diff --git "a/Translator/Translator_using_NLP_1.ipynb" "b/Translator/Translator_using_NLP_1.ipynb"
new file mode 100644--- /dev/null
+++ "b/Translator/Translator_using_NLP_1.ipynb"
@@ -0,0 +1,4587 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4",
+      "authorship_tag": "ABX9TyPMkVYj1hDIsFFu5HfA2RfY",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/GaurangRawat/NLP/blob/main/Translator/Translator_using_NLP_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Importing Libraries\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "mCTLLIgwIcxd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uiQ4TAd86B6p",
+        "outputId": "bfeeb12d-09cd-4562-a91a-c863b4bbd4b2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting contractions\n",
+            "  Downloading contractions-0.1.73-py2.py3-none-any.whl.metadata (1.2 kB)\n",
+            "Collecting textsearch>=0.0.21 (from contractions)\n",
+            "  Downloading textsearch-0.0.24-py2.py3-none-any.whl.metadata (1.2 kB)\n",
+            "Collecting anyascii (from textsearch>=0.0.21->contractions)\n",
+            "  Downloading anyascii-0.3.2-py3-none-any.whl.metadata (1.5 kB)\n",
+            "Collecting pyahocorasick (from textsearch>=0.0.21->contractions)\n",
+            "  Downloading pyahocorasick-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
+            "Downloading contractions-0.1.73-py2.py3-none-any.whl (8.7 kB)\n",
+            "Downloading textsearch-0.0.24-py2.py3-none-any.whl (7.6 kB)\n",
+            "Downloading anyascii-0.3.2-py3-none-any.whl (289 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m289.9/289.9 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading pyahocorasick-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (118 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.3/118.3 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: pyahocorasick, anyascii, textsearch, contractions\n",
+            "Successfully installed anyascii-0.3.2 contractions-0.1.73 pyahocorasick-2.1.0 textsearch-0.0.24\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install contractions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from textblob import Word\n",
+        "import nltk\n",
+        "nltk.data.path.append(\"/kaggle/working/\")\n",
+        "nltk.download('wordnet')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EkWPxKu7FquT",
+        "outputId": "aac9a4a5-a461-4f62-8e86-97a9d40ccd1b"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Libraray for Data Manipulation\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Libraray for Data Visualisation\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "sns.set(style=\"white\",font_scale=1.5)\n",
+        "sns.set(rc={\"axes.facecolor\":\"#FFFAF0\",\"figure.facecolor\":\"#FFFAF0\"})\n",
+        "sns.set_context(\"poster\",font_scale = .7)\n",
+        "\n",
+        "# Library for preprocessing\n",
+        "import contractions\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "from sklearn.preprocessing import LabelBinarizer\n",
+        "from nltk.corpus import stopwords\n",
+        "from nltk.stem.porter import PorterStemmer\n",
+        "from wordcloud import WordCloud,STOPWORDS\n",
+        "from nltk.stem import WordNetLemmatizer\n",
+        "from nltk.tokenize import word_tokenize,sent_tokenize\n",
+        "import spacy\n",
+        "import math\n",
+        "from gensim.models import Word2Vec\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from joblib import Parallel, delayed\n",
+        "import re,string,unicodedata\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from textblob import TextBlob\n",
+        "from scipy.sparse import lil_matrix\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+        "from nltk import pos_tag\n",
+        "\n",
+        "# Library for model building\n",
+        "from tensorflow.keras.regularizers import l2\n",
+        "import tensorflow\n",
+        "import keras\n",
+        "from keras import layers\n",
+        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+        "from keras.models import Sequential,Model\n",
+        "#from attention import BahdanauAttention\n",
+        "from keras.layers import SimpleRNN,LSTM,GRU, Embedding, Dense, SpatialDropout1D, Dropout, BatchNormalization, Bidirectional, Attention, Input\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from tensorflow.keras.initializers import GlorotUniform\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "\n",
+        "#Library to overcome Warnings\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "import os\n",
+        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+        "    for filename in filenames:\n",
+        "        print(os.path.join(dirname, filename))"
+      ],
+      "metadata": {
+        "id": "Mc_-SNPJGIkb"
+      },
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Importing Dataset\n",
+        "----"
+      ],
+      "metadata": {
+        "id": "7YVVaAZcIpJN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = pd.read_csv('Dataset_English_Hindi.csv')"
+      ],
+      "metadata": {
+        "id": "SlWxWvnoGwXL"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.sample(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "XcbIG0l4HEQw",
+        "outputId": "c24ecca7-513e-4bda-b427-60e9076d97fb"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                 English  \\\n",
+              "56903  What Badruddin wantedand finally got , was an ...   \n",
+              "59663  Members should not approach the Chair personal...   \n",
+              "36852  Later on the diabetic gets dryness of the mout...   \n",
+              "25418  Haemophiliac men , if they survive and reach t...   \n",
+              "69314  Many Muslims were attracted back towards Islam...   \n",
+              "\n",
+              "                                                   Hindi  \n",
+              "56903  बदरूद्दीन जो चाहते थे और जो उन्होंने आखिरकार ह...  \n",
+              "59663  सदस्यों को स्वयं अध्यक्ष-पीठ के पास नहीं जाना ...  \n",
+              "36852  तत्पश्चात रोगी का मूंह , तालू तथा गला सुख जाता...  \n",
+              "25418  हीमोफिलिया से पीड़ित पुरूष यदि प्रजनन करने योग...  \n",
+              "69314  सूफियों के कारण कई मुसलमान धर्म की ओर वापस आकर...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c6bd3072-146a-4114-9439-d009619ef6bc\" class=\"colab-df-container\">\n",
+              "    <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>English</th>\n",
+              "      <th>Hindi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>56903</th>\n",
+              "      <td>What Badruddin wantedand finally got , was an ...</td>\n",
+              "      <td>बदरूद्दीन जो चाहते थे और जो उन्होंने आखिरकार ह...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>59663</th>\n",
+              "      <td>Members should not approach the Chair personal...</td>\n",
+              "      <td>सदस्यों को स्वयं अध्यक्ष-पीठ के पास नहीं जाना ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36852</th>\n",
+              "      <td>Later on the diabetic gets dryness of the mout...</td>\n",
+              "      <td>तत्पश्चात रोगी का मूंह , तालू तथा गला सुख जाता...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25418</th>\n",
+              "      <td>Haemophiliac men , if they survive and reach t...</td>\n",
+              "      <td>हीमोफिलिया से पीड़ित पुरूष यदि प्रजनन करने योग...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>69314</th>\n",
+              "      <td>Many Muslims were attracted back towards Islam...</td>\n",
+              "      <td>सूफियों के कारण कई मुसलमान धर्म की ओर वापस आकर...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6bd3072-146a-4114-9439-d009619ef6bc')\"\n",
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+              "\n",
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+              "      height: 32px;\n",
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+              "      width: 32px;\n",
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+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c6bd3072-146a-4114-9439-d009619ef6bc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-f5025c09-546e-40cb-9be7-c929287300c2\">\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
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+              "  .colab-df-quickchart:hover {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
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+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
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+              "    border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f5025c09-546e-40cb-9be7-c929287300c2 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"English\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Members should not approach the Chair personally . They may send chits to the Officers at the Table , if necessary .\",\n          \"Many Muslims were attracted back towards Islam because of Sufis.\",\n          \"Later on the diabetic gets dryness of the mouth , palate and throat , thirst , lassitude and hyperaesthesia in some regions and hypoaesthesia in other regions of the body -LRB- C . S . Nidanasthana 4/47 -RRB- .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hindi\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\u0938\\u0926\\u0938\\u094d\\u092f\\u094b\\u0902 \\u0915\\u094b \\u0938\\u094d\\u0935\\u092f\\u0902 \\u0905\\u0927\\u094d\\u092f\\u0915\\u094d\\u0937-\\u092a\\u0940\\u0920 \\u0915\\u0947 \\u092a\\u093e\\u0938 \\u0928\\u0939\\u0940\\u0902 \\u091c\\u093e\\u0928\\u093e \\u091a\\u093e\\u0939\\u093f\\u090f , \\u092f\\u0926\\u093f \\u0906\\u0935\\u0936\\u094d\\u092f\\u0915 \\u0939\\u094b \\u0924\\u094b \\u0909\\u0928\\u094d\\u0939\\u0947\\u0902 \\u092a\\u091f\\u0932 \\u0905\\u0927\\u093f\\u0915\\u093e\\u0930\\u093f\\u092f\\u094b\\u0902 \\u0915\\u0947 \\u092a\\u093e\\u0938 \\u091a\\u093f\\u091f \\u092d\\u0947\\u091c\\u0928\\u0940 \\u091a\\u093e\\u0939\\u093f\\u090f .\",\n          \"\\u0938\\u0942\\u092b\\u093f\\u092f\\u094b\\u0902 \\u0915\\u0947 \\u0915\\u093e\\u0930\\u0923 \\u0915\\u0908 \\u092e\\u0941\\u0938\\u0932\\u092e\\u093e\\u0928 \\u0927\\u0930\\u094d\\u092e \\u0915\\u0940 \\u0913\\u0930 \\u0935\\u093e\\u092a\\u0938 \\u0906\\u0915\\u0930\\u094d\\u0937\\u093f\\u0924 \\u0939\\u094b\\u0928\\u0947 \\u0932\\u0917\\u0947\\u0964\",\n          \"\\u0924\\u0924\\u094d\\u092a\\u0936\\u094d\\u091a\\u093e\\u0924 \\u0930\\u094b\\u0917\\u0940 \\u0915\\u093e \\u092e\\u0942\\u0902\\u0939 , \\u0924\\u093e\\u0932\\u0942 \\u0924\\u0925\\u093e \\u0917\\u0932\\u093e \\u0938\\u0941\\u0916 \\u091c\\u093e\\u0924\\u093e \\u0939\\u0948 , \\u0905\\u0927\\u093f\\u0915 \\u092a\\u094d\\u092f\\u093e\\u0938 \\u0932\\u0917\\u0924\\u0940 \\u0939\\u0948 , \\u0906\\u0932\\u0938\\u094d\\u092f \\u0930\\u0939\\u0924\\u093e \\u0939\\u0948 \\u0924\\u0925\\u093e \\u0936\\u0930\\u0940\\u0930 \\u0915\\u0947 \\u0915\\u093f\\u0938\\u0940 \\u092d\\u093e\\u0917 \\u092e\\u0947\\u0902 \\u0915\\u092e \\u0935 \\u0915\\u093f\\u0938\\u0940 \\u092e\\u0947\\u0902 \\u0905\\u0927\\u093f\\u0915 \\u0938\\u0902\\u0935\\u0947\\u0926\\u0928\\u093e \\u092e\\u0939\\u0938\\u0942\\u0938 \\u0939\\u094b\\u0924\\u0940 \\u0939\\u0948 . ( \\u091a . \\u0938 . \\u0928\\u093f\\u0926\\u093e\\u0928\\u093e\\u0938\\u094d\\u0925\\u093e\\u0928 4/47 ) .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Data Exploration\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "kjspwIIJIx3e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qLYG47UAHZdK",
