diff --git a/[DM]Apriori.ipynb b/[DM]Apriori.ipynb index 8d266bfba74fb98506e8a2508a469f48b38670e1..10d688728df4cf053d4268e3b86192ea8b5669e9 100644 --- a/[DM]Apriori.ipynb +++ b/[DM]Apriori.ipynb @@ -45,7 +45,7 @@ "import seaborn as sns\n", "import time" ], - "execution_count": 1, + "execution_count": null, "outputs": [] }, { @@ -61,7 +61,7 @@ "data = pd.read_csv('DM_data.csv')\n", "data.info()" ], - "execution_count": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -127,7 +127,7 @@ "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler" ], - "execution_count": 3, + "execution_count": null, "outputs": [] }, { @@ -138,7 +138,7 @@ "source": [ "from mlxtend.frequent_patterns import apriori,association_rules" ], - "execution_count": 4, + "execution_count": null, "outputs": [] }, { @@ -160,7 +160,7 @@ "\n", "#StandardScaler로 data scaling" ], - "execution_count": 5, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -705,7 +705,7 @@ "\n", "#classification을 위해 scaling 시킨 data들을 음수면 0, 양수면 1로 encoding" ], - "execution_count": 6, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1244,7 +1244,7 @@ "\n", "#train data와 test data를 7:3 의 비율로 split" ], - "execution_count": 7, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1270,7 +1270,7 @@ "\n", "df.head()" ], - "execution_count": 8, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1582,7 +1582,7 @@ "result_desc = frequent_itemsets.sort_values(['support'],ascending =[False])\n", "result_desc" ], - "execution_count": 9, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1708,7 +1708,7 @@ "rules = rules.sort_values(['confidence','lift'], ascending=[False , False])\n", "rules" ], - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1917,7 +1917,7 @@ "rules_list = rules[rules['consequents'] == {\"class\"}]\n", "rules_list" ], - "execution_count": 11, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2086,7 +2086,7 @@ "test = pd.DataFrame(X_test, columns=data.drop(columns = [\"index_num\"]).columns)\n", "test.head(n=10)" ], - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2627,7 +2627,7 @@ "print(test_df.shape)\n", "print(test_err.shape)" ], - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -2658,7 +2658,7 @@ "print(test_df.shape)\n", "print(test_err.shape)" ], - "execution_count": 14, + "execution_count": null, "outputs": [ { "output_type": "stream",