customer leaving reason.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from itertools import combinations\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>3328</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Int'l Plan VMail Plan  CustServ Calls  Churn?\n",
       "0            no        yes               1  False.\n",
       "1            no        yes               1  False.\n",
       "2            no         no               0  False.\n",
       "3           yes         no               2  False.\n",
       "4           yes         no               3  False.\n",
       "...         ...        ...             ...     ...\n",
       "3328         no        yes               2  False.\n",
       "3329         no         no               3  False.\n",
       "3330         no         no               2  False.\n",
       "3331        yes         no               2  False.\n",
       "3332         no        yes               0  False.\n",
       "\n",
       "[3333 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.txt')\n",
    "deletingCol = []\n",
    "for i in df.columns:\n",
    "    if i not in ['CustServ Calls', \"Int'l Plan\", 'VMail Plan', 'Churn?']:\n",
    "        deletingCol.append(i)\n",
    "df = df.drop(deletingCol, axis=1)\n",
    "df.columns\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IntlPlan</th>\n",
       "      <th>VMailPlan</th>\n",
       "      <th>CustServCalls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>no</td>\n",
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       "      <td>False.</td>\n",
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       "      <th>3</th>\n",
       "      <td>yes</td>\n",
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       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     IntlPlan VMailPlan  CustServCalls   Churn\n",
       "0          no       yes              1  False.\n",
       "1          no       yes              1  False.\n",
       "2          no        no              0  False.\n",
       "3         yes        no              2  False.\n",
       "4         yes        no              3  False.\n",
       "...       ...       ...            ...     ...\n",
       "3328       no       yes              2  False.\n",
       "3329       no        no              3  False.\n",
       "3330       no        no              2  False.\n",
       "3331      yes        no              2  False.\n",
       "3332       no       yes              0  False.\n",
       "\n",
       "[3333 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={'CustServ Calls':'CustServCalls',\"Int'l Plan\":'IntlPlan', 'VMail Plan':'VMailPlan', 'Churn?':'Churn'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>IntlPlan</th>\n",
       "      <th>VMailPlan</th>\n",
       "      <th>CustServCalls</th>\n",
       "      <th>Churn</th>\n",
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       "      <th>806</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>805</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IntlPlan  VMailPlan  CustServCalls  Churn\n",
       "542       True       True              9   True\n",
       "2380     False      False              9   True\n",
       "1502     False       True              8  False\n",
       "1912     False      False              8   True\n",
       "902      False      False              7  False\n",
       "...        ...        ...            ...    ...\n",
       "2760      True      False              0   True\n",
       "806      False      False              0  False\n",
       "805      False      False              0  False\n",
       "804      False      False              0  False\n",
       "3332     False       True              0  False\n",
       "\n",
       "[3333 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(by=['CustServCalls'], ascending=False)\n",
    "df = df.replace({'IntlPlan': {'yes': True, 'no': False}})\n",
    "df = df.replace({'VMailPlan': {'yes': True, 'no': False}})\n",
    "df['Churn'] = df['Churn'].astype(str)\n",
    "df = df.replace({'Churn': {'True.': True, 'False.': False}})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IntlPlan</th>\n",
       "      <th>VMailPlan</th>\n",
       "      <th>CustServCalls</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      IntlPlan  VMailPlan  CustServCalls  Churn\n",
