Quora_Question_Pair_Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quora Question Pair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Business Problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Description\n",
    "\n",
    "\n",
    "Quora is a place to gain and share knowledge—about anything. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers. This empowers people to learn from each other and to better understand the world.\n",
    "\n",
    "Over 100 million people visit Quora every month, so it's no surprise that many people ask similarly worded questions. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term.\n",
    "\n",
    "\n",
    "\n",
    "#### Problem Statement --\n",
    "\n",
    "- Identify which questions asked on Quora are duplicates of questions that have already been asked.<br>\n",
    "- This could be useful to instantly provide answers to questions that have already been answered.<br>\n",
    "- We are tasked with predicting whether a pair of questions are duplicates or not.<br>\n",
    "\n",
    "\n",
    "#### Source - \n",
    "\n",
    "Source : https://www.kaggle.com/c/quora-question-pairs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Real world/Business Objectives and Constraints\n",
    "\n",
    "\n",
    "a.The cost of a mis-classification can be very high.<br>\n",
    "b.You would want a probability of a pair of questions to be duplicates so that you can choose any threshold of choice.<br>\n",
    "c.No strict latency concerns.<br>\n",
    "d.Interpretability is partially important.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Machine Learning Probelm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Data Overview\n",
    "\n",
    "- Data is in a file train.csv\n",
    "- train.csv contains 6 columns : id , qid1 , qid2 , question1 , question2 , is_duplicate\n",
    "- Size of train.csv - 58.7 MB\n",
    "- Number of rows in Train.csv = 404,290"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Mapping the real world problem to an ML problem\n",
    "\n",
    "\n",
    "### a. Type of Machine Leaning Problem\n",
    "It is a binary classification problem, for a given pair of questions we need to predict if they are duplicate or not.\n",
    "\n",
    "### b. Performance Metric\n",
    "Source: https://www.kaggle.com/c/quora-question-pairs#evaluation\n",
    "\n",
    "Metric(s):\n",
    "\n",
    "- log-loss : https://www.kaggle.com/wiki/LogarithmicLoss\n",
    "- Binary Confusion Matrix\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Train and Test Construction\n",
    "We build train and test by randomly splitting in the ratio of 70:30 or 80:20 whatever we choose as we have sufficient points to work with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Importing Library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vivek Kumar Goel\\anaconda3\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n",
      "C:\\Users\\Vivek Kumar Goel\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "#library for advanced EDA\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.stem import PorterStemmer\n",
    "import re\n",
    "\n",
    "\n",
    "#library for advanced EDA using NLP and Fuzzy \n",
    "\n",
    "from fuzzywuzzy import fuzz\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "import urllib.request\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "\n",
    "#library for text featuring\n",
    "\n",
    "import spacy\n",
    "import sklearn\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from tqdm import tqdm\n",
    "\n",
    "#library for ml models\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics.classification import accuracy_score, log_loss\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from collections import Counter\n",
    "from scipy.sparse import hstack\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from collections import Counter, defaultdict\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import math\n",
    "from sklearn.metrics import normalized_mutual_info_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import precision_recall_curve, auc, roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Reading Data and Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the data \n",
    "\n",
    "data = pd.read_csv(\"train.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'qid1', 'qid2', 'question1', 'question2', 'is_duplicate'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column name \n",
    "\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of row in data is 404290 and column is 6\n"
     ]
    }
   ],
   "source": [
    "# shape of data\n",
    "\n",
    "row , col = data.shape\n",
    "print(f\"The number of row in data is {row} and column is {col}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>404290.000000</td>\n",
       "      <td>404290.000000</td>\n",
       "      <td>404290.000000</td>\n",
       "      <td>404290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>202144.500000</td>\n",
       "      <td>217243.942418</td>\n",
       "      <td>220955.655337</td>\n",
       "      <td>0.369198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>116708.614502</td>\n",
       "      <td>157751.700002</td>\n",
       "      <td>159903.182629</td>\n",
       "      <td>0.482588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>101072.250000</td>\n",
       "      <td>74437.500000</td>\n",
       "      <td>74727.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>202144.500000</td>\n",
       "      <td>192182.000000</td>\n",
       "      <td>197052.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>303216.750000</td>\n",
       "      <td>346573.500000</td>\n",
       "      <td>354692.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>404289.000000</td>\n",
       "      <td>537932.000000</td>\n",
       "      <td>537933.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id           qid1           qid2   is_duplicate\n",
       "count  404290.000000  404290.000000  404290.000000  404290.000000\n",
       "mean   202144.500000  217243.942418  220955.655337       0.369198\n",
       "std    116708.614502  157751.700002  159903.182629       0.482588\n",
       "min         0.000000       1.000000       2.000000       0.000000\n",
       "25%    101072.250000   74437.500000   74727.000000       0.000000\n",
       "50%    202144.500000  192182.000000  197052.000000       0.000000\n",
       "75%    303216.750000  346573.500000  354692.500000       1.000000\n",
       "max    404289.000000  537932.000000  537933.000000       1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# information of data \n",
    "\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            404290 non-null  int64 \n",
      " 1   qid1          404290 non-null  int64 \n",
      " 2   qid2          404290 non-null  int64 \n",
      " 3   question1     404289 non-null  object\n",
      " 4   question2     404288 non-null  object\n",
      " 5   is_duplicate  404290 non-null  int64 \n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion - \n",
    "\n",
    "- In question1 we have one null value and in question2 we have 2 null value . Remaining all the columns donot have any null value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Distribution of data points among output classes\n",
    "\n",
    "- Number of duplicate(smilar) and non-duplicate(non similar) questions<br><br>\n",
    "- '0' represent the question pair is not similar\n",
    "- '1' represent the question pair is similar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    255027\n",
       "1    149263\n",
       "Name: is_duplicate, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['is_duplicate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29044e7ca00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting graph \n",
    "data['is_duplicate'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion - \n",
    "\n",
