1.Quora.ipynb

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   "source": [
    "![Quora-1.png](attachment:Quora-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "J6rnhr2Xs5Vs"
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   "source": [
    "<h1 style=\"text-align:center;font-size:30px;\" > Quora Question Pairs </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o9fciGc7s5Vu"
   },
   "source": [
    "<h1> 1. Business Problem </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LRzmxjKxs5Vw"
   },
   "source": [
    "<h2> 1.1 Description </h2>"
   ]
  },
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    "id": "1nlaIYe9s5Vx"
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   "source": [
    "<p>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.</p>\n",
    "<p>\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",
    "</p>\n",
    "<br>\n",
    "> Credits: Kaggle \n"
   ]
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   "source": [
    "__ Problem Statement __\n",
    "- Identify which questions asked on Quora are duplicates of questions that have already been asked. \n",
    "- This could be useful to instantly provide answers to questions that have already been answered. \n",
    "- We are tasked with predicting whether a pair of questions are duplicates or not. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "34hYn911s5V0"
   },
   "source": [
    "<h2> 1.2 Sources/Useful Links</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7YIjqVPgs5V4"
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   "source": [
    "- Source : https://www.kaggle.com/c/quora-question-pairs\n",
    "<br><br>____ Useful Links ____\n",
    "- Discussions : https://www.kaggle.com/anokas/data-analysis-xgboost-starter-0-35460-lb/comments\n",
    "- Kaggle Winning Solution and other approaches: https://www.dropbox.com/sh/93968nfnrzh8bp5/AACZdtsApc1QSTQc7X0H3QZ5a?dl=0\n",
    "- Blog 1 : https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning\n",
    "- Blog 2 : https://towardsdatascience.com/identifying-duplicate-questions-on-quora-top-12-on-kaggle-4c1cf93f1c30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "jlNRUR4Ws5V5"
   },
   "source": [
    "<h2>1.3 Real world/Business Objectives and Constraints </h2>"
   ]
  },
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   "source": [
    "1. The cost of a mis-classification can be very high.\n",
    "2. You would want a probability of a pair of questions to be duplicates so that you can choose any threshold of choice.\n",
    "3. No strict latency concerns.\n",
    "4. Interpretability is partially important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VIam5Aaks5V9"
   },
   "source": [
    "<h1>2. Machine Learning Probelm </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jnty9Bhls5V-"
   },
   "source": [
    "<h2> 2.1 Data </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rty1PZv3s5V_"
   },
   "source": [
    "<h3> 2.1.1 Data Overview </h3>"
   ]
  },
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   "source": [
    "<p> \n",
    "- Data will be in a file Train.csv <br>\n",
    "- Train.csv contains 5 columns : qid1, qid2, question1, question2, is_duplicate <br>\n",
    "- Size of Train.csv - 60MB <br>\n",
    "- Number of rows in Train.csv = 404,290\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "v9grbSNds5WC"
   },
   "source": [
    "<h3> 2.1.2 Example Data point </h3>"
   ]
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   "source": [
    "<pre>\n",
    "\"id\",\"qid1\",\"qid2\",\"question1\",\"question2\",\"is_duplicate\"\n",
    "\"0\",\"1\",\"2\",\"What is the step by step guide to invest in share market in india?\",\"What is the step by step guide to invest in share market?\",\"0\"\n",
    "\"1\",\"3\",\"4\",\"What is the story of Kohinoor (Koh-i-Noor) Diamond?\",\"What would happen if the Indian government stole the Kohinoor (Koh-i-Noor) diamond back?\",\"0\"\n",
    "\"7\",\"15\",\"16\",\"How can I be a good geologist?\",\"What should I do to be a great geologist?\",\"1\"\n",
    "\"11\",\"23\",\"24\",\"How do I read and find my YouTube comments?\",\"How can I see all my Youtube comments?\",\"1\"\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9qPVfeEjs5WF"
   },
   "source": [
    "<h2> 2.2 Mapping the real world problem to an ML problem </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JfBn0LYPs5WI"
   },
   "source": [
    "<h3> 2.2.1 Type of Machine Leaning Problem </h3>"
   ]
  },
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   "cell_type": "markdown",
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   },
   "source": [
    "<p> It is a binary classification problem, for a given pair of questions we need to predict if they are duplicate or not. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "keZOL1las5WL"
   },
   "source": [