       "542       True       True           True   True\n",
       "2380     False      False           True   True\n",
       "1502     False       True           True  False\n",
       "1912     False      False           True   True\n",
       "902      False      False           True  False\n",
       "...        ...        ...            ...    ...\n",
       "2760      True      False          False   True\n",
       "806      False      False          False  False\n",
       "805      False      False          False  False\n",
       "804      False      False          False  False\n",
       "3332     False       True          False  False\n",
       "\n",
       "[3333 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace({'CustServCalls': [1,2,3,4,5,6,7,8,9]}, {'CustServCalls': True})\n",
    "df = df.replace({'CustServCalls': [0]}, {'CustServCalls': False})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'IntlPlan': 0, 'VMailPlan': 0, 'CustServCall': 0, 'Churn': 0}\n",
      "{'IntlPlan': 323, 'VMailPlan': 922, 'CustServCall': 2636, 'Churn': 483}\n",
      "support is : 33.33\n",
      "['IntlPlan', 'VMailPlan', 'CustServCall', 'Churn']\n"
     ]
    }
   ],
   "source": [
    "items = ['IntlPlan', 'VMailPlan', 'CustServCall', 'Churn']\n",
    "itemsFrequency = dict.fromkeys(items,0)\n",
    "baskets = []\n",
    "frequentItems = {}\n",
    "print(itemsFrequency)\n",
    "for i in df.itertuples(): \n",
    "    basket= []\n",
    "    if i[1] == True:\n",
    "        itemsFrequency['IntlPlan'] += 1\n",
    "        basket.append('IntlPlan')\n",
    "    if i[2] == True:\n",
    "        itemsFrequency['VMailPlan'] += 1\n",
    "        basket.append('VMailPlan')\n",
    "    if i[3] == True:\n",
    "        itemsFrequency['CustServCall'] += 1\n",
    "        basket.append('CustServCall')\n",
    "    if i[4] == True:\n",
    "        itemsFrequency['Churn'] += 1\n",
    "        basket.append('Churn')\n",
    "    baskets.append(basket)\n",
    "print(itemsFrequency)\n",
    "support = 0.01*df.shape[0]\n",
    "print(f\"support is : {support}\")\n",
    "L1 = []\n",
    "for key,value in itemsFrequency.items():\n",
    "    if value >= support:\n",
    "        frequentItems[key] = value\n",
    "        L1.append(key)\n",
    "print(L1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('IntlPlan', 'VMailPlan'), ('CustServCall', 'IntlPlan'), ('Churn', 'IntlPlan'), ('CustServCall', 'VMailPlan'), ('Churn', 'VMailPlan'), ('Churn', 'CustServCall')]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('IntlPlan', 'VMailPlan'),\n",
       " ('CustServCall', 'IntlPlan'),\n",
       " ('Churn', 'IntlPlan'),\n",
       " ('CustServCall', 'VMailPlan'),\n",
       " ('Churn', 'VMailPlan'),\n",
       " ('Churn', 'CustServCall')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2 = [tuple(sorted(x)) for x in list(combinations(L1,2))]\n",
    "pairFrequency = dict.fromkeys(c2,0)\n",
    "L2 = []\n",
    "frequentPairs = {}\n",
    "\n",
    "for basket in baskets:\n",
    "    basket_pairs = [tuple(sorted(x)) for x in list(combinations(basket,2))]\n",
    "    for duo in basket_pairs:\n",
    "        if duo in c2:\n",
    "            pairFrequency[duo] += 1\n",
    "for key,value in pairFrequency.items():\n",
    "    if value >= support :\n",
    "        frequentPairs[key]=value\n",
    "        L2.append(key)\n",
    "print(L2)\n",
    "L2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('CustServCall', 'IntlPlan', 'VMailPlan'), ('Churn', 'IntlPlan', 'VMailPlan'), ('Churn', 'CustServCall', 'IntlPlan'), ('Churn', 'CustServCall', 'VMailPlan')]\n"
     ]
    }
   ],
   "source": [
    "c3 = []\n",
    "L3 = []\n",
    "frequentTriples = {}\n",
    "\n",
    "for pair in list(combinations(L2,2)):\n",
    "    triple = set(pair[0]).union(set(pair[1]))\n",
    "    if len(triple)==3 and all(tuple(sorted(x)) in L2 for x in list(combinations(triple,2))):\n",
    "        c3.append(tuple(sorted(triple)))\n",
    "        \n",
    "tripleFrequency = dict.fromkeys(c3,0)\n",
    "\n",
    "for basket in baskets:\n",
    "    subs = [tuple(sorted(x)) for x in list(combinations(basket,3))]\n",
    "    for trio in subs:\n",
    "        if trio in c3:\n",
    "            tripleFrequency[trio] += 1\n",
    "            \n",
    "for key,value in tripleFrequency.items():\n",