    "- Here we can conclude approx 37% (149263) question pair are duplicate in our data set and 63% question pair are not duplicate ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Number of unique questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of unique questions are  : 537933\n",
      "Number of questions that appeared more than once are : 111780 (20.77953945937505%)\n",
      "Maximun number of time a single question is appeared is : 157\n"
     ]
    }
   ],
   "source": [
    "#getting all the question ids \n",
    "q_ids = pd.Series(data['qid1'].tolist() + data['qid2'].tolist())\n",
    "\n",
    "#getting unique question id \n",
    "unique_id = len(np.unique(q_ids))\n",
    "\n",
    "print(f\"Total number of unique questions are  : {unique_id}\")\n",
    "\n",
    "#checking the question appeared more than once \n",
    "\n",
    "qs_more_than_once = np.sum(q_ids.value_counts() > 1)\n",
    "\n",
    "print(f\"Number of questions that appeared more than once are : {qs_more_than_once} ({((qs_more_than_once / unique_id)*100)}%)\")\n",
    "\n",
    "#Checking max no. of time a question have appeared \n",
    "\n",
    "print(f\"Maximun number of time a single question is appeared is : {max(q_ids.value_counts())}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting graph \n",
    "\n",
    "x = [\"unique_questions\" , \"Repeated Questions\"]\n",
    "y =  [unique_id , qs_more_than_once]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title (\"Plot representing unique and repeated questions  \")\n",
    "sns.barplot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2559      157\n",
      "30782     120\n",
      "4044      111\n",
      "2561       88\n",
      "14376      79\n",
      "         ... \n",
      "75109       1\n",
      "81254       1\n",
      "85352       1\n",
      "83305       1\n",
      "168274      1\n",
      "Length: 537933, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#getting the most asked question \n",
    "\n",
    "q_vals = q_ids.value_counts()\n",
    "print(q_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question at qid 2559 is : ['What are the best ways to lose weight?']\n",
      "\n",
      "\n",
      "Question at qid 30782 is : []\n",
      "\n",
      "\n",
      "Question at qid 4044 is : ['How can I lose weight quickly?']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#here we observe that questions with q_ids 2559 , 30782 , 4044 are some questions that \n",
    "#are repeated multiple time\n",
    "\n",
    "#seeing the question\n",
    "\n",
    "print(f\"Question at qid 2559 is : {data.loc[data['qid1']==2559]['question1'].head(1).values}\")\n",
    "print(\"\\n\")\n",
    "print(f\"Question at qid 30782 is : {data.loc[data['qid1']==30782]['question1'].head(1).values}\")\n",
    "print(\"\\n\")\n",
    "print(f\"Question at qid 4044 is : {data.loc[data['qid1']==4044]['question1'].head(1).values}\")\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Checking for Duplicates Pair\n",
    "\n",
    "- we are checking rows having duplicate question pair or not \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate questions : 0\n"
     ]
    }
   ],
   "source": [
    "duplicate_row = data[data.duplicated(['qid1', 'qid2' , 'is_duplicate'])]\n",
    "print (f\"Number of duplicate questions : {duplicate_row.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 Plotting Questions based on there frequency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of times a single question is repeated: 157\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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O9cG86QW2ouckef3wi0HXJnlapr/wpRfYMrr70qp6U5IrMl3e6sNJ9ia5VfQCI1dVb0jyiCQnV9XBJP8pM34m6u5PVNVFmf6yxA1Jfr67vz2XwmEdzOiHFye5WZJ3Tn9HIh/s7mfqB8ZsrV7o7testa9eYMxmfF+4IMkFVXVlkm8lOW+YHTyaXqgbZzsDAAAAAADA4rMEIgAAAAAAAKMiAAMAAAAAAGBUBGAAAAAAAACMigAMAAAAAACAURGAAQAAAAAAMCoCMAAAYENUVVfVb6x6/IKq+uUTdO4Lq+qJJ+JcR7nOk6rqqqp673pfaxFV1c2q6l1V9ZGqevIGX/vxVXXGqscvr6ozN7IGAABg8xCAAQAAG+WbSZ5QVSfPu5DVquqkY9j96Un+bXf/1HrVczTHWO9Gu3+Sm3T3/br7v2/wtR+f5O8DsO5+aXe/a4NrAAAANgkBGAAAsFFuSLI3yfMP33D4DK6q+n/Dn4+oqvdV1UVV9amq2lNV51bVZVX18aq6y6rTnFlVfzrs95jh+JOq6hVVdXlVfayq/s2q8763qv5bko+vUc85w/mvrKpfHcZemuThSV5VVa84bP8arnPlcNyTV2170TD20araM4wtDTOlPlpVV1TVXYaa3rbquN+uqqcOXx+oqpdW1f9K8qSq+umq+sBw7B9W1a1W7feyYfzjVXWPYfxWVfXaYexjVfUzw/is8+ypqk8O+/76Gq/Pbavqj4btH6yq+1TV7ZP8QZL7DTPA7nLYMT8+PN8PHHqthvGnVtVvr9rvbVX1iGOpr6oemuSxSV5x6Nqr31NV9aiq+vDw/C+oqpsd6fUCAAAWnwAMAADYSOcnObeqJsdwzH2TPC/JvZM8JcnduvtBSV6d5Dmr9tue5CeT7Mw0pLp5pjO2Vrr7gUkemOQZVXX6sP+Dkryku89YdY5U1Y8m+dUkj0xyvyQPrKrHd/fLk3woybnd/cLDanzCsO99k5yZaRBzh6p6dKYzk/5Jd983ya8N+78+yfnD2EOTfPH7eB2+0d0PT/KuJL+U5MzufsBQ079btd+XhvFXJnnBMPYfh9fh3t19nyTvqelMvO85T1XdNsm/SHLPYd9fWaOWlyX58LD9F5P8Xndfl+RfJ/nTYQbYXx52zGuTPLe7H/J9PNccS33d/b+TvDXJCw+/9vA+uDDJk7v73km2JXnWUV4vAABgwQnAAACADdPdX0nye0meewyHXd7dX+zubyb5yyTvGMY/nmnodchF3f2d7v50kmuT3CPJTyf5V1X1kSSXJrldkrsO+1/W3Z9Z43oPTPIn3X19d9+QaVj1E0ep8eFJ3tDd3+7uv0nyvuE8ZyZ5bXd/PUm6+8tV9cNJTu3uNw9j3zi0/SgOLSn44EyX+vuz4Xmdl+THVu138fDnn+fG1+fMTMPHDNf8v0c4z1eSfCPJq6vqCUnWqu3hSX5/ONd7ktzuSKHmsO3W3f2+Yej3v4/n+4PUt9rdk3ymuz81PH5dvvvvc63XCwAAWHDb5l0AAACw5fxWkisynRF0yA0ZfkGvqirJTVdt++aqr7+z6vF38t2fafqw63SSSvKc7n776g3DEntfm1FfHfUZfP/H1Bp1zdr371+Dwc0P236o3kryzu4+Z8Z5Dr0+386Nr8+sOtY8T1U9KMmjkpyd5NmZzoY72nM4/PyH7z9r+6zn/YPUd7RaV1vr9QIAABacGWAAAMCG6u4vJ7ko0+UJDzmQ5MeHrx+X5CbHceonVdUPDfeeunOSq5O8PcmzquomSVJVd6uqWx7lPJcm+cmqOrmqTkpyTqYzuo7k/UmeXNN7jp2S6QyjyzKdrfZzVXWL4fq3HWbBHayqxw9jNxu2/1WSM4bHk0wDnrV8MMnDqmppOP4WVXW3o9T3jkyDogzH3GbWeYb7bE26+5Ikv5Dp0o5rPd9zh+Mekekygl+ZdfHu/tskK1X18GHo3FWbD2R637Afqqo7Zro05czneYT6vprkh9e4/F8k2X7oPJkuo3m0v08AAGDB+e02AABgHn4jqwKZJL+b5C1VdVmSd2f27KwjuTrTYONHA4eQkQAAAURJREFUkjyzu79RVa/OdFm7K4aZZddnek+umbr7i1X14iTvzXT20CXd/ZajXPvNSR6S5KOZznR6UXf/dZI/rqr7JflQVX0rySWZ3jPrKUn+a1W9PMnfJXlSd19bVRcl+ViSTyf58Iz6rq+qpyZ5Q1XdbBj+pSSfWmv/wa8kOb+qrsx0ptPLuvviGef5aqZ/Fzcfnv/z1zjfLyd5bVV9LNMlCM874qsz9bQkF1TV1zMNJg/5sySfyXRJyysznR14pOc5q743JvndqnpukiceOvnwPnhakj+sqm1JLk/yqu+jXgAAYIFV95FWqQAAAIATq6q2J3lbd99rzqUAAAAjZQlEAAAAAAAARsUMMAAAAAAAAEbFDDAAAAAAAABGRQAGAAAAAADAqAjAAAAAAAAAGBUBGAAAAAAAAKMiAAMAAAAAAGBU/j/XMG9itkiGxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "\n",
    "plt.hist(q_ids.value_counts(), bins=250)\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.title('Log-Histogram of question appearance counts')\n",
    "\n",
    "plt.xlabel('Number of occurences of question')\n",
    "\n",
    "plt.ylabel('Number of questions')\n",
    "\n",
    "print (f'Maximum number of times a single question is repeated: {max(q_ids.value_counts())}\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7 Checking and Removing Null Value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>105780</th>\n",
       "      <td>105780</td>\n",
       "      <td>174363</td>\n",
       "      <td>174364</td>\n",
       "      <td>How can I develop android app?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201841</th>\n",
       "      <td>201841</td>\n",
       "      <td>303951</td>\n",
       "      <td>174364</td>\n",
       "      <td>How can I create an Android app?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363362</th>\n",
       "      <td>363362</td>\n",
       "      <td>493340</td>\n",
       "      <td>493341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>My Chinese name is Haichao Yu. What English na...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id    qid1    qid2                         question1  \\\n",
       "105780  105780  174363  174364    How can I develop android app?   \n",
       "201841  201841  303951  174364  How can I create an Android app?   \n",
       "363362  363362  493340  493341                               NaN   \n",
       "\n",
       "                                                question2  is_duplicate  \n",
       "105780                                                NaN             0  \n",
       "201841                                                NaN             0  \n",
       "363362  My Chinese name is Haichao Yu. What English na...             0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(1)]       # Checking if any value is null in our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion - \n",
