essay_score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6076d257",
   "metadata": {},
   "source": [
    "# Task :- \n",
    "## Automatic essay scoring based on the content of the essay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ff0ff4",
   "metadata": {},
   "source": [
    "# Lets import some libraries required "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f90e7382",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd #data manipulation\n",
    "import numpy as np #numerical computation\n",
    "import seaborn as sns #data visualization \n",
    "print(\"Setup complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa813857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(\"train.csv\")\n",
    "print(\"train set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7820087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test set\n"
     ]
    }
   ],
   "source": [
    "data_test=pd.read_csv('test.csv')\n",
    "print('test set')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495e61df",
   "metadata": {},
   "source": [
    "# Lets check if the dataset is correctly imported or not by looking at top 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a0a4758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "      <th>evaluator_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1_323</td>\n",
       "      <td>At present age, our education system is not go...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_238</td>\n",
       "      <td>I am agree the tightly defined curriculum of o...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_212</td>\n",
       "      <td>I strongly agree with the statement that tight...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1_117</td>\n",
       "      <td>Our education system is nice quitely but i dis...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1_229</td>\n",
       "      <td>i am totally agree with the statement that tig...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  promptId uniqueId  \\\n",
       "0           0         1    1_323   \n",
       "1           1         1    1_238   \n",
       "2           2         1    1_212   \n",
       "3           3         1    1_117   \n",
       "4           4         1    1_229   \n",
       "\n",
       "                                               essay  evaluator_rating  \n",
       "0  At present age, our education system is not go...               3.0  \n",
       "1  I am agree the tightly defined curriculum of o...               4.0  \n",
       "2  I strongly agree with the statement that tight...               2.0  \n",
       "3  Our education system is nice quitely but i dis...               2.0  \n",
       "4  i am totally agree with the statement that tig...               3.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6eb3d26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>Curriculum has been adopted in many schools. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>I strongly agree with the statement ,  The tig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>Imagination and creativity is the most importa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>In our eduction system leaves no room for imag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>I will agree at some what extend, because if w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  promptId uniqueId  \\\n",
       "0           0         1    1_315   \n",
       "1           1         1    1_214   \n",
       "2           2         1    1_196   \n",
       "3           3         1    1_178   \n",
       "4           4         1    1_201   \n",
       "\n",
       "                                               essay  \n",
       "0  Curriculum has been adopted in many schools. T...  \n",
       "1  I strongly agree with the statement ,  The tig...  \n",
       "2  Imagination and creativity is the most importa...  \n",
       "3  In our eduction system leaves no room for imag...  \n",
       "4  I will agree at some what extend, because if w...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1099b851",
   "metadata": {},
   "source": [
    "## promptId is the unique ID for the specific question that the student has to answer\n",
    "## uniqueId is the ID for essay in the dataset \n",
    "### evaluator_rating is the target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b18bb32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "      <th>evaluator_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1_323</td>\n",
       "      <td>At present age, our education system is not go...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1_238</td>\n",
       "      <td>I am agree the tightly defined curriculum of o...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1_212</td>\n",
       "      <td>I strongly agree with the statement that tight...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1_117</td>\n",
       "      <td>Our education system is nice quitely but i dis...</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1_229</td>\n",
       "      <td>i am totally agree with the statement that tig...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   promptId uniqueId                                              essay  \\\n",
       "0         1    1_323  At present age, our education system is not go...   \n",
       "1         1    1_238  I am agree the tightly defined curriculum of o...   \n",
       "2         1    1_212  I strongly agree with the statement that tight...   \n",
       "3         1    1_117  Our education system is nice quitely but i dis...   \n",
       "4         1    1_229  i am totally agree with the statement that tig...   \n",
       "\n",
       "   evaluator_rating  \n",
       "0               3.0  \n",
       "1               4.0  \n",
       "2               2.0  \n",
       "3               2.0  \n",
       "4               3.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets drop Unnamed:0 \n",
    "\n",
    "data=data.drop('Unnamed: 0',axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2895f00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>Curriculum has been adopted in many schools. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>I strongly agree with the statement ,  The tig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>Imagination and creativity is the most importa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>In our eduction system leaves no room for imag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>I will agree at some what extend, because if w...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   promptId uniqueId                                              essay\n",