    "    if value >= support :\n",
    "        frequentTriples[key] = value\n",
    "        L3.append(key)\n",
    "print(L3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "c4 = []\n",
    "L4 = []\n",
    "frequentQuadro = {}\n",
    "\n",
    "for pair in list(combinations(L3,2)):\n",
    "    quadro = set(pair[0]).union(set(pair[1]))\n",
    "    if len(quadro)==4 and all(tuple(sorted(x)) in L3 for x in list(combinations(quadro,2))):\n",
    "        c4.append(tuple(sorted(quadro)))\n",
    "        \n",
    "quadroFrequency = dict.fromkeys(c4,0)\n",
    "\n",
    "for basket in baskets:\n",
    "    subs = [tuple(sorted(x)) for x in list(combinations(basket,4))]\n",
    "    for quadro in subs:\n",
    "        if quadro in c4:\n",
    "            quadroFrequency[quadro] += 1\n",
    "            \n",
    "for key,value in quadroFrequency.items():\n",
    "    if value >= support :\n",
    "        frequentQuadro[key] = value\n",
    "        L4.append(key)\n",
    "print(c4)\n",
    "print(L4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_rule_1(confidence, support, key, value, key1, key2):\n",
    "    output = []\n",
    "    lhs = key[key1]\n",
    "    rhs = key[key2]\n",
    "    lhs_sup = float(frequentItems[lhs])/n\n",
    "    rhs_sup = float(frequentItems[rhs])/n\n",
    "    rule_sup = float(value)/n\n",
    "    conf = rule_sup/lhs_sup\n",
    "    lift = conf/rhs_sup\n",
    "    if conf >= confidence and lhs_sup > support:\n",
    "        output.append({'lhs':lhs,\n",
    "                     'rhs':rhs,\n",
    "                     'lhs_support': lhs_sup,\n",
    "                     'rule_support': rule_sup,\n",
    "                     'rule_confidence':conf,\n",
    "                     'lift':lift})\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "[{'lhs': 'IntlPlan', 'rhs': 'VMailPlan', 'lhs_support': 0.0969096909690969, 'rule_support': 0.027602760276027604, 'rule_confidence': 0.2848297213622291, 'lift': 1.0296501749461058}]\n",
      "[{'lhs': 'VMailPlan', 'rhs': 'IntlPlan', 'lhs_support': 0.27662766276627665, 'rule_support': 0.027602760276027604, 'rule_confidence': 0.09978308026030368, 'lift': 1.0296501749461058}]\n",
      "[{'lhs': 'CustServCall', 'rhs': 'IntlPlan', 'lhs_support': 0.7908790879087909, 'rule_support': 0.07200720072007201, 'rule_confidence': 0.09104704097116843, 'lift': 0.9395039862442861}]\n",
      "[{'lhs': 'IntlPlan', 'rhs': 'CustServCall', 'lhs_support': 0.0969096909690969, 'rule_support': 0.07200720072007201, 'rule_confidence': 0.7430340557275542, 'lift': 0.9395039862442861}]\n",
      "[{'lhs': 'IntlPlan', 'rhs': 'Churn', 'lhs_support': 0.0969096909690969, 'rule_support': 0.0411041104110411, 'rule_confidence': 0.4241486068111455, 'lift': 2.9268888333365384}]\n",
      "[{'lhs': 'CustServCall', 'rhs': 'VMailPlan', 'lhs_support': 0.7908790879087909, 'rule_support': 0.21872187218721872, 'rule_confidence': 0.2765553869499241, 'lift': 0.9997387252755933}]\n",
      "[{'lhs': 'VMailPlan', 'rhs': 'CustServCall', 'lhs_support': 0.27662766276627665, 'rule_support': 0.21872187218721872, 'rule_confidence': 0.7906724511930585, 'lift': 0.9997387252755933}]\n",
      "[{'lhs': 'VMailPlan', 'rhs': 'Churn', 'lhs_support': 0.27662766276627665, 'rule_support': 0.024002400240024, 'rule_confidence': 0.08676789587852493, 'lift': 0.5987523746648521}]\n",
      "[{'lhs': 'CustServCall', 'rhs': 'Churn', 'lhs_support': 0.7908790879087909, 'rule_support': 0.11731173117311731, 'rule_confidence': 0.1483308042488619, 'lift': 1.0235746802514631}]\n"
     ]
    }
   ],
   "source": [
    "support = 0.01\n",
    "confidence = 0.05\n",
    "n = len(baskets)\n",
    "\n",
    "rules_1 = []\n",
    "\n",
    "for key,value in frequentPairs.items():\n",
    "    if 'Churn' in key:\n",
    "        rules_1.append(find_rule_1(confidence, support, key, value, 1, 0))\n",
    "    else:\n",
    "        rules_1.append(find_rule_1(confidence, support, key, value, 0, 1))\n",
    "        rules_1.append(find_rule_1(confidence, support, key, value, 1, 0))\n",
    "\n",
    "print(len(rules_1))\n",
    "for i in rules_1:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_rule_2(confidence, support, key, value, key1, key2, key3):\n",
    "    output = []\n",
    "    rhs = tuple(sorted([key[key1], key[key2]]))\n",
    "    lhs = tuple(sorted([key[key3]]))\n",