    "\n",
    "As here we can see there are only 3 datapoint having null value so we can remove them ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, qid1, qid2, question1, question2, is_duplicate]\n",
       "Index: []"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)   #Dropping Null Value\n",
    "data[data.isnull().any(1)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.8 Basic Feature Extraction\n",
    "\n",
    "Let us now construct a few features like:\n",
    "\n",
    "- freq_qid1 = Frequency of qid1's\n",
    "- freq_qid2 = Frequency of qid2's\n",
    "- q1len = Length of q1\n",
    "- q2len = Length of q2\n",
    "- q1_n_words = Number of words in Question 1\n",
    "- q2_n_words = Number of words in Question 2\n",
    "- word_Common = (Number of common unique words in Question 1 and Question 2)\n",
    "- word_Total =(Total num of words in Question 1 + Total num of words in Question 2)\n",
    "- word_share = (word_common)/(word_Total)\n",
    "- freq_q1+freq_q2 = sum total of frequency of qid1 and qid2\n",
    "- freq_q1-freq_q2 = absolute difference of frequency of qid1 and qid2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_Total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  freq_qid1  \\\n",
       "0  What is the step by step guide to invest in sh...             0          1   \n",
       "1  What would happen if the Indian government sto...             0          4   \n",
       "2  How can Internet speed be increased by hacking...             0          1   \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0          1   \n",
       "4            Which fish would survive in salt water?             0          3   \n",
       "\n",
       "   freq_qid2  q1len  q2len  q1_n_words  q2_n_words  word_Common  word_Total  \\\n",
       "0          1     66     57          14          12           10          23   \n",
       "1          1     51     88           8          13            4          20   \n",
       "2          1     73     59          14          10            4          24   \n",
       "3          1     50     65          11           9            0          19   \n",
       "4          1     76     39          13           7            2          20   \n",
       "\n",
       "   word_share  freq_q1+q2  freq_q1-q2  \n",
       "0    0.434783           2           0  \n",
       "1    0.200000           5           3  \n",
       "2    0.166667           2           0  \n",
       "3    0.000000           2           0  \n",
       "4    0.100000           4           2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['freq_qid1'] = data.groupby('qid1')['qid1'].transform('count')     #finding the frequency of Question1\n",
    "data['freq_qid2'] = data.groupby('qid2')['qid2'].transform('count')     #finding the frequency of Question2\n",
    " \n",
    "#---------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "data['q1len']=data['question1'].str.len()  # Finding length of Question 1\n",
    "data['q2len']=data['question2'].str.len()  # Finding length of Question 2\n",
    "\n",
    "#---------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "data['q1_n_words']=data['question1'].apply(lambda row : len(row.split()))   # Finding Number of Words in Question 1\n",
    "data['q2_n_words']=data['question2'].apply(lambda row : len(row.split()))   # Finding Number of Words in Question 2\n",
    "\n",
    "\n",
    "#---------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "# Fucntion to find the number of common words in Question 1 and Question 2\n",
    "\n",
    "def common_word(row):\n",
    "    '''\n",
    "    We are converting both Question 1 and Question 2 to set (and also converting them to lower so that every word have same) and finding there intersection so that we can get common words\n",
    "    Then we are simply finding the lenth of those common words\n",
    "    '''\n",
    "    return len((set(row['question1'].lower().split())).intersection(set(row['question2'].lower().split())))\n",
    "\n",
    "\n",
    "# Creating New Column with Number of Common Words\n",
    "data['word_Common'] = data.apply(common_word, axis=1)\n",
    "\n",
    "\n",
    "#---------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# Total Number of Distict words in both question1 and question2\n",
    "\n",
    "def total_word(row):\n",
    "    '''\n",
    "    Coverting them to lower form then removing extra spaces and them removing the repeted words by converting them to sets \n",
    "    Then finding Length of Both the questions and adding them to find total words in both\n",
    "    '''\n",
    "    w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "    w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "    return (len(w1) + len(w2))\n",
    "\n",
    "# Getting total number of (unique) words in both question1 and question2\n",
    "data['word_Total'] = data.apply(total_word, axis=1)\n",
    "\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "def word_share(row):\n",
    "    '''\n",
    "    Here we are finding total number of shared word and dividing by total number of words [ (A intersection B)/A+B]\n",
    "    '''\n",
    "    w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "    w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "    return 1.0 * len(w1 & w2)/(len(w1) + len(w2))    # Finding the number of common words between question1 and question2 and dividing by total words between both of them\n",
    "data['word_share'] = data.apply(word_share, axis=1)\n",
    "\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "data['freq_q1+q2'] = data['freq_qid1']+data['freq_qid2']\n",
    "data['freq_q1-q2'] = abs(data['freq_qid1']-data['freq_qid2'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.9 Analysis of some of the extracted features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9.1 Asking some basic question to our extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum length of the questions in question1 : 1\n",
      "Minimum length of the questions in question2 : 1\n",
      "\n",
      "\n",
      "Number of Questions with minimum length question1 :  70\n",
      "Number of Questions with minimum length question2 : 26\n",
      "\n",
      "\n",
      "Maximum length of the questions in question1 :   125\n",
      "Maximum length of the questions in question2 :   237\n",
      "\n",
      "\n",
      "Number of Questions with minimum length question1 : 1\n",
      "Number of Questions with minimum length question2 : 13\n",
      "\n",
      "\n",
      "Maximum number of Common word :  41\n",
      "Maximum number of Shared Word : 0.5\n"
     ]
    }
   ],
   "source": [
    "print(f\"Minimum length of the questions in question1 : {min(data['q1_n_words'])}\")\n",
    "print(f\"Minimum length of the questions in question2 : {min(data['q2_n_words'])}\")\n",
    "print('\\n')\n",
    "\n",
    "print(f\"Number of Questions with minimum length question1 :  {data[data['q1_n_words']== 1].shape[0]}\")\n",
    "print(f\"Number of Questions with minimum length question2 : {data[data['q2_n_words']== 1].shape[0]}\")\n",
    "print('\\n')\n",
    "\n",
    "print(f\"Maximum length of the questions in question1 :   {max(data['q1_n_words'])}\")\n",
    "print(f\"Maximum length of the questions in question2 :   {max(data['q2_n_words'])}\")\n",
    "print('\\n')\n",
    "\n",
    "print(f\"Number of Questions with minimum length question1 : {data[data['q1_n_words']>120].shape[0]}\")\n",
    "print(f\"Number of Questions with minimum length question2 : {data[data['q2_n_words']> 230].shape[0]}\")\n",
    "print('\\n')\n",
    "\n",
    "print(f\"Maximum number of Common word :  {max(data['word_Common'])}\")\n",
    "print(f\"Maximum number of Shared Word : {max(data['word_share'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9.2 Analysing column \"word_share\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'word_share', data = data[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['word_share'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['word_share'][0:] , label = \"0\" , color = 'green' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion- \n",