    "<h3> 2.2.2 Performance Metric </h3>"
   ]
  },
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   "cell_type": "markdown",
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    "id": "YHktaBrMs5WN"
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   "source": [
    "Source: https://www.kaggle.com/c/quora-question-pairs#evaluation\n",
    "\n",
    "Metric(s): \n",
    "* log-loss : https://www.kaggle.com/wiki/LogarithmicLoss\n",
    "* Binary Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FmDMBWJjs5WO"
   },
   "source": [
    "<h2> 2.3 Train and Test Construction </h2>"
   ]
  },
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   "source": [
    "<p>  </p>\n",
    "<p> 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. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cW_MVIlps5WQ"
   },
   "source": [
    "<h1>3. Exploratory Data Analysis </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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    "colab_type": "code",
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    "outputId": "0e1df4ed-4a74-4b0e-e84e-1b3862bbf55d"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from subprocess import check_output\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "import os\n",
    "import gc\n",
    "\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import distance\n",
    "from nltk.stem import PorterStemmer\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "__T8jddGs5Wc"
   },
   "source": [
    "<h2> 3.1 Reading data and basic stats </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
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    "colab_type": "code",
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    "outputId": "2e17a7bc-9a5b-4c43-d35b-081cc9f92528"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 404290\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "\n",
    "print(\"Number of data points:\",df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
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    "id": "34zXGW8gs5Wj",
    "outputId": "ab7d570a-9eeb-477a-b7cb-663ff6fd04fa"
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>What is the step by step guide to invest in sh...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
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       "      <th>4</th>\n",
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       "      <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  "
      ]
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    "df.head()"
   ]
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   },
   "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",
      "id              404290 non-null int64\n",
      "qid1            404290 non-null int64\n",
      "qid2            404290 non-null int64\n",
      "question1       404290 non-null object\n",
      "question2       404288 non-null object\n",
      "is_duplicate    404290 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "We are given a minimal number of data fields here, consisting of:\n",
    "\n",
    "- id:  Looks like a simple rowID\n",
    "- qid{1, 2}:  The unique ID of each question in the pair\n",
    "- question{1, 2}:  The actual textual contents of the questions.\n",
    "- is_duplicate:  The label that we are trying to predict - whether the two questions are duplicates of each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZulqVzTDs5Wx"
   },
   "source": [
    "<h3> 3.2.1 Distribution of data points among output classes</h3>\n",
    "- Number of duplicate(smilar) and non-duplicate(non similar) questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
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    {
     "data": {
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       "<matplotlib.axes._subplots.AxesSubplot at 0x22b00727d30>"
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     "execution_count": 10,
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     "data": {
      "image/png": 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pwTrQQ2MpcM/Q+52tJkkag8XjXsAsMqJWj+uUrAPWtbc/SvKNX+qqFpbDgO+NexGzybvH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\nKdRMn+OVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b7737e198>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"is_duplicate\")['id'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-usI2K2bs5W4",
    "outputId": "ff0a6a8b-65ad-487a-d5ec-df3c223ba620"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~> Total number of question pairs for training:\n",
      "   404290\n"
     ]
    }
   ],
   "source": [
    "print('~> Total number of question pairs for training:\\n   {}'.format(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YiPia6Pjs5W_",
    "outputId": "3cde4cec-4314-4c14-e807-b35e969bf9e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~> Question pairs are not Similar (is_duplicate = 0):\n",
