diff --git a/Prediction Models/Research topic Prediction/README.md b/Prediction Models/Research topic Prediction/README.md
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+# Research-topic-Prediction
+
+### Problem Statement
+Researchers have access to large online archives of scientific articles. As a consequence, finding relevant articles has become more difficult. Tagging or topic modelling provides a way to give token of identification to research articles which facilitates recommendation and search process.
+
+Given the abstract and title for a set of research articles, predict the topics for each article included in the test set. 
+
+Note that a research article can possibly have more than 1 topic. The research article abstracts and titles are sourced from the following 6 topics: 
+
+1. Computer Science
+
+2. Physics
+
+3. Mathematics
+
+4. Statistics
+
+5. Quantitative Biology
+
+6. Quantitative Finance
+
+## Approach
+
+### Data Preprocessing:
+
+We clean the text data by removing punctuations, converting text to lowercase, and removing unnecessary characters. This helps standardize the text input for better performance during the model training.
+Feature Extraction:
+
+We extract features from the research abstracts and titles using text vectorization techniques. The two main methods used are CountVectorizer (which counts word occurrences) and TF-IDF (Term Frequency-Inverse Document Frequency), which assigns importance to words based on their frequency across documents.
+### Model Selection:
+
+We use a Linear Support Vector Machine (LinearSVC) with a multi-output classification approach. This allows the model to predict multiple topics for each article simultaneously.
+Evaluation:
+
+The model is evaluated using common classification metrics such as precision, recall, F1-score, and accuracy. These metrics give insight into how well the model performs across different research topics.
+### Prediction:
+
+After training, the model predicts the topics for unseen research articles, and the results are formatted for submission. Each prediction shows whether a particular article belongs to one or more of the six topics.
+## Project Highlights
+Multi-label Classification: Each article can be tagged with more than one topic, so the model needs to handle multiple outputs simultaneously.
+Text Processing: Effective text preprocessing and vectorization are key to extracting meaningful features from the research articles’ titles and abstracts.
+### Model Performance:
+ The model demonstrates good performance in most categories, but there is room for improvement, particularly in addressing class imbalances for topics with fewer articles.
+## Future Work
+Future improvements could include addressing the data imbalance, exploring advanced machine learning models such as deep learning techniques, and refining the feature extraction process for better prediction accuracy.
+
+This project provides a foundation for automating topic prediction in research articles, potentially enhancing search engines and recommendation systems in academic databases.
\ No newline at end of file
diff --git a/Prediction Models/Research topic Prediction/Research-topic-Prediction.ipynb b/Prediction Models/Research topic Prediction/Research-topic-Prediction.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "OdBntg3UTJg3"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "train = pd.read_csv('/content/drive/My Drive/ml/train.csv')\n",
+    "test = pd.read_csv('/content/drive/My Drive/ml/test.csv')\n",
+    "subission_pd = pd.read_csv('/content/drive/My Drive/ml/sample_submission_UVKGLZE.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 68
+    },
+    "colab_type": "code",
+    "id": "c8wedvYeT4nm",
+    "outputId": "1a91f831-6556-49e3-dcba-e87c7567241e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape: (20972, 9)\n",
+      "Test shape: (8989, 3)\n",
+      "Sample shape: (8989, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Train shape:',train.shape)\n",
+    "print('Test shape:',test.shape)\n",
+    "print('Sample shape:',subission_pd.shape)\n",
+    "\n",
+    "l = ['Computer Science', 'Physics', 'Mathematics', 'Statistics', 'Quantitative Biology', 'Quantitative Finance']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "_8-A4zRtT8Ss",
+    "outputId": "91bb890b-601a-42ef-cb0c-1e2aaafcba28"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(18874, 2) (2098, 2)\n",
+      "(18874, 6) (2098, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = test.drop(['ID'],axis=1)\n",
+    "\n",
+    "X = train.loc[:,['TITLE','ABSTRACT']]\n",
+    "y = train.loc[:,l]\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42, shuffle=True)\n",
+    "\n",
+    "print(X_train.shape, X_test.shape)\n",
+    "print(y_train.shape, y_test.shape)\n",
+    "\n",
+    "y_test.reset_index(drop=True,inplace=True)\n",
+    "X_test.reset_index(drop=True,inplace=True)\n",
+    "\n",
+    "y1 = np.array(y_train)\n",
+    "y2 = np.array(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ENRy0yNfUaCK"
+   },
+   "outputs": [],
+   "source": [
+    "#Removing Punctuations\n",
+    "\n",
+    "X_train.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "X_test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "\n",
+    "test.replace('[^a-zA-Z]',' ', regex=True, inplace=True)\n",
+    "\n",
+    "#Converting to lower case characters\n",
+    "\n",
+    "for index in X_train.columns:\n",