    "\n",
    "- The distributions for normalized word_share have some overlap on the far right-hand side, i.e., there are quite a lot of questions with high word similarity\n",
    "- The average word share and Common no. of words of qid1 and qid2 is more when they are duplicate(Similar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9.3  Analysing column \"word_Common\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'word_Common', data = data[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['word_Common'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['word_Common'][0:] , label = \"0\" , color = 'green' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion - \n",
    "\n",
    "- The distributions of the word_Common feature in similar and non-similar questions are highly overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.9.4 Analysing column \"word_Total\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'word_Total', data = data[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['word_Total'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['word_Total'][0:] , label = \"0\" , color = 'green' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.10 EDA: Advanced Feature Extraction\n",
    "\n",
    "Preprocessing of Text :\n",
    "- Removing html tags\n",
    "- Removing Punctuations\n",
    "- Performing stemming\n",
    "- Removing Stopwords\n",
    "- Expanding contractions etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.1 Creating stopwords \n",
    "\n",
    "- Removing stopwords like how , whom , not , etc may be useful to differentiate between questions\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# snow=SnowballStemmer('english')\n",
    "snow=nltk.stem.SnowballStemmer('english')\n",
    "\n",
    "# Creating custom stopwords\n",
    "\n",
    "stopwords= set(['the', 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\",\\\n",
    "            \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', \\\n",
    "            'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their',\\\n",
    "            'theirs', 'themselves', 'this', 'that', \"that'll\", 'these', 'those', \\\n",
    "            'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', \\\n",
    "            'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', \\\n",
    "            'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after',\\\n",
    "            'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further',\\\n",
    "            'then', 'once', 'here', 'there', 'all', 'any', 'both', 'each', 'few', 'more',\\\n",
    "            'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', \\\n",
    "            's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', \\\n",
    "            've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn',\\\n",
    "            \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn',\\\n",
    "            \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", \\\n",
    "            'won', \"won't\", 'wouldn', \"wouldn't\"])\n",
    "\n",
    "\n",
    "\n",
    "# function to perform \"Stemming\" and removing stopwords\n",
    "\n",
    "def removeStopWord(word):\n",
    "  token=word.split(\" \")   ## coverting string to token (list of word) \\\\ like [\"this\",\"is\",\"token\"]\n",
    "  removestop=[snow.stem(x) for x in token if x not in stopwords]   ##removing stopwords and also doing Stemming\n",
    "  removed=\" \".join(removestop)  ##joing back the list into sentence\n",
    "  return removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.2 Replacing common words like 1000 to 1k , removing special character and stopwords   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(x):\n",
    "    x = str(x).lower()  # Lowering all text to covert all of them to there base form\n",
    "    \n",
    "    # Replacing commonly use words or numbers like 1,000 to 1k and 1,000,000 to 1m and currency symbol to there respective names and many other symbols to there name\n",
    "    \n",
    "    x = x.replace(\",000,000\", \"m\").replace(\",000\", \"k\").replace(\"′\", \"'\").replace(\"’\", \"'\")\\\n",
    "                           .replace(\"won't\", \"will not\").replace(\"cannot\", \"can not\").replace(\"can't\", \"can not\")\\\n",
    "                           .replace(\"n't\", \" not\").replace(\"what's\", \"what is\").replace(\"it's\", \"it is\")\\\n",
    "                           .replace(\"'ve\", \" have\").replace(\"i'm\", \"i am\").replace(\"'re\", \" are\")\\\n",
    "                           .replace(\"he's\", \"he is\").replace(\"she's\", \"she is\").replace(\"'s\", \" is\")\\\n",
    "                           .replace(\"%\", \" percent \").replace(\"₹\", \" rupee \").replace(\"$\", \" dollar \")\\\n",
    "                           .replace(\"€\", \" euro \").replace(\"'ll\", \" will\").replace(\"@\",\"at\")\n",
    "    \n",
    "    # Renaming 1000 to 1k and 1000000 to 1m (the onces which may not be seperated with commans)\n",
    "    x = re.sub(r\"([0-9]+)000000\", r\"\\1m\", x)\n",
    "    x = re.sub(r\"([0-9]+)000\", r\"\\1k\", x)\n",
    "    \n",
    "    \n",
    "    #Remove any special character like [= , ' ; \"\" ']\n",
    "    \n",
    "    pattern = re.compile('\\W')\n",
    "    \n",
    "    if type(x) == type(''):\n",
    "        x = re.sub(pattern, ' ', x)\n",
    "    \n",
    "    #Removing Stopwords And Doing Stemming\n",
    "    x=removeStopWord(x)          \n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Doing Pre-processing on both question1 and question2\n",
    "\n",
    "data['cleanQ1']=data['question1'].apply(preprocess)\n",
    "data['cleanQ2']=data['question2'].apply(preprocess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.3 How Preprocessing changed our Question Pair \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Questions 1 without pre-processing\n",
      "How can I increase the speed of my internet connection while using a VPN?\n",
      "\n",
      "Questions 1 after pre-processing\n",
      "how increas speed internet connect use vpn \n",
      "\n",
      "Questions 2 without pre-processing\n",
      "How can Internet speed be increased by hacking through DNS?\n",
      "\n",
      "Questions 2 after pre-processing\n",
      "how internet speed increas hack dns \n"
     ]
    }
   ],
   "source": [
    "print('Questions 1 without pre-processing')\n",
    "print(data['question1'][2])\n",
    "print()\n",
    "print('Questions 1 after pre-processing')\n",
    "print(data['cleanQ1'][2])\n",
    "print()\n",
    "print('Questions 2 without pre-processing')\n",
    "print(data['question2'][2])\n",
    "print()\n",
    "print('Questions 2 after pre-processing')\n",
    "print(data['cleanQ2'][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.4 Advanced EDA using NLP and Fuzzy Features\n",
    "\n",
    "Definition:\n",
    "\n",
    "- Token: You get a token by splitting sentence a space\n",
    "- Stop_Word : stop words as per NLTK.\n",
    "- Word : A token that is not a stop_word\n",
    "\n",
    "Features :\n",
    "\n",
    "- Simple Ratio : Measurement of edit distance (Minimum number of edits required to convert one sentence to other )\n",
    "\n",
    "- Partial Ratio : How much accuratly a part of sentence match to other sentence (\"Chennai Super Kings\", \"Super Kings\")\n",
    "\n",
    "- Token Sort Ratio : Tokenizing the string in question, sorting the tokens alphabetically, and then joining them back into a string\n",
    "\n",
    "- Token Set Ratio : Tokenize both strings,split the tokens into two groups of intersection and remainder. We use those sets to build up a comparison string.\n",
    "\n",
    "- Last_Word : Checks if last word is same in both Q1 and Q2\n",
    "\n",
    "- First_Word : Checks if First word is same in both Q1 and Q2\n",
    "\n",
    "- Length_diff : Finds the length diffrence between Q1 and Q2\n",
