diff --git a/skeleton.py b/skeleton.py
new file mode 100644
index 0000000..4b93ac0
--- /dev/null
+++ b/skeleton.py
@@ -0,0 +1,53 @@
+import numpy as np
+
+from mrjob.job import MRJob
+from itertools import combinations, permutations
+
+from scipy.stats.stats import pearsonr
+
+
+class RestaurantSimilarities(MRJob):
+
+    def steps(self):
+        "the steps in the map-reduce process"
+        thesteps = [
+            self.mr(mapper=self.line_mapper, reducer=self.users_items_collector),
+            self.mr(mapper=self.pair_items_mapper, reducer=self.calc_sim_collector)
+        ]
+        return thesteps
+
+    def line_mapper(self,_,line):
+        "this is the complete implementation"
+        user_id,business_id,stars,business_avg,user_avg=line.split(',')
+        yield user_id, (business_id,stars,business_avg,user_avg)
+
+
+    def users_items_collector(self, user_id, values):
+        """
+        #iterate over the list of tuples yielded in the previous mapper
+        #and append them to an array of rating information
+        """
+        pass
+
+
+    def pair_items_mapper(self, user_id, values):
+        """
+        ignoring the user_id key, take all combinations of business pairs
+        and yield as key the pair id, and as value the pair rating information
+        """
+	   pass #your code here
+
+    def calc_sim_collector(self, key, values):
+        """
+        Pick up the information from the previous yield as shown. Compute
+        the pearson correlation and yield the final information as in the
+        last line here.
+        """
+        (rest1, rest2), common_ratings = key, values
+	    #your code here
+        yield (rest1, rest2), (rho, n_common)
+
+
+#Below MUST be there for things to work
+if __name__ == '__main__':
+    RestaurantSimilarities.run()