After applying feature engineering.ipynb

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   "source": [
    "I have tried random forest,xgboost and k nearest algorithms. Random forest gives comparitevley good results and that is not enough so try to more feautes in the dataset and see if the accuracy incresae or not\n"
   ]
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    "Adding some features in the dataset these features are \n",
    "1 -> question 1 length\n",
    "2 -> question 2 length\n",
    "3 -> total words in question 1\n",
    "4 -> total words in question 2\n",
    "5 -> Total number of words in question 1 and question 2 both\n",
    "6 -> No of unique word common in both question 1 and question 2\n",
    "7 -> word share in both that is word common/word total\n",
    "\n",
    "for every row added new 7 columns and 3000 bow features of question 1 and 3000 bow features of question 2 so total number of features are 3000+3000+7 that is 6007\n",
    "\n",
    "Now by adding these changes will apply random forest onces again to see if their is increase in the accuracy or not"
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   "cell_type": "code",
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   "id": "75a97e6c",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
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   "source": [
    "df=pd.read_csv('train.csv')"
   ]
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   "source": [
    "df.shape"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(new_df['is_duplicate'].value_counts())\n",
    "print((new_df['is_duplicate'].value_counts()/new_df['is_duplicate'].count())*100)\n",
    "new_df['is_duplicate'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "490324de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique questons   55299\n",
      "Number of duplicates  3480\n"
     ]
    }
   ],
   "source": [
    "#finding count of duplicate questions in the whole dataset\n",
    "qid=pd.Series(new_df['qid1'].tolist()+new_df['qid2'].tolist())\n",
    "#print(qid)\n",
    "print(\"Number of unique questons  \",np.unique(qid).shape[0])\n",
    "\n",
    "x=qid.value_counts()>1\n",
    "print(\"Number of duplicates \",x[x].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8844b43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(qid.value_counts().values,bins=180)\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ced0b7",
   "metadata": {},
   "source": [
    "Faeture engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c90b5f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['q1_len']=new_df['question1'].str.len()\n",
    "new_df['q2_len']=new_df['question2'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9f701b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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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>q1_len</th>\n",
       "      <th>q2_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398782</th>\n",
       "      <td>398782</td>\n",
       "      <td>496695</td>\n",
       "      <td>532029</td>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115086</th>\n",
       "      <td>115086</td>\n",
       "      <td>187729</td>\n",
       "      <td>187730</td>\n",
       "      <td>I am poor but I want to invest. What should I do?</td>\n",
       "      <td>I am quite poor and I want to be very rich. Wh...</td>\n",
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       "    <tr>\n",
       "      <th>327711</th>\n",
       "      <td>327711</td>\n",
       "      <td>454161</td>\n",
       "      <td>454162</td>\n",
       "      <td>I am from India and live abroad. I met a guy f...</td>\n",
       "      <td>T.I.E.T to Thapar University to Thapar Univers...</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367788</th>\n",
       "      <td>367788</td>\n",
       "      <td>498109</td>\n",
       "      <td>491396</td>\n",
       "      <td>Why do so many people in the U.S. hate the sou...</td>\n",
       "      <td>My boyfriend doesnt feel guilty when he hurts ...</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151235</th>\n",
       "      <td>151235</td>\n",
       "      <td>237843</td>\n",
       "      <td>50930</td>\n",
       "      <td>Consequences of Bhopal gas tragedy?</td>\n",
       "      <td>What was the reason behind the Bhopal gas trag...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id    qid1    qid2  \\\n",
       "398782  398782  496695  532029   \n",
       "115086  115086  187729  187730   \n",
       "327711  327711  454161  454162   \n",
       "367788  367788  498109  491396   \n",
       "151235  151235  237843   50930   \n",
       "\n",
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
       "367788  Why do so many people in the U.S. hate the sou...   \n",
       "151235                Consequences of Bhopal gas tragedy?   \n",
       "\n",