    "\n",
    "- StopWord_Ratio : Number of stopwords in both Questions\n",
    "\n",
    "- Token_Ratio : Number of tokens in both Questions\n",
    "\n",
    "- Longest_Substr_ratio : Ratio of the Longest Substring that is found in between Q1 and Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating columns \n",
    "\n",
    "# data['Simple_Ratio']=data.apply(lambda x: fuzz.ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "# data['Partial_Ratio']=data.apply(lambda x: fuzz.partial_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "# data['Token_Sort_Ratio']=data.apply(lambda x: fuzz.token_sort_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "# data['Token_Set_Ratio']=data.apply(lambda x: fuzz.token_set_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "\n",
    "# data['Last_Word']=data.apply(lambda x: int(x['question1'].split()[-1] == x['question2'].split()[-1]),axis=1)\n",
    "\n",
    "\n",
    "# data['First_Word']=data.apply(lambda x: int(x['question1'].split()[0] == x['question2'].split()[0]),axis=1)\n",
    "\n",
    "\n",
    "# data['Length_diff']=data.apply(lambda x: abs(len(x['question1'].split())-len(x['question2'].split())),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Simple_Ratio']=data.apply(lambda x: fuzz.ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "data['Partial_Ratio']=data.apply(lambda x: fuzz.partial_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "data['Token_Sort_Ratio']=data.apply(lambda x: fuzz.token_sort_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n",
    "\n",
    "data['Token_Set_Ratio']=data.apply(lambda x: fuzz.token_set_ratio(x['cleanQ1'],x['cleanQ2']) ,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Last_Word']=data.apply(lambda x: int(x['question1'].split()[-1] == x['question2'].split()[-1]),axis=1)\n",
    "\n",
    "\n",
    "data['First_Word']=data.apply(lambda x: int(x['question1'].split()[0] == x['question2'].split()[0]),axis=1)\n",
    "\n",
    "\n",
    "data['Length_diff']=data.apply(lambda x: abs(len(x['question1'].split())-len(x['question2'].split())),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions to find common_StopWord_Ratio , common_Token_Ratio , etc \n",
    "\n",
    "def common_StopWord_Ratio(q1,q2):\n",
    "    q1_token=q1.split() # Splitting Words to make then tokens\n",
    "    q2_token=q2.split()\n",
    "    \n",
    "    # We are takking out all the stopwords in both the Question and finding there intersection (Common stopwords)\n",
    "    q1=set([word for word in q1_token if word in stopwords])\n",
    "    q2=set([word for word in q2_token if word in stopwords])\n",
    "    common=q1.intersection(q2)\n",
    "    ratio=len(common)/len(set(q1_token))+len(set(q2_token))\n",
    "    \n",
    "    return ratio\n",
    "\n",
    "def common_Token_Ratio(q1,q2):\n",
    "    q1_token=q1.split()\n",
    "    q2_token=q2.split()\n",
    "    # We are takking out all the token in both the Question and finding there intersection (Common tokens)\n",
    "    q1=set([x for x in q1_token if x not in stopwords])\n",
    "    q2=set([x for x in q2_token if x not in stopwords])\n",
    "    \n",
    "    common=q1.intersection(q2)\n",
    "    ratio=len(common)/len(set(q1_token))+len(set(q2_token))\n",
    "    \n",
    "    return ratio\n",
    "\n",
    "def get_longest_substr_ratio(a, b):\n",
    "    \n",
    "    strs = list(distance.lcsubstrings(a, b))\n",
    "    if len(strs) == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return round(len(strs[0]) / (min(len(a), len(b)) + 1),5)\n",
    "\n",
    "def common_Characters(q1,q2):\n",
    "    q1=\"\".join(q1.split())\n",
    "    q2=\"\".join(q2.split())\n",
    "    c=0\n",
    "    d=dict(Counter(q1))\n",
    "    for i in q2:\n",
    "        if i in d and d[i]!=0:\n",
    "            c+=1;d[i]-=1\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating columns for above feature\n",
    "\n",
    "# data['StopWord_Ratio']=data.apply(lambda x: common_StopWord_Ratio(x[\"question1\"], x[\"question2\"]), axis=1)\n",
    "\n",
    "\n",
    "# data['Token_Ratio']=data.apply(lambda x: common_Token_Ratio(x[\"question1\"], x[\"question2\"]), axis=1)\n",
    "\n",
    "\n",
    "# data['Longest_Substr_ratio']=data.apply(lambda x: get_longest_substr_ratio(x[\"question1\"], x[\"question2\"]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3.10.5 Removing Uncleaned Question Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>...</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "      <th>cleanQ1</th>\n",
       "      <th>cleanQ2</th>\n",
       "      <th>Simple_Ratio</th>\n",
       "      <th>Partial_Ratio</th>\n",
       "      <th>Token_Sort_Ratio</th>\n",
       "      <th>Token_Set_Ratio</th>\n",
       "      <th>Last_Word</th>\n",
       "      <th>First_Word</th>\n",
       "      <th>Length_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>what step step guid invest share market india</td>\n",
       "      <td>what step step guid invest share market</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>what stori kohinoor  koh noor  diamond</td>\n",
       "      <td>what would happen indian govern stole kohinoor...</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
       "      <td>65</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>how increas speed internet connect use vpn</td>\n",
       "      <td>how internet speed increas hack dns</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>whi mental lone  how solv</td>\n",
       "      <td>find remaind when  math 23  24   math  divid 2...</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>which one dissolv water quik sugar  salt  meth...</td>\n",
       "      <td>which fish would surviv salt water</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2  is_duplicate  freq_qid1  freq_qid2  q1len  q2len  \\\n",
       "0   0     1     2             0          1          1     66     57   \n",
       "1   1     3     4             0          4          1     51     88   \n",
       "2   2     5     6             0          1          1     73     59   \n",
       "3   3     7     8             0          1          1     50     65   \n",
       "4   4     9    10             0          3          1     76     39   \n",
       "\n",
       "   q1_n_words  q2_n_words  ...  freq_q1-q2  \\\n",
       "0          14          12  ...           0   \n",
       "1           8          13  ...           3   \n",
       "2          14          10  ...           0   \n",
       "3          11           9  ...           0   \n",
       "4          13           7  ...           2   \n",
       "\n",
       "                                             cleanQ1  \\\n",
       "0     what step step guid invest share market india    \n",
       "1            what stori kohinoor  koh noor  diamond    \n",
       "2        how increas speed internet connect use vpn    \n",
       "3                         whi mental lone  how solv    \n",
       "4  which one dissolv water quik sugar  salt  meth...   \n",
       "\n",
       "                                             cleanQ2  Simple_Ratio  \\\n",
       "0           what step step guid invest share market             93   \n",
       "1  what would happen indian govern stole kohinoor...            67   \n",
       "2               how internet speed increas hack dns             41   \n",
       "3  find remaind when  math 23  24   math  divid 2...            31   \n",
       "4                which fish would surviv salt water             34   \n",
       "\n",
       "   Partial_Ratio Token_Sort_Ratio Token_Set_Ratio  Last_Word  First_Word  \\\n",
       "0            100               93             100          0           1   \n",
       "1             85               65              91          0           1   \n",
       "2             42               73              85          0           1   \n",
       "3             42               26              31          0           0   \n",
       "4             51               51              64          0           1   \n",