      "   63.08%\n",
      "\n",
      "~> Question pairs are Similar (is_duplicate = 1):\n",
      "   36.92%\n"
     ]
    }
   ],
   "source": [
    "print('~> Question pairs are not Similar (is_duplicate = 0):\\n   {}%'.format(100 - round(df['is_duplicate'].mean()*100, 2)))\n",
    "print('\\n~> Question pairs are Similar (is_duplicate = 1):\\n   {}%'.format(round(df['is_duplicate'].mean()*100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wGX03QVRs5XF"
   },
   "source": [
    "<h3> 3.2.2 Number of unique questions </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "185770    6\n",
       "28680     6\n",
       "71195     6\n",
       "39359     6\n",
       "104421    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.qid1 + df.qid2 ).value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [],
   "source": [
    "np.value_counts() > 1 and "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [],
   "source": [
    "#que_id = df.qid1 + df.qid2\n",
    "np.unique(que_id).value_couts() > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VOKa6aU2s5XG",
    "outputId": "8f644b1d-27c0-4d63-84e2-bb2a42419be2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total num of  Unique Questions are: 537933\n",
      "\n",
      "Number of unique questions that appear more than one time: 111780 (20.77953945937505%)\n",
      "\n",
      "Max number of times a single question is repeated: 157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qids = pd.Series(df['qid1'].tolist() + df['qid2'].tolist())\n",
    "unique_qs = len(np.unique(qids))\n",
    "qs_morethan_onetime = np.sum(qids.value_counts() > 1)\n",
    "print ('Total number of  Unique Questions are: {}\\n'.format(unique_qs))\n",
    "#print len(np.unique(qids))\n",
    "\n",
    "print ('Number of unique questions that appear more than one time: {} ({}%)\\n'.format(qs_morethan_onetime,qs_morethan_onetime/unique_qs*100))\n",
    "\n",
    "print ('Max number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) \n",
    "\n",
    "q_vals=qids.value_counts()\n",
    "\n",
    "q_vals=q_vals.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "plcvbd4Cs5XM",
    "outputId": "8e137cc1-e0c4-44f4-9cc2-703302206d4f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0yQ5JNgUOAs6c5zFJkiTdpbo+wlZVtyZ5IXAOsBFwYlVdOs/DkiRJukt1HdgA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\nSZ0zsEmSJHXu/wOOW4dlTW6sEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b00c2ceb8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "x = [\"unique_questions\" , \"Repeated Questions\"]\n",
    "y =  [unique_qs , qs_morethan_onetime]\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": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G-CwGaMms5XS"
   },
   "source": [
    "<h3>3.2.3 Checking for Duplicates </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YCiDBHm5s5XT",
    "outputId": "d8011926-4086-4c9a-9fcf-59663a584ec4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate questions 0\n"
     ]
    }
   ],
   "source": [
    "#checking whether there are any repeated pair of questions\n",
    "\n",
    "pair_duplicates = df[['qid1','qid2','is_duplicate']].groupby(['qid1','qid2']).count().reset_index()\n",
    "\n",
    "print (\"Number of duplicate questions\",(pair_duplicates).shape[0] - df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iaHTnnt8s5XX"
   },
   "source": [
    "<h3> 3.2.4 Number of occurrences of each question </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dPZwk-C8s5Xa",
    "outputId": "0d6d5978-2306-4ed3-cf27-f2a0b974e47d"
   },
   "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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/JTl4slCaeneS+y0oDwAAAABLYJ6ZSq+tqlcluTiTWUuPSfKahaYCAAAAYE+b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\nJEmSJKm3/wewDUouuwNUgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b0188b3c8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.hist(qids.value_counts(), bins=160)\n",
    "\n",
    "plt.yscale('log', nonposy='clip')\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 ('Maximum number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h_WdYxlYs5Xj"
   },
   "source": [
    "<h3> 3.2.5 Checking for NULL values </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r0x1gR2fs5Xk",
    "outputId": "721aef48-e628-40c6-d567-25466f4283e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            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  \n"
     ]
    }
   ],
   "source": [
    "#Checking whether there are any rows with null values\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CCYmufv6s5Xo"
   },
   "source": [
    "- There are two rows with null values in question2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yLBRyACgs5Xp",