       "                                                question2  is_duplicate  \\\n",
       "398782  What is the best marketing automation tool for...             1   \n",
       "115086  I am quite poor and I want to be very rich. Wh...             0   \n",
       "327711  T.I.E.T to Thapar University to Thapar Univers...             0   \n",
       "367788  My boyfriend doesnt feel guilty when he hurts ...             0   \n",
       "151235  What was the reason behind the Bhopal gas trag...             0   \n",
       "\n",
       "        q1_len  q2_len  \n",
       "398782      76      77  \n",
       "115086      49      57  \n",
       "327711     105     120  \n",
       "367788      59     146  \n",
       "151235      35      50  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33ff027f",
   "metadata": {},
   "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>q1_len</th>\n",
       "      <th>q2_len</th>\n",
       "      <th>q1_num_words</th>\n",
       "      <th>q2_num_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398782</th>\n",
       "      <td>398782</td>\n",
       "      <td>496695</td>\n",
       "      <td>532029</td>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115086</th>\n",
       "      <td>115086</td>\n",
       "      <td>187729</td>\n",
       "      <td>187730</td>\n",
       "      <td>I am poor but I want to invest. What should I do?</td>\n",
       "      <td>I am quite poor and I want to be very rich. Wh...</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327711</th>\n",
       "      <td>327711</td>\n",
       "      <td>454161</td>\n",
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       "      <td>I am from India and live abroad. I met a guy f...</td>\n",
       "      <td>T.I.E.T to Thapar University to Thapar Univers...</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
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       "      <td>25</td>\n",
       "      <td>17</td>\n",
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       "      <th>367788</th>\n",
       "      <td>367788</td>\n",
       "      <td>498109</td>\n",
       "      <td>491396</td>\n",
       "      <td>Why do so many people in the U.S. hate the sou...</td>\n",
       "      <td>My boyfriend doesnt feel guilty when he hurts ...</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>146</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151235</th>\n",
       "      <td>151235</td>\n",
       "      <td>237843</td>\n",
       "      <td>50930</td>\n",
       "      <td>Consequences of Bhopal gas tragedy?</td>\n",
       "      <td>What was the reason behind the Bhopal gas trag...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id    qid1    qid2  \\\n",
       "398782  398782  496695  532029   \n",
       "115086  115086  187729  187730   \n",
       "327711  327711  454161  454162   \n",
       "367788  367788  498109  491396   \n",
       "151235  151235  237843   50930   \n",
       "\n",
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
       "367788  Why do so many people in the U.S. hate the sou...   \n",
       "151235                Consequences of Bhopal gas tragedy?   \n",
       "\n",
       "                                                question2  is_duplicate  \\\n",
       "398782  What is the best marketing automation tool for...             1   \n",
       "115086  I am quite poor and I want to be very rich. Wh...             0   \n",
       "327711  T.I.E.T to Thapar University to Thapar Univers...             0   \n",
       "367788  My boyfriend doesnt feel guilty when he hurts ...             0   \n",
       "151235  What was the reason behind the Bhopal gas trag...             0   \n",
       "\n",
       "        q1_len  q2_len  q1_num_words  q2_num_words  \n",
       "398782      76      77            12            12  \n",
       "115086      49      57            12            15  \n",
       "327711     105     120            25            17  \n",
       "367788      59     146            12            30  \n",
       "151235      35      50             5             9  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['q1_num_words']=new_df['question1'].apply(lambda row:len(row.split(\" \")))\n",
    "new_df['q2_num_words']=new_df['question2'].apply(lambda row:len(row.split(\" \")))\n",
    "new_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9fd6dbf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def common_words(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 len(w1&w2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed34e141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0</td>\n",
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       "367788  367788  498109  491396   \n",
       "151235  151235  237843   50930   \n",
       "\n",
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
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       "\n",