       "\n",
       "   Length_diff  \n",
       "0            2  \n",
       "1            5  \n",
       "2            4  \n",
       "3            2  \n",
       "4            6  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting question1 from our data frame\n",
    "del data['question1']\n",
    "\n",
    "# Deleting question2 from our data frame\n",
    "del data['question2']\n",
    "\n",
    "# Printing dataset after deletion\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.6 Analyzing Extracted Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in class 1 (duplicate pairs) : 298526\n",
      "Number of data points in class 0 (non duplicate pairs) : 510048\n"
     ]
    }
   ],
   "source": [
    "# Creating two dataframe of both duplicate and non duplicate and then extacting all the text and saving then in text file\n",
    "data_duplicate = data[data['is_duplicate'] == 1]\n",
    "datap_nonduplicate = data[data['is_duplicate'] == 0]\n",
    "\n",
    "# Converting 2d array of q1 and q2 and flatten the array: like {{1,2},{3,4}} to {1,2,3,4}\n",
    "p = np.dstack([data_duplicate['cleanQ1'], data_duplicate['cleanQ2']]).flatten()\n",
    "n = np.dstack([datap_nonduplicate[\"cleanQ1\"], datap_nonduplicate[\"cleanQ2\"]]).flatten()\n",
    "\n",
    "print (\"Number of data points in class 1 (duplicate pairs) :\",len(p))\n",
    "print (\"Number of data points in class 0 (non duplicate pairs) :\",len(n))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.10.7 Analyzing each features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the Simple_Ratio\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'Simple_Ratio', data = data[0:] , )\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['Simple_Ratio'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['Simple_Ratio'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the Partial_Ratio\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'Partial_Ratio', data = data[0:] , )\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['Partial_Ratio'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['Partial_Ratio'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the Token_Sort_Ratio\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'Token_Sort_Ratio', data = data[0:] , )\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['Token_Sort_Ratio'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['Token_Sort_Ratio'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of the Token_Set_Ratio\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.violinplot(x = 'is_duplicate', y = 'Token_Set_Ratio', data = data[0:] , )\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(data[data['is_duplicate'] == 1.0]['Token_Set_Ratio'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(data[data['is_duplicate'] == 0.0]['Token_Set_Ratio'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'qid1', 'qid2', 'is_duplicate', 'freq_qid1', 'freq_qid2', 'q1len',\n",
       "       'q2len', 'q1_n_words', 'q2_n_words', 'word_Common', 'word_Total',\n",
       "       'word_share', 'freq_q1+q2', 'freq_q1-q2', 'cleanQ1', 'cleanQ2',\n",
       "       'Simple_Ratio', 'Partial_Ratio', 'Token_Sort_Ratio', 'Token_Set_Ratio',\n",
       "       'Last_Word', 'First_Word', 'Length_diff'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using TSNE for Dimentionality reduction for 15 Features(Generated after cleaning the data) to 3 dimention\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.manifold import TSNE\n",
    "datap_subsampled = data[0:5000]\n",
    "X = MinMaxScaler().fit_transform(datap_subsampled[['Simple_Ratio', 'Partial_Ratio', 'Token_Sort_Ratio', 'Token_Set_Ratio','q1len', 'q2len', 'q1_n_words','q2_n_words', 'word_Common', 'word_Total', 'word_share','Last_Word', 'First_Word', 'Length_diff']])\n",
    "y = datap_subsampled['is_duplicate'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 5000 samples in 0.054s...\n",
      "[t-SNE] Computed neighbors for 5000 samples in 0.698s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 5000\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 5000\n",
      "[t-SNE] Mean sigma: 0.084912\n",
      "[t-SNE] Computed conditional probabilities in 0.374s\n",
      "[t-SNE] Iteration 50: error = 80.9654007, gradient norm = 0.0471294 (50 iterations in 3.177s)\n",
      "[t-SNE] Iteration 100: error = 71.2368469, gradient norm = 0.0100794 (50 iterations in 2.471s)\n",
      "[t-SNE] Iteration 150: error = 69.7338333, gradient norm = 0.0047685 (50 iterations in 2.427s)\n",
      "[t-SNE] Iteration 200: error = 69.0948105, gradient norm = 0.0042093 (50 iterations in 2.472s)\n",
      "[t-SNE] Iteration 250: error = 68.7337723, gradient norm = 0.0029269 (50 iterations in 2.489s)\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 68.733772\n",
      "[t-SNE] Iteration 300: error = 1.8972559, gradient norm = 0.0011795 (50 iterations in 2.527s)\n",
      "[t-SNE] Iteration 350: error = 1.5131139, gradient norm = 0.0004803 (50 iterations in 2.483s)\n",
      "[t-SNE] Iteration 400: error = 1.3522749, gradient norm = 0.0002742 (50 iterations in 2.452s)\n",
      "[t-SNE] Iteration 450: error = 1.2655349, gradient norm = 0.0001885 (50 iterations in 2.450s)\n",
      "[t-SNE] Iteration 500: error = 1.2124174, gradient norm = 0.0001404 (50 iterations in 2.466s)\n",
      "[t-SNE] Iteration 550: error = 1.1780016, gradient norm = 0.0001161 (50 iterations in 2.421s)\n",
      "[t-SNE] Iteration 600: error = 1.1552255, gradient norm = 0.0000997 (50 iterations in 2.498s)\n",
      "[t-SNE] Iteration 650: error = 1.1399904, gradient norm = 0.0000904 (50 iterations in 2.459s)\n",
      "[t-SNE] Iteration 700: error = 1.1285889, gradient norm = 0.0000822 (50 iterations in 2.477s)\n",
      "[t-SNE] Iteration 750: error = 1.1204561, gradient norm = 0.0000770 (50 iterations in 2.449s)\n",
      "[t-SNE] Iteration 800: error = 1.1141772, gradient norm = 0.0000723 (50 iterations in 2.493s)\n",
      "[t-SNE] Iteration 850: error = 1.1094086, gradient norm = 0.0000685 (50 iterations in 2.501s)\n",
      "[t-SNE] Iteration 900: error = 1.1054152, gradient norm = 0.0000623 (50 iterations in 2.500s)\n",
      "[t-SNE] Iteration 950: error = 1.1016458, gradient norm = 0.0000599 (50 iterations in 2.500s)\n",
      "[t-SNE] Iteration 1000: error = 1.0982625, gradient norm = 0.0000588 (50 iterations in 2.484s)\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.098263\n"
     ]
    }
   ],
   "source": [
    "tsne2d = TSNE(\n",
    "    n_components=2,\n",
    "    init='random', # pca\n",
    "    random_state=101,\n",
    "    method='barnes_hut',\n",
    "    n_iter=1000,\n",
    "    verbose=2,\n",
    "    angle=0.5\n",
    ").fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "\n",
    "df = pd.DataFrame({'x':tsne2d[:,0], 'y':tsne2d[:,1] ,'label':y})\n",
    "\n",
    "palette = sns.color_palette(\"bright\", 2)\n",
    "# draw the plot in appropriate place in the grid\n",
    "sns.scatterplot(data=df, x='x', y='y', hue='label',legend='full', palette=palette)\n",
    "plt.title(\"perplexity : {} and max_iter : {}\".format(30, 1000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1=data[['cleanQ1','cleanQ2','is_duplicate']]\n",
    "# data1['cleanQ1'] = data['cleanQ1'].apply(lambda x: str(x))\n",
    "# data1['cleanQ2'] = data['cleanQ2'].apply(lambda x: str(x))\n",
    "# data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # merging questions of both Q1 and Q2 to a single list in which first 404287 index will be of question 1 and then rest of question 2\n",
    "# questions = list(data1['cleanQ1']) + list(data1['cleanQ2'])  # len(questions): 808574  [404287 of q1 and 404287 of q2]\n",
    "\n",
    "# tfidf = TfidfVectorizer() #  Convert a collection of raw documents to a matrix of TF-Idata features\n",
    "\n",
    "# tfidf.fit_transform(questions)  # Converting out text to a matrix of TF-Idata features\n",
    "\n",
    "# # mapping our feature_names with threre resptive tf-idata score  ( dict key:word and value:tf-idata score )\n",
