diff --git a/x1_simple_duckdb_splink3.py b/x1_simple_duckdb_splink3.py
deleted file mode 100644
index d94e48ced8..0000000000
--- a/x1_simple_duckdb_splink3.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import splink.duckdb.comparison_library as cl
-from splink.duckdb.blocking_rule_library import block_on
-from splink.duckdb.linker import DuckDBLinker
-from splink.internals.datasets import splink_datasets
-
-df = splink_datasets.historical_50k
-df = df.head(1000)
-
-settings_dict = {
-    "link_type": "dedupe_only",
-    "blocking_rules_to_generate_predictions": [
-        block_on(["postcode_fake", "first_name"]),
-        block_on(["first_name", "surname"]),
-        block_on(["dob", "substr(postcode_fake,1,2)"]),
-        block_on(["postcode_fake", "substr(dob,1,3)"]),
-        block_on(["postcode_fake", "substr(dob,4,5)"]),
-    ],
-    "comparisons": [
-        cl.exact_match(
-            "first_name",
-            term_frequency_adjustments=True,
-        ),
-        cl.jaro_winkler_at_thresholds(
-            "surname",
-            distance_threshold_or_thresholds=[0.9, 0.8],
-        ),
-        cl.levenshtein_at_thresholds(
-            "postcode_fake", distance_threshold_or_thresholds=[1, 2]
-        ),
-    ],
-}
-
-
-linker = DuckDBLinker(df, settings_dict)
-
-linker.estimate_u_using_random_sampling(max_pairs=1e6)
-
-linker.estimate_parameters_using_expectation_maximisation(
-    block_on(["first_name", "surname"])
-)
-
-linker.estimate_parameters_using_expectation_maximisation(
-    block_on(["dob", "substr(postcode_fake, 1,3)"])
-)
-
-df_e = linker.predict()