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       "398782  What is the best marketing automation tool for...             1   \n",
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       "327711  T.I.E.T to Thapar University to Thapar Univers...             0   \n",
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       "151235  What was the reason behind the Bhopal gas trag...             0   \n",
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       "        q1_len  q2_len  q1_num_words  q2_num_words  word_common  \n",
       "398782      76      77            12            12           11  \n",
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    "new_df['word_common']=new_df.apply(common_words,axis=1)\n",
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  {
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   "execution_count": 18,
   "id": "95b8c69b",
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   "source": [
    "def total_words(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 len(w1)+len(w2)\n",
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   "id": "39694f7b",
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       "            id    qid1    qid2  \\\n",
       "398782  398782  496695  532029   \n",
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       "367788  367788  498109  491396   \n",
       "151235  151235  237843   50930   \n",
       "\n",
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
       "367788  Why do so many people in the U.S. hate the sou...   \n",
       "151235                Consequences of Bhopal gas tragedy?   \n",
       "\n",
       "                                                question2  is_duplicate  \\\n",
       "398782  What is the best marketing automation tool for...             1   \n",
       "115086  I am quite poor and I want to be very rich. Wh...             0   \n",
       "327711  T.I.E.T to Thapar University to Thapar Univers...             0   \n",
       "367788  My boyfriend doesnt feel guilty when he hurts ...             0   \n",
       "151235  What was the reason behind the Bhopal gas trag...             0   \n",
       "\n",
       "        q1_len  q2_len  q1_num_words  q2_num_words  word_common  word_total  \n",
       "398782      76      77            12            12           11          24  \n",
       "115086      49      57            12            15            7          23  \n",
       "327711     105     120            25            17            2          34  \n",
       "367788      59     146            12            30            0          32  \n",
       "151235      35      50             5             9            3          13  "
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   "source": [
    "new_df['word_total']=new_df.apply(total_words,axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "581b7a40",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.23</td>\n",
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       "            id    qid1    qid2  \\\n",
       "398782  398782  496695  532029   \n",
       "115086  115086  187729  187730   \n",
       "327711  327711  454161  454162   \n",
       "367788  367788  498109  491396   \n",
       "151235  151235  237843   50930   \n",
       "\n",
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
       "367788  Why do so many people in the U.S. hate the sou...   \n",
       "151235                Consequences of Bhopal gas tragedy?   \n",
       "\n",
       "                                                question2  is_duplicate  \\\n",
       "398782  What is the best marketing automation tool for...             1   \n",
       "115086  I am quite poor and I want to be very rich. Wh...             0   \n",
       "327711  T.I.E.T to Thapar University to Thapar Univers...             0   \n",
       "367788  My boyfriend doesnt feel guilty when he hurts ...             0   \n",
       "151235  What was the reason behind the Bhopal gas trag...             0   \n",
       "\n",
       "        q1_len  q2_len  q1_num_words  q2_num_words  word_common  word_total  \\\n",
       "398782      76      77            12            12           11          24   \n",
       "115086      49      57            12            15            7          23   \n",
       "327711     105     120            25            17            2          34   \n",
       "367788      59     146            12            30            0          32   \n",
       "151235      35      50             5             9            3          13   \n",
       "\n",
       "        word_share  \n",
       "398782        0.46  \n",
       "115086        0.30  \n",
       "327711        0.06  \n",
       "367788        0.00  \n",
       "151235        0.23  "
      ]
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     "execution_count": 31,
     "metadata": {},