+        "outputId": "1eda8572-14fc-4143-ab98-b421b99b9962"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(130476, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Statistical Summary\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "FQi4JETcJGmI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GtJxEOUEJAEP",
+        "outputId": "1a2fd0fb-e6e9-481d-dc1c-6dbb664e6102"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 130476 entries, 0 to 130475\n",
+            "Data columns (total 2 columns):\n",
+            " #   Column   Non-Null Count   Dtype \n",
+            "---  ------   --------------   ----- \n",
+            " 0   English  130474 non-null  object\n",
+            " 1   Hindi    130164 non-null  object\n",
+            "dtypes: object(2)\n",
+            "memory usage: 2.0+ MB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Duplicated Data"
+      ],
+      "metadata": {
+        "id": "j4_nYhmLJiSi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FSa7krGQJPRn",
+        "outputId": "d86e94b0-bdb4-4c2b-c005-9c01cb03fc42"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2788"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Missing Data"
+      ],
+      "metadata": {
+        "id": "MlxdghoNJt7L"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "missing_data = data.isnull().sum().to_frame().rename(columns={0:\"Total No. of Missing Values\"})\n",
+        "missing_data[\"% of Missing Values\"] = round((missing_data[\"Total No. of Missing Values\"]/len(data))*100,2)\n",
+        "missing_data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "id": "mAYVDvUbJdaN",
+        "outputId": "1446afa7-3b19-42d8-d26a-14f655a20643"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Total No. of Missing Values  % of Missing Values\n",
+              "English                            2                 0.00\n",
+              "Hindi                            312                 0.24"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-973b0328-1781-4680-9d12-b948d39e4310\" class=\"colab-df-container\">\n",
+              "    <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>Total No. of Missing Values</th>\n",
+              "      <th>% of Missing Values</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>English</th>\n",
+              "      <td>2</td>\n",
+              "      <td>0.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hindi</th>\n",
+              "      <td>312</td>\n",
+              "      <td>0.24</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-973b0328-1781-4680-9d12-b948d39e4310')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-973b0328-1781-4680-9d12-b948d39e4310 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-973b0328-1781-4680-9d12-b948d39e4310');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-79de36d8-9bed-41a4-a1f9-0d9e503e0614\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79de36d8-9bed-41a4-a1f9-0d9e503e0614')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-79de36d8-9bed-41a4-a1f9-0d9e503e0614 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0ff2341a-b2e4-4115-9928-3c23df33054c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('missing_data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
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+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0ff2341a-b2e4-4115-9928-3c23df33054c button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('missing_data');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "missing_data",
+              "summary": "{\n  \"name\": \"missing_data\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Total No. of Missing Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 219,\n        \"min\": 2,\n        \"max\": 312,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          312,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"% of Missing Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16970562748477142,\n        \"min\": 0.0,\n        \"max\": 0.24,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.24,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "round(data.describe().T,2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "zg0cVvVPK3Vj",
+        "outputId": "cb2bc766-9dcf-40de-bac3-fa512b91428b"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          count  unique         top freq\n",
+              "English  130474  126959  (Laughter)  555\n",
+              "Hindi    130164  100228      (हँसी)  212"
+            ],
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+              "      <th>English</th>\n",
+              "      <td>130474</td>\n",
+              "      <td>126959</td>\n",
+              "      <td>(Laughter)</td>\n",
+              "      <td>555</td>\n",
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+              "      <th>Hindi</th>\n",
+              "      <td>130164</td>\n",
+              "      <td>100228</td>\n",
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+              "      <td>212</td>\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-b2a3ee79-568c-42fe-b376-44eb251c567a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "      --disabled-bg-color: #3B4455;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "    cursor: pointer;\n",
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+              "  }\n",
+              "\n",
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+              "\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
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+              "\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-5f3e76b7-f85e-4b31-951f-a0004ad2c14c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"round(data\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"130164\",\n        \"max\": \"130474\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"130164\",\n          \"130474\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 100228,\n        \"max\": 126959,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100228,\n          126959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"(\\u0939\\u0901\\u0938\\u0940)\",\n          \"(Laughter)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"212\",\n        \"max\": \"555\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"212\",\n          \"555\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Preprocessing\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "AMATOzuYK-dd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Dropping Duplicated and Null Values"
+      ],
+      "metadata": {
+        "id": "Uy8f8cmTLz2t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.dropna(inplace=True)\n",
+        "data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "St2sEDh2J5M1",
+        "outputId": "36b60cb4-637c-4a6e-e2fb-5c1a48daf963"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                  English  \\\n",
+              "0                                                   Help!   \n",
+              "1                                                   Jump.   \n",
+              "2                                                   Jump.   \n",
+              "3                                                   Jump.   \n",
+              "4                                                  Hello!   \n",
+              "...                                                   ...   \n",
+              "130471  Examples of art deco construction can be found...   \n",
+              "130472                          and put it in our cheeks.   \n",
+              "130473  As for the other derivatives of sulphur , the ...   \n",
+              "130474  its complicated functioning is defined thus in...   \n",
+              "130475  They've just won four government contracts to ...   \n",
+              "\n",
+              "                                                    Hindi  \n",
+              "0                                                   बचाओ!  \n",
+              "1                                                   उछलो.  \n",
+              "2                                                   कूदो.  \n",
+              "3                                                  छलांग.  \n",
+              "4                                                 नमस्ते।  \n",
+              "...                                                   ...  \n",
+              "130471  आर्ट डेको शैली के निर्माण मैरीन ड्राइव और ओवल ...  \n",
+              "130472                    और अपने गालों में डाल लेते हैं।  \n",
+              "130473  जहां तक गंधक के अन्य उत्पादों का प्रश्न है , द...  \n",
+              "130474  Zरचना-प्रकिया को उसने एक पहेली में यों बांधा है .  \n",
+              "130475  हाल ही में उन्हें सरकारी ठेका मिला है करीब सौ ...  \n",
+              "\n",
+              "[130162 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-721aac95-e4dc-474b-b459-324a415e125b\" class=\"colab-df-container\">\n",
+              "    <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>English</th>\n",
+              "      <th>Hindi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Help!</td>\n",
