.net/blog/20200726-ibm-data-science/03_data_map-min.png "Initial data map") * Clustering: /k-means/ @@ -306,7 +306,7 @@ map_clusters #+END_SRC #+CAPTION: Clustered Map -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/04_clusters-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/04_clusters-min.png]] * Investigate Clusters @@ -326,7 +326,7 @@ for x in range(0,6): #+END_SRC #+CAPTION: Venues per Cluster -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/05_venues_per_cluster-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/05_venues_per_cluster-min.png]] Our last piece of analysis is to summarize the categories of businesses within each cluster. With these results, we can clearly see that restaurants, coffee @@ -355,10 +355,10 @@ with pd.option_context('display.max_rows', None, 'display.max_columns', None): #+END_SRC #+CAPTION: Venues per Cluster, pt. 1 -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/06_categories_per_cluster_pt1-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/06_categories_per_cluster_pt1-min.png]] #+CAPTION: Venues per Cluster, pt. 2 -[[https://img.0x4b1d.org/blog/20200726-ibm-data-science/07_categories_per_cluster_pt2-min.png]] +[[https://img.cleberg.net/blog/20200726-ibm-data-science/07_categories_per_cluster_pt2-min.png]] * Discussion -- cgit v1.2.3-70-g09d2