    "    lhs_sup = float(frequentItems[lhs[0]])/n\n",
    "    rhs_sup = float(frequentPairs[tuple(sorted(x for x in rhs))])/n\n",
    "    rule_sup = float(value)/n\n",
    "    conf = rule_sup/lhs_sup\n",
    "    lift = conf/rhs_sup\n",
    "    if conf >= confidence and lhs_sup > support:\n",
    "        output.append({'lhs':lhs,\n",
    "                     'rhs':rhs,\n",
    "                     'lhs_support': lhs_sup,\n",
    "                     'rule_support': rule_sup,\n",
    "                     'rule_confidence':conf,\n",
    "                     'lift':lift})\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "[{'lhs': ('VMailPlan',), 'rhs': ('CustServCall', 'IntlPlan'), 'lhs_support': 0.27662766276627665, 'rule_support': 0.019801980198019802, 'rule_confidence': 0.07158351409978307, 'lift': 0.9941160520607374}]\n",
      "[{'lhs': ('IntlPlan',), 'rhs': ('CustServCall', 'VMailPlan'), 'lhs_support': 0.0969096909690969, 'rule_support': 0.019801980198019802, 'rule_confidence': 0.2043343653250774, 'lift': 0.9342200817949012}]\n",
      "[{'lhs': ('IntlPlan',), 'rhs': ('Churn', 'VMailPlan'), 'lhs_support': 0.0969096909690969, 'rule_support': 0.010801080108010801, 'rule_confidence': 0.11145510835913314, 'lift': 4.643498452012385}]\n",
      "[{'lhs': ('IntlPlan',), 'rhs': ('Churn', 'CustServCall'), 'lhs_support': 0.0969096909690969, 'rule_support': 0.029402940294029404, 'rule_confidence': 0.30340557275541796, 'lift': 2.5863191150736777}]\n",
      "[{'lhs': ('VMailPlan',), 'rhs': ('Churn', 'CustServCall'), 'lhs_support': 0.27662766276627665, 'rule_support': 0.020102010201020103, 'rule_confidence': 0.07266811279826464, 'lift': 0.6194445523187111}]\n"
     ]
    }
   ],
   "source": [
    "support = 0.01\n",
    "confidence = 0.05\n",
    "n = len(baskets)\n",
    "\n",
    "rules_2 = []\n",
    "\n",
    "for key,value in frequentTriples.items():\n",
    "    if 'Churn' in key:\n",
    "        rules_2.append(find_rule_2(confidence, support, key, value,0,1,2))\n",
    "        rules_2.append(find_rule_2(confidence, support, key, value, 0,2,1))\n",
    "    else:\n",
    "        rules_2.append(find_rule_2(confidence, support, key, value,0,1,2))\n",
    "        rules_2.append(find_rule_2(confidence, support, key, value,0,2,1))\n",
    "        rules_2.append(find_rule_2(confidence, support, key, value,1,2,0))\n",
    "\n",
    "rules_2 = [x for x in rules_2 if x != []]        \n",
    "print(len(rules_2))\n",
    "for i in rules_2:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lhs</th>\n",
       "      <th>rhs</th>\n",
       "      <th>lhs_support</th>\n",
       "      <th>rule_support</th>\n",
       "      <th>rule_confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(IntlPlan,)</td>\n",
       "      <td>(Churn, VMailPlan)</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.010801</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>4.643498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IntlPlan</td>\n",
       "      <td>Churn</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.041104</td>\n",
       "      <td>0.424149</td>\n",
       "      <td>2.926889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(IntlPlan,)</td>\n",
       "      <td>(Churn, CustServCall)</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.029403</td>\n",
       "      <td>0.303406</td>\n",
       "      <td>2.586319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IntlPlan</td>\n",
       "      <td>VMailPlan</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.027603</td>\n",
       "      <td>0.284830</td>\n",
       "      <td>1.029650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VMailPlan</td>\n",
       "      <td>IntlPlan</td>\n",
       "      <td>0.276628</td>\n",
       "      <td>0.027603</td>\n",
       "      <td>0.099783</td>\n",
       "      <td>1.029650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CustServCall</td>\n",
       "      <td>Churn</td>\n",
       "      <td>0.790879</td>\n",
       "      <td>0.117312</td>\n",
       "      <td>0.148331</td>\n",
       "      <td>1.023575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CustServCall</td>\n",
       "      <td>VMailPlan</td>\n",
       "      <td>0.790879</td>\n",
       "      <td>0.218722</td>\n",
       "      <td>0.276555</td>\n",