+              "      <td>बचाओ!</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>उछलो.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>कूदो.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>छलांग.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Hello!</td>\n",
+              "      <td>नमस्ते।</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130471</th>\n",
+              "      <td>Examples of art deco construction can be found...</td>\n",
+              "      <td>आर्ट डेको शैली के निर्माण मैरीन ड्राइव और ओवल ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130472</th>\n",
+              "      <td>and put it in our cheeks.</td>\n",
+              "      <td>और अपने गालों में डाल लेते हैं।</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130473</th>\n",
+              "      <td>As for the other derivatives of sulphur , the ...</td>\n",
+              "      <td>जहां तक गंधक के अन्य उत्पादों का प्रश्न है , द...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130474</th>\n",
+              "      <td>its complicated functioning is defined thus in...</td>\n",
+              "      <td>Zरचना-प्रकिया को उसने एक पहेली में यों बांधा है .</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130475</th>\n",
+              "      <td>They've just won four government contracts to ...</td>\n",
+              "      <td>हाल ही में उन्हें सरकारी ठेका मिला है करीब सौ ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>130162 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-721aac95-e4dc-474b-b459-324a415e125b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-721aac95-e4dc-474b-b459-324a415e125b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-721aac95-e4dc-474b-b459-324a415e125b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-fill-color: #666;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
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+              "    background-color: var(--hover-bg-color);\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-1835aaa0-d33e-4d50-84f4-7ed0dd430725 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_382889bb-925a-4cfe-ab1d-e48642b14d65\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
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+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
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+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "      (() => {\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('data');\n",
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+              "type": "dataframe",
+              "variable_name": "data"
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+          "metadata": {},
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+    {
+      "cell_type": "code",
+      "source": [
+        "data.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "uVIMXP95KReW",
+        "outputId": "b139915e-84f7-4d0c-af65-0b7610b45431"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "English    0\n",
+              "Hindi      0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
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+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
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+          "metadata": {},
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+        "data.drop_duplicates(inplace=True)\n",
+        "data"
+      ],
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+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
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+        "outputId": "caf1c8a9-f944-44a9-a336-b4e5a18427be"
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+      "execution_count": 13,
+      "outputs": [
+        {
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+          "data": {
+            "text/plain": [
+              "                                                  English  \\\n",
+              "0                                                   Help!   \n",
+              "1                                                   Jump.   \n",
+              "2                                                   Jump.   \n",
+              "3                                                   Jump.   \n",
+              "4                                                  Hello!   \n",
+              "...                                                   ...   \n",
+              "130471  Examples of art deco construction can be found...   \n",
+              "130472                          and put it in our cheeks.   \n",
+              "130473  As for the other derivatives of sulphur , the ...   \n",
+              "130474  its complicated functioning is defined thus in...   \n",
+              "130475  They've just won four government contracts to ...   \n",
+              "\n",
+              "                                                    Hindi  \n",
+              "0                                                   बचाओ!  \n",
+              "1                                                   उछलो.  \n",
+              "2                                                   कूदो.  \n",
+              "3                                                  छलांग.  \n",
+              "4                                                 नमस्ते।  \n",
+              "...                                                   ...  \n",
+              "130471  आर्ट डेको शैली के निर्माण मैरीन ड्राइव और ओवल ...  \n",
+              "130472                    और अपने गालों में डाल लेते हैं।  \n",
+              "130473  जहां तक गंधक के अन्य उत्पादों का प्रश्न है , द...  \n",
+              "130474  Zरचना-प्रकिया को उसने एक पहेली में यों बांधा है .  \n",
+              "130475  हाल ही में उन्हें सरकारी ठेका मिला है करीब सौ ...  \n",
+              "\n",
+              "[127375 rows x 2 columns]"
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+              "</style>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>English</th>\n",
+              "      <th>Hindi</th>\n",
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+              "    <tr>\n",
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+              "      <th>1</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>उछलो.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>कूदो.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Jump.</td>\n",
+              "      <td>छलांग.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Hello!</td>\n",
+              "      <td>नमस्ते।</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>130471</th>\n",
+              "      <td>Examples of art deco construction can be found...</td>\n",
+              "      <td>आर्ट डेको शैली के निर्माण मैरीन ड्राइव और ओवल ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130472</th>\n",
+              "      <td>and put it in our cheeks.</td>\n",
+              "      <td>और अपने गालों में डाल लेते हैं।</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130473</th>\n",
+              "      <td>As for the other derivatives of sulphur , the ...</td>\n",
+              "      <td>जहां तक गंधक के अन्य उत्पादों का प्रश्न है , द...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130474</th>\n",
+              "      <td>its complicated functioning is defined thus in...</td>\n",
+              "      <td>Zरचना-प्रकिया को उसने एक पहेली में यों बांधा है .</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130475</th>\n",
+              "      <td>They've just won four government contracts to ...</td>\n",
+              "      <td>हाल ही में उन्हें सरकारी ठेका मिला है करीब सौ ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>127375 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f3ea2c3-4134-4de8-8b98-2d78a7c52a0e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5f3ea2c3-4134-4de8-8b98-2d78a7c52a0e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5f3ea2c3-4134-4de8-8b98-2d78a7c52a0e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4bcddbf8-b7fb-484b-bc91-d23300a46e13\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4bcddbf8-b7fb-484b-bc91-d23300a46e13')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4bcddbf8-b7fb-484b-bc91-d23300a46e13 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_192ee3c3-4a45-44fb-8e31-edaa32f0209e\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_192ee3c3-4a45-44fb-8e31-edaa32f0209e button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('data');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LK78JF-RLfdK",
+        "outputId": "06438120-fb64-4e76-eae0-1a83497f1d23"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UZaitZPTLqJ_",
+        "outputId": "0f5516dd-0679-467d-a419-71e8d6af9670"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(127375, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Lowering Case"
+      ],
+      "metadata": {
+        "id": "M52Yqzc5L60K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['English'] = data['English'].str.lower()"
+      ],
+      "metadata": {
+        "id": "Fn5jdaLYLvM7"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Remove HTML tags"
+      ],
+      "metadata": {
+        "id": "dxatTFGcrfat"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re\n",
+        "def remove_html_tags(text):\n",
+        "    pattern = r'[^a-zA-Z0-9\\s]'\n",
+        "    text = re.sub(pattern,'',text)\n",
+        "    return text"
+      ],
+      "metadata": {
+        "id": "P7txA56ariGk"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['English'] = data['English'].apply(remove_html_tags)"
+      ],
+      "metadata": {
+        "id": "oWDJZIwFrkRJ"
+      },
+      "execution_count": 18,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Removing URL"
+      ],
+      "metadata": {
+        "id": "vARaRPhm7X5q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def remove_url(text):\n",
+        "    pattern = re.compile(r'https?://\\S+|www\\.\\S+')\n",
+        "    return pattern.sub(r'',text)\n",
+        "\n",
+        "data['English'] = data['English'].apply(remove_url)\n",
+        "data['Hindi'] = data['Hindi'].apply(remove_url)"
+      ],
+      "metadata": {
+        "id": "PsYIL2eqrnlg"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Chat Word Treatment"
+      ],
+      "metadata": {
+        "id": "ZpNevdlO7j-e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "chat_words = {\n",