       "0         1    1_315  Curriculum has been adopted in many schools. T...\n",
       "1         1    1_214  I strongly agree with the statement ,  The tig...\n",
       "2         1    1_196  Imagination and creativity is the most importa...\n",
       "3         1    1_178  In our eduction system leaves no room for imag...\n",
       "4         1    1_201  I will agree at some what extend, because if w..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test=data_test.drop('Unnamed: 0',axis=1)\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55f35065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1240 entries, 0 to 1239\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   promptId          1240 non-null   int64  \n",
      " 1   uniqueId          1240 non-null   object \n",
      " 2   essay             1240 non-null   object \n",
      " 3   evaluator_rating  1240 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 38.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca6f5f95",
   "metadata": {},
   "source": [
    "# Bag of words on essay column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53165a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk #nlp library\n",
    "import re #text preprocessing\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.stem import WordNetLemmatizer #either use stemming or lemmatizer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0db03d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    wordnet=WordNetLemmatizer()\n",
    "    text=text.lower() #lowering all the words in the text\n",
    "    text=re.sub('[^a-zA-Z]','',text) # substituting all characters other than A-Z and a-z with space\n",
    "    text=[wordnet.lemmatize(word) for word in text]\n",
    "    text=''.join(text).lower()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ce67ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['essay']=data['essay'].apply(lambda x:clean_text(x)) #train set\n",
    "data_test['essay']=data_test['essay'].apply(lambda x:clean_text(x)) #test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49bbac9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv=CountVectorizer()\n",
    "vecs=cv.fit_transform(data['essay']).toarray() #train\n",
    "vec_test=cv.fit_transform(data_test['essay']).toarray() #test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f20a7bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0     1     2     3     4     5     6     7     8     9     ...  1230  \\\n",
      "0        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "1        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "2        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "3        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "4        0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
      "1235     0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "1236     0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "1237     0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "1238     0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "1239     0     0     0     0     0     0     0     0     0     0  ...     0   \n",
      "\n",
      "      1231  1232  1233  1234  1235  1236  1237  1238  1239  \n",
      "0        0     0     0     0     0     0     0     0     0  \n",
      "1        0     0     0     0     0     0     0     0     0  \n",
      "2        0     0     0     0     0     0     0     0     0  \n",
      "3        0     0     0     0     0     0     0     0     0  \n",
      "4        0     0     0     0     0     0     0     0     0  \n",
      "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
      "1235     0     0     0     0     0     0     0     0     0  \n",
      "1236     0     0     0     0     0     0     0     0     0  \n",
      "1237     0     0     0     0     0     0     0     0     0  \n",
      "1238     0     0     0     0     0     0     0     0     0  \n",
      "1239     0     0     0     0     0     0     0     0     0  \n",
      "\n",
      "[1240 rows x 1240 columns]\n"
     ]
    }
   ],
   "source": [
    "vecs=pd.DataFrame(vecs)\n",
    "print(vecs)\n",
    "vec_test=pd.DataFrame(vec_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5b97a1fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      promptId uniqueId                                              essay  \\\n",
      "0            1    1_323  atpresentageoureducationsystemisnotgoodbecause...   \n",
      "1            1    1_238  iamagreethetightlydefinedcurriculumofoureducat...   \n",
      "2            1    1_212  istronglyagreewiththestatementthattightschedul...   \n",
      "3            1    1_117  oureducationsystemisnicequitelybutidisagreebec...   \n",
      "4            1    1_229  iamtotallyagreewiththestatementthattightlydefi...   \n",
      "...        ...      ...                                                ...   \n",
      "1235         5    5_419  theentireworldisintheraceofproducingarmsandwea...   \n",
      "1236         5    5_420  theraceinthedevelopmentofweaponsareproceedingd...   \n",
      "1237         5    5_421  inanerawhereeverysecondpersonhopesandworkstowa...   \n",
      "1238         5    5_422  introductionsincethebeginningofthetimeofthehum...   \n",
      "1239         5    5_423  toconqueranationfirstdisarmitscitizensadolfhit...   \n",
      "\n",
      "      evaluator_rating  0  1  2  3  4  5  ...  1230  1231  1232  1233  1234  \\\n",
      "0                  3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1                  4.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "2                  2.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "3                  2.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "4                  3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "...                ... .. .. .. .. .. ..  ...   ...   ...   ...   ...   ...   \n",
      "1235               3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1236               2.5  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1237               4.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1238               3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1239               4.5  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "\n",
      "      1235  1236  1237  1238  1239  \n",
      "0        0     0     0     0     0  \n",
      "1        0     0     0     0     0  \n",
      "2        0     0     0     0     0  \n",
      "3        0     0     0     0     0  \n",