    "outputId": "076046a9-1510-41ef-cf98-15b38661dca4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [id, qid1, qid2, question1, question2, is_duplicate]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Filling the null values with ' '\n",
    "df = df.fillna('')\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l9Qcl5xfs5Xs"
   },
   "source": [
    "<h2>3.3 Basic Feature Extraction (before cleaning) </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RRzvPYzGs5Xu"
   },
   "source": [
    "Let us now construct a few features like:\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd,numpy as np\n",
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "                             'foo', 'bar', 'foo', 'foo'],\n",
    "                       'B' : ['one', 'one', 'two', 'three',\n",
    "                             'two', 'two', 'one', 'three'],\n",
    "                       'C' : np.random.randn(8),\n",
    "                       'D' : np.random.randn(8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.830911</td>\n",
       "      <td>-0.755606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.306842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.669381</td>\n",
       "      <td>-1.283149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>0.527199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.145645</td>\n",
       "      <td>-1.000449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.789617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-1.181012</td>\n",
       "      <td>0.545725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>0.535757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -0.830911 -0.755606\n",
       "1  bar    one  0.301124  0.306842\n",
       "2  foo    two  0.669381 -1.283149\n",
       "3  bar  three  1.591871  0.527199\n",
       "4  foo    two  1.145645 -1.000449\n",
       "5  bar    two -0.190438 -0.789617\n",
       "6  foo    one -1.181012  0.545725\n",
       "7  foo  three -0.491200  0.535757"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_a = df.groupby('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>one</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.306842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>one</td>\n",
       "      <td>-0.830911</td>\n",
       "      <td>-0.755606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       B         C         D\n",
       "A                           \n",
       "bar  one  0.301124  0.306842\n",
       "foo  one -0.830911 -0.755606"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_a.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>two</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.789617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>three</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>0.535757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         B         C         D\n",
       "A                             \n",
       "bar    two -0.190438 -0.789617\n",
       "foo  three -0.491200  0.535757"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_a.last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A\n",
       "bar    1.702557\n",
       "foo   -0.688098\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_a['C'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bar': Int64Index([1, 3, 5], dtype='int64'),\n",
       " 'foo': Int64Index([0, 2, 4, 6, 7], dtype='int64')}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_a.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grouped_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A\n",
       "bar          barbarbar\n",
       "foo    foofoofoofoofoo\n",
       "Name: A, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_a['A'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>1.702557</td>\n",
       "      <td>0.044424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-0.688098</td>\n",
       "      <td>-1.957723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  1.702557  0.044424\n",
       "foo -0.688098 -1.957723"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#want aggregation in all\n",
    "\n",
    "grouped_a.aggregate(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">C</th>\n",
       "      <th colspan=\"8\" halign=\"left\">D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>0.301124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.306842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.306842</td>\n",
       "      <td>0.306842</td>\n",
       "      <td>0.306842</td>\n",
       "      <td>0.306842</td>\n",
       "      <td>0.306842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>1.591871</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.527199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.527199</td>\n",
       "      <td>0.527199</td>\n",
       "      <td>0.527199</td>\n",
       "      <td>0.527199</td>\n",