+        "    \"AFAIK\": \"As Far As I Know\",\n",
+        "    \"AFK\": \"Away From Keyboard\",\n",
+        "    \"ASAP\": \"As Soon As Possible\",\n",
+        "    \"ATK\": \"At The Keyboard\",\n",
+        "    \"ATM\": \"At The Moment\",\n",
+        "    \"A3\": \"Anytime, Anywhere, Anyplace\",\n",
+        "    \"BAK\": \"Back At Keyboard\",\n",
+        "    \"BBL\": \"Be Back Later\",\n",
+        "    \"BBS\": \"Be Back Soon\",\n",
+        "    \"BFN\": \"Bye For Now\",\n",
+        "    \"B4N\": \"Bye For Now\",\n",
+        "    \"BRB\": \"Be Right Back\",\n",
+        "    \"BRT\": \"Be Right There\",\n",
+        "    \"BTW\": \"By The Way\",\n",
+        "    \"B4\": \"Before\",\n",
+        "    \"CU\": \"See You\",\n",
+        "    \"CUL8R\": \"See You Later\",\n",
+        "    \"CYA\": \"See You\",\n",
+        "    \"FAQ\": \"Frequently Asked Questions\",\n",
+        "    \"FC\": \"Fingers Crossed\",\n",
+        "    \"FWIW\": \"For What It's Worth\",\n",
+        "    \"FYI\": \"For Your Information\",\n",
+        "    \"GAL\": \"Get A Life\",\n",
+        "    \"GG\": \"Good Game\",\n",
+        "    \"GN\": \"Good Night\",\n",
+        "    \"GMTA\": \"Great Minds Think Alike\",\n",
+        "    \"GR8\": \"Great!\",\n",
+        "    \"G9\": \"Genius\",\n",
+        "    \"IC\": \"I See\",\n",
+        "    \"ICQ\": \"I Seek you (also a chat program)\",\n",
+        "    \"ILU\": \"I Love You\",\n",
+        "    \"IMHO\": \"In My Honest/Humble Opinion\",\n",
+        "    \"IMO\": \"In My Opinion\",\n",
+        "    \"IOW\": \"In Other Words\",\n",
+        "    \"IRL\": \"In Real Life\",\n",
+        "    \"KISS\": \"Keep It Simple, Stupid\",\n",
+        "    \"LDR\": \"Long Distance Relationship\",\n",
+        "    \"LMAO\": \"Laugh My A.. Off\",\n",
+        "    \"LOL\": \"Laughing Out Loud\",\n",
+        "    \"LTNS\": \"Long Time No See\",\n",
+        "    \"L8R\": \"Later\",\n",
+        "    \"MTE\": \"My Thoughts Exactly\",\n",
+        "    \"M8\": \"Mate\",\n",
+        "    \"NRN\": \"No Reply Necessary\",\n",
+        "    \"OIC\": \"Oh I See\",\n",
+        "    \"PITA\": \"Pain In The A..\",\n",
+        "    \"PRT\": \"Party\",\n",
+        "    \"PRW\": \"Parents Are Watching\",\n",
+        "    \"QPSA\": \"Que Pasa?\",\n",
+        "    \"ROFL\": \"Rolling On The Floor Laughing\",\n",
+        "    \"ROFLOL\": \"Rolling On The Floor Laughing Out Loud\",\n",
+        "    \"ROTFLMAO\": \"Rolling On The Floor Laughing My A.. Off\",\n",
+        "    \"SK8\": \"Skate\",\n",
+        "    \"STATS\": \"Your sex and age\",\n",
+        "    \"ASL\": \"Age, Sex, Location\",\n",
+        "    \"THX\": \"Thank You\",\n",
+        "    \"TTFN\": \"Ta-Ta For Now!\",\n",
+        "    \"TTYL\": \"Talk To You Later\",\n",
+        "    \"U\": \"You\",\n",
+        "    \"U2\": \"You Too\",\n",
+        "    \"U4E\": \"Yours For Ever\",\n",
+        "    \"WB\": \"Welcome Back\",\n",
+        "    \"WTF\": \"What The F...\",\n",
+        "    \"WTG\": \"Way To Go!\",\n",
+        "    \"WUF\": \"Where Are You From?\",\n",
+        "    \"W8\": \"Wait...\",\n",
+        "    \"7K\": \"Sick:-D Laughter\",\n",
+        "    \"TFW\": \"That feeling when\",\n",
+        "    \"MFW\": \"My face when\",\n",
+        "    \"MRW\": \"My reaction when\",\n",
+        "    \"IFYP\": \"I feel your pain\",\n",
+        "    \"LOL\": \"Laughing out loud\",\n",
+        "    \"TNTL\": \"Trying not to laugh\",\n",
+        "    \"JK\": \"Just kidding\",\n",
+        "    \"IDC\": \"I don’t care\",\n",
+        "    \"ILY\": \"I love you\",\n",
+        "    \"IMU\": \"I miss you\",\n",
+        "    \"ADIH\": \"Another day in hell\",\n",
+        "    \"IDC\": \"I don’t care\",\n",
+        "    \"ZZZ\": \"Sleeping, bored, tired\",\n",
+        "    \"WYWH\": \"Wish you were here\",\n",
+        "    \"TIME\": \"Tears in my eyes\",\n",
+        "    \"BAE\": \"Before anyone else\",\n",
+        "    \"FIMH\": \"Forever in my heart\",\n",
+        "    \"BSAAW\": \"Big smile and a wink\",\n",
+        "    \"BWL\": \"Bursting with laughter\",\n",
+        "    \"LMAO\": \"Laughing my a** off\",\n",
+        "    \"BFF\": \"Best friends forever\",\n",
+        "    \"CSL\": \"Can’t stop laughing\",\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "EUV0ppp97XJ0"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Removing Emojis"
+      ],
+      "metadata": {
+        "id": "Z87581eU7siL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def remove_emoji(text):\n",
+        "    emoji_pattern = re.compile(\"[\"\n",
+        "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
+        "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
+        "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
+        "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
+        "                           u\"\\U00002702-\\U000027B0\"\n",
+        "                           u\"\\U000024C2-\\U0001F251\"\n",
+        "                           \"]+\", flags=re.UNICODE)\n",
+        "    return emoji_pattern.sub(r'', text)"
+      ],
+      "metadata": {
+        "id": "vzbTSI537nyX"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['English'] = data['English'].apply(remove_emoji)\n",
+        "data['Hindi'] = data['Hindi'].apply(remove_emoji)"
+      ],
+      "metadata": {
+        "id": "Uk8r6XNU7w7a"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Removing Contraction"
+      ],
+      "metadata": {
+        "id": "7nGqPmrM71qS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def expand_contractions(text):\n",
+        "    expanded_text = contractions.fix(text)\n",
+        "    return expanded_text\n",
+        "\n",
+        "data['English'] = data['English'].apply(expand_contractions)"
+      ],
+      "metadata": {
+        "id": "Nq7ohxVa7xht"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Removing Non-Hindi and Alphanumeric Characters"
+      ],
+      "metadata": {
+        "id": "ZvVcDuh78Dro"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def preprocess_text(text, language='english'):\n",
+        "    if not isinstance(text, str):\n",
+        "        return text\n",
+        "\n",
+        "    if language == 'english':\n",
+        "        pattern = re.compile(r'[^a-zA-Z0-9\\s]')\n",
+        "        return pattern.sub(r'', text)\n",
+        "    elif language == 'hindi':\n",
+        "        pattern = re.compile(r'[^\\u0900-\\u097F\\s]')\n",
+        "        return pattern.sub(r'', text)\n",
+        "    else:\n",
+        "        raise ValueError(\"Unsupported Language, Supported languages are 'english' and 'hindi'\")\n",
+        "\n",
+        "data['English'] = data['English'].apply(lambda x: preprocess_text(x, language='english'))\n",
+        "data['Hindi'] = data['Hindi'].apply(lambda x: preprocess_text(x, language='hindi'))"
+      ],
+      "metadata": {
+        "id": "KOJjxSY177JI"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.sample(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "qJ6wwZtq8JnU",
+        "outputId": "590b2a9e-61fb-441c-eb3a-99247c6ef24b"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                  English  \\\n",
+              "83123                           half century of my poetry   \n",
+              "72501            he asked   who is your teacher  brother    \n",
+              "81176           and even there it is him who advises them   \n",
+              "103027       if they saw me running up and down this hill   \n",
+              "119415  the number stanzas in linga puran is eleven th...   \n",
+              "\n",
+              "                                                    Hindi  \n",
+              "83123                             मेरी कविताई की आधी सदी   \n",
+              "72501   तब सारिपुत्र ने पूछा  ऋऋभनऋऊण्श्छ्ष्ते आपका गु...  \n",
+              "81176         तब भी सलाह तो राष्ट्रपति ही उन्हें देते हैं  \n",
+              "103027  अगर वे मुझे ऐसे  पहाड़ी पर इधरउधर भागते हुए दे...  \n",
+              "119415   लिंगपुराण में श्लोकों की संख्या ग्यारह हजार हैं।  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-62d95cbe-fc03-48f0-9df1-530faeacdaa9\" class=\"colab-df-container\">\n",
+              "    <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>English</th>\n",
+              "      <th>Hindi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>83123</th>\n",
+              "      <td>half century of my poetry</td>\n",
+              "      <td>मेरी कविताई की आधी सदी</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>72501</th>\n",
+              "      <td>he asked   who is your teacher  brother</td>\n",
+              "      <td>तब सारिपुत्र ने पूछा  ऋऋभनऋऊण्श्छ्ष्ते आपका गु...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81176</th>\n",
+              "      <td>and even there it is him who advises them</td>\n",
+              "      <td>तब भी सलाह तो राष्ट्रपति ही उन्हें देते हैं</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103027</th>\n",
+              "      <td>if they saw me running up and down this hill</td>\n",
+              "      <td>अगर वे मुझे ऐसे  पहाड़ी पर इधरउधर भागते हुए दे...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>119415</th>\n",
+              "      <td>the number stanzas in linga puran is eleven th...</td>\n",
+              "      <td>लिंगपुराण में श्लोकों की संख्या ग्यारह हजार हैं।</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62d95cbe-fc03-48f0-9df1-530faeacdaa9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-62d95cbe-fc03-48f0-9df1-530faeacdaa9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-62d95cbe-fc03-48f0-9df1-530faeacdaa9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-86a2d4e0-dd5a-4ba5-a2ba-2076c7360697\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86a2d4e0-dd5a-4ba5-a2ba-2076c7360697')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-86a2d4e0-dd5a-4ba5-a2ba-2076c7360697 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"English\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"he asked   who is your teacher  brother \",\n          \"the number stanzas in linga puran is eleven thousand\",\n          \"and even there it is him who advises them\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hindi\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\u0924\\u092c \\u0938\\u093e\\u0930\\u093f\\u092a\\u0941\\u0924\\u094d\\u0930 \\u0928\\u0947 \\u092a\\u0942\\u091b\\u093e  \\u090b\\u090b\\u092d\\u0928\\u090b\\u090a\\u0923\\u094d\\u0936\\u094d\\u091b\\u094d\\u0937\\u094d\\u0924\\u0947 \\u0906\\u092a\\u0915\\u093e \\u0917\\u0941\\u0930\\u0942 \\u0915\\u090b\\u0928 \\u0939\\u0948\\u090b\",\n          \"\\u0932\\u093f\\u0902\\u0917\\u092a\\u0941\\u0930\\u093e\\u0923 \\u092e\\u0947\\u0902 \\u0936\\u094d\\u0932\\u094b\\u0915\\u094b\\u0902 \\u0915\\u0940 \\u0938\\u0902\\u0916\\u094d\\u092f\\u093e \\u0917\\u094d\\u092f\\u093e\\u0930\\u0939 \\u0939\\u091c\\u093e\\u0930 \\u0939\\u0948\\u0902\\u0964\",\n          \"\\u0924\\u092c \\u092d\\u0940 \\u0938\\u0932\\u093e\\u0939 \\u0924\\u094b \\u0930\\u093e\\u0937\\u094d\\u091f\\u094d\\u0930\\u092a\\u0924\\u093f \\u0939\\u0940 \\u0909\\u0928\\u094d\\u0939\\u0947\\u0902 \\u0926\\u0947\\u0924\\u0947 \\u0939\\u0948\\u0902\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "WordCloud"