      "4        0     0     0     0     0  \n",
      "...    ...   ...   ...   ...   ...  \n",
      "1235     0     0     0     0     0  \n",
      "1236     0     0     0     0     0  \n",
      "1237     0     0     0     0     0  \n",
      "1238     0     0     0     0     0  \n",
      "1239     0     0     0     0     0  \n",
      "\n",
      "[1240 rows x 1244 columns]\n"
     ]
    }
   ],
   "source": [
    "data_train=pd.concat([data,vecs],axis=1)\n",
    "print(data_train)\n",
    "data_test=pd.concat([data_test,vec_test],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0ee9aa",
   "metadata": {},
   "source": [
    "# independent and dependent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "afc0b687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.0\n",
      "1       4.0\n",
      "2       2.0\n",
      "3       2.0\n",
      "4       3.0\n",
      "       ... \n",
      "1235    3.0\n",
      "1236    2.5\n",
      "1237    4.0\n",
      "1238    3.0\n",
      "1239    4.5\n",
      "Name: evaluator_rating, Length: 1240, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "columns=['evaluator_rating','essay']\n",
    "x= data_train.drop(columns,axis=1)\n",
    "y= data_train['evaluator_rating']\n",
    "print(y)\n",
    "data_test=data_test.drop('essay',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "609531b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>295</th>\n",
       "      <th>296</th>\n",
       "      <th>297</th>\n",
       "      <th>298</th>\n",
       "      <th>299</th>\n",
       "      <th>300</th>\n",
       "      <th>301</th>\n",
       "      <th>302</th>\n",
       "      <th>303</th>\n",
       "      <th>304</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5</td>\n",
       "      <td>5_146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>5</td>\n",
       "      <td>5_65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>5</td>\n",
       "      <td>5_151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>5</td>\n",
       "      <td>5_404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5</td>\n",
       "      <td>5_360</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 307 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     promptId uniqueId  0  1  2  3  4  5  6  7  ...  295  296  297  298  299  \\\n",
       "0           1    1_315  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "1           1    1_214  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "2           1    1_196  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "3           1    1_178  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "4           1    1_201  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "..        ...      ... .. .. .. .. .. .. .. ..  ...  ...  ...  ...  ...  ...   \n",
       "300         5    5_146  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "301         5     5_65  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "302         5    5_151  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "303         5    5_404  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "304         5    5_360  0  0  0  0  0  0  0  0  ...    0    0    0    0    0   \n",
       "\n",
       "     300  301  302  303  304  \n",
       "0      0    0    0    0    0  \n",
       "1      0    0    0    0    0  \n",
       "2      0    0    0    0    0  \n",
       "3      0    0    0    0    0  \n",
       "4      0    0    0    0    0  \n",
       "..   ...  ...  ...  ...  ...  \n",
       "300    0    0    0    0    0  \n",
       "301    0    0    0    0    0  \n",
       "302    0    0    0    0    0  \n",
       "303    0    0    0    0    0  \n",
       "304    0    0    0    0    0  \n",
       "\n",
       "[305 rows x 307 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee9619c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1240, 1242), (305, 307))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape,data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839a9e35",
   "metadata": {},
   "source": [
    "# Observe we have mismatch in the train and test shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c0c0291",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_diff=set(x)-set(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b401fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>1230</th>\n",
       "      <th>1231</th>\n",
       "      <th>1232</th>\n",
       "      <th>1233</th>\n",
       "      <th>1234</th>\n",
       "      <th>1235</th>\n",
       "      <th>1236</th>\n",
       "      <th>1237</th>\n",
       "      <th>1238</th>\n",
       "      <th>1239</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5</td>\n",
       "      <td>5_146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>5</td>\n",
       "      <td>5_65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>5</td>\n",
       "      <td>5_151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>5</td>\n",
       "      <td>5_404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5</td>\n",
       "      <td>5_360</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 1242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     promptId uniqueId  0  1  2  3  4  5  6  7  ...  1230  1231  1232  1233  \\\n",
       "0           1    1_315  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "1           1    1_214  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "2           1    1_196  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "3           1    1_178  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "4           1    1_201  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "..        ...      ... .. .. .. .. .. .. .. ..  ...   ...   ...   ...   ...   \n",
       "300         5    5_146  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "301         5     5_65  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "302         5    5_151  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "303         5    5_404  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "304         5    5_360  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "     1234  1235  1236  1237  1238  1239  \n",
       "0     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "..    ...   ...   ...   ...   ...   ...  \n",
       "300   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "301   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "302   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "303   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "304   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[305 rows x 1242 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_difference_df = pd.DataFrame(data=np.zeros((data_test.shape[0], len(features_diff))),columns=list(features_diff))\n",