       "      <td>0.999739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VMailPlan</td>\n",
       "      <td>CustServCall</td>\n",
       "      <td>0.276628</td>\n",
       "      <td>0.218722</td>\n",
       "      <td>0.790672</td>\n",
       "      <td>0.999739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(VMailPlan,)</td>\n",
       "      <td>(CustServCall, IntlPlan)</td>\n",
       "      <td>0.276628</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.071584</td>\n",
       "      <td>0.994116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CustServCall</td>\n",
       "      <td>IntlPlan</td>\n",
       "      <td>0.790879</td>\n",
       "      <td>0.072007</td>\n",
       "      <td>0.091047</td>\n",
       "      <td>0.939504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IntlPlan</td>\n",
       "      <td>CustServCall</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.072007</td>\n",
       "      <td>0.743034</td>\n",
       "      <td>0.939504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(IntlPlan,)</td>\n",
       "      <td>(CustServCall, VMailPlan)</td>\n",
       "      <td>0.096910</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.204334</td>\n",
       "      <td>0.934220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(VMailPlan,)</td>\n",
       "      <td>(Churn, CustServCall)</td>\n",
       "      <td>0.276628</td>\n",
       "      <td>0.020102</td>\n",
       "      <td>0.072668</td>\n",
       "      <td>0.619445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VMailPlan</td>\n",
       "      <td>Churn</td>\n",
       "      <td>0.276628</td>\n",
       "      <td>0.024002</td>\n",
       "      <td>0.086768</td>\n",
       "      <td>0.598752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lhs                        rhs  lhs_support  rule_support  \\\n",
       "11   (IntlPlan,)         (Churn, VMailPlan)     0.096910      0.010801   \n",
       "4       IntlPlan                      Churn     0.096910      0.041104   \n",
       "12   (IntlPlan,)      (Churn, CustServCall)     0.096910      0.029403   \n",
       "0       IntlPlan                  VMailPlan     0.096910      0.027603   \n",
       "1      VMailPlan                   IntlPlan     0.276628      0.027603   \n",
       "8   CustServCall                      Churn     0.790879      0.117312   \n",
       "5   CustServCall                  VMailPlan     0.790879      0.218722   \n",
       "6      VMailPlan               CustServCall     0.276628      0.218722   \n",
       "9   (VMailPlan,)   (CustServCall, IntlPlan)     0.276628      0.019802   \n",
       "2   CustServCall                   IntlPlan     0.790879      0.072007   \n",
       "3       IntlPlan               CustServCall     0.096910      0.072007   \n",
       "10   (IntlPlan,)  (CustServCall, VMailPlan)     0.096910      0.019802   \n",
       "13  (VMailPlan,)      (Churn, CustServCall)     0.276628      0.020102   \n",
       "7      VMailPlan                      Churn     0.276628      0.024002   \n",
       "\n",
       "    rule_confidence      lift  \n",
       "11         0.111455  4.643498  \n",
       "4          0.424149  2.926889  \n",
       "12         0.303406  2.586319  \n",
       "0          0.284830  1.029650  \n",
       "1          0.099783  1.029650  \n",
       "8          0.148331  1.023575  \n",
       "5          0.276555  0.999739  \n",
       "6          0.790672  0.999739  \n",
       "9          0.071584  0.994116  \n",
       "2          0.091047  0.939504  \n",
       "3          0.743034  0.939504  \n",
       "10         0.204334  0.934220  \n",
       "13         0.072668  0.619445  \n",
       "7          0.086768  0.598752  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules_1 = [i[0] for i in rules_1]\n",
    "rules_2 = [i[0] for i in rules_2]\n",
    "results = rules_1+rules_2\n",
    "output = pd.DataFrame(results)\n",
    "output = output.sort_values('lift',ascending=False)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('CustServCall', 'IntlPlan', 'VMailPlan'): 66,\n",
       " ('Churn', 'IntlPlan', 'VMailPlan'): 36,\n",
       " ('Churn', 'CustServCall', 'IntlPlan'): 98,\n",
       " ('Churn', 'CustServCall', 'VMailPlan'): 67}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tripleFrequency"
   ]
  }
 ],
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