+      ],
+      "metadata": {
+        "id": "QwWOzYIu8vkD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to preprocess text and generate word cloud\n",
+        "def generate_wordcloud(text_list, language):\n",
+        "    # Join the list of words into a single string\n",
+        "    text_str = ' '.join(map(str, text_list))\n",
+        "\n",
+        "    # Generate word cloud without specifying font path\n",
+        "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap='viridis').generate(text_str)\n",
+        "\n",
+        "    # Display word cloud\n",
+        "    plt.figure(figsize=(10, 5))\n",
+        "    plt.imshow(wordcloud, interpolation='bilinear')\n",
+        "    plt.title(f\"Word Cloud for {language} Text\")\n",
+        "    plt.axis('off')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "H2IYoQHR8Mz4"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Generate word clouds for English and Hindi columns\n",
+        "generate_wordcloud(data['English'], 'english')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "kgyC2A6I8zSU",
+        "outputId": "b40dee17-ebdd-455f-b44e-3ae1816a3533"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NAE6C4VK82bI",
+        "outputId": "d634b5ea-ecc2-4980-baaa-bddec5ae4577"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(127375, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = data[data['English'].str.len() <= 50]"
+      ],
+      "metadata": {
+        "id": "m9_M-p_f86Ls"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2l3QTTeh8_DN",
+        "outputId": "d6d793e6-4dab-4891-9f2e-ebb7ed1052fd"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(50019, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Add <start> and <end> tokens to Hindi sentences\n",
+        "data['Hindi'] = ['<start> ' + sentence + ' <end>' for sentence in data['Hindi']]"
+      ],
+      "metadata": {
+        "id": "dR04MdpJ9Dn5"
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Tokenize the Data"
+      ],
+      "metadata": {
+        "id": "WM0YJDab9K4I"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tok = Tokenizer()\n",
+        "tok.fit_on_texts(data['English'])"
+      ],
+      "metadata": {
+        "id": "3IuFNMJe9HZb"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tok_hindi = Tokenizer()\n",
+        "tok_hindi.fit_on_texts(data['Hindi'])"
+      ],
+      "metadata": {
+        "id": "9QpH47gn9Nfy"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(tok.word_index),len(tok_hindi.word_index)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Dk899_An9P9V",
+        "outputId": "8e18793f-b7aa-4990-8aec-461eb33074b8"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(22657, 24545)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(tok.word_index),len(tok_hindi.word_index)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xK_A_vBl9SOx",
+        "outputId": "68238150-a960-4f99-d179-ee1a5df8f82d"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(22657, 24545)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tok.document_count"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WDxGXUGM9VlT",
+        "outputId": "6cd44f37-0d51-4c6a-d991-d57b5b879863"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "50019"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Converting Data into Numerical Form"
+      ],
+      "metadata": {
+        "id": "TVXz00qq9a8b"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['English'] = tok.texts_to_sequences(data['English'])\n",
+        "data['Hindi'] = tok_hindi.texts_to_sequences(data['Hindi'])"
+      ],
+      "metadata": {
+        "id": "QRmvSmJV9YI2"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "gaIevX0p9fR4",
+        "outputId": "b4979b30-7015-487b-afb8-e0f4a5d19e7e"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                    English  \\\n",
+              "0                                     [147]   \n",
+              "1                                    [2835]   \n",
+              "2                                    [2835]   \n",
+              "3                                    [2835]   \n",
+              "4                                    [3803]   \n",
+              "...                                     ...   \n",
+              "130461        [62, 52, 10, 14, 6, 936, 157]   \n",
+              "130462      [1, 22656, 14, 10039, 26, 4587]   \n",
+              "130464  [5, 49, 13, 97, 9, 4, 254, 56, 459]   \n",
+              "130466              [363, 47, 16, 214, 343]   \n",
+              "130472           [2, 188, 12, 7, 56, 22657]   \n",
+              "\n",
+              "                                                    Hindi  \n",
+              "0                                           [1, 12946, 2]  \n",
+              "1                                           [1, 12947, 2]  \n",
+              "2                                           [1, 12948, 2]  \n",
+              "3                                            [1, 4554, 2]  \n",
+              "4                                            [1, 9649, 2]  \n",
+              "...                                                   ...  \n",
+              "130461               [1, 98, 48, 21, 11, 1826, 164, 9, 2]  \n",
+              "130462  [1, 106, 2923, 9401, 17, 3263, 8, 3247, 46, 51...  \n",
+              "130464          [1, 86, 22, 179, 9, 14, 13, 1281, 850, 2]  \n",
+              "130466              [1, 1116, 37, 34, 1399, 15, 7, 16, 2]  \n",
+              "130472             [1, 6, 41, 24545, 5, 1156, 445, 33, 2]  \n",
+              "\n",
+              "[50019 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-24e49515-b7f8-4641-a0dc-3d25318d7e17\" class=\"colab-df-container\">\n",
+              "    <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>English</th>\n",
+              "      <th>Hindi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>[147]</td>\n",
+              "      <td>[1, 12946, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>[2835]</td>\n",
+              "      <td>[1, 12947, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>[2835]</td>\n",
+              "      <td>[1, 12948, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>[2835]</td>\n",
+              "      <td>[1, 4554, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>[3803]</td>\n",
+              "      <td>[1, 9649, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130461</th>\n",
+              "      <td>[62, 52, 10, 14, 6, 936, 157]</td>\n",
+              "      <td>[1, 98, 48, 21, 11, 1826, 164, 9, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130462</th>\n",
+              "      <td>[1, 22656, 14, 10039, 26, 4587]</td>\n",
+              "      <td>[1, 106, 2923, 9401, 17, 3263, 8, 3247, 46, 51...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130464</th>\n",
+              "      <td>[5, 49, 13, 97, 9, 4, 254, 56, 459]</td>\n",
+              "      <td>[1, 86, 22, 179, 9, 14, 13, 1281, 850, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130466</th>\n",
+              "      <td>[363, 47, 16, 214, 343]</td>\n",
+              "      <td>[1, 1116, 37, 34, 1399, 15, 7, 16, 2]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>130472</th>\n",
+              "      <td>[2, 188, 12, 7, 56, 22657]</td>\n",
+              "      <td>[1, 6, 41, 24545, 5, 1156, 445, 33, 2]</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>50019 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24e49515-b7f8-4641-a0dc-3d25318d7e17')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-24e49515-b7f8-4641-a0dc-3d25318d7e17 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-24e49515-b7f8-4641-a0dc-3d25318d7e17');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-771cb8be-2c74-4328-a47b-f584d5e90e4a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-771cb8be-2c74-4328-a47b-f584d5e90e4a')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-771cb8be-2c74-4328-a47b-f584d5e90e4a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_2b63e4ea-33a7-45e0-91f6-f0b4c62026cd\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2b63e4ea-33a7-45e0-91f6-f0b4c62026cd button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('data');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 50019,\n  \"fields\": [\n    {\n      \"column\": \"English\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hindi\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def calculate_max_sequence_length(eng,hindi):\n",
+        "    max_length_combined = max(max(len(seq) for seq in eng), max(len(seq) for seq in hindi))\n",
+        "\n",
+        "    return max_length_combined\n",
+        "\n",
+        "max_length_combined = calculate_max_sequence_length(data['English'],data['Hindi'])\n",
+        "max_length_combined"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UIQrcoIp9haX",
+        "outputId": "b8eb214b-1dfe-4eed-ee81-df822ee76c7f"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "260"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for num in data['English'][0]:\n",
+        "    print(num, end=' ')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sXrYCRem9kh0",
+        "outputId": "7122380e-852f-4c3a-c450-b9f314785ee0"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "147 "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Print corresponding words horizontally\n",
+        "for num in data['English'][0]:\n",