    "data_test=data_test.join(feature_difference_df)\n",
    "data_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c150b02",
   "metadata": {},
   "source": [
    "# Lets build a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9110b081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr=LinearRegression()\n",
    "lr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b4696c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=lr.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2aaf4011",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(lr,open('bagofwords.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3c4315e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     predicted_score\n",
      "0           2.987726\n",
      "1           2.951872\n",
      "2           2.998296\n",
      "3          -0.045243\n",
      "4           4.498698\n",
      "..               ...\n",
      "300         2.997396\n",
      "301         3.982436\n",
      "302         2.062256\n",
      "303         0.002845\n",
      "304         2.546826\n",
      "\n",
      "[305 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "ypred=pd.DataFrame(ypred,columns=['predicted_score'])\n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4bfc0b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>Curriculum has been adopted in many schools. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>I strongly agree with the statement ,  The tig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>Imagination and creativity is the most importa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>In our eduction system leaves no room for imag...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>I will agree at some what extend, because if w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>5_146</td>\n",
       "      <td>Earth is a creation of God and everything that...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>5_65</td>\n",
       "      <td>production of arms and weapons in this present...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>5</td>\n",
       "      <td>5_151</td>\n",
       "      <td>Race to become more powerful can destroy the e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>303</td>\n",
       "      <td>5</td>\n",
       "      <td>5_404</td>\n",
       "      <td>In its attempt to harness the power of the ato...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>304</td>\n",
       "      <td>5</td>\n",
       "      <td>5_360</td>\n",
       "      <td>Racein the production of arms and weapons in t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  promptId uniqueId  \\\n",
       "0             0         1    1_315   \n",
       "1             1         1    1_214   \n",
       "2             2         1    1_196   \n",
       "3             3         1    1_178   \n",
       "4             4         1    1_201   \n",
       "..          ...       ...      ...   \n",
       "300         300         5    5_146   \n",
       "301         301         5     5_65   \n",
       "302         302         5    5_151   \n",
       "303         303         5    5_404   \n",
       "304         304         5    5_360   \n",
       "\n",
       "                                                 essay  \n",
       "0    Curriculum has been adopted in many schools. T...  \n",
       "1    I strongly agree with the statement ,  The tig...  \n",
       "2    Imagination and creativity is the most importa...  \n",
       "3    In our eduction system leaves no room for imag...  \n",
       "4    I will agree at some what extend, because if w...  \n",
       "..                                                 ...  \n",
       "300  Earth is a creation of God and everything that...  \n",
       "301  production of arms and weapons in this present...  \n",
       "302  Race to become more powerful can destroy the e...  \n",
       "303  In its attempt to harness the power of the ato...  \n",
       "304  Racein the production of arms and weapons in t...  \n",
       "\n",
       "[305 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.read_csv('test.csv')\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaddd938",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>essay</th>\n",
       "      <th>predicted_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>Curriculum has been adopted in many schools. T...</td>\n",
       "      <td>2.987726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>I strongly agree with the statement ,  The tig...</td>\n",
       "      <td>2.951872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>Imagination and creativity is the most importa...</td>\n",
       "      <td>2.998296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>In our eduction system leaves no room for imag...</td>\n",
       "      <td>-0.045243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>I will agree at some what extend, because if w...</td>\n",
       "      <td>4.498698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>5_146</td>\n",
       "      <td>Earth is a creation of God and everything that...</td>\n",
       "      <td>2.997396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>5</td>\n",
       "      <td>5_65</td>\n",
       "      <td>production of arms and weapons in this present...</td>\n",
       "      <td>3.982436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>5</td>\n",
       "      <td>5_151</td>\n",
       "      <td>Race to become more powerful can destroy the e...</td>\n",
       "      <td>2.062256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>303</td>\n",
       "      <td>5</td>\n",
       "      <td>5_404</td>\n",
       "      <td>In its attempt to harness the power of the ato...</td>\n",
       "      <td>0.002845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>304</td>\n",
       "      <td>5</td>\n",
       "      <td>5_360</td>\n",
       "      <td>Racein the production of arms and weapons in t...</td>\n",
       "      <td>2.546826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  promptId uniqueId  \\\n",
       "0             0         1    1_315   \n",
       "1             1         1    1_214   \n",
       "2             2         1    1_196   \n",
       "3             3         1    1_178   \n",
       "4             4         1    1_201   \n",
       "..          ...       ...      ...   \n",
       "300         300         5    5_146   \n",
