guh
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 5,
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"id": "b7ee7a83",
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"metadata": {},
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"outputs": [],
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@@ -21,7 +21,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 6,
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"id": "edda6af5",
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"metadata": {},
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"outputs": [
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@@ -450,7 +450,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "0d381aa2",
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"metadata": {},
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"outputs": [
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@@ -534,7 +534,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 8,
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"id": "ca5e9c28",
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"metadata": {},
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"outputs": [
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@@ -581,7 +581,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 9,
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"id": "daecf558",
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"metadata": {},
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"outputs": [
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@@ -591,7 +591,7 @@
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"<Axes: xlabel='AgeInDays', ylabel='Count'>"
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]
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},
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"execution_count": 16,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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},
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@@ -623,7 +623,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 10,
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"id": "5521b827",
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"metadata": {},
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"outputs": [
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@@ -633,7 +633,7 @@
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"<Axes: xlabel='Hour', ylabel='Count'>"
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]
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},
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"execution_count": 17,
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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@@ -655,7 +655,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 11,
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"id": "3f9a5c00",
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"metadata": {},
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"outputs": [
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@@ -809,7 +809,7 @@
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"4 False False False "
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]
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},
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"execution_count": 18,
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -824,7 +824,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 12,
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"id": "17384e1b",
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"metadata": {},
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"outputs": [
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@@ -867,7 +867,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 13,
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"id": "b4104bd2",
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"metadata": {},
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"outputs": [
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@@ -938,7 +938,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 14,
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"id": "b82c29be",
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"metadata": {},
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"outputs": [
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@@ -985,7 +985,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 17,
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"id": "8ed14aaf",
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"metadata": {},
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"outputs": [
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@@ -1024,7 +1024,7 @@
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"\n",
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"knn_search = KNeighborsClassifier()\n",
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"param_grid = {\"n_neighbors\": np.arange(1, 100)}\n",
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"knn_gscv = GridSearchCV(knn_search, param_grid, cv=5, scoring=\"precision\") # most important metric from business perspective.\n",
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"knn_gscv = GridSearchCV(knn_search, param_grid, cv=5, scoring=\"precision\", n_jobs=8) # most important metric from business perspective.\n",
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"\n",
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"# fit the model to the training set\n",
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"knn_gscv.fit(x_train, y_train)\n",
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@@ -1036,7 +1036,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": null,
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"id": "22d2e4ae",
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"metadata": {},
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"outputs": [
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131166
project1.csv
Normal file
131166
project1.csv
Normal file
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