+        "    word = tok.index_word.get(num, 'UNK')\n",
+        "    print(word, end=' ')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "37usTrJb9nCf",
+        "outputId": "68747055-cffa-4ec1-9379-a2443de2b6f9"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "help "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for num in data['Hindi'][0]:\n",
+        "    word = tok_hindi.index_word.get(num)\n",
+        "    print(word,end = \" \")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ga23QcPz9ppw",
+        "outputId": "392f7e87-62b3-48be-ff52-b599e8025a40"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "start बचाओ end "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Seperating the data in dependent and independent"
+      ],
+      "metadata": {
+        "id": "lE4dgeuU9yhq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "hindi = data['Hindi']\n",
+        "english = data['English']"
+      ],
+      "metadata": {
+        "id": "ym4ju2TE9sn0"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Padding"
+      ],
+      "metadata": {
+        "id": "_q-xRP61-DUs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = pad_sequences(english,maxlen = max_length_combined,padding = 'post')\n",
+        "y = pad_sequences(hindi,maxlen = max_length_combined,padding = 'post')"
+      ],
+      "metadata": {
+        "id": "OkpRBOtk-CmK"
+      },
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hEQAoeFK-HqJ",
+        "outputId": "4a0d4b0b-eade-4f06-b525-ec994516d18c"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 147,    0,    0, ...,    0,    0,    0],\n",
+              "       [2835,    0,    0, ...,    0,    0,    0],\n",
+              "       [2835,    0,    0, ...,    0,    0,    0],\n",
+              "       ...,\n",
+              "       [   5,   49,   13, ...,    0,    0,    0],\n",
+              "       [ 363,   47,   16, ...,    0,    0,    0],\n",
+              "       [   2,  188,   12, ...,    0,    0,    0]], dtype=int32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i7wRIjIv-JCm",
+        "outputId": "e6c972ec-e516-49b9-f5e9-33c616410ce8"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[    1, 12946,     2, ...,     0,     0,     0],\n",
+              "       [    1, 12947,     2, ...,     0,     0,     0],\n",
+              "       [    1, 12948,     2, ...,     0,     0,     0],\n",
+              "       ...,\n",
+              "       [    1,    86,    22, ...,     0,     0,     0],\n",
+              "       [    1,  1116,    37, ...,     0,     0,     0],\n",
+              "       [    1,     6,    41, ...,     0,     0,     0]], dtype=int32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Seperating Train , Test and Evaluation Data"
+      ],
+      "metadata": {
+        "id": "HTI0evVO-Or7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=42)"
+      ],
+      "metadata": {
+        "id": "v2_90dCu-KFL"
+      },
+      "execution_count": 47,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check the shape of the resulting arrays\n",
+        "print(\"Shape of x_train:\", x_train.shape)\n",
+        "print(\"Shape of x_test:\", x_test.shape)\n",
+        "print(\"Shape of y_train:\", y_train.shape)\n",
+        "print(\"Shape of y_test:\", y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WWYKoGY8-VeW",
+        "outputId": "954095a8-3e6c-467c-be1b-98b2ae1cb6f8"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of x_train: (40015, 260)\n",
+            "Shape of x_test: (10004, 260)\n",
+            "Shape of y_train: (40015, 260)\n",
+            "Shape of y_test: (10004, 260)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Modelling\n",
+        "---"
+      ],
+      "metadata": {
+        "id": "6Qx63EzM-og-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ENCODER-DECODER MODEL"
+      ],
+      "metadata": {
+        "id": "THUKjYu_-xBl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_length_input = x_train.shape[1]\n",
+        "max_length_output = y_train.shape[1]\n",
+        "input_vocab_size = len(tok.word_index) + 1\n",
+        "output_vocab_size = len(tok_hindi.word_index) + 1\n",
+        "\n",
+        "# Define Encoder model\n",
+        "encoder_inputs = Input(shape=(max_length_input,))\n",
+        "encoder_embedding = Embedding(input_dim=input_vocab_size, output_dim=260)(encoder_inputs)\n",
+        "encoder_lstm = LSTM(156, return_state=True)\n",
+        "encoder_outputs, state_h, state_c = encoder_lstm(encoder_embedding)\n",
+        "encoder_states = [state_h, state_c]\n",
+        "\n",
+        "# Define Decoder model\n",
+        "decoder_inputs = Input(shape=(max_length_output,))\n",
+        "decoder_embedding = Embedding(input_dim=output_vocab_size, output_dim=260)(decoder_inputs)\n",
+        "decoder_lstm = LSTM(156, return_sequences=True, return_state=True)\n",
+        "decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)\n",
+        "decoder_dense = Dense(output_vocab_size, activation='softmax')\n",
+        "decoder_outputs = decoder_dense(decoder_outputs)\n",
+        "\n",
+        "# Define the full Encoder-Decoder model\n",
+        "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Print model summary\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 417
+        },
+        "id": "vT0LzuvL-ahZ",
+        "outputId": "6a2140ed-6fea-44b3-b8ed-47989755fdaa"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ input_layer_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
+              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m260\u001b[0m)       │      \u001b[38;5;34m5,891,080\u001b[0m │ input_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m260\u001b[0m)       │      \u001b[38;5;34m6,381,960\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)               │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m156\u001b[0m), (\u001b[38;5;45mNone\u001b[0m,   │        \u001b[38;5;34m260,208\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
+              "│                           │ \u001b[38;5;34m156\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m156\u001b[0m)]     │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)             │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m156\u001b[0m),     │        \u001b[38;5;34m260,208\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
+              "│                           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m156\u001b[0m), (\u001b[38;5;45mNone\u001b[0m,    │                │ lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m], lstm[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m2\u001b[0m] │\n",
+              "│                           │ \u001b[38;5;34m156\u001b[0m)]                  │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m24546\u001b[0m)     │      \u001b[38;5;34m3,853,722\u001b[0m │ lstm_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
+              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ input_layer_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)       │      <span style=\"color: #00af00; text-decoration-color: #00af00\">5,891,080</span> │ input_layer[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)       │      <span style=\"color: #00af00; text-decoration-color: #00af00\">6,381,960</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)               │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">260,208</span> │ embedding[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
+              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>)]     │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)             │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>),     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">260,208</span> │ embedding_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
+              "│                           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,    │                │ lstm[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>], lstm[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>] │\n",
+              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>)]                  │                │                        │\n",
+              "├─────���─────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24546</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,853,722</span> │ lstm_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
+              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16,647,178\u001b[0m (63.50 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16,647,178</span> (63.50 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16,647,178\u001b[0m (63.50 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16,647,178</span> (63.50 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+        "\n",
+        "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
+        "model_checkpoint = ModelCheckpoint('model_checkpoint.keras', save_best_only=True)  # Updated filepath\n",
+        "\n",
+        "# Train the model with callbacks\n",
+        "history = model.fit(\n",
+        "    x=[x_train, y_train],# Exclude last token from decoder input\n",
+        "    y=y_train,   # Exclude first token from decoder target\n",
+        "    batch_size=32,\n",
+        "    epochs=5,\n",
+        "    validation_data=([x_test, y_test], y_test),\n",
+        "    callbacks=[early_stopping, model_checkpoint],\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7RB1AHIk-1BI",
+        "outputId": "2503612f-9f33-40ca-f200-459a6d744977"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m324s\u001b[0m 254ms/step - accuracy: 0.9661 - loss: 0.9194 - val_accuracy: 0.9838 - val_loss: 0.1327\n",
+            "Epoch 2/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m319s\u001b[0m 255ms/step - accuracy: 0.9854 - loss: 0.1191 - val_accuracy: 0.9897 - val_loss: 0.0897\n",
+            "Epoch 3/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m322s\u001b[0m 255ms/step - accuracy: 0.9907 - loss: 0.0804 - val_accuracy: 0.9930 - val_loss: 0.0642\n",
+            "Epoch 4/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m322s\u001b[0m 255ms/step - accuracy: 0.9936 - loss: 0.0568 - val_accuracy: 0.9949 - val_loss: 0.0487\n",