       "      <td>0.527199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>-0.190438</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.789617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.789617</td>\n",
       "      <td>-0.789617</td>\n",
       "      <td>-0.789617</td>\n",
       "      <td>-0.789617</td>\n",
       "      <td>-0.789617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.005961</td>\n",
       "      <td>0.247559</td>\n",
       "      <td>-1.181012</td>\n",
       "      <td>-1.093487</td>\n",
       "      <td>-1.005961</td>\n",
       "      <td>-0.918436</td>\n",
       "      <td>-0.830911</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.104941</td>\n",
       "      <td>0.920180</td>\n",
       "      <td>-0.755606</td>\n",
       "      <td>-0.430274</td>\n",
       "      <td>-0.104941</td>\n",
       "      <td>0.220392</td>\n",
       "      <td>0.545725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>-0.491200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.535757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.535757</td>\n",
       "      <td>0.535757</td>\n",
       "      <td>0.535757</td>\n",
       "      <td>0.535757</td>\n",
       "      <td>0.535757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.907513</td>\n",
       "      <td>0.336770</td>\n",
       "      <td>0.669381</td>\n",
       "      <td>0.788447</td>\n",
       "      <td>0.907513</td>\n",
       "      <td>1.026579</td>\n",
       "      <td>1.145645</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.141799</td>\n",
       "      <td>0.199899</td>\n",
       "      <td>-1.283149</td>\n",
       "      <td>-1.212474</td>\n",
       "      <td>-1.141799</td>\n",
       "      <td>-1.071124</td>\n",
       "      <td>-1.000449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              C                                                              \\\n",
       "          count      mean       std       min       25%       50%       75%   \n",
       "A   B                                                                         \n",
       "bar one     1.0  0.301124       NaN  0.301124  0.301124  0.301124  0.301124   \n",
       "    three   1.0  1.591871       NaN  1.591871  1.591871  1.591871  1.591871   \n",
       "    two     1.0 -0.190438       NaN -0.190438 -0.190438 -0.190438 -0.190438   \n",
       "foo one     2.0 -1.005961  0.247559 -1.181012 -1.093487 -1.005961 -0.918436   \n",
       "    three   1.0 -0.491200       NaN -0.491200 -0.491200 -0.491200 -0.491200   \n",
       "    two     2.0  0.907513  0.336770  0.669381  0.788447  0.907513  1.026579   \n",
       "\n",
       "                        D                                                    \\\n",
       "                max count      mean       std       min       25%       50%   \n",
       "A   B                                                                         \n",
       "bar one    0.301124   1.0  0.306842       NaN  0.306842  0.306842  0.306842   \n",
       "    three  1.591871   1.0  0.527199       NaN  0.527199  0.527199  0.527199   \n",
       "    two   -0.190438   1.0 -0.789617       NaN -0.789617 -0.789617 -0.789617   \n",
       "foo one   -0.830911   2.0 -0.104941  0.920180 -0.755606 -0.430274 -0.104941   \n",
       "    three -0.491200   1.0  0.535757       NaN  0.535757  0.535757  0.535757   \n",
       "    two    1.145645   2.0 -1.141799  0.199899 -1.283149 -1.212474 -1.141799   \n",
       "\n",
       "                               \n",
       "                75%       max  \n",
       "A   B                          \n",
       "bar one    0.306842  0.306842  \n",
       "    three  0.527199  0.527199  \n",
       "    two   -0.789617 -0.789617  \n",
       "foo one    0.220392  0.545725  \n",
       "    three  0.535757  0.535757  \n",
       "    two   -1.071124 -1.000449  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Iq4DZ-rYs5Xv",
    "outputId": "d34e66da-d84b-49ea-8852-4beb9da688ba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
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       "      <td>12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</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",
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       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <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>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>20.0</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         10.0        23.0   \n",
       "1          1     51     88           8          13          4.0        20.0   \n",
       "2          1     73     59          14          10          4.0        24.0   \n",
       "3          1     50     65          11           9          0.0        19.0   \n",