+    "  X_train[index] = X_train[index].str.lower()\n",
+    "\n",
+    "for index in X_test.columns:\n",
+    "  X_test[index] = X_test[index].str.lower()\n",
+    "\n",
+    "for index in test.columns:\n",
+    "  test[index] = test[index].str.lower()\n",
+    "\n",
+    "#Removing one letter words\n",
+    "\n",
+    "X_train['ABSTRACT'] = X_train['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "X_test['ABSTRACT'] = X_test['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "\n",
+    "test['ABSTRACT'] = test['ABSTRACT'].str.replace(r'\\b\\w\\b', '').str.replace(r'\\s+', ' ')\n",
+    "\n",
+    "#Removing multiple blank spaces\n",
+    "\n",
+    "X_train = X_train.replace('\\s+', ' ', regex=True)\n",
+    "X_test = X_test.replace('\\s+', ' ', regex=True)\n",
+    "\n",
+    "test = test.replace('\\s+', ' ', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 374
+    },
+    "colab_type": "code",
+    "id": "VJD5hEUkUfNY",
+    "outputId": "1b4eff7d-4a42-44a3-8e4e-ae4f2ef4a533"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /root/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>combined</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13275</th>\n",
+       "      <td>clustering in hilbert space of a quantum optim...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19273</th>\n",
+       "      <td>graph heat mixture model learning  graph infer...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6427</th>\n",
+       "      <td>fast and unsupervised methods for multilingual...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19168</th>\n",
+       "      <td>natasha faster non convex stochastic optimizat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14148</th>\n",
+       "      <td>kustaanheimo stiefel transformation with an ar...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                combined\n",
+       "13275  clustering in hilbert space of a quantum optim...\n",
+       "19273  graph heat mixture model learning  graph infer...\n",
+       "6427   fast and unsupervised methods for multilingual...\n",
+       "19168  natasha faster non convex stochastic optimizat...\n",
+       "14148  kustaanheimo stiefel transformation with an ar..."
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('wordnet')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('averaged_perceptron_tagger')\n",
+    "from nltk import sent_tokenize, word_tokenize\n",
+    "from nltk.stem.snowball import SnowballStemmer\n",
+    "from nltk.stem.wordnet import WordNetLemmatizer\n",
+    "from nltk.corpus import stopwords\n",
+    "\n",
+    "\n",
+    "stop_words = set(stopwords.words('english')) \n",
+    "# len(stop_words)\n",
+    "# X_train['ABSTRACT'] = X_train['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "# X_test['ABSTRACT'] = X_test['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "\n",
+    "# test['ABSTRACT'] = test['ABSTRACT'].apply(lambda x: ' '.join(term for term in x.split() if term not in stop_words))\n",
+    "\n",
+    "X_train['combined'] = X_train['TITLE']+' '+X_train['ABSTRACT']\n",
+    "X_test['combined'] = X_test['TITLE']+' '+X_test['ABSTRACT']\n",
+    "\n",
+    "test['combined'] = test['TITLE']+' '+test['ABSTRACT']\n",
+    "\n",
+    "X_train = X_train.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "X_test = X_test.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "\n",
+    "test = test.drop(['TITLE','ABSTRACT'],axis=1)\n",
+    "\n",
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZZpJCI5cYCap"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "X_lines = []\n",
+    "for row in range(0,X.shape[0]):\n",
+    "  X_lines.append(' '.join(str(x) for x in X.iloc[row,:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "aKsTUdeRUrMd"
+   },
+   "outputs": [],
+   "source": [
+    "train_lines = []\n",
+    "for row in range(0,X_train.shape[0]):\n",
+    "  train_lines.append(' '.join(str(x) for x in X_train.iloc[row,:]))\n",
+    "\n",
+    "test_lines = []\n",
+    "for row in range(0,X_test.shape[0]):\n",
+    "  test_lines.append(' '.join(str(x) for x in X_test.iloc[row,:]))\n",
+    "\n",
+    "predtest_lines = []\n",
+    "for row in range(0,test.shape[0]):\n",
+    "  predtest_lines.append(' '.join(str(x) for x in test.iloc[row,:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "jGDABzRWZlOi",
+    "outputId": "a9321f83-b977-43e7-9d7b-243bfeeaa754"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18874"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(train_lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "O05zD3CTUu_x"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "countvector = CountVectorizer(ngram_range=(1,2))\n",
+    "X_train_cv = countvector.fit_transform(train_lines)\n",
+    "X_test_cv = countvector.transform(test_lines)\n",
+    "\n",
+    "test_cv = countvector.transform(predtest_lines)\n",
+    "\n",
+    "#Using TfidfVectorizer\n",
+    "\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
+    "\n",
+    "tfidfvector = TfidfTransformer()\n",
+    "X_train_tf = tfidfvector.fit_transform(X_train_cv)\n",
+    "X_test_tf = tfidfvector.fit_transform(X_test_cv)\n",
+    "\n",
+    "test_tf = tfidfvector.fit_transform(test_cv)\n",
+    "\n",
+    "X_cv = countvector.transform(X_lines)\n",
+    "\n",
+    "X_tf = tfidfvector.fit_transform(X_cv) #x_tf,y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153
+    },
+    "colab_type": "code",
+    "id": "CcnLYLSQV3wk",