+            "Epoch 5/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m322s\u001b[0m 255ms/step - accuracy: 0.9953 - loss: 0.0411 - val_accuracy: 0.9960 - val_loss: 0.0379\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ENCODER-DECODER MODEL EVALUATION"
+      ],
+      "metadata": {
+        "id": "Oo4LZvWvk1J_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['accuracy'],color = 'blue',label = 'accuracy')\n",
+        "plt.plot(history.history['val_accuracy'],color = 'red',label = 'val_accuracy')\n",
+        "plt.title(\"Training And Validation Accuracy Score\")\n",
+        "plt.xlabel(\"accuracy\")\n",
+        "plt.ylabel(\"epochs\")\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "X4e_ooBD-5QD",
+        "outputId": "e78ec629-19ac-4274-9d92-7c85fea01c74"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'],color = 'blue',label = 'loss')\n",
+        "plt.plot(history.history['val_loss'],color = 'red',label = 'val_loss')\n",
+        "plt.title(\"Training And Validation Loss Score\")\n",
+        "plt.xlabel(\"loss\")\n",
+        "plt.ylabel(\"epochs\")\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "24DMCMQFAv0E",
+        "outputId": "2c826f29-b641-4d89-b2cd-f7192bc0e8f9"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "PREDICTION FROM ENCODER-DECODER MODEL"
+      ],
+      "metadata": {
+        "id": "GJU5SAbZk_uJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n",
+        "\n",
+        "# Reverse token dictionaries\n",
+        "rev_tok_hindi = {idx: word for word, idx in tok_hindi.word_index.items()}\n",
+        "\n",
+        "# Select a subset of test data\n",
+        "num_samples = 10\n",
+        "x_test_subset = x_test[:num_samples]\n",
+        "y_test_padded_subset = y_test[:num_samples]\n",
+        "\n",
+        "# Predict\n",
+        "predictions = model.predict([x_test_subset, y_test_padded_subset], batch_size=16)\n",
+        "print('Shape of predictions:', predictions.shape)\n",
+        "\n",
+        "# Convert predictions to token indices\n",
+        "predicted_tokens_np = np.argmax(predictions, axis=-1)\n",
+        "print('Shape of predicted_tokens:', predicted_tokens_np.shape)\n",
+        "\n",
+        "# Map indices to tokens and remove 'start' and 'end' tokens\n",
+        "predicted_sentences = []\n",
+        "for sample in predicted_tokens_np:\n",
+        "    sentence = ' '.join([rev_tok_hindi.get(token, '<unknown>') for token in sample if token != 0 and token not in [tok_hindi.word_index.get('start'), tok_hindi.word_index.get('end')]])  # Exclude padding, 'start', and 'end' tokens\n",
+        "    predicted_sentences.append(sentence)\n",
+        "\n",
+        "# Reverse token dictionary for English (assuming you have `tok_english` for English tokens)\n",
+        "rev_tok_english = {idx: word for word, idx in tok.word_index.items()}\n",
+        "\n",
+        "# Map the English test inputs to their corresponding sentences\n",
+        "english_sentences = []\n",
+        "for sample in x_test_subset:\n",
+        "    sentence = ' '.join([rev_tok_english.get(token, '<unknown>') for token in sample if token != 0])  # Exclude padding token\n",
+        "    english_sentences.append(sentence)\n",
+        "\n",
+        "# Print English sentence followed by predicted Hindi sentence\n",
+        "for idx, (eng_sentence, hin_sentence) in enumerate(zip(english_sentences, predicted_sentences)):\n",
+        "    print(f'English sentence {idx + 1}: {eng_sentence}')\n",
+        "    print(f'Predicted Hindi translation {idx + 1}: {hin_sentence}\\n')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "I8Sl7eaIAzSO",
+        "outputId": "19e7d4ce-4067-40d3-ebb5-1928221e6d1b"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 420ms/step\n",
+            "Shape of predictions: (10, 260, 24546)\n",
+            "Shape of predicted_tokens: (10, 260)\n",
+            "English sentence 1: islams golden age began in the rule of abbasioan\n",
+            "Predicted Hindi translation 1: के राज में इस्लाम का स्वर्ण युग शुरु हुआ।\n",
+            "\n",
+            "English sentence 2: even of salinas\n",
+            "Predicted Hindi translation 2: श्रेणीबिहार तक की\n",
+            "\n",
+            "English sentence 3: adhikashh poetic creation he has chosen post\n",
+            "Predicted Hindi translation 3: के उन्होंने पद ही चुने हैं।\n",
+            "\n",
+            "English sentence 4: was i had this really\n",
+            "Predicted Hindi translation 4: ये थी कि मुझे एक सचमुच\n",
+            "\n",
+            "English sentence 5: and transform it\n",
+            "Predicted Hindi translation 5: और करते है\n",
+            "\n",
+            "English sentence 6: if they really had technology assessment\n",
+            "Predicted Hindi translation 6: अगर उनके पास सहायता होती\n",
+            "\n",
+            "English sentence 7: included in secondary pollutant\n",
+            "Predicted Hindi translation 7: में शामिल है\n",
+            "\n",
+            "English sentence 8: cut down on sweets chips and soda\n",
+            "Predicted Hindi translation 8: चीजें और का प्रयोग कम करना होगा\n",
+            "\n",
+            "English sentence 9: pranithas mother was a woman in prostitution\n",
+            "Predicted Hindi translation 9: की मां एक हैं। थी\n",
+            "\n",
+            "English sentence 10: outer origin\n",
+            "Predicted Hindi translation 10: बाहरी कडियाँ\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ENCODER-DECODER MODEL WITH ATTENTION"
+      ],
+      "metadata": {
+        "id": "W6hf0zqflHhT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, Attention, Concatenate\n",
+        "from tensorflow.keras.models import Model\n",
+        "\n",
+        "# Define Encoder model\n",
+        "encoder_inputs = Input(shape=(max_length_input,))\n",
+        "encoder_embedding = Embedding(input_dim=input_vocab_size, output_dim=260)(encoder_inputs)\n",
+        "encoder_lstm = LSTM(156, return_sequences=True, return_state=True)  # Ensure return_sequences=True\n",
+        "encoder_outputs, state_h, state_c = encoder_lstm(encoder_embedding)\n",
+        "encoder_states = [state_h, state_c]\n",
+        "\n",
+        "# Define Decoder model\n",
+        "decoder_inputs = Input(shape=(max_length_output,))\n",
+        "decoder_embedding = Embedding(input_dim=output_vocab_size, output_dim=260)(decoder_inputs)\n",
+        "decoder_lstm = LSTM(156, return_sequences=True, return_state=True)\n",
+        "decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)\n",
+        "\n",
+        "# Attention Layer\n",
+        "attention = Attention()([decoder_outputs, encoder_outputs])\n",
+        "\n",
+        "# Concatenate attention output with decoder outputs\n",
+        "decoder_concat_input = Concatenate(axis=-1)([decoder_outputs, attention])\n",
+        "\n",
+        "# Dense Layer for output prediction\n",
+        "decoder_dense = Dense(output_vocab_size, activation='softmax')\n",
+        "decoder_outputs = decoder_dense(decoder_concat_input)\n",
+        "\n",
+        "# Define the full Encoder-Decoder model\n",
+        "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Print model summary\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        },
+        "id": "vl3yWHdGA3jw",
+        "outputId": "5eb3a36d-f1ce-4882-bb24-8b78bb276020"
+      },
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
+              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ input_layer_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m)            │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
+              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m260\u001b[0m)       │      \u001b[38;5;34m5,891,080\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_3 (\u001b[38;5;33mEmbedding\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m260\u001b[0m)       │      \u001b[38;5;34m6,381,960\u001b[0m │ input_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)             │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m156\u001b[0m),     │        \u001b[38;5;34m260,208\u001b[0m │ embedding_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
+              "│                           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m156\u001b[0m), (\u001b[38;5;45mNone\u001b[0m,    │                │                        │\n",
+              "│                           │ \u001b[38;5;34m156\u001b[0m)]                  │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)             │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m156\u001b[0m),     │        \u001b[38;5;34m260,208\u001b[0m │ embedding_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
+              "│                           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m156\u001b[0m), (\u001b[38;5;45mNone\u001b[0m,    │                │ lstm_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m],          │\n",
+              "│                           │ \u001b[38;5;34m156\u001b[0m)]                  │                │ lstm_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m2\u001b[0m]           │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ attention (\u001b[38;5;33mAttention\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m156\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ lstm_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
+              "│                           │                        │                │ lstm_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m312\u001b[0m)       │              \u001b[38;5;34m0\u001b[0m │ lstm_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
+              "│                           │                        │                │ attention[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m260\u001b[0m, \u001b[38;5;34m24546\u001b[0m)     │      \u001b[38;5;34m7,682,898\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
+              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+              "│ input_layer_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ input_layer_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)            │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