       "4          1     76     39          13           7          2.0        20.0   \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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('df_fe_without_preprocessing_train.csv'):\n",
    "    df = pd.read_csv(\"df_fe_without_preprocessing_train.csv\",encoding='latin-1')\n",
    "else:\n",
    "    df['freq_qid1'] = df.groupby('qid1')['qid1'].transform('count') \n",
    "    df['freq_qid2'] = df.groupby('qid2')['qid2'].transform('count')\n",
    "    df['q1len'] = df['question1'].str.len() \n",
    "    df['q2len'] = df['question2'].str.len()\n",
    "    df['q1_n_words'] = df['question1'].apply(lambda row: len(row.split(\" \")))\n",
    "    df['q2_n_words'] = df['question2'].apply(lambda row: len(row.split(\" \")))\n",
    "\n",
    "    def normalized_word_Common(row):\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)\n",
    "    df['word_Common'] = df.apply(normalized_word_Common, axis=1)\n",
    "\n",
    "    def normalized_word_Total(row):\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) + len(w2))\n",
    "    df['word_Total'] = df.apply(normalized_word_Total, axis=1)\n",
    "\n",
    "    def normalized_word_share(row):\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))\n",
    "    df['word_share'] = df.apply(normalized_word_share, axis=1)\n",
    "\n",
    "    df['freq_q1+q2'] = df['freq_qid1']+df['freq_qid2']\n",
    "    df['freq_q1-q2'] = abs(df['freq_qid1']-df['freq_qid2'])\n",
    "\n",
    "    df.to_csv(\"df_fe_without_preprocessing_train.csv\", index=False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-zLujovVs5X3"
   },
   "source": [
    "<h3> 3.3.1 Analysis of some of the extracted features </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zRIFQTkCs5X3"
   },
   "source": [
    "- Here are some questions have only one single words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jSS0X82Ds5X5",
    "outputId": "5dacd7b2-d955-4435-9639-f1c6acd9b580"
   },
   "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",
      "Number of Questions with minimum length [question1] : 67\n",
      "Number of Questions with minimum length [question2] : 24\n"
     ]
    }
   ],
   "source": [
    "print (\"Minimum length of the questions in question1 : \" , min(df['q1_n_words']))\n",
    "\n",
    "print (\"Minimum length of the questions in question2 : \" , min(df['q2_n_words']))\n",
    "\n",
    "print (\"Number of Questions with minimum length [question1] :\", df[df['q1_n_words']== 1].shape[0])\n",
    "print (\"Number of Questions with minimum length [question2] :\", df[df['q2_n_words']== 1].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kFzTIHW3s5YB"
   },
   "source": [
    "<h4> 3.3.1.1 Feature: word_share </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4rwGLFDs5YD",
    "outputId": "0103aaa0-3f5a-4eb4-cd22-164a57d7aef0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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321seKSwswtjsGPk1NgFAUVGR4yRyLIsXvcHUyjglwezdQWPWoCjxRIJly5a5jiIiInJU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\ng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G2z4VM/+AHcdRc8+SS8+KJzAc9Ci2qllFJ+6T/+A+Li4L77nE7indMFdeLhbXw8ZwGn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\nVCullFJKKdVOWlQrpZRSSinVTv8Lj5rGr+Oy1ZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b050d3978>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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 = df[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['word_share'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['word_share'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RcwMI4xps5YJ"
   },
   "source": [
    "- 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": {
    "colab_type": "text",
    "id": "K0AbOS65s5YL"
   },
   "source": [
    "<h4> 3.3.1.2 Feature: word_Common </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_mCFvztcs5YM",
    "outputId": "008ac763-a832-4c11-88fa-5da52cdb9305"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SSxUV0Oljwhl/BwDou6SAnu9kw7w50tlohzyaE53tSHuqI+3p8XfuzYU0HWkA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\njDHG9ML/ADoq1bfzxvIdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b016ccdd8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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_Common', data = df[0:])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(df[df['is_duplicate'] == 1.0]['word_Common'][0:] , label = \"1\", color = 'red')\n",
    "sns.distplot(df[df['is_duplicate'] == 0.0]['word_Common'][0:] , label = \"0\" , color = 'blue' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Ej1ouEVs5YR"
   },
   "source": [
    "<p> The distributions of the word_Common feature in similar and non-similar questions are highly overlapping </p>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "1.Quora.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}