+    "outputId": "eec93633-b54c-4efb-9403-19467293f4c0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiOutputClassifier(estimator=LinearSVC(C=0.5, class_weight='balanced',\n",
+       "                                          dual=True, fit_intercept=True,\n",
+       "                                          intercept_scaling=1,\n",
+       "                                          loss='squared_hinge', max_iter=1000,\n",
+       "                                          multi_class='ovr', penalty='l2',\n",
+       "                                          random_state=42, tol=0.0001,\n",
+       "                                          verbose=0),\n",
+       "                      n_jobs=None)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.multioutput import MultiOutputClassifier\n",
+    "\n",
+    "model = LinearSVC(C=0.5, class_weight='balanced', random_state=42)\n",
+    "models = MultiOutputClassifier(model)\n",
+    "\n",
+    "models.fit(X_train_tf, y1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136
+    },
+    "colab_type": "code",
+    "id": "MITfjxaeWGNf",
+    "outputId": "b5b6e7e6-ac08-43ba-ced2-d19bd292fe39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 0, 0, 1, 0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 1, 0, 0, 1, 0],\n",
+       "       [1, 0, 0, 1, 0, 0],\n",
+       "       [0, 1, 0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = models.predict(X_test_tf)\n",
+    "preds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 272
+    },
+    "colab_type": "code",
+    "id": "f09g5ZevWI6e",
+    "outputId": "a1410f03-caed-44f3-dd73-b19e1592f054"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.81      0.91      0.85       853\n",
+      "           1       0.88      0.89      0.88       623\n",
+      "           2       0.84      0.84      0.84       580\n",
+      "           3       0.72      0.86      0.78       516\n",
+      "           4       0.53      0.40      0.46        58\n",
+      "           5       0.86      0.69      0.77        26\n",
+      "\n",
+      "   micro avg       0.81      0.86      0.83      2656\n",
+      "   macro avg       0.77      0.76      0.76      2656\n",
+      "weighted avg       0.81      0.86      0.83      2656\n",
+      " samples avg       0.84      0.89      0.84      2656\n",
+      "\n",
+      "0.6611058150619638\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "\n",
+    "#print(confusion_matrix(y2,preds))\n",
+    "print(classification_report(y2,preds))\n",
+    "print(accuracy_score(y2,preds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hlszaq_ujFje"
+   },
+   "outputs": [],
+   "source": [
+    "# for i in range(100):\n",
+    "#     print(str(y2[i])+str(preds[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "LgQTqGrhjI7w"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136
+    },
+    "colab_type": "code",
+    "id": "mi9nVKeqWK-p",
+    "outputId": "c27fb35b-0f4a-410e-ecdf-1b1b0aae4dae"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0, 1, 0, 0],\n",
+       "       [0, 1, 0, 0, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, 0, 1, 0],\n",
+       "       [0, 0, 0, 1, 0, 0],\n",
+       "       [1, 0, 0, 0, 0, 0]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predssv = models.predict(test_tf)\n",
+    "predssv"
+   ]
+  },
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+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
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+    "colab_type": "code",
+    "id": "EBUnVCwlWPOZ",
+    "outputId": "e99d8905-3d96-4471-8c2e-a43e2d4eef76"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th>ID</th>\n",
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+      "text/plain": [
+       "      ID  Computer Science  ...  Quantitative Biology  Quantitative Finance\n",
+       "0  20973                 0  ...                     0                     0\n",
+       "1  20974                 0  ...                     0                     0\n",
+       "2  20975                 1  ...                     0                     0\n",
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+       "\n",
+       "[5 rows x 7 columns]"
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+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = pd.read_csv('/content/drive/My Drive/ml/test.csv')\n",
+    "\n",
+    "submit = pd.DataFrame({'ID': test.ID, 'Computer Science': predssv[:,0],'Physics':predssv[:,1],\n",
+    "                       'Mathematics':predssv[:,2],'Statistics':predssv[:,3],'Quantitative Biology':predssv[:,4],\n",
+    "                       'Quantitative Finance':predssv[:,5]})\n",
+    "submit.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ybS28j2AWbio"
+   },
+   "outputs": [],
+   "source": [
+    "submit.to_csv('submission2.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyMhmjaEvacjvO1SJCWFndxi",
+   "collapsed_sections": [],
+   "mount_file_id": "1-RAfjiGOy2brJkcnEiA6loSQDSn5oPQ1",
+   "name": "independenceday_challange",
+   "provenance": []
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+ },
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+}
diff --git a/Prediction Models/Research topic Prediction/submission6.csv b/Prediction Models/Research topic Prediction/submission6.csv
new file mode 100644
index 00000000..e3d7db90
--- /dev/null
+++ b/Prediction Models/Research topic Prediction/submission6.csv	
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