+              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │                        │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)       │      <span style=\"color: #00af00; text-decoration-color: #00af00\">5,891,080</span> │ input_layer_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ embedding_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>)       │      <span style=\"color: #00af00; text-decoration-color: #00af00\">6,381,960</span> │ input_layer_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)             │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>),     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">260,208</span> │ embedding_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
+              "│                           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,    │                │                        │\n",
+              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>)]                  │                │                        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)             │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>),     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">260,208</span> │ embedding_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
+              "│                           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>,    │                │ lstm_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>],          │\n",
+              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>)]                  │                │ lstm_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>]           │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ attention (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Attention</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">156</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ lstm_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
+              "│                           │                        │                │ lstm_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">312</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ lstm_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
+              "│                           │                        │                │ attention[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
+              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">260</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24546</span>)     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">7,682,898</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
+              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,476,354\u001b[0m (78.11 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,476,354</span> (78.11 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,476,354\u001b[0m (78.11 MB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,476,354</span> (78.11 MB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
+        "model_checkpoint = ModelCheckpoint('model_checkpoint.keras', save_best_only=True)  # Updated filepath\n",
+        "\n",
+        "# Train the model with callbacks\n",
+        "history = model.fit(\n",
+        "    x=[x_train, y_train],# Exclude last token from decoder input\n",
+        "    y=y_train,   # Exclude first token from decoder target\n",
+        "    batch_size=32,\n",
+        "    epochs=5,\n",
+        "    validation_data=([x_test, y_test], y_test),\n",
+        "    callbacks=[early_stopping, model_checkpoint],\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KNiOp7rYBDWO",
+        "outputId": "65c8270b-7a3a-40d6-b31d-b2e763ac9274"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m400s\u001b[0m 318ms/step - accuracy: 0.9686 - loss: 0.6460 - val_accuracy: 0.9856 - val_loss: 0.1218\n",
+            "Epoch 2/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m441s\u001b[0m 317ms/step - accuracy: 0.9874 - loss: 0.1066 - val_accuracy: 0.9914 - val_loss: 0.0783\n",
+            "Epoch 3/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m448s\u001b[0m 322ms/step - accuracy: 0.9923 - loss: 0.0677 - val_accuracy: 0.9943 - val_loss: 0.0551\n",
+            "Epoch 4/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m441s\u001b[0m 321ms/step - accuracy: 0.9949 - loss: 0.0461 - val_accuracy: 0.9959 - val_loss: 0.0414\n",
+            "Epoch 5/5\n",
+            "\u001b[1m1251/1251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m442s\u001b[0m 321ms/step - accuracy: 0.9964 - loss: 0.0313 - val_accuracy: 0.9968 - val_loss: 0.0328\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ENCODER-DECODER MODEL WITH ATTENTION EVALUATION"
+      ],
+      "metadata": {
+        "id": "ql0T8R0vlSSW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['accuracy'],color = 'blue',label = 'accuracy')\n",
+        "plt.plot(history.history['val_accuracy'],color = 'red',label = 'val_accuracy')\n",
+        "plt.title(\"Training And Validation Accuracy Score\")\n",
+        "plt.xlabel(\"accuracy\")\n",
+        "plt.ylabel(\"epochs\")\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "FCjEVjWYBH4k",
+        "outputId": "608ac3fc-cf2f-47a2-f846-acb7e9781566"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(history.history['loss'],color = 'blue',label = 'loss')\n",
+        "plt.plot(history.history['val_loss'],color = 'red',label = 'val_loss')\n",
+        "plt.title(\"Training And Validation Loss Score\")\n",
+        "plt.xlabel(\"loss\")\n",
+        "plt.ylabel(\"epochs\")\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "uVWlk5jMBMiU",
+        "outputId": "7ed3d680-5ac8-475d-9a77-f3c5530de1db"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "PREDICTION FROM ENCODER-DECODER MODEL WITH ATTENTION"
+      ],
+      "metadata": {
+        "id": "O0KvpV5clcju"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n",
+        "\n",
+        "# Reverse token dictionaries\n",
+        "rev_tok_hindi = {idx: word for word, idx in tok_hindi.word_index.items()}\n",
+        "\n",
+        "# Select a subset of test data\n",
+        "num_samples = 10\n",
+        "x_test_subset = x_test[:num_samples]\n",
+        "y_test_padded_subset = y_test[:num_samples]\n",
+        "\n",
+        "# Predict\n",
+        "predictions = model.predict([x_test_subset, y_test_padded_subset], batch_size=16)\n",
+        "print('Shape of predictions:', predictions.shape)\n",
+        "\n",
+        "# Convert predictions to token indices\n",
+        "predicted_tokens_np = np.argmax(predictions, axis=-1)\n",
+        "print('Shape of predicted_tokens:', predicted_tokens_np.shape)\n",
+        "\n",
+        "# Map indices to tokens and remove 'start' and 'end' tokens\n",
+        "predicted_sentences = []\n",
+        "for sample in predicted_tokens_np:\n",
+        "    sentence = ' '.join([rev_tok_hindi.get(token, '<unknown>') for token in sample if token != 0 and token not in [tok_hindi.word_index.get('start'), tok_hindi.word_index.get('end')]])  # Exclude padding, 'start', and 'end' tokens\n",
+        "    predicted_sentences.append(sentence)\n",
+        "\n",
+        "# Reverse token dictionary for English (assuming you have `tok_english` for English tokens)\n",
+        "rev_tok_english = {idx: word for word, idx in tok.word_index.items()}\n",
+        "\n",
+        "# Map the English test inputs to their corresponding sentences\n",
+        "english_sentences = []\n",
+        "for sample in x_test_subset:\n",
+        "    sentence = ' '.join([rev_tok_english.get(token, '<unknown>') for token in sample if token != 0])  # Exclude padding token\n",
+        "    english_sentences.append(sentence)\n",
+        "\n",
+        "# Print English sentence followed by predicted Hindi sentence\n",
+        "for idx, (eng_sentence, hin_sentence) in enumerate(zip(english_sentences, predicted_sentences)):\n",
+        "    print(f'English sentence {idx + 1}: {eng_sentence}')\n",
+        "    print(f'Predicted Hindi translation {idx + 1}: {hin_sentence}\\n')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bbeuRSgjBTQ_",
+        "outputId": "4b71a613-12b7-4228-ea2e-e25c5cfca010"
+      },
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297ms/step\n",
+            "Shape of predictions: (10, 260, 24546)\n",
+            "Shape of predicted_tokens: (10, 260)\n",
+            "English sentence 1: islams golden age began in the rule of abbasioan\n",
+            "Predicted Hindi translation 1: अंगरक्षक के राज में इस्लाम का स्वर्ण युग शुरु हुआ।\n",
+            "\n",
+            "English sentence 2: even of salinas\n",
+            "Predicted Hindi translation 2: घ् तक की\n",
+            "\n",
+            "English sentence 3: adhikashh poetic creation he has chosen post\n",
+            "Predicted Hindi translation 3: श्रेणीऊर्जा के कायस्थ उन्होंने पद ही चुने हैं।\n",
+            "\n",
+            "English sentence 4: was i had this really\n",
+            "Predicted Hindi translation 4: ये थी कि मुझे एक सचमुच\n",
+            "\n",
+            "English sentence 5: and transform it\n",
+            "Predicted Hindi translation 5: और करते है\n",
+            "\n",
+            "English sentence 6: if they really had technology assessment\n",
+            "Predicted Hindi translation 6: अगर उनके पास तकनिकी सहायता होती\n",
+            "\n",
+            "English sentence 7: included in secondary pollutant\n",
+            "Predicted Hindi translation 7: फिश प्रोटीन में शामिल है\n",
+            "\n",
+            "English sentence 8: cut down on sweets chips and soda\n",
+            "Predicted Hindi translation 8: फिश चीजें और का प्रयोग कम करना होगा\n",
+            "\n",
+            "English sentence 9: pranithas mother was a woman in prostitution\n",
+            "Predicted Hindi translation 9: की मां एक थी\n",
+            "\n",
+            "English sentence 10: outer origin\n",
+            "Predicted Hindi translation 10: बाहरी कडियाँ\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Saving the best weight"
+      ],
+      "metadata": {
+        "id": "3rwwLJWkllaj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save the entire model\n",
+        "model.save('encoder_decoder_model.h5')\n",
+        "print('Model saved succesfully!!')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EWYBMbWZBUjQ",
+        "outputId": "258b6980-c661-4097-962a-d42ceb1e98a0"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model saved succesfully!!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "z6MtRA0HL-sW"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file