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   "source": [
    "new_df['word_share'] = round(new_df['word_common']/new_df['word_total'],2)\n",
    "new_df.head()"
   ]
  },
  {
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   "id": "c1eea173",
   "metadata": {},
   "source": [
    "Analysis of Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f39afdd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum characters  2\n",
      "Maximum characters  391\n",
      "Average number of characters  59\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(new_df['q1_len'])\n",
    "print(\"Minimum characters \",new_df['q1_len'].min())\n",
    "print(\"Maximum characters \",new_df['q1_len'].max())\n",
    "print(\"Average number of characters \",int(new_df['q1_len'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a43d84ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum characters  6\n",
      "Maximum characters  1151\n",
      "Average number of characters  60\n"
     ]
    },
    {
     "data": {
      "image/png": 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qdrZ1G5JWsCYBnOTrSe5KckeSbV3tqUluSvLV7uuxQ+tfmGR7kvuTnNOiZ0katZZHwD9TVadX1abu/TuAm6vqVODm7j1JTgM2A88GzgXel2SqRcOSNEpLaQjiPODKbvlK4CVD9WuqqldVDwLbgbMm354kjVarAC7g00m+mGRLV3t6Ve0E6L4+rasfDzw0tO2OriZJy9raRt/3+VX1cJKnATcluW+BdTNPbd6Lb7sw3wJw0kknHXmXB1FV7Nmzh6OPPnrs30vSytPkCLiqHu6+7gKuZzCk8EiS4wC6r7u61XcAJw5tfgLw8AH2u7WqNlXVpo0bN46r/Sd4LbCkIzHxAE7y5CRHzy0DvwDcDdwAnN+tdj7wiW75BmBzkg1JTgFOBW6fbNcH5rXAkg5XiyGIpwPXJ5n7/h+tqhuTfAG4NskFwD8BvwJQVfckuRa4F5gG3lJVMw36lqSRmngAV9XXgH89T/2fgbMPsM3FwMVjbu2wzM0JIUmHaildhrYszY0Dz846KY+kQ2MAj4DjwJIOhwEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAE8Aj4/TtLhMIAlqREDWJIaMYAlqREDWJIaMYBHZGa677zAkg6JATwGvV7PMJZ0UAawJDViAEtSIwawJDViAEtSIwbwiMw9HbnKh3NKWhwDeERmZ6a54Irb6Pf7rVuRtEwYwCNU4OVnkhbNAJakRgzgEXIcWNKhMIBH6PHed3jd5bc6DixpUQzgEVuzdl3rFiQtEwbwPHq9HrOzDiNIGi8DWJIaMYAlqZGJB3CSE5N8NslXktyT5De6+juTfCPJHd3rRUPbXJhke5L7k5wz6Z4Pxcz0414LLGlR1jb4ntPAf6iqLyU5Gvhikpu6z95TVZcMr5zkNGAz8Gzgh4DPJHlmVc1MtGtJGrGJHwFX1c6q+lK3/BjwFeD4BTY5D7imqnpV9SCwHThr/J1K0ng1HQNOcjJwBvD5rvTWJHcmuSLJsV3teOChoc12cIDATrIlybYk23bv3j2utiVpJJoFcJLvB64D3lZVe4DLgB8GTgd2Au+eW3Wezee9RqyqtlbVpqratHHjxtE3LUkj1CSAk6xjEL4fqaqPA1TVI1U1U1WzwAfYN8ywAzhxaPMTgIcn2a8kjUOLqyACXA58par+cKh+3NBqLwXu7pZvADYn2ZDkFOBU4PZx9Tc3n8MBDrIXvb3zQUg6mBZHwM8HXgP87H6XnL0ryV1J7gR+BvhNgKq6B7gWuBe4EXjLOK+A6Pf7nL/1Fuow74SbnZnmjVfe7nwQkg5q4pehVdXfMf+47icX2OZi4OKxNbWfqbXrmJk+/Ix3PghJi+GdcJLUiAEsSY0YwGPgiThJi2EAj4EP6JS0GAbwmKxZu751C5KWOAN4TGam+86KJmlBBvAS0Ov1DGtpFTKAx8hglbQQA3iMqoo9e/bw6KOPLhjEXjUhrU4G8JhUFY899hiv++DfHfRqiH6/z2v++9961YS0yhjAYzI7M81b/nQbWbO4u729akJafQzgMXJOCEkLMYAlqREDeIL27t3Lnj17PNkmCTCAv0ev12P2MOcCns/wFQ6ebJM0zAAes9mZabZ8+PPs2bOHvXv3UpTXBksCGkzIviolvO4Df8fs7Axr1z/piRs0NmzY0LozSQ0ZwBOyZu06mN33C0dVsXfv3ieWJa0+BvCEzEw/PlhYMwUMbr7Y/MefZmpqPZe9+sc43IeASlq+HAOesOnH+zz66KM8+uijZM0UMzPTvOHy/3nYDwGVtHx5BDzkSB9JvxhP3CGXPHGjhjdsSKuTATxk7pH04/7FIGvXsSbzPRha0mriEMR+pjwalTQhBrAkNWIAS1IjBrAkNWIAS1IjBvCQUU/EI0kLMYCXiPmeC+ez4qSVzQBeIh7vfZtXX3bLd01V2e/3ecWlNzt9pbRCLZsATnJukvuTbE/yjlHvfxJ3wR3MbBW7d+/mO9/5Dnv37mXv3r1M7fesuLlJfPbu3euRsbTMLYs74ZJMAf8N+HlgB/CFJDdU1b2j2P/cE4wncRfcQubmDr7kZT/Cb11/HzU7w9S6DezatYujjz4aGBwVX/ChW0mm+JM3voD16/cFdJInprjs9/tPfNbv91m3bh39fp+qemK9dHfj7fvPh++qSxqvLIejqCTPA95ZVed07y8EqKr/cqBtNm3aVNu2bVvU/nu9Hi+75C+YnS1qdnbedWanHydTaxcMp1GtM73320xtOOqJdWbnZlLrrFm7jtnpx6maZWrdBmp2hqyZImvW8IHXPQ+AX/vQrXzgdc9jw4YNXHDF/+J9rzqTX/vQrcDgbr8PveH5AE8E9svf81dMrd3A1b/+c85TrFXnMP/OH/GRynIJ4F8Gzq2qN3TvXwP8RFW9db/1tgBburfPAu5f5Lf4QeCbI2p3UpZbz8utX7DnSVhu/cK+nr9ZVeceyY6WxRAE8/9P8z3/c1TVVmDrIe882VZVmw6nsVaWW8/LrV+w50lYbv3CaHteLifhdgAnDr0/AXi4US+SNBLLJYC/AJya5JQk64HNwA2Ne5KkI7IshiCqajrJW4G/BqaAK6rqnhF+i0MetlgCllvPy61fsOdJWG79wgh7XhYn4SRpJVouQxCStOIYwJLUyKoP4HHf4nw4kpyY5LNJvpLkniS/0dWfmuSmJF/tvh47tM2F3c9wf5JzGvU9leTLSf5ymfT7lCQfS3Jf92f9vGXQ8292fyfuTnJ1kicttZ6TXJFkV5K7h2qH3GOSM5Pc1X323ozpFs0D9PsH3d+LO5Ncn+QpY+m3qlbti8EJvQeAZwDrgb8HTlsCfR0H/Fi3fDTwD8BpwLuAd3T1dwC/3y2f1vW+ATil+5mmGvT9duCjwF9275d6v1cCb+iW1wNPWco9A8cDDwJHde+vBX51qfUM/DTwY8DdQ7VD7hG4HXgeg/sAPgX84gT7/QVgbbf8++Pqd7UfAZ8FbK+qr1VVH7gGOK9xT1TVzqr6Urf8GPAVBv/4zmMQGnRfX9ItnwdcU1W9qnoQ2M7gZ5uYJCcA/wb44FB5Kfd7DIN/eJcDVFW/qv7PUu65sxY4Ksla4PsYXA+/pHquqs8B39qvfEg9JjkOOKaqbq1Bul01tM3Y+62qT1fVdPf2Ngb3Hoy839UewMcDDw2939HVlowkJwNnAJ8Hnl5VO2EQ0sDTutWWws/xR8B/BIYn01jK/T4D2A18qBs2+WCSJ7OEe66qbwCXAP8E7AQerapPs4R7HnKoPR7fLe9fb+H1DI5oYcT9rvYAXtQtzq0k+X7gOuBtVbVnoVXnqU3s50jyS8CuqvriYjeZpzbpP/e1DH7tvKyqzgD+H4NfjQ+kec/duOl5DH71/SHgyUlevdAm89SWzN/vzoF6XBK9J7kImAY+MleaZ7XD7ne1B/CSvcU5yToG4fuRqvp4V36k+1WH7uuurt7653g+8OIkX2cwjPOzSf6UpdvvXA87qurz3fuPMQjkpdzzzwEPVtXuqnoc+Djwkyztnuccao872Pdr/3B9YpKcD/wS8KpuWAFG3O9qD+AleYtzd/b0cuArVfWHQx/dAJzfLZ8PfGKovjnJhiSnAKcyOCEwEVV1YVWdUFUnM/gz/JuqevVS7bfr+X8DDyV5Vlc6G7iXJdwzg6GH5yb5vu7vyNkMzg8s5Z7nHFKP3TDFY0me2/2srx3aZuySnAv8DvDiqvr20Eej7XfcZ0SX+gt4EYOrDB4ALmrdT9fTTzH49eVO4I7u9SLgB4Cbga92X586tM1F3c9wP2M6W7zI3l/IvqsglnS/wOnAtu7P+X8Axy6Dnv8zcB9wN/AnDM7GL6megasZjFE/zuDI8ILD6RHY1P2cDwCX0t25O6F+tzMY65379/f+cfTrrciS1MhqH4KQpGYMYElqxACWpEYMYElqxACWpEYMYElqxADWirbQtILzrPvCdFNpSpNgAGuluwl4TlX9KIMbbi5s3I/0BANYK0aSi7pJsj/TTVb+W3XgaQUPtq8ndxN1f6GbLe28rv6rST6e5MZucvF3jevn0cq3LJ6KLB1MkjMZzENxBoO/118C9p+d7fXAny1ylxcxmNPi9d2wxe1JPtN9dnr3fXrA/Un+uKoemn830oEZwFopXgBcX93EKUm+a1KleaYVPJhfYDDD2291758EnNQt31xVj3b7vRf4l3z3HLHSohjAWknmndhkaFrBs2vxk58E+HdVdf9++/oJBke+c2bw35EOk2PAWik+B7w0yVFJjgb+LSw4reDB/DXw63MPVkxyxqgblgxgrQg1eIbenzGYOvA64G+7jy5l8GDTm5LckeT9i9zl7wHrgDu7p+X+3mg7lnA6Sq1MSd4J/N+quqR1L9KBeAQsSY14BKxVJ8k5wO/vV36wql7aoh+tXgawJDXiEIQkNWIAS1IjBrAkNWIAS1Ij/x8WlZSBYuyAgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(new_df['q2_len'])\n",
