Update Apriori

[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",