    "# word2tfidf = dict(zip(tfidf.get_feature_names(), tfidf.idf_)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plotting Key Value Pair\n",
    "# wrd=list(word2tfidf.keys())[::-1][:10]   # finding keys of dict and then reversing them to get last 10 keys\n",
    "# for i in wrd:\n",
    "#     print(i,word2tfidf[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(['qid1', 'qid2','cleanQ1', 'cleanQ2'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>is_duplicate</th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_Total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "      <th>Simple_Ratio</th>\n",
       "      <th>Partial_Ratio</th>\n",
       "      <th>Token_Sort_Ratio</th>\n",
       "      <th>Token_Set_Ratio</th>\n",
       "      <th>Last_Word</th>\n",
       "      <th>First_Word</th>\n",
       "      <th>Length_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>93</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>85</td>\n",
       "      <td>65</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>73</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   is_duplicate  freq_qid1  freq_qid2  q1len  q2len  q1_n_words  q2_n_words  \\\n",
       "0             0          1          1     66     57          14          12   \n",
       "1             0          4          1     51     88           8          13   \n",
       "2             0          1          1     73     59          14          10   \n",
       "3             0          1          1     50     65          11           9   \n",
       "4             0          3          1     76     39          13           7   \n",
       "\n",
       "   word_Common  word_Total  word_share  freq_q1+q2  freq_q1-q2  Simple_Ratio  \\\n",
       "0           10          23    0.434783           2           0            93   \n",
       "1            4          20    0.200000           5           3            67   \n",
       "2            4          24    0.166667           2           0            41   \n",
       "3            0          19    0.000000           2           0            31   \n",
       "4            2          20    0.100000           4           2            34   \n",
       "\n",
       "   Partial_Ratio  Token_Sort_Ratio  Token_Set_Ratio  Last_Word  First_Word  \\\n",
       "0            100                93              100          0           1   \n",
       "1             85                65               91          0           1   \n",
       "2             42                73               85          0           1   \n",
       "3             42                26               31          0           0   \n",
       "4             51                51               64          0           1   \n",
       "\n",
       "   Length_diff  \n",
       "0            2  \n",
       "1            5  \n",
       "2            4  \n",
       "3            2  \n",
       "4            6  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.drop(['id'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: is_duplicate, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true=data['is_duplicate']\n",
    "y_true.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['is_duplicate'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
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       "      <th>freq_q1-q2</th>\n",
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       "      <th>Partial_Ratio</th>\n",
       "      <th>Token_Sort_Ratio</th>\n",
       "      <th>Token_Set_Ratio</th>\n",
       "      <th>Last_Word</th>\n",
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       "      <td>65</td>\n",
       "      <td>11</td>\n",
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       "      <td>19</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   freq_qid1  freq_qid2  q1len  q2len  q1_n_words  q2_n_words  word_Common  \\\n",
       "0          1          1     66     57          14          12           10   \n",
       "1          4          1     51     88           8          13            4   \n",
       "2          1          1     73     59          14          10            4   \n",
       "3          1          1     50     65          11           9            0   \n",
       "4          3          1     76     39          13           7            2   \n",
       "\n",
       "   word_Total  word_share  freq_q1+q2  freq_q1-q2  Simple_Ratio  \\\n",
       "0          23    0.434783           2           0            93   \n",
       "1          20    0.200000           5           3            67   \n",
       "2          24    0.166667           2           0            41   \n",
       "3          19    0.000000           2           0            31   \n",
       "4          20    0.100000           4           2            34   \n",
       "\n",
       "   Partial_Ratio  Token_Sort_Ratio  Token_Set_Ratio  Last_Word  First_Word  \\\n",
       "0            100                93              100          0           1   \n",
       "1             85                65               91          0           1   \n",
       "2             42                73               85          0           1   \n",
       "3             42                26               31          0           0   \n",
       "4             51                51               64          0           1   \n",
       "\n",
       "   Length_diff  \n",
       "0            2  \n",
       "1            5  \n",
       "2            4  \n",
       "3            2  \n",
       "4            6  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "# transform data\n",
    "scaled = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>freq_q1-q2</th>\n",
       "      <th>Simple_Ratio</th>\n",
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       "      <th>Last_Word</th>\n",
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       "      <td>65</td>\n",
       "      <td>11</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   freq_qid1  freq_qid2  q1len  q2len  q1_n_words  q2_n_words  word_Common  \\\n",
       "0          1          1     66     57          14          12           10   \n",
       "1          4          1     51     88           8          13            4   \n",
       "2          1          1     73     59          14          10            4   \n",
       "3          1          1     50     65          11           9            0   \n",
       "4          3          1     76     39          13           7            2   \n",
       "\n",
       "   word_Total  word_share  freq_q1+q2  freq_q1-q2  Simple_Ratio  \\\n",
       "0          23    0.434783           2           0            93   \n",
       "1          20    0.200000           5           3            67   \n",
       "2          24    0.166667           2           0            41   \n",
       "3          19    0.000000           2           0            31   \n",
       "4          20    0.100000           4           2            34   \n",
       "\n",
       "   Partial_Ratio  Token_Sort_Ratio  Token_Set_Ratio  Last_Word  First_Word  \\\n",
       "0            100                93              100          0           1   \n",
       "1             85                65               91          0           1   \n",
       "2             42                73               85          0           1   \n",
       "3             42                26               31          0           0   \n",
       "4             51                51               64          0           1   \n",
       "\n",
       "   Length_diff  \n",
       "0            2  \n",
       "1            5  \n",
       "2            4  \n",
       "3            2  \n",
       "4            6  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test, y_train, y_test = train_test_split(scaled, y_true, stratify=y_true, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train data : (283000, 18)\n",
      "Number of data points in test data : (121287, 18)\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of data points in train data :\",X_train.shape)\n",