    "print(\"Minimum characters \",new_df['q2_len'].min())\n",
    "print(\"Maximum characters \",new_df['q2_len'].max())\n",
    "print(\"Average number of characters \",int(new_df['q2_len'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0925fdff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum characters  1\n",
      "Maximum characters  72\n",
      "Average number of characters  10\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAb6UlEQVR4nO3df7Dd9V3n8eeroQFKiSU0ZWPCLFixFtAGSSO1VltwJdtRAcfadHYb1q0bh9Ld4q8d0Bmtf2RGd+iP7e4Gh1oW0Fo2alloLVQaa2kdBFNKCeGHpIWWSJak96KldsUlvPeP8730eDm5ucA953POvc/HzJnzPe/vj/O+l/DKN5/z/X5OqgpJ0ui9qHUDkrRUGcCS1IgBLEmNGMCS1IgBLEmNHNG6gWHZuHFj3Xzzza3bkCSADCou2jPgr3/9661bkKQ5LdoAlqRxZwBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiNDC+AkRyW5I8mXkuxO8ltd/T1J/jbJXd3jzX37XJZkT5IHkpzbVz8zya5u3QeTDLyrRJImyTBvRX4SOLuqvpnkxcDnk9zUrXt/VV3ev3GSU4FNwGnAdwKfTvI9VXUQuALYAvwV8ElgI3ATkjTBhnYGXD3f7F6+uHvM9fUb5wHXVdWTVfUQsAfYkGQ1sKKqbqve13dcC5w/rL4laVSGOgacZFmSu4D9wC1VdXu36l1J7k5yVZLjutoa4JG+3fd2tTXd8uz6oPfbkmRnkp0HDhxYyB9FkhbcUAO4qg5W1TpgLb2z2dPpDSe8ElgH7APe220+aFy35qgPer8rq2p9Va1ftWrVC+x+OKqKqakp/C4+SSO5CqKq/g74C2BjVT3WBfPTwIeADd1me4ET+3ZbCzza1dcOqE+k6elpNl1+PdPT061bkdTYMK+CWJXkZd3y0cCPAfd3Y7ozLgDu6ZZvBDYlOTLJycApwB1VtQ94IslZ3dUPm4EbhtX3KCx/ybGtW5A0BoZ5FcRq4Joky+gF/faq+kSS30+yjt4wwsPALwBU1e4k24F7gaeAi7srIAAuAq4GjqZ39YNXQEiaeEML4Kq6GzhjQP3tc+yzFdg6oL4TOH1BG5SkxrwTTpIaMYAlqREDWJIaMYAlqREDWJIaMYAb8q44aWkzgBvyrjhpaTOAG/OuOGnpMoAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDeMic81fSoRjAQ+acv5IOxQAeAef8lTSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjRjAktSIASxJjQwtgJMcleSOJF9KsjvJb3X1lUluSfJg93xc3z6XJdmT5IEk5/bVz0yyq1v3wSQZVt+SNCrDPAN+Eji7ql4DrAM2JjkLuBTYUVWnADu61yQ5FdgEnAZsBLYlWdYd6wpgC3BK99g4xL4laSSGFsDV883u5Yu7RwHnAdd09WuA87vl84DrqurJqnoI2ANsSLIaWFFVt1VvSrFr+/aRpIk11DHgJMuS3AXsB26pqtuBE6pqH0D3/Ipu8zXAI3277+1qa7rl2fVB77clyc4kOw8cOLCgP4skLbShBnBVHayqdcBaemezp8+x+aBx3ZqjPuj9rqyq9VW1ftWqVc+5X0kapZFcBVFVfwf8Bb2x28e6YQW65/3dZnuBE/t2Wws82tXXDqhL0kQb5lUQq5K8rFs+Gvgx4H7gRuDCbrMLgRu65RuBTUmOTHIyvQ/b7uiGKZ5IclZ39cPmvn0kaWIdMcRjrwau6a5keBGwvao+keQ2YHuSdwBfA94CUFW7k2wH7gWeAi6uqoPdsS4CrgaOBm7qHpI00YYWwFV1N3DGgPoUcM4h9tkKbB1Q3wnMNX4sSRPHO+EkqREDWJIaGeYYsJ6jqnrm25NXrlyJd1xLi5tnwGNkenqazdt2sHnbDr/GXloCPAMeM8uPWdG6BUkj4hmwJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSI0ML4CQnJvlMkvuS7E7y7q7+niR/m+Su7vHmvn0uS7InyQNJzu2rn5lkV7fug0kyrL4laVSOGOKxnwJ+uaruTHIs8IUkt3Tr3l9Vl/dvnORUYBNwGvCdwKeTfE9VHQSuALYAfwV8EtgI3DTE3iVp6IZ2BlxV+6rqzm75CeA+YM0cu5wHXFdVT1bVQ8AeYEOS1cCKqrqtqgq4Fjh/WH1L0qiMZAw4yUnAGcDtXeldSe5OclWS47raGuCRvt32drU13fLs+qD32ZJkZ5KdBw4cWMgfQZIW3NADOMlLgT8BLqmqb9AbTnglsA7YB7x3ZtMBu9cc9WcXq66sqvVVtX7VqlUvtHVJGqqhBnCSF9ML349U1ccAquqxqjpYVU8DHwI2dJvvBU7s230t8GhXXzugLkkTbZhXQQT4MHBfVb2vr766b7MLgHu65RuBTUmOTHIycApwR1XtA55IclZ3zM3ADcPqW5JGZZhXQbweeDuwK8ldXe3XgLclWUdvGOFh4BcAqmp3ku3AvfSuoLi4uwIC4CLgauBoelc/eAWEpIk3tACuqs8zePz2k3PssxXYOqC+Ezh94bqTpPa8E06SGjGAJakRA1iSGjGAJakRA1iSGjGAx1RVMTU1RW/6C0mLkQE8ph5//HE2XX4909PTrVuRNCQG8Bhb/pJjW7cgaYgMYElqZJi3Ii85VfXMkMHKlSvxizskzcUz4AU0PT3N5m072Lxth2O3kg7LM+AFtvyYFa1bkDQhPAOWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEaGFsBJTkzymST3Jdmd5N1dfWWSW5I82D0f17fPZUn2JHkgybl99TOT7OrWfTBJhtW3JI3KMM+AnwJ+uapeDZwFXJzkVOBSYEdVnQLs6F7TrdsEnAZsBLYlWdYd6wpgC3BK99g4xL4laSSGFsBVta+q7uyWnwDuA9YA5wHXdJtdA5zfLZ8HXFdVT1bVQ8AeYEOS1cCKqrqtqgq4tm8fSZpYIxkDTnIScAZwO3BCVe2DXkgDr+g2WwM80rfb3q62plueXR/0PluS7Eyy88CBAwv6M0jSQht6ACd5KfAnwCVV9Y25Nh1Qqznqzy5WXVlV66tq/apVq557s5I0QvMK4CSvn09twDYvphe+H6mqj3Xlx7phBbrn/V19L3Bi3+5rgUe7+toBdUmaaPM9A/5v86w9o7tS4cPAfVX1vr5VNwIXdssXAjf01TclOTLJyfQ+bLujG6Z4IslZ3TE39+0jSRPriLlWJnkd8EPAqiS/1LdqBbBs8F7PeD3wdmBXkru62q8Bvw1sT/IO4GvAWwCqaneS7cC99K6guLiqDnb7XQRcDRwN3NQ9JGmizRnAwHLgpd12x/bVvwH8zFw7VtXnGTx+C3DOIfbZCmwdUN8JnH6YXiVposwZwFX1WeCzSa6uqq+OqCdJWhIOdwY848gkVwIn9e9TVWcPoylJWgrmG8B/BPwu8HvAwcNsK0mah/kG8FNVdcVQO5GkJWa+l6F9PMk7k6zuJtNZmWTlUDuTpEVuvmfAM9ft/mpfrYDvWth2JGnpmFcAV9XJw25EkpaaeQVwks2D6lV17cK2I0lLx3yHIF7bt3wUvRsp7qQ3NaQk6XmY7xDEf+x/neQ7gN8fSkeStEQ83+kov0VvshxJ0vM03zHgj/PtOXiXAa8Gtg+rKUlaCuY7Bnx53/JTwFerau+hNpYkHd68hiC6SXnupzcj2nHAPw2zKUlaCub7jRg/C9xBb+7enwVuTzLndJSSpLnNdwji14HXVtV+gCSrgE8DfzysxiRpsZtvAL9oJnw7U4zoG5XVU1VMT08DsHLlSnrfziRpks03gG9O8ingo93rtwKfHE5LGmR6eprN23YAcO07z+H4449v3JGkF+pw3wn33cAJVfWrSX4a+GF6XzN0G/CREfSnPsuPWdG6BUkL6HDDCB8AngCoqo9V1