       "301         301         5     5_65   \n",
       "302         302         5    5_151   \n",
       "303         303         5    5_404   \n",
       "304         304         5    5_360   \n",
       "\n",
       "                                                 essay  predicted_score  \n",
       "0    Curriculum has been adopted in many schools. T...         2.987726  \n",
       "1    I strongly agree with the statement ,  The tig...         2.951872  \n",
       "2    Imagination and creativity is the most importa...         2.998296  \n",
       "3    In our eduction system leaves no room for imag...        -0.045243  \n",
       "4    I will agree at some what extend, because if w...         4.498698  \n",
       "..                                                 ...              ...  \n",
       "300  Earth is a creation of God and everything that...         2.997396  \n",
       "301  production of arms and weapons in this present...         3.982436  \n",
       "302  Race to become more powerful can destroy the e...         2.062256  \n",
       "303  In its attempt to harness the power of the ato...         0.002845  \n",
       "304  Racein the production of arms and weapons in t...         2.546826  \n",
       "\n",
       "[305 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_prediction=pd.concat([test,ypred],axis=1)\n",
    "test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "224a6c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prediction.to_csv(\"test_prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0526ee89",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_ontrain=lr.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5c0059cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7291098964415303e-25"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as mae\n",
    "\n",
    "mae(ypred_ontrain,y) #on train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "96cb62ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('test.csv')\n",
    "test=test.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a91cd1bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test['essay']=test['essay'].apply(lambda x:clean_text(x)) #test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ce0e41",
   "metadata": {},
   "source": [
    "# TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b6e6ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfid=TfidfVectorizer()\n",
    "vecs=cv.fit_transform(data['essay']).toarray() #train\n",
    "vec_test=cv.fit_transform(test['essay']).toarray() #test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b15bb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vecs=pd.DataFrame(vecs)\n",
    "vec_test=pd.DataFrame(vec_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "577e4730",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      promptId uniqueId                                              essay  \\\n",
      "0            1    1_323  atpresentageoureducationsystemisnotgoodbecause...   \n",
      "1            1    1_238  iamagreethetightlydefinedcurriculumofoureducat...   \n",
      "2            1    1_212  istronglyagreewiththestatementthattightschedul...   \n",
      "3            1    1_117  oureducationsystemisnicequitelybutidisagreebec...   \n",
      "4            1    1_229  iamtotallyagreewiththestatementthattightlydefi...   \n",
      "...        ...      ...                                                ...   \n",
      "1235         5    5_419  theentireworldisintheraceofproducingarmsandwea...   \n",
      "1236         5    5_420  theraceinthedevelopmentofweaponsareproceedingd...   \n",
      "1237         5    5_421  inanerawhereeverysecondpersonhopesandworkstowa...   \n",
      "1238         5    5_422  introductionsincethebeginningofthetimeofthehum...   \n",
      "1239         5    5_423  toconqueranationfirstdisarmitscitizensadolfhit...   \n",
      "\n",
      "      evaluator_rating  0  1  2  3  4  5  ...  1230  1231  1232  1233  1234  \\\n",
      "0                  3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1                  4.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "2                  2.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "3                  2.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "4                  3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "...                ... .. .. .. .. .. ..  ...   ...   ...   ...   ...   ...   \n",
      "1235               3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1236               2.5  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1237               4.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1238               3.0  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "1239               4.5  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
      "\n",
      "      1235  1236  1237  1238  1239  \n",
      "0        0     0     0     0     0  \n",
      "1        0     0     0     0     0  \n",
      "2        0     0     0     0     0  \n",
      "3        0     0     0     0     0  \n",
      "4        0     0     0     0     0  \n",
      "...    ...   ...   ...   ...   ...  \n",
      "1235     0     0     0     0     0  \n",
      "1236     0     0     0     0     0  \n",
      "1237     0     0     0     0     0  \n",
      "1238     0     0     0     0     0  \n",
      "1239     0     0     0     0     0  \n",
      "\n",
      "[1240 rows x 1244 columns]\n"
     ]
    }
   ],
   "source": [
    "data_train=pd.concat([data,vecs],axis=1)\n",
    "print(data_train)\n",
    "test=pd.concat([test,vec_test],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8dac9a3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.0\n",
      "1       4.0\n",
      "2       2.0\n",
      "3       2.0\n",
      "4       3.0\n",
      "       ... \n",
      "1235    3.0\n",
      "1236    2.5\n",
      "1237    4.0\n",
      "1238    3.0\n",
      "1239    4.5\n",
      "Name: evaluator_rating, Length: 1240, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "columns=['evaluator_rating','essay']\n",
    "x= data_train.drop(columns,axis=1)\n",
    "y= data_train['evaluator_rating']\n",
    "print(y)\n",
    "test=test.drop('essay',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "714c5ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1240, 1242), (305, 307))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape,test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7dad5d",
   "metadata": {},
   "source": [
    "# Shape mismatch is observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2c985454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>promptId</th>\n",