    "print(\"Number of data points in test data :\",X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.6307985865724381 Class 1:  0.36920141342756185\n",
      "---------- Distribution of output variable in train data ----------\n",
      "Class 0:  0.3691986775169639 Class 1:  0.3691986775169639\n"
     ]
    }
   ],
   "source": [
    "print(\"-\"*10, \"Distribution of output variable in train data\", \"-\"*10)\n",
    "train_distr = Counter(y_train)\n",
    "train_len = len(y_train)\n",
    "print(\"Class 0: \",int(train_distr[0])/train_len,\"Class 1: \", int(train_distr[1])/train_len)\n",
    "print(\"-\"*10, \"Distribution of output variable in train data\", \"-\"*10)\n",
    "test_distr = Counter(y_test)\n",
    "test_len = len(y_test)\n",
    "print(\"Class 0: \",int(test_distr[1])/test_len, \"Class 1: \",int(test_distr[1])/test_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function plots the confusion matrices given y_i, y_i_hat.\n",
    "def plot_confusion_matrix(test_y, predict_y):\n",
    "    C = confusion_matrix(test_y, predict_y)\n",
    "    # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j\n",
    "    \n",
    "    A =(((C.T)/(C.sum(axis=1))).T)\n",
    "    #divid each element of the confusion matrix with the sum of elements in that column\n",
    "    \n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.T = [[1, 3],\n",
    "    #        [2, 4]]\n",
    "    # C.sum(axis = 1)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =1) = [[3, 7]]\n",
    "    # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]\n",
    "    #                           [2/3, 4/7]]\n",
    "\n",
    "    # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]\n",
    "    #                           [3/7, 4/7]]\n",
    "    # sum of row elements = 1\n",
    "    \n",
    "    B =(C/C.sum(axis=0))\n",
    "    #divid each element of the confusion matrix with the sum of elements in that row\n",
    "    # C = [[1, 2],\n",
    "    #     [3, 4]]\n",
    "    # C.sum(axis = 0)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array\n",
    "    # C.sum(axix =0) = [[4, 6]]\n",
    "    # (C/C.sum(axis=0)) = [[1/4, 2/6],\n",
    "    #                      [3/4, 4/6]] \n",
    "    plt.figure(figsize=(20,4))\n",
    "    \n",
    "    labels = [1,2]\n",
    "    # representing A in heatmap format\n",
    "    cmap=sns.light_palette(\"blue\")\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.heatmap(C, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 2)\n",
    "    sns.heatmap(B, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Precision matrix\")\n",
    "    \n",
    "    plt.subplot(1, 3, 3)\n",
    "    # representing B in heatmap format\n",
    "    sns.heatmap(A, annot=True, cmap=cmap, fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "    plt.xlabel('Predicted Class')\n",
    "    plt.ylabel('Original Class')\n",
    "    plt.title(\"Recall matrix\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building a random model (Finding worst-case log-loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss on Test Data using Random Model 0.887417930560734\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_y = np.zeros((test_len,2))\n",
    "for i in range(test_len):\n",
    "    rand_probs = np.random.rand(1,2)\n",
    "    predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
    "print(\"Log loss on Test Data using Random Model\",log_loss(y_test, predicted_y, eps=1e-15))\n",
    "\n",
    "predicted_y =np.argmax(predicted_y, axis=1)\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 0.43576104259800197\n",
      "For values of alpha =  0.0001 The log loss is: 0.43143513522182303\n",
      "For values of alpha =  0.001 The log loss is: 0.4303481058710582\n",
      "For values of alpha =  0.01 The log loss is: 0.44236039209422784\n",
      "For values of alpha =  0.1 The log loss is: 0.47421670227132806\n",
      "For values of alpha =  1 The log loss is: 0.5012774966204472\n",
      "For values of alpha =  10 The log loss is: 0.5115099784697646\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.001 The train log loss is: 0.42997047302466035\n",
      "For values of best alpha =  0.001 The test log loss is: 0.4303481058710582\n",
      "Total number of data points : 121287\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 2)] # hyperparam for SGD classifier.\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "\n",
    "\n",
    "log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
    "    clf.fit(X_train, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predicted_y =np.argmax(predict_y,axis=1)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVM with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of alpha =  1e-05 The log loss is: 0.46457225187938556\n",
      "For values of alpha =  0.0001 The log loss is: 0.4412620902969132\n",
      "For values of alpha =  0.001 The log loss is: 0.4415049126049381\n",
      "For values of alpha =  0.01 The log loss is: 0.44866370649008647\n",
      "For values of alpha =  0.1 The log loss is: 0.5244023412742739\n",
      "For values of alpha =  1 The log loss is: 0.6585278256453735\n",
      "For values of alpha =  10 The log loss is: 0.6585278256453736\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For values of best alpha =  0.0001 The train log loss is: 0.4409166229628016\n",
      "For values of best alpha =  0.0001 The test log loss is: 0.4412620902969132\n",
      "Total number of data points : 121287\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = [10 ** x for x in range(-5, 2)] # hyperparam for SGD classifier.\n",
    "\n",
    "# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html\n",
    "# ------------------------------\n",
    "# default parameters\n",
    "# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, \n",
    "# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, \n",
    "# class_weight=None, warm_start=False, average=False, n_iter=None)\n",
    "\n",
    "# some of methods\n",
    "# fit(X, y[, coef_init, intercept_init, …])\tFit linear model with Stochastic Gradient Descent.\n",
    "# predict(X)\tPredict class labels for samples in X.\n",
    "\n",
    "#-------------------------------\n",
    "# video link: \n",
    "#------------------------------\n",
    "\n",
    "\n",
    "log_error_array=[]\n",
    "for i in alpha:\n",
    "    clf = SGDClassifier(alpha=i, penalty='l1', loss='hinge', random_state=42)\n",
    "    clf.fit(X_train, y_train)\n",
    "    sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "    sig_clf.fit(X_train, y_train)\n",
    "    predict_y = sig_clf.predict_proba(X_test)\n",
    "    log_error_array.append(log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "    print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(alpha, log_error_array,c='g')\n",
    "for i, txt in enumerate(np.round(log_error_array,3)):\n",
    "    ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],log_error_array[i]))\n",
    "plt.grid()\n",
    "plt.title(\"Cross Validation Error for each alpha\")\n",
    "plt.xlabel(\"Alpha i's\")\n",
    "plt.ylabel(\"Error measure\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "best_alpha = np.argmin(log_error_array)\n",
    "clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l1', loss='hinge', random_state=42)\n",
    "clf.fit(X_train, y_train)\n",
    "sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
    "sig_clf.fit(X_train, y_train)\n",
    "\n",
    "predict_y = sig_clf.predict_proba(X_train)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predict_y = sig_clf.predict_proba(X_test)\n",
    "print('For values of best alpha = ', alpha[best_alpha], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))\n",
    "predicted_y =np.argmax(predict_y,axis=1)\n",
    "print(\"Total number of data points :\", len(predicted_y))\n",
    "plot_confusion_matrix(y_test, predicted_y)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
 "nbformat": 4,
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}