S9V1S/SO/v9wHBbk6TF7XABfFJV3T272H1J5klD6UiSlojDBfBRc6w7eiEbkaSl5nAB/NdJ/sPsYpJ3AF8YTkuStDQc7iqIS4Drk/wbvh2464HlwAVD7EuSFr05A7iqHgN+KMmbgNO78p9W1Z8PvTNJWuTmOx/wZ4DPDLkXSVpSvJtNkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoZWgAnuSrJ/iT39NXek+Rvk9zVPd7ct+6yJHuSPJDk3L76mUl2des+GGcil7RIDPMM+Gpg44D6+6tqXff4JECSU4FNwGndPtuSLOu2vwLYApzSPQYdU5ImztACuKpuBabnufl5wHVV9WRVPQTsATYkWQ2sqKrbqqqAa4Hzh9KwJI1YizHgdyW5uxuiOK6rrQEe6dtmb1db0y3Prg+UZEuSnUl2HjhwYKH7lqQFNeoAvgJ4JbAO2Ae8t6sPGtetOeoDVdWVVbW+qtavWrXqBbYqScM10gCuqseq6mBVPQ18CNjQrdoLnNi36Vrg0a6+dkBdkibeSAO4G9OdcQEwc4XEjcCmJEcmOZneh213VNU+4IkkZ3VXP2wGbhhlz5I0LPP9WvrnLMlHgTcCL0+yF/hN4I1J1tEbRngY+AWAqtqdZDtwL/AUcHFVHewOdRG9KyqOBm7qHpI08YYWwFX1tgHlD8+x/VZg64D6Tr49GbwkLRreCSdJjRjAktSIASxJjRjAE6yqmJqaoneToKRJYwBPsOnpaTZdfj3T0/O941vSOBnaVRBLRVU9E4AtzkSXv+TYkb+npIVhAL9A09PTbN62A4APbDqjcTeSJokBvACWH7OidQuSJpBjwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAG8SDg3sDR5DOBFwrmBpcljAC8izg0sTRYDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaGVoAJ7kqyf4k9/TVVia5JcmD3fNxfesuS7InyQNJzu2rn5lkV7fug0kyrJ4laZSGeQZ8NbBxVu1SYEdVnQLs6F6T5FRgE3Bat8+2JMu6fa4AtgCndI/Zx9QATk8pjb+hBXBV3QrMnhvxPOCabvka4Py++nVV9WRVPQTsATYkWQ2sqKrbqpck1/btozk4PaU0/kY9BnxCVe0D6J5f0dXXAI/0bbe3q63plmfXB0qyJcnOJDsPHDiwoI1PIqenlMbbuHwIN2hct+aoD1RVV1bV+qpav2rVqgVrTpKGYdQB/Fg3rED3vL+r7wVO7NtuLfBoV187oC5JE2/UAXwjcGG3fCFwQ199U5Ijk5xM78O2O7phiieSnNVd/bC5bx9JmmhHDOvAST4KvBF4eZK9wG8Cvw1sT/IO4GvAWwCqaneS7cC9wFPAxVV1sDvURfSuqDgauKl7SNLEG1oAV9XbDrHqnENsvxXYOqC+Ezh9AVuTpLEwLh/CSdKSYwBLUiMGsCQ1YgBLUiMGsCQ1YgAvAU7MI40nA3gJcGIeaTwZwEuEE/NI48cAlqRGDGBJasQAlqRGDGBJamRok/EsVlX1zNUEK1eubNyNpElmAD9H09PTbN62A4Br3zlwYjdJmhcD+HlYfsyK1i1IWgQcA5akRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRpwLYgmaPaNbksYdSUuTZ8BL0MyMbpu37fCLOqWGPANeopzRTWrPM2BJasQAlqRGDGBJasQAlqRGmgRwkoeT7EpyV5KdXW1lkluSPNg9H9e3/WVJ9iR5IMm5LXqWpIXW8gz4TVW1rqrWd68vBXZU1SnAju41SU4FNgGnARuBbUmWtWhYkhbSOA1BnAdc0y1fA5zfV7+uqp6sqoeAPcCG0be3eFUVU1NTTE1NUVWt25GWjFYBXMCfJflCki1d7YSq2gfQPb+iq68BHunbd29Xe5YkW5LsTLLzwIEDQ2p98fHGDKmNVjdivL6qHk3yCuCWJPfPse2g+2QHnqZV1ZXAlQDr16/3VO458MYMafSanAFX1aPd837genpDCo8lWQ3QPe/vNt8LnNi3+1rg0dF1K0nDMfIATnJMkmNnloEfB+4BbgQu7Da7ELihW74R2JTkyCQnA6cAd4y2a0laeC2GIE4Aru9m4DoC+MOqujnJXwPbk7wD+BrwFoCq2p1kO3Av8BRwcVUdbNC3JC2okQdwVX0FeM2A+hRwziH22QpsHXJr4ttXRIBTVUrD5mxo+mcef/xxLrnuiwBc+85zOP744xt3JC1eBrCexSsipNEYpxsxNKZmhiW8SUNaWAawDmt6eppNl1/vTRrSAjOANS/LX3Js6xakRccAlqRGDGBJasQA1nPiB3LSwjGA9Zz4gZy0cAxgPWd+ICctDANYkhoxgCWpEQNYL4gfyknPnwGsF8QP5aTnz8l45qGqngkYz/SezQ/lpOfHAJ6HmS+tBPjApjMadzPe+v+ycj5haW4G8Dw5ReP89P9l5XzC0twMYC04/7KS5scP4TQ0XiEhzc0A1tA8/vjjXiEhzcEA1lB5hYR0aAawJDXih3Aamf5L1I477jgef/xxwMvVtHQZwBqZ2ddTX3LdFwEvV9PSZQBrpPovUfNyNS11jgFrbMxctvb0008zNTXlJWxa9AxgjY2ZiX2+/OUvs3nbDjZv2+ElbFrUHILQWJm5bM3hCS0FngFrInhXnRYjA1gTwXmHtRg5BKGJ0X9XndNeajEwgDWR+q8pvuais0liEGviOAShibX8mBUsP2bFsyb9mRkvdsxY425iAjjJxiQPJNmT5NLW/Wi89A9PzJwdb96245kg7g9jP9DTuJiIAE6yDPgfwL8GTgXeluTUtl1pnPWfHc++pnj2B3r9gTw7nPvPpmduEJkruOcK97ne51Db+hfF4jYpY8AbgD1V9RWAJNcB5wH3LuSbTE1NDaxPT0/zT//wDaA3x+3M8sz/wHOtm56e5p++9cQhXz9rXf+xDrFu3seax/s8n59hrveZ788w1/vM+/c4z/fp/+94qOef/++f4Pfe9RMAzyyvXLmS6elp3nnVZwHYesH386sf+ctn1g3Sf6zZ28z1PoOOM/O+2/79jx7y/TRaCz1nSSbhb9ckPwNsrKqf716/HfjBqnrXrO22AFu6l68CHjjMoV8OfH2B210I49iXPc3fOPZlT/M3jL6+XlUbZxcn5Qx40Efbz/qbo6quBK6c90GTnVW1/oU0Ngzj2Jc9zd849mVP8zfKviZiDBjYC5zY93ot8GijXiRpQUxKAP81cEqSk5MsBzYBNzbuSZJekIkYgqiqp5K8C/gUsAy4qqp2L8Ch5z1cMWLj2Jc9zd849mVP8zeyvibiQzhJWowmZQhCkhYdA1iSGlmyATwOtzYnuSrJ/iT39NVWJrklyYPd83Ej7unEJJ9Jcl+S3UnePSZ9HZXkjiRf6vr6rXHoq+thWZIvJvnEGPX0cJJdSe5KsnMc+krysiR/nOT+7s/X61r2lORV3e9n5vGNJJeMsqclGcBjdGvz1cDsi7MvBXZU1SnAju71KD0F/HJVvRo4C7i4+9207utJ4Oyqeg2wDtiY5Kwx6Avg3cB9fa/HoSeAN1XVur5rWlv39V+Bm6vqe4HX0PudNeupqh7ofj/rgDOBbwHXj7SnmfvSl9IDeB3wqb7XlwGXNerlJOCevtcPAKu75dXAA41/VzcA/2qc+gJeAtwJ/GDrvuhdk74DOBv4xLj8NwQeBl4+q9asL2AF8BDdB//j0NOsPn4c+MtR97Qkz4CBNcAjfa/3drVxcEJV7QPonl/RqpEkJwFnALePQ1/dP/XvAvYDt1TVOPT1AeA/A0/31Vr3BL07Rf8syRe6W/Rb9/VdwAHgf3bDNb+X5JjGPfXbBHy0Wx5ZT0s1gOd1a/NSluSlwJ8Al1TVN1r3A1BVB6v3z8W1wIYkp7fsJ8lPAPur6gst+ziE11fVD9AbZrs4yY807ucI4AeAK6rqDOAfaDc08890N3f9FPBHo37vpRrA43xr82NJVgN0z/tH3UCSF9ML349U1cfGpa8ZVfV3wF/QGz9v2dfrgZ9K8jBwHXB2kj9o3BMAVfVo97yf3rjmhsZ97QX2dv9qAfhjeoHc/HdF7y+pO6vqse71yHpaqgE8zrc23whc2C1fSG8MdmSSBPgwcF9VvW+M+lqV5GXd8tHAjwH3t+yrqi6rqrVVdRK9P0N/XlX/tmVPAEmOSXLszDK98c17WvZVVf8HeCTJq7rSOfSmk236u+q8jW8PP8Aoe2ox4D0OD+DNwN8AXwZ+vVEPHwX2Af+P3hnCO4Dj6X2o82D3vHLEPf0wveGYu4G7usebx6Cv7we+2PV1D/AbXb1pX339vZFvfwjX+nf1XcCXusfumT/fY9DXOmBn99/wfwPHjUFPLwGmgO/oq42sJ29FlqRGluoQhCQ1ZwBLUiMGsCQ1YgBLUiMGsCQ1YgBLUiMGsMZekrd0U1A+nWTsvkV3oSV548zUllrcDGBNgnuAnwZubd3IMHTTo2oJMoA1VpL8ejdR/qeTfDTJr1TVfVX1wDz3/3dJPpbk5m5C7f/St+6bfcs/k+TqbvnqJFd0E9F/JcmPpjdZ/n0z2xzivX42yfu65Xcn+Uq3/Mokn++Wz+lm/9rVHfPIrv5wkt/otntLel8QcH/3+qf73uNH+yYM/+LMLcZaHAxgjY0kZ9KbU+EMeiH02ud5qHXAW4HvA96a5MS5Nwd6t8WeDfwi8HHg/cBpwPclWXeIfW4F3tAtvwGYSrKG3u3cn0tyFL1J999aVd9Hb0awi/r2/8eq+mF6t+V+CPjJ7jj/om+bXwEurt4scG8A/u88fhZNCANY4+QNwPVV9a3qTYH5fCdI2lFVf19V/0hvwpd/OY99Pl69+/J3AY9V1a6qepreXAonDdqhehPMvLQ7Kz0R+EPgR7qf43PAq4CHqupvul2u6dbP+F/d8/d22z3Y9fAHfdv8JfC+JP8JeFlVPTWPn0UTwgDWuFmIyUme7Fs+SO/Mc/axjzrEPk/P2v/pvv0HuQ34OXrfovA5euH7OnrBOWje6X7/0Lc88Oeuqt8Gfh44GvirJN97mGNqghjAGie3AhckObo7q/zJBT7+Y0leneRFwAULdMxb6Q0T3EpvtrY3AU9W1d/Tmy7zpCTf3W37duCzA45xP3Bykld2r982syLJK7uz8d+hN5OYAbyIGMAaG1V1J71/lt9Fb0L4zwEkuSDJXnpnln+a5FPP8y0uBT4B/Dm9aUAXwufoDT/cWlUH6X3V1ecBuiGQnwP+KMkuemfTvzv7AN12W+j9bJ8Hvtq3+pIk9yT5Er3x35sWqG+NAaej1NhK8h7gm1V1eetepGHwDFiSGvEMWBMpybnA78wqP1RVCzW2O/v9bgeOnFV+e1XtGsb7aWkwgCWpEYcgJKkRA1iSGjGAJakRA1iSGvn/1MYsC7vI1YkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(new_df['q1_num_words'])\n",
