Sentiment_Analysis_ar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import statements ##\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31962 entries, 0 to 31961\n",
      "Data columns (total 3 columns):\n",
      "id       31962 non-null int64\n",
      "label    31962 non-null int64\n",
      "tweet    31962 non-null object\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 749.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    29720\n",
       "1     2242\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_features(features,data):\n",
    "    data.drop(features,inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ouch   junior is angry     got7  junior  yugyo      '"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "## example ## \n",
    "re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])\", \" \",\"ouch...junior is angryð#got7 #junior #yugyo..., @user\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_tweet(tweet):\n",
    "    return \" \".join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])\", \" \",tweet.lower()).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['processed_tweets'] = train_data['tweet'].apply(process_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "      <th>processed_tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "      <td>when a father is dysfunctional and is so selfi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "      <td>thanks for lyft credit i can t use cause they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "      <td>model i love u take with u all the time in ur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "      <td>factsguide society now motivation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>[2/2] huge fan fare and big talking before the...</td>\n",
       "      <td>2 2 huge fan fare and big talking before they ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>@user camping tomorrow @user @user @user @use...</td>\n",
       "      <td>camping tomorrow danny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>the next school year is the year for exams.ð...</td>\n",
       "      <td>the next school year is the year for exams can...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>we won!!! love the land!!! #allin #cavs #champ...</td>\n",
       "      <td>we won love the land allin cavs champions clev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user welcome here !  i'm   it's so #gr...</td>\n",
       "      <td>welcome here i m it s so gr8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet  \\\n",
       "0   1      0   @user when a father is dysfunctional and is s...   \n",
       "1   2      0  @user @user thanks for #lyft credit i can't us...   \n",
       "2   3      0                                bihday your majesty   \n",
       "3   4      0  #model   i love u take with u all the time in ...   \n",
       "4   5      0             factsguide: society now    #motivation   \n",
       "5   6      0  [2/2] huge fan fare and big talking before the...   \n",
       "6   7      0   @user camping tomorrow @user @user @user @use...   \n",
       "7   8      0  the next school year is the year for exams.ð...   \n",
       "8   9      0  we won!!! love the land!!! #allin #cavs #champ...   \n",
       "9  10      0   @user @user welcome here !  i'm   it's so #gr...   \n",
       "\n",
       "                                    processed_tweets  \n",
       "0  when a father is dysfunctional and is so selfi...  \n",
       "1  thanks for lyft credit i can t use cause they ...  \n",
       "2                                bihday your majesty  \n",
       "3      model i love u take with u all the time in ur  \n",
       "4                  factsguide society now motivation  \n",
       "5  2 2 huge fan fare and big talking before they ...  \n",
       "6                             camping tomorrow danny  \n",
       "7  the next school year is the year for exams can...  \n",
       "8  we won love the land allin cavs champions clev...  \n",
       "9                       welcome here i m it s so gr8  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_features(['id','tweet'],train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31962 entries, 0 to 31961\n",
      "Data columns (total 2 columns):\n",
      "label               31962 non-null int64\n",
      "processed_tweets    31962 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 499.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(train_data[\"processed_tweets\"],train_data[\"label\"], test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer(stop_words='english')\n",
    "transformer = TfidfTransformer(norm='l2',sublinear_tf=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_counts = count_vect.fit_transform(x_train)\n",
    "x_train_tfidf = transformer.fit_transform(x_train_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25569, 33735)\n",
      "(25569, 33735)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_counts.shape)\n",
    "print(x_train_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_counts = count_vect.transform(x_test)\n",
    "x_test_tfidf = transformer.transform(x_test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6393, 33735)\n",
      "(6393, 33735)\n"
     ]
    }
   ],
   "source": [
    "print(x_test_counts.shape)\n",
    "print(x_test_tfidf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1=LogisticRegression()\n",
    "model1.fit(x_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "predLR=model1.predict(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5924   13]\n",
      " [ 317  139]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,f1_score\n",
    "c_m = confusion_matrix(y_test,predLR)\n",
    "print(c_m)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9483810417644298"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,predLR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model2=DecisionTreeClassifier()\n",