       "      <th>uniqueId</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>1230</th>\n",
       "      <th>1231</th>\n",
       "      <th>1232</th>\n",
       "      <th>1233</th>\n",
       "      <th>1234</th>\n",
       "      <th>1235</th>\n",
       "      <th>1236</th>\n",
       "      <th>1237</th>\n",
       "      <th>1238</th>\n",
       "      <th>1239</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1_315</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1_214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1_196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1_178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1_201</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>5</td>\n",
       "      <td>5_146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>5</td>\n",
       "      <td>5_65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>5</td>\n",
       "      <td>5_151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>5</td>\n",
       "      <td>5_404</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5</td>\n",
       "      <td>5_360</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 1242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     promptId uniqueId  0  1  2  3  4  5  6  7  ...  1230  1231  1232  1233  \\\n",
       "0           1    1_315  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "1           1    1_214  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "2           1    1_196  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "3           1    1_178  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "4           1    1_201  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "..        ...      ... .. .. .. .. .. .. .. ..  ...   ...   ...   ...   ...   \n",
       "300         5    5_146  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "301         5     5_65  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "302         5    5_151  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "303         5    5_404  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "304         5    5_360  0  0  0  0  0  0  0  0  ...   0.0   0.0   0.0   0.0   \n",
       "\n",
       "     1234  1235  1236  1237  1238  1239  \n",
       "0     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "..    ...   ...   ...   ...   ...   ...  \n",
       "300   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "301   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "302   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "303   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "304   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[305 rows x 1242 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_diff=set(x)-set(test)\n",
    "feature_difference_df = pd.DataFrame(data=np.zeros((test.shape[0], len(features_diff))),columns=list(features_diff))\n",
    "test=test.join(feature_difference_df)\n",
    "data_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247f1634",
   "metadata": {},
   "source": [
    "# Lets build a model of linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2961d552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lrs=LinearRegression()\n",
    "lrs.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4294fa47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7291098964415303e-25"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred_tfidf_test=lrs.predict(test)\n",
    "ypred_tfidf_train=lrs.predict(x)\n",
    "mae(ypred_tfidf_train,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f9a09966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.021527573194211"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred_tfidf_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "52eaf40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_tfidf_test=pd.DataFrame(ypred_tfidf_test,columns=['predicted_score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2753a3a",
   "metadata": {},
   "source": [
    "# Similar value is seen in both TFIDF and Bag of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ef2dc33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'scores')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(ypred)\n",
    "plt.title(\"predicted score\")\n",
    "plt.ylabel(\"scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bff436ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'scores')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAACSCAYAAABPNdH2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAr60lEQVR4nO2deXgcxZm430+yLNvybcu3jWxjToM5jI0xECAQX5gjIVw5ICQ/Akuu3SQQkoUlBGJvdgNslmzIAUkgHAGySUhIAmQhAcJpY4MPfEvGh2TLli1LtiRbUv3+6J5Rz0x3T3fPdE/PuN7n0aOe7q6qr6q++rq6qr4uUUqh0Wg0mtKjrNACaDQajSYctIHXaDSaEkUbeI1GoylRtIHXaDSaEkUbeI1GoylRtIHXaDSaEqXkDLyI1InI+ebxN0XkZxGkeY6IbA07nVJFRFaJyDmFlqOUEJEHROS2fN+bJZ4aEVEi0ivXuFzSyIusPtMMPV9hUXQC+0Ep9V0v94nIL4CtSql/DVei0kJEaoBaoEIp1Rk0HqXU8XkTygPmw+RXSqlxUaYbJUqpG8K4NxdEpA74nFLqr0HjiErWUiHWPfhifGJGTdzLKO7ylSIiUl5oGYKgdSUElFKR/gF1wK3AamAP8HOgj3ntHGArcAvQADyC8RD6BrAR2A08CQy1xPcpYLN57Vtm/Oeb1+7A6Kkl7j0TeA3YC2wBrgWuBw4BB4FW4A/mvWOA3wCNGL3UL1ni6Qv8wpR/NfB1jDcAu/wKcC+wE2gG3gOmWuL5vil/M/Aq0Ne8dhGwypT1b8CxaWV4ixlXB8ab2OmWvL0LnONSB8eace4107jIcu0XwA+BZ4EW4E1gskM8HwDKLLdWYJZZpv8w89wE3AVMBl4062gX8CgwOC0/1jp7EnjYTH8VMD1A2VYC/2nKuAN4wCzvKqAN6LbIPSbqdhCg3WSrsx8BfwL2A+eb5+6y3HMzUA9sBz5n1tuRlvB3pbXBr5rlWg98xhLPAmAZsA+jDd1huVZjxtvLRv5HzDJvM8v8Zsv9nzXr6WXz3qcw2n8z8DJwfFpePcmalv6VwJK0c/8MPOM3X1Z9dbAzjm0Ro31swtDtWuAToepNARS1DlgJjAeGYhgDa4V1Av+O0UD7Al8B3gDGmed+DDxu3n+cqSxnm9fuMcNnGHhgglmoVwEVwDDgpHSlMX+XAUuB24HewCSzUuaY1xcDr5jyjzfz42Tg55hxDcYwSMcCo81rP8RotGOBcuAMMx9HYTTUC0xZbwY2AL0tZbjcTLuvGX43MN+U/QLzd7WNPBVmXN8083aeWS5HW8qiCZiB8eB4FHjCIW81pDVoU4E7gS+a4fsCR5oyVQLVGI32vjSdsNZZu5mXcmAR8EaAsr0PeMasowHAH4BFVsMQte7n0Ga81FkzMNus/z6kGsK5GAbzeKAfhrF1M/CdwJ1muvOBA8AQy/UTzHROxHh4XuKkDzZt32oYE/c/jPHgTXRurjPrrNKsx+WWMJ5lTUu7n1lmUyzn3gau9Jsvm3zcQY+dcWyLZh73WeptNJaHVyi6UwBlrQNusPyeD2y0FPJBzB69ee594MOW36Mxety9MAzwE5ZrVWZ4OwN/K/BbB5mSSmP+ngl8kHbPrcDPzeNNwFzLtetxNvDnAeswnupllvNlGL2ZaTZhbgOeTLt3G2ZPwCzD6yzXbwEeSYvjOeAam7jPwmjsVlkex+yxmGXxs7T6WeOQtxTFN89dm152NuEuAZal6YS1zv5quXYc0OazbAXjATnZcm4WUGvRs2Iy8F7q7GEnnQYewny4mb+PxN3At6XV6U7gdAfZ7gPuddKHtHuT9Zx2/ySXvA827xmUB1l/BdxuHk/BMPj9/ObLJh930GNnHNsihn3aC3wM82EW9l+hxuC3WI43YwyHJGhUSrVbfh8B/FZE9orIXgyD3wWMNMMl41JK7cd4WtoxHmOYxwtHAGMSaZrpftNMk/R0zTzYopR6Ebgfo7e+Q0R+IiIDgeEYPS07mcZY41RKdZvpjbXcY03/CODjafKeifEwtIt7ixmnVX5r3A2W4wNAf6f8OWCVDREZISJPiMg2EdmH0dCGu4RPT7+P3fisS9lWY/TYllrK4y/m+WLES51twZl0fXW7F2C3Sp00T+qAiMwUkZdEpFFEmoEbcK9LLyTlEZFyEVksIhtNXakzLzml4SirDY9hvMEDXA38Til1wEw3X/lybIumfbrCjLteRJ4VkWMCpOGZQhn48ZbjCRjjgglU2r1bgHlKqcGWvz5KqW0YY27JuESkH8bQix1bMMaC7bBLszYtzQFKqfnm9ZR0zTw4opT6gVLqVIxX5KMwxux3YQxF2Mm0HUNRABARMdPb5iDzFoxeg1XeKqXUYoe4x4uIte4npMXtlfRyczq/yDx3olJqIPBJjF52zriUbRvG62+iPAYppRIN30nuuOKlztzyVI8xxJlgvNONHngMY+hrvFJqEMbchte69KIvVwMXY8wjDMLoPeMjDTeeB4aLyEkYhv4xyzU/+dqP0YFIMMpy7NoWlVLPKaUuwOh8rQF+mod8OVIoA3+TiIwTkaEYPeNfu9z7AHC3iBwBICLVInKxee1p4EIROVNEemOMxTnl6VHgfBG5XER6icgws6LBGG+bZLn3LWCfiNwiIn3NXsVUETnNvP4kcKuIDBGRcRjjzbaIyGlm76ACQzHagS6zN/YQcI+IjDHTmCUilWb8C0Tkw2a4r2JMpr7mkMyvgIUiMseMp4+5Nt9uGeCbphw3i0iFuWRwIfCEUx5caMSYOJuU5b4BGHMle0VkLIYRzpksZftT4F4RGWHeO1ZE5phBdwDDRGRQPuSIgFzr7EngMyJyrNkJuj0HWQYATUqpdhGZgWGQvZLezpzi78B4E+8HeFrq7AWzp/808B8YczMvpKXrNV/LgSvNupgOXGa55tgWRWSkiFwkIlVmHlsxRiNCo1AG/jGMp+km8+8ul3v/C+PJ+ryItGBMuM4EUEqtAm4y46vHWNVi63CklPoAYzz5qxiTiMuBaeblB4HjzFeq3ymlujAa0EkYM927gJ9h9CgAvo3xilxr5uMRF/kHYhibPfSs9vlP89rXgBUYkz1NGJPLZUqptRi93P82014ILFRKHXTI2xaMXs83MYzuFgwjmlG/ZhwXAfPMuP8H+LRSao1LHmwxX2/vBv5hlt3pDrd+GzgFYyLwWeB//ablgFvZ3oIxMfmG+ar/V+BoU+41GGPYm0y5x6RHHCdyrTOl1J+BHwAvYZTJ6+aljgDi/BNwp9kWb8d4eHhlEfCvZpl/zeGehzHqchvGCrU3AsjoxmMYbwdPpQ3t+MnXbRhv3nswdDv5JpClLZZh2J/tGO39Q2a6oSHmJEBk5MPZQaPRBEdEjsVY+VWpcnBQ08SfWDs6aTSa/CAil4pIbxEZgvGm+Adt3EsfbeA1msODz2MMGWzEGPe9sbDiaKIg8iEajUaj0USD7sFrNBpNiaINvEaj0ZQosfp62/Dhw1VNTU2hxdCUKEuXLt2llIrcm1XrtSZM3PQ6Vga+pqaGJUuWFFoMTYkiIo6flAgTrdeaMHHTaz1Eo9FoNCVKSRn4+uY2duxrZ0vTgZzj2nvgIK+sbyQuq4zW72ihuzsesmiiZ21DC9v2ttHSfijnuFZsbaZu1/48SJU7+zs689JeNfbEaogmV2YtejF5XLd4QU5xnXSn8ZmK7398Gh87tbA7u63Y2szC+1/llrnHcOM5Tt9L05Qqf3xvO194bBkAk6qrePGr5wSOa1/7IRbe/yqQexvJB5/42Zss37I3FrKUIiXVgw+D1fX7Ci0CW/cYPZx3t+wtrCCagrC2oSV5vKkxt573gY5Qv23lm+Vap0NFG/gsxGSERnMYo3VQExRt4DWamKOK7vP1mrigDXwWdOPSaDTFijbwGk3M0UM0mqBoA58F3bg0Gk2xog28RhNzdB9DE5SiM/A79rWzu7WDNQ37WLmtmY7OLv743nZbh6Tte9t4fePu5LVdrR3s3NfOrtYO3tu6l6b9xg54u1s7aGhu5+26Jt9OF+/X7wvkgLRuRwuHurp9h/NCa0cnm3f7X07XfqiLjY2tnu5VSvG+zRLSxhajjOub29iz33aHQVe27W2j+UCPM8+GnS0c7Ewtpw07W2k/lLnc76W1O1m9vfDLWoOypsHQpedWNdDa0cm6HS2s2Npse+/bdU18sLtHVxP6lAiT0PkNO1tobOngtY27aO3wvr9Ha0dnSvxeaTvYxSaPOhSELU0HAjl7bdvbxt4D3vSxaf9Bduxrzzi/tqGFzq5u3q/fF8gB0tpe2g91sWFnajl1dGaeA9jZ0s5zqxpS2oVXis7RaeZ3/y/l95B+Few5cIj6+ZkVcsZiw/HptguP47NnTmT6XZm7BNYtXsCpaee9Ol0s3dzEx370Ot+afyz/7+xsewn3sKXpAB+592Wumz2R2xce5zmcV676yRus2Nbs23nki48v44XVO1h711wqe5W73vvzf9Rx5x9X8+vrT2fmpGHJ86fd3VOW5WXCxu/O9yXD7MUvMqRfBctu/wg7W9o5/56XuWrGeBZ99EQAmtsOcf49f+fik8bwX1eenBL2a0++y9ypo7j70hN8pRkHltQ1cdkDr3P0yAGs3dGScu2GD6U6tz23qoHPP7IUMHQ1oU/zpo7izysbAPjOJVOZe/wozr/n5WS4E8cN4qefnu5Jnst+9BprGlp869A/PbqUl9Y2suHuefQq995/VEohIlnvO+t7LzFlRH9e+JcP+ZJr9uIXGVDZixXfnpP13lO+Yzg5WvO+efd+5tz3MkeN7M+6Ha0s+ugJXDVjguf0n3l3O196fBn/84lTmH/CaL761Ls8+149K789h/6Vhhn+1m9X8vTSrbxz2wUMreqdDLtiazOff2Qpv79pNtP6DfacJhRhDz6dPeZTbcnmJsd7Vm6z7wV5we1JvaWpzYh/u7/4d7Uaex0v/WBPYLncWBEwv6+u3wVAZ1f23kkiz1v2tDne0xXw0wqJOt3XZvx/s7anbtsOGj33NzbtzginAA82IpZ8YL45pht3O9J7eQl9emH1juS51dv3ZfTY39va7HlOaU1DdjnseMXUIb8176dDvN6ml+uFFh9vMOkkynjdDiPtNT4dINeb9Zqou9c3GvprfRNN6HRre6qcibIJottFb+C90B2zmdJkTyV2chVagnScBbIrOqUU4hKmWMm2VDehT+l6Xl6ACg2q2vFqCZmk58fL24YbdqGdokwkHUS3S8bAuylULnbUS1C/8SeqKa5KHTu5LAK5tati7sHngpM+2ZVF2OWTMEJ+/Ufi8lG/qPGS7UTZHNY9eLdyCqsHn2tjiZtOJw1FTATzW75KufX5ixiP1ZFebWVlmaURVdX6TSfuH0rNt3h+dDuXtEvHwLv14EOKNyjJ19iY9ZVzfe0MC7tSsj3ncaKu1HDKso19D5+AacatLUSFXb7Tz8V6DF5EykVkmYj8MdyUnBUklx5pGIoX1Thx0Hz7CRVmb7/M1GhrGm4lF6WJiE6v3XFbomunZ+EP0Rj4fWuOyUujI5lj8LnGaFM3jtqtslx3Jooe/JeB98NOxE1BusNZbp4zYSt10PhVTMrL91yFinQMPhK9Bvf8d3Y7Tyzb9g5jOkQTfwOfm4COwT2NwRv/g+h2qOvgRWQcsAC4G/iXoPGs2t5MZ5di2vjBjve8tHZnym+rk81fVjUknZrS2bAzcznYKsuyx/0dXazc1kzT/oPMnDSU2l37OWbUQAAOmcsJ3epo1fZmjh45gPfrW5g6diAiYltRrR2dvLRmJxeeONpxmGHdjhbqm9voONTNxsZWevcqY3Df3uze38GeAweZMLQf5WU9z+zfLd9G+6FuJldXMXF4FX9aUc9FJ41l+942po4dlFIGb9ftSS6rSxiG9kNd/G7ZNhZOG8PyLXsZMaCSbgWD+lbw5iZj6WJC1lfWN1IzrCpD5saWDjq7uxk9qC9grGPf3drBkro9VA+s5KiRA9jV0kHT/oMM7FuRDPfA3zdywXEjAdi8+wBrG1qoKBcOms5hjS0dGWm1dHRGYijypdcJZ6bq/pWu921LW4pqNTbPr27giKGZ5b7sgz22Om914Ono7OKvq3dy0oTBCFBRXkb1AHdZEuzv6KRhXzvDqnrT0t7J+KH9AOvwYw8rtxnLM08YNygzIpPV9fuYOnYgb9U20ausjM7ubnqXl3Gwq5uOQ0adHz1qQPL+p5dupe1QFx8+ZgQbG1tpae9kxsShdHR2M3Zw3+R9f15RT62N09/ahha2NB3gjCOH8fe1jRw9agAD+1awfW9qWbcf6uL51TsYblmXDkZvek3DPiYN70/vXkab29TYSme34u26JiZX9+eYUQNYYeb9PXPp8i9eq+OMycOS5bSzpYO9bYc42NmdbHe1u/ZzhKUtJdpl7Aw8cB9wMzDA6QYRuR64HmDChEzHgVXbm1nwA2MHmvuvPjnjeoL0N9Uz//3FlN8J54V0rI4gCRLpgdG4Lvxv4/fE4VXU7trPW9/8MCMG9uHuZ1cDOHq/rmnYx4IfvMqk6io2Ne7n3iumcenJPbtDWY3R1H97DjC8aq+dPdE2vk279qfsWpWNf3ny3Yxzd/zBkPmX183gQ0cZG7Gnl0GiLD/14Ju8XbeHx9/6gHcdPCrB8LT71INv2V5LOD4lnEYuuv9VNnvwkFz85zXJhg0w577MetrU2Mqk6v5Az6YYv3itjjsuOj5r/DlyHznqNcAnzd2MAO65fJpjYs+uqE/5/dtl25LHX3hsGdeeUZMRZk1DS4oeJ7j4h/9IHt/z/Dp+/PKmlOteHZuueegtlmzew6C+FTS3HcoIZ30IJdqPW9wf+9FrntJN8LWnDN2+zeZaIp1X1jdy46Pv2IZP6NMRw/q56uMlP/wHaxpamDKif8r51fXNzL2vlqtnTuC7pmPded//e1a5m/Yf5LIHXmeE+SBNlI2Va3/+dkpZff3p94CYDdGIyIXATqXUUrf7lFI/UUpNV0pNr66uzrje0NzT4/Czm83+g/nZuaZ/n55nYK25j2XCYSLhkOPkAr1jn9HLTMid7qBi19l8vz6Yg4lfPnD5JEOicb5dZzhiuRl3gJZ27w4kXox7z73u9b3b0kPd2ZLpyRwG+dJrCL6b0ca0drDe5i3UC7nsprRks6EbzW2p7vMJIxSHVTFe9p3Npo8Jh69056p60y69szkcZ0U74jbJOhu4SETqgCeA80TkV34jsfZy47I+IqgDSUL53ddyR9My3PIQh8YJ2SfqUnUjMu3Ii17nkzg5d4nviZPiJNcSD2JCgqQZmoFXSt2qlBqnlKoBrgReVEp90nc8eZcsd8pCnMmLarLJ7TMhfieUwpLZz4MmqsnVfOl1qROLZY8hKoUkV3gFDB/AXMetB58X4uJ0YyW9oJ0kTK+PRLikt59N3qLKrdt68biUeDY54qgbhSCoHQvD/vU4y+U/7jhRiB58kDKN5GuSSqm/AX+LIq18Y2dE7DwEvZAIFQdfHPchmni0zrjI4UQx63VYOH0XpyCEKINK/o9BPl2Ifw/eclwIw2hXfYE9BD1kIKp2UeY6ROM9nkL2ouPdtKIjTO/dKJzlipGce/B5kSI78TfwRaApTpWV3u7Se/C2X0SMqGm4zSPEovdFduMSxwn4QhA0717mkgJ/SC8eKhQauX4QNshChiBJFcGGHz3ZenndrshTT3zf2srvl29nuMU5pW73AVZua046D73zwR5Wbd/H+CF9U8I9vXQrvcqERvPb0mt3tPBWbROvbuhJo6W9k7dqm2jY1860cYNobjvEk0u25D1f976wjuVb9tKvd+bGHrf9biX9entTjdc27k46erjxpxX1nG7ZGMQLiWWaTizbsof+lb1YXd/sa6lmHPnHhszv23vl7+saA4V7bWNmmg++WstZU4Ynfy/ZvIcjhvVj5MA+dHcrHnljMyfaOCzd/+J6oGcJ8bIP9tCtFPvaeuqldtd+Nu5sZdSgPnR2KxqanfcSyIXv/WUN7Ye6+eXrdRnX7nhmla2TnB1uy0gTS1XX72zllfWNTBnh6BJhS4PNjlFWVm1v5lCXon5vbmUkcZqomj59ulqyZEnKuT+vqHd0Vogbf/vaOdQMr6LmG88CcMqEwbzzwd7CCnUY4uRQIyJLlVLetjTKI3Z6DST1pBioW7yAn72yibuejeTrDBobnvvK2SnevAnc9Dr2QzTFRFOaw9Nqn7u+aDRxZp2H3aY04RFk+Db2Bj4+7xf+CXO9vEYTNXFyqNJ4I/4GvogsfMaGC9rAa0oIt5VXmvAJYgtjX2VxX2fqhrbvGo2mkHgy8CIyWUQqzeNzRORLIjI4VMlMiqkHnz6gpO17vCmkXms0fgmzB/8boEtEjgQeBCYCj/lP7vAiqMerJjK0XmuKhjAnWbuVUp3ApcB9Sql/Bkb7Ti0AxdSBz9jWqzBiaLxTML3WaKLAq6PTIRG5CrgGWGieq3C5P2/EaZ1+Nr733FqGWXZ+SXwvXhNbCqbXxcaXn1jG75dvL7QYhzVhfmzsM8ANwN1KqVoRmQgU9BvYceSt2qZCi6Dxh9Zrj2jjXnhGDerjO4ynIRql1GrgFuAd83etUmqx79QCcMqEIVEkoykRPnm6/fZ4dhRSrwHbz0Qc7hw1sn/2mw5ThmfZu9cOr6toFgLLgb+Yv08SkWd8pxaAvroRZDCknx5FyAeF1GsguS+npofB/Xpnv0njGa+TrHcAM4C9AEqp5RgrDkJHT1Rq/OBznPIOCqTXEO5nfjUa8G7gO5VS6TsvF8/sp+awwadSar3WlDReJ1lXisjVQLmITAG+BLwWnlg96F6OJkQKptcaTRR47cF/ETge6MBwBGkGvhKSTClo856Jfug543OIpmB6DcW1BDgqtGbnl6w9eBEpB55RSp0PfCt8kdLTjzpFTXHjzWgWWq9BjwVpwidrD14p1QUcEJHMbVw0mpjhtVMcB73WHXhN2Hgdg28HVojIC8D+xEml1JdCkcqC/gZ1JlecNp4f/W1j5OlOGdGf9TtbI0/XD7Mm+9oWsGB6rbHn49PH82ZEDoOjB/Whvtl967xix+sY/LPAbcDLwFLLX/jkaN8vOG6kr/t/d9NsHr5uRm6Jhsi6u+Zx85yjeejazB26Xr3lXM47ZgQAd158PGu+M5dXbj435Z7/uOzE5HGlzV6q6+6ax03nTrZN+5kvnJmL6JHgc9/Xwuk10W9uvurbcyJNzw+D+law7q55XHbqONvrq+/skX3Vt+ew7q55HDt6YPLc58+elHL/dy6ZmvL7qRtmse6ueSnnrjmjJnn8uTMjWx0bKZ568EqpX4pIb+Ao89RapVQkH1rJdQx+YB9/TkFVvcs51NWdW6Ihktjgutxm94UBlRUMNb+F06einD4V5VRWlNmGT9zT0dmdcb2i3P65XwwbPvhRl0LqdSGoqvT6wh49Irhu3m7dBD6RD+v96Z2V3uWZmpAev/WOCg8bxxcjnmpcRM4BfgnUYZTLeBG5Rin1cmiSJdLOMbzfL/YW9aSu+NtVynfZFMNwmQ8RC6nXoMfgrQQpC7eq9rKbWlG3dY94faR/H/iIUmotgIgcBTwOnBqWYPmiVCvRLlvWvCYO3Q186RWOz4dQ0eq1xr1tl+u9GADvY/AViUYAoJRaR0SfVc11zbd/I1YcimHX4bGT3E3P/W5IUgzPA58yFkyvNbljrer09uCpB18kbT0XvBr4JSLyoLmt2Tki8lOyTEaJyHgReUlE3heRVSLy5SAC5loFQYxSMVd7+q4vbg/IUuzk+MySb72G/Ol21JOspYbViGdstlOCuh0Er0M0NwI3YbhyC8aqg//JEqYT+KpS6h0RGQAsFZEXzE+0eibqiipmxbAa88Sxaw/eZ2aLoWh8vvEF0WvIk25r+95DoI6YJUx6x6YUhx+D4NXA9wL+Syl1DyS9AF2/daqUqgfqzeMWEXkfGAv4agS5U3pGzAn7IZrDbQzeF771GvKn20H22NT04DbEoidZDbwO0fwf0Nfyuy/wV6+JiEgNcDLwps2160VkiYgsaWxszAjb22HJnve0/YeZMLRfTmkWChEyBiOt+T+tJnXzlKtmjLeNZ/aRwx3ij3+L8PnQykmvwVm3s+k1wHWzo197PbSqhL63bqnqM4+sTrnktGfCLAc/ibOm2Ot8sePVevZRSiVdGM1jT1ZQRPpj7F7/FaXUvvTrSqmfKKWmK6WmV1dXZ4TvVV7GD68+xaOYuVMmwoiBmVtjXXry2FDTnTi8Knn8yGeDOVpZezR2q2h+ff2s5PGCE0dz07lHpoRPrDw4rWYoG+5OdQpJsPG78x2vxQJ/z6DAeg3uup1NrwE+/yF7h7Iwue3CYzPO/fwzp4Wa5sUnjQk1/l99dmaKB/OGu+fRL23Nf2Ko8tHPzeTqmZm7fp0xeTgb7p7Hb26clXGtmPFq4PeLSNLKish0oC1bIBGpwGgAjyql/jeYiFBVGd2uTk49wLC3V+tb0RN/n4r8pWXNTlmZJMd9y0UyeuTW8fpeNm9OgvEQsLsWF3y+ZATSa/PevOh21NgNawzsE64DlNVJKZ8k9DXdAc9OPxPtuqxM6GUGTNf/XuVltg6ExYzXkv8y8JSIbMcYBBgDXOEWQIzSexB4PzHGWQw4GYiwRyfyMRorEmy5WE/4+A/BZMNnDnzrNRSvboO9Hoc92RuWWiUeVl7k97ouvvhbQCpeH1cTMcYZbwReANaS3SbNBj4FnCciy82/+YElDYjvVfBOBj7Cqs9HSol8pBt4V+eQLC2xGOy/z4dUEL2GmOh2sRCW2iSq2ouBt+voZHMWLAW89uBvU0o9JSKDgQswPAB/BMx0CqCUepUifCAWamVJPo26lfSOi1tjKIV18T6z4FuvoXh124mw1/KE1aaSBt5DDryKUGrOT1578F3m/wXAA0qp3wMlNB3fQxye4LnIkNglKBGHU4/W9mGQxcIXwxCOTxEPG70uJGF1HPwM0VgfMm7iFIGK+8Krgd8mIj8GLgf+JCKVPsIWFL8V5tTbiLbigyUmSLIvk60nYtcoSmFdvM8eWNHqdT4Jfww+7B68zbW03yler2nh7eIsFbwq8+XAc8BcpdReYCjw9bCEyid+X7mc7g673vM2yZpDRKXwgSafDbRo9VrT8+Dw8skHu4VftmPwh+MQjVLqgFLqf5VS683f9Uqp58MVLV6cd6y/jUNyYXj/nlECJ8cMO6yqmW7oPmGu/T1hnLFD3fwTRmWE/9TpR3gXMqb08vGQ0nptEPbm31ZdnDFxaMo1q/+HXy4x19dPGdEfcPdVqR6Q6dtiR1x78FdMt3dKzEZJvI7WLsrjAgabCp5cXcWHjrJ3VgmSfu2i+RlhrMn2tzhpPH796Z7jd3oVrl00n7svPQGAydX9qV00n7lTR6fc8+UPT+Er50/xlE5cuefyabFeo++Xh6+bwe9vmh1qGq994zzHa9//+DRfuj1mUB9qF81P2TUsnS9/2NCx02qGULd4AT+/NriT1UdPGUftovmMG2L4pt17xUm28tYums+gvt4+EhpXA7/ooycEChffLV4sZOtfuI3x+a0wu1e0bGPTfscY7e5XLtf9xG87HukxvmKYRHUjbGe0QhB2lbi98YgE0+2Mpbk2HtbWNHLBi277yUNch2j8fto7GS7PchQ99ksNo630oKnFUzU1QYlC7dwMR9D0XcNJ+s94aW2R93Ey0AY+jUI5P7iNn3uOo8SU0z+lVwBhG8Bykbytg0/0lNP10LpOPW4GPZ14S+efkjfw+XEgiroHH3CZpEjgCTP9cDg8CfrqH5S461nc5fNLyRt4v9gZ88hXD+YhvWIfTw9CqWVZkLznKV0v3JbGBu5opIXzMgYfZdVlmVGLSIpoKHkD73uSyOacvw92+UoutDh8p1liiq2xJ72Wy0UcfSfC0MNEe4zrblal1kkoeQPvl1y92/IyJJRD2Ji2m0gosbYZzdxPKEY82vTySczF84028Gn0tVlqd8Vp2Z0MPn/2JON/lk0cnHaoutKyu5KIcOK4QRw1sr9jPE7XFp5oOH+cMHaQqxzpxL3hHa6EXS8V5WWePtZ13jEjst7zSQdHucqKHp2v7m/siDh3aqajXYLPzK6xPX/0yAEMdtipKV8M6BNu/FFzWBn4e6+YlvWeyl6ZBv4TM1MVt27xAjZ+N9Wh4tb5x1K3eAG3zD2GusULqFu8IHntqRt6dolZZ7MbUt3iBXx6Vk3ytwC/ufEM/vjFsxzlHD2or63zxtypo6hbvCAnD0E/vHrLuZGkcziST9tupw9fn3N0yhj86ZOGUrd4AReemOoEV7d4AQ9de1pSr7/3McOR6bJTxyXvGVbVmxvPSe3cXHjiaOoWL6DC0qmpGV7F+3fO5XNnGR0iuwfYvy083jYPz/3z2Sy//SNZcpob1jY1M83rthgpCkenXLAqULGMM4uQ0igiSTNguDh9oKwUJ5bzpbOuq6scx+CDzT1lC2f3lpxv3ERwe18pS8lH3sQpGIdVDz6f+Kl7vxNKhXgQBVXmUvhAWWyJuGhz0zt37+xiodQ6CdrAB8SPHvhdm15MOharHnyhBQiBfBWvkxOSHbka5mQSHiKKm0EtK8I3fjdK3sCnrMHNY33FTTELhe7Bh0c+DYxdJyObCvtJXWyGNhKTt8WkIdZ2XQpNvOQNfDESp15xNrLt4xolMRIlLxgf+/J2Xy5kmP4AXfiUT2342GmpEFXmNc1S0Cdt4CPAb3spiKNTwEQlRhpUCg3SSuQO1OkfAvPj/1Fik5OlQoyaZzgUo7IVk8ix6sEXVcl5w0uecs11ek/by7r4IPEWG6WgT0Vh4I8ZNSCSNLxuCuAX36toYmQ0s6HH4HPjutkTHa951YP09ed2WP0snNNL++3DwAX95nucVT3OsnmlKAz86EF9GdgntyX7F00bY9tgqsw1uU/feAbv/lu4ThRe8apXYW+15oVYzRfESBSv3L7wONfrXor363OOcb1et3gB151pPEhSDXHAAvP4OY98vQloglMUBv5wozBj8MHC6R58uERRuumGOEi/IWWSNbGKxtMkq9afMCkaAx+0LxAH9fHbkymmIZo42fcYiZIXolaD9NUv/iZZcxM2jjofR5n8UjQGPijJ701L6RmAfJLLJiOa8Mj/9+Azjx0/Fxw8FSPewOHjQSlodvEY+GLWlmKWvYgotYdNoZdJ5iset85DnKsszrJ5JVQDLyJzRWStiGwQkW/kElfgIZqEizalUWFhocvGO/nUaw+phRu9DUEmR1M/6mfG4yOafC8YyMfYfik0idAMvIiUAz8E5gHHAVeJiPuSgUDp5DtGTbEShSpEpddGWmHEmp0gY/BWet6kshvtODffUngjDLMHPwPYoJTapJQ6CDwBXBw0slyf8IWsLD1CU1LkVa+zkfcxeJtjZ/3MLXE/TTZWy21LiDAN/Fhgi+X3VvNcCiJyvYgsEZEljY2NjpH907lHpvy+9GQjqi+eNwWA848dmbw2ZUTPbkeJzQsuOmkM08YNBlJ3VUrEW9krtSjGD+0LwMJpY1LiTXe6ctp9BmDEgEpmTByaIo+VScOrOH7MQMfwVi49eSxHj0xN+wbTweXjlo0XgnLWlOG25z96ylg+efqEjPNuDjpRMLx/74xzR0fgEEee9Rqgb0Xq99ETu4PVDKtiaL+efFrzfOoRQ1Lq5dozalLi+MTMCfSpKEtpFwAnjOvZ6etMs84TOp3YuSyxkcfUsfa6Of2IIYDRNhL6b9WHhE4n4jn3mGoAJlVnbjrSv08vKsolw1mrqnd5chepa2bZ7xTlRqL92jHvBMMmzJps5N9Ovy+fPo7Lp+fergqNhOUsIyIfB+YopT5n/v4UMEMp9UWnMNOnT1dLliwJlF53t6JLqcg3yogKpRRd3YpeMc1fZ1d3UrbEsfWc2/3d3caor9Oa+q5uhQBl5vVEWKUUnd0q+bmEsixrNkVkqVJqeoDsWeOIVK8BDnZ207tXPOs9H7jpSaHp7OqmvEySIwDZdNvpfrf4E9etbfxgZzcV5eKpzbvpdZg7Om0FrJuZjgO2h5VYWZlQFusRvdwQEXqVxzd/ViVMHLsppvVaNsOcbvgTYUWEiujLJFK9BkrauIO7nhSadNmy6bbT/V7it7bxRJ3n2ubDLNm3gSkiMlFEegNXAs+EmJ5GEwVarzVFQ2g9eKVUp4h8AXgOKAceUkqtCis9jSYKtF5rionQxuCDICKNwGabS8OBXRGLEyU6f9FwhFKqOupEXfQa4lM2YaHzFz6Oeh0rA++EiCzJdXIszuj8Hb6Uetno/BWW+M5uaDQajSYntIHXaDSaEqVYDPxPCi1AyOj8Hb6Uetno/BWQohiD12g0Go1/iqUHr9FoNBqfxN7AR/tp1vAQkToRWSEiy0VkiXluqIi8ICLrzf9DLPffauZ5rYjMKZzk9ojIQyKyU0RWWs75zo+InGqWywYR+YGUwif8PFIKuq31OuZ6rZSK7R+GI8lGYBLQG3gXOK7QcgXMSx0wPO3c94BvmMffAP7dPD7OzGslMNEsg/JC5yFN9rOBU4CVueQHeAuYhfHpwj8D8wqdt4jKryR0W+t1vPU67j34SD/NWgAuBn5pHv8SuMRy/gmlVIdSqhbYgFEWsUEp9TLQlHbaV35EZDQwUCn1ujJaxcOWMKVOKeu21uuY6HXcDbynT7MWCQp4XkSWisj15rmRSql6APP/CPN8sebbb37Gmsfp5w8HirWO09F63UPs9DrMr0nmA7txq2Jd9jNbKbVdREYAL4jIGpd7Synf4JyfUsunH0ol71qvU4mVXse9Bx/5p1nDQim13fy/E/gtxqvpDvN1DvP/TvP2Ys233/xsNY/Tzx8OFGsdp6D1Ot56HXcDXxKfZhWRKhEZkDgGPgKsxMjLNeZt1wC/N4+fAa4UkUoRmQhMwZi0iTu+8mO+7raIyOnmKoNPW8KUOkWv21qvi0CvCz1r7WFWez6wDmOG+luFlidgHiZhzLa/C6xK5AMYBvwfsN78P9QS5ltmntcSw5UlwONAPXAIo8fy2SD5AaZjGIWNwP2YzneHw1+x67bW6/jrtfZk1Wg0mhIl7kM0Go1GowmINvAajUZTomgDr9FoNCWKNvAajUZTomgDr9FoNCWKNvAajUZTomgDr9FoNCWKNvAajUZTovx/xZpVjAZUVXMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "plt.plot(ypred_ontrain)\n",
    "plt.title(\"predicted score on train set\")\n",
    "plt.ylabel(\"scores\")\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(y)\n",
    "plt.title(\"original train values\")\n",
    "plt.ylabel(\"scores\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec13796",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}