    "print(\"Minimum characters \",new_df['q1_num_words'].min())\n",
    "print(\"Maximum characters \",new_df['q1_num_words'].max())\n",
    "print(\"Average number of characters \",int(new_df['q1_num_words'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eedea3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum characters  1\n",
      "Maximum characters  237\n",
      "Average number of characters  11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(new_df['q2_num_words'])\n",
    "print(\"Minimum characters \",new_df['q2_num_words'].min())\n",
    "print(\"Maximum characters \",new_df['q2_num_words'].max())\n",
    "print(\"Average number of characters \",int(new_df['q2_num_words'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d468c4b8",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.distplot(new_df[new_df['is_duplicate']==0]['word_common'],label='non duplicate')\n",
    "sns.distplot(new_df[new_df['is_duplicate']==1]['word_common'],label=\"duplicate\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d81a8ce",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.distplot(new_df[new_df['is_duplicate']==0]['word_total'],label='non duplicate')\n",
    "sns.distplot(new_df[new_df['is_duplicate']==1]['word_total'],label=\"duplicate\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4f5e505d",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.distplot(new_df[new_df['is_duplicate']==0]['word_share'],label='non duplicate')\n",
    "sns.distplot(new_df[new_df['is_duplicate']==1]['word_share'],label=\"duplicate\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06592458",
   "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>question1</th>\n",
       "      <th>question2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398782</th>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "      <td>What is the best marketing automation tool for...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115086</th>\n",
       "      <td>I am poor but I want to invest. What should I do?</td>\n",
       "      <td>I am quite poor and I want to be very rich. Wh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327711</th>\n",
       "      <td>I am from India and live abroad. I met a guy f...</td>\n",
       "      <td>T.I.E.T to Thapar University to Thapar Univers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367788</th>\n",
       "      <td>Why do so many people in the U.S. hate the sou...</td>\n",
       "      <td>My boyfriend doesnt feel guilty when he hurts ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151235</th>\n",
       "      <td>Consequences of Bhopal gas tragedy?</td>\n",
       "      <td>What was the reason behind the Bhopal gas trag...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                question1  \\\n",
       "398782  What is the best marketing automation tool for...   \n",
       "115086  I am poor but I want to invest. What should I do?   \n",
       "327711  I am from India and live abroad. I met a guy f...   \n",
       "367788  Why do so many people in the U.S. hate the sou...   \n",
       "151235                Consequences of Bhopal gas tragedy?   \n",
       "\n",
       "                                                question2  \n",
       "398782  What is the best marketing automation tool for...  \n",
       "115086  I am quite poor and I want to be very rich. Wh...  \n",
       "327711  T.I.E.T to Thapar University to Thapar Univers...  \n",
       "367788  My boyfriend doesnt feel guilty when he hurts ...  \n",
       "151235  What was the reason behind the Bhopal gas trag...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ques_df = new_df[['question1','question2']]\n",
    "ques_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "caf2881a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 8)\n"
     ]
    },
    {
     "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>q1_len</th>\n",
       "      <th>q2_len</th>\n",
       "      <th>q1_num_words</th>\n",
       "      <th>q2_num_words</th>\n",
       "      <th>word_common</th>\n",
       "      <th>word_total</th>\n",
