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‎Apriori Movies Recommend System.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"#import library\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from mlxtend.frequent_patterns import apriori,association_rules"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>V1</th>\n",
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" <th>V2</th>\n",
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" <th>V3</th>\n",
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" <th>V4</th>\n",
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" <th>V5</th>\n",
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" <th>Sixth Sense</th>\n",
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" <th>Gladiator</th>\n",
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" <th>LOTR1</th>\n",
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" <th>Harry Potter1</th>\n",
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" <th>Patriot</th>\n",
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" <th>LOTR2</th>\n",
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" <th>Harry Potter2</th>\n",
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" <th>LOTR</th>\n",
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" <th>Braveheart</th>\n",
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" <th>Green Mile</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Sixth Sense</td>\n",
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" <td>LOTR1</td>\n",
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" <td>Harry Potter1</td>\n",
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" <td>Green Mile</td>\n",
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" <td>LOTR2</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>Braveheart</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>LOTR1</td>\n",
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" <td>LOTR2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>Sixth Sense</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>Sixth Sense</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>Sixth Sense</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>Harry Potter1</td>\n",
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" <td>Harry Potter2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>Gladiator</td>\n",
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" <td>Patriot</td>\n",
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" <td>Sixth Sense</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>Sixth Sense</td>\n",
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" <td>LOTR</td>\n",
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" <td>Gladiator</td>\n",
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" <td>Green Mile</td>\n",
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" <td>NaN</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" V1 V2 V3 V4 V5 \\\n",
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"0 Sixth Sense LOTR1 Harry Potter1 Green Mile LOTR2 \n",
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"1 Gladiator Patriot Braveheart NaN NaN \n",
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"2 LOTR1 LOTR2 NaN NaN NaN \n",
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"3 Gladiator Patriot Sixth Sense NaN NaN \n",
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"4 Gladiator Patriot Sixth Sense NaN NaN \n",
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"5 Gladiator Patriot Sixth Sense NaN NaN \n",
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"6 Harry Potter1 Harry Potter2 NaN NaN NaN \n",
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"7 Gladiator Patriot NaN NaN NaN \n",
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"8 Gladiator Patriot Sixth Sense NaN NaN \n",
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"9 Sixth Sense LOTR Gladiator Green Mile NaN \n",
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"\n",
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" Sixth Sense Gladiator LOTR1 Harry Potter1 Patriot LOTR2 \\\n",
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"0 1 0 1 1 0 1 \n",
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"1 0 1 0 0 1 0 \n",
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"2 0 0 1 0 0 1 \n",
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"3 1 1 0 0 1 0 \n",
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"4 1 1 0 0 1 0 \n",
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"5 1 1 0 0 1 0 \n",
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"6 0 0 0 1 0 0 \n",
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"7 0 1 0 0 1 0 \n",
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"8 1 1 0 0 1 0 \n",
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"9 1 1 0 0 0 0 \n",
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"\n",
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" Harry Potter2 LOTR Braveheart Green Mile \n",
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"0 0 0 0 1 \n",
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"1 0 0 1 0 \n",
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"2 0 0 0 0 \n",
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"3 0 0 0 0 \n",
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"4 0 0 0 0 \n",
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"5 0 0 0 0 \n",
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"6 1 0 0 0 \n",
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"7 0 0 0 0 \n",
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"8 0 0 0 0 \n",
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"9 0 1 0 1 "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#read csv file\n",
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"movie = pd.read_csv(\"~/Downloads/Data Science/data set/my_movies.csv\")\n",
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"movie"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"movie.drop(['V1','V2','V3','V4','V5'], axis=1, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Building the model \n",
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"movie_apiori = apriori(movie, min_support = 0.1, use_colnames = True) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" antecedents consequents \\\n",
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"102 (Green Mile, Gladiator) (LOTR) \n",
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"107 (LOTR) (Green Mile, Gladiator) \n",
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"132 (Green Mile, Gladiator, Sixth Sense) (LOTR) \n",
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"136 (Green Mile, Gladiator) (LOTR, Sixth Sense) \n",
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"141 (LOTR, Sixth Sense) (Green Mile, Gladiator) \n",
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"\n",
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" antecedent support consequent support support confidence lift \\\n",
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"102 0.1 0.1 0.1 1.0 10.0 \n",
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"107 0.1 0.1 0.1 1.0 10.0 \n",
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"132 0.1 0.1 0.1 1.0 10.0 \n",
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"136 0.1 0.1 0.1 1.0 10.0 \n",
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"141 0.1 0.1 0.1 1.0 10.0 \n",
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"\n",
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" leverage conviction \n",
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"102 0.09 inf \n",
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"107 0.09 inf \n",
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"132 0.09 inf \n",
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"136 0.09 inf \n",
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"141 0.09 inf \n"
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]
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}
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],
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"source": [
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"# Collecting the inferred rules in a dataframe \n",
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"rules = association_rules(movie_apiori, metric =\"lift\") \n",
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"rules = rules.sort_values(['confidence', 'lift'], ascending =[False, False]) \n",
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"print(rules.head())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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