    "model2.fit(x_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "predDT=model2.predict(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5775  162]\n",
      " [ 169  287]]\n"
     ]
    }
   ],
   "source": [
    "c_m = confusion_matrix(y_test,predDT)\n",
    "print(c_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9482246206788675"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predDT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model3=SVC()\n",
    "model3.fit(x_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "predSVC=model3.predict(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5924   13]\n",
      " [ 257  199]]\n"
     ]
    }
   ],
   "source": [
    "c_m = confusion_matrix(y_test,predSVC)\n",
    "print(c_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9577663068981699"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predSVC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model4 = RandomForestClassifier(n_estimators=200)\n",
    "model4.fit(x_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "predRF = model.predict(x_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5900,   37],\n",
       "       [ 221,  235]], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,f1_score\n",
    "c_m = confusion_matrix(y_test,predRF)\n",
    "c_m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9596433599249179"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predRF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall =  0.9939363314805457\n",
      "precision =  0.9645308924485125\n",
      "F1_score =  0.9790128577353795\n"
     ]
    }
   ],
   "source": [
    "tp,fn,fp,tn = c_m[0][0],c_m[0][1],c_m[1][0],c_m[1][1]\n",
    "precision = tp/(tp+fp)\n",
    "recall = tp/(tp+fn)\n",
    "f1score = 2 * (recall * precision) / (recall + precision)\n",
    "print(\"recall = \",recall)\n",
    "print(\"precision = \", precision)\n",
    "print(\"F1_score = \", f1score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod_accuracy=[accuracy_score(y_test,predLR),accuracy_score(y_test,predDT),accuracy_score(y_test,predSVC),accuracy_score(y_test,predRF)]\n",
    "models=[\"Logistic regression\",\"Decision Tree\",\"Support Vector Machines\",\"Random Forest\"]\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(models,mod_accuracy,'bo',linestyle='solid',linewidth=2, markersize=10)\n",
    "plt.xlabel(\"Models used\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Models vs Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17197 entries, 0 to 17196\n",
      "Data columns (total 2 columns):\n",
      "id       17197 non-null int64\n",
      "tweet    17197 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 268.8+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['processed_tweet'] = test_data['tweet'].apply(process_tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>id</th>\n",
       "      <th>tweet</th>\n",
       "      <th>processed_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>31963</td>\n",
       "      <td>#studiolife #aislife #requires #passion #dedic...</td>\n",
       "      <td>studiolife aislife requires passion dedication...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>31964</td>\n",
       "      <td>@user #white #supremacists want everyone to s...</td>\n",
       "      <td>white supremacists want everyone to see the ne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31965</td>\n",
       "      <td>safe ways to heal your #acne!!    #altwaystohe...</td>\n",
       "      <td>safe ways to heal your acne altwaystoheal heal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>31966</td>\n",
       "      <td>is the hp and the cursed child book up for res...</td>\n",
       "      <td>is the hp and the cursed child book up for res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31967</td>\n",
       "      <td>3rd #bihday to my amazing, hilarious #nephew...</td>\n",
       "      <td>3rd bihday to my amazing hilarious nephew eli ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              tweet  \\\n",
       "0  31963  #studiolife #aislife #requires #passion #dedic...   \n",
       "1  31964   @user #white #supremacists want everyone to s...   \n",
       "2  31965  safe ways to heal your #acne!!    #altwaystohe...   \n",
       "3  31966  is the hp and the cursed child book up for res...   \n",
       "4  31967    3rd #bihday to my amazing, hilarious #nephew...   \n",
       "\n",
       "                                     processed_tweet  \n",
       "0  studiolife aislife requires passion dedication...  \n",
       "1  white supremacists want everyone to see the ne...  \n",
       "2  safe ways to heal your acne altwaystoheal heal...  \n",
       "3  is the hp and the cursed child book up for res...  \n",
       "4  3rd bihday to my amazing hilarious nephew eli ...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_features(['tweet'],test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_counts = count_vect.fit_transform(train_data['processed_tweets'])\n",
    "test_counts = count_vect.transform(test_data['processed_tweet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31962, 38763)\n",
      "(17197, 38763)\n"
     ]
    }
   ],
   "source": [
    "print(train_counts.shape)\n",
    "print(test_counts.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tfidf = transformer.fit_transform(train_counts)\n",
    "test_tfidf = transformer.transform(test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31962, 38763)\n",
      "(17197, 38763)\n"
     ]
    }
   ],
   "source": [
    "print(train_tfidf.shape)\n",
    "print(test_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_tfidf,train_data['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_result = pd.DataFrame({'id':test_data['id'],'label':predictions})\n",
    "final_result.to_csv('output2.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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