       "      <th>word_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398782</th>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115086</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327711</th>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>120</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367788</th>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>146</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151235</th>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_duplicate  q1_len  q2_len  q1_num_words  q2_num_words  word_common  \\\n",
       "398782             1      76      77            12            12           11   \n",
       "115086             0      49      57            12            15            7   \n",
       "327711             0     105     120            25            17            2   \n",
       "367788             0      59     146            12            30            0   \n",
       "151235             0      35      50             5             9            3   \n",
       "\n",
       "        word_total  word_share  \n",
       "398782          24        0.46  \n",
       "115086          23        0.30  \n",
       "327711          34        0.06  \n",
       "367788          32        0.00  \n",
       "151235          13        0.23  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = new_df.drop(columns=['id','qid1','qid2','question1','question2'])\n",
    "print(final_df.shape)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4ce7f69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "# merge texts\n",
    "questions = list(ques_df['question1']) + list(ques_df['question2'])\n",
    "\n",
    "cv = CountVectorizer(max_features=3000)\n",
    "q1_arr, q2_arr = np.vsplit(cv.fit_transform(questions).toarray(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6230c297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 6000)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df1 = pd.DataFrame(q1_arr, index= ques_df.index)\n",
    "temp_df2 = pd.DataFrame(q2_arr, index= ques_df.index)\n",
    "temp_df = pd.concat([temp_df1, temp_df2], axis=1)\n",
    "temp_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ac9c6fe6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30000, 6008)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>q1_len</th>\n",
       "      <th>q2_len</th>\n",
       "      <th>q1_num_words</th>\n",
       "      <th>q2_num_words</th>\n",
       "      <th>word_common</th>\n",
       "      <th>word_total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>146</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 6008 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        is_duplicate  q1_len  q2_len  q1_num_words  q2_num_words  word_common  \\\n",
       "398782             1      76      77            12            12           11   \n",
       "115086             0      49      57            12            15            7   \n",
       "327711             0     105     120            25            17            2   \n",
       "367788             0      59     146            12            30            0   \n",
       "151235             0      35      50             5             9            3   \n",
       "\n",
       "        word_total  word_share  0  1  ...  2990  2991  2992  2993  2994  2995  \\\n",
       "398782          24        0.46  0  0  ...     0     0     0     0     0     0   \n",
       "115086          23        0.30  0  0  ...     0     0     0     0     0     0   \n",
       "327711          34        0.06  0  0  ...     0     0     0     0     0     0   \n",
       "367788          32        0.00  0  0  ...     0     0     0     1     0     0   \n",
       "151235          13        0.23  0  0  ...     0     0     0     0     0     0   \n",
       "\n",
       "        2996  2997  2998  2999  \n",
       "398782     0     0     0     0  \n",
       "115086     0     0     0     0  \n",
       "327711     0     0     0     0  \n",
       "367788     0     0     0     0  \n",
       "151235     0     0     0     0  \n",
       "\n",
       "[5 rows x 6008 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.concat([final_df, temp_df], axis=1)\n",
    "print(final_df.shape)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29fceaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(final_df.iloc[:,1:].values,final_df.iloc[:,0].values,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c6e77bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7708333333333334"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3c83a2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7645"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train,y_train)\n",
    "y_pred = xgb.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e44f2b",
   "metadata": {},
   "source": [
    "Previously random forest accuracy was 74% and now it is 77%\n",
    "Previously xgboost accuracy was 68% and now it is 76 %\n",
    "The accuracy has incresed but not that much so will try to apply more feature engineering techniques "
   ]
  }
 ],
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