stock Prediction using nlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e06aa0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c4766ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('stock_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e805826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kickers on my watchlist XIDE TIT SOQ PNK CPW B...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user: AAP MOVIE. 55% return for the FEA/GEED i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user I'd be afraid to short AMZN - they are lo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MNTA Over 12.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OI  Over 21.37</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PGNX  Over 3.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AAP - user if so then the current downtrend wi...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Monday's relative weakness. NYX WIN TIE TAP IC...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GOOG - ower trend line channel test &amp; volume s...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AAP will watch tomorrow for ONG entry.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  Sentiment\n",
       "0  Kickers on my watchlist XIDE TIT SOQ PNK CPW B...          1\n",
       "1  user: AAP MOVIE. 55% return for the FEA/GEED i...          1\n",
       "2  user I'd be afraid to short AMZN - they are lo...          1\n",
       "3                                  MNTA Over 12.00            1\n",
       "4                                   OI  Over 21.37            1\n",
       "5                                  PGNX  Over 3.04            1\n",
       "6  AAP - user if so then the current downtrend wi...         -1\n",
       "7  Monday's relative weakness. NYX WIN TIE TAP IC...         -1\n",
       "8  GOOG - ower trend line channel test & volume s...          1\n",
       "9             AAP will watch tomorrow for ONG entry.          1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fd8ff08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5791, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c792b947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5791 entries, 0 to 5790\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Text       5791 non-null   object\n",
      " 1   Sentiment  5791 non-null   int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b56aaed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5791.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.272664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.962192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sentiment\n",
       "count  5791.000000\n",
       "mean      0.272664\n",
       "std       0.962192\n",
       "min      -1.000000\n",
       "25%      -1.000000\n",
       "50%       1.000000\n",
       "75%       1.000000\n",
       "max       1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27af1ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    3685\n",
       "-1    2106\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sentiment value count\n",
    "data['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "996823fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Software\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "#plotting the sentiment value count\n",
    "sns.countplot(data['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b5c4317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Text', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#loength of the text using KDEplot\n",
    "length = data[\"Text\"].str.len()\n",
    "sns.kdeplot(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4ea48ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Puja\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from nltk import word_tokenize, WordNetLemmatizer\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed0fd534",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\Puja\\AppData\\Roaming\\nltk_data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'few', 'too', 'our', 'been', 'them', 'being', 'any', \"you're\", \"it's\", 'did', 'have', 'were', 'such', 'only', 'between', 'shouldn', 'itself', 'whom', \"didn't\", 'once', 'if', \"won't\", 'does', 'you', 'where', 'herself', 'because', 'ours', 'shan', 'there', 'off', \"hadn't\", \"you'd\", 'through', 'about', \"shouldn't\", 'hasn', 've', 'by', 'o', 'here', 'having', 'until', 'all', 'it', 'an', 'into', 'yourself', 'ma', 'then', 'the', 'this', 'doing', 'can', 'while', 'doesn', 'my', 'mustn', 'wouldn', \"weren't\", 'other', 'him', 'as', 'she', 'i', 's', 'himself', 'ain', 'has', \"you'll\", 'under', \"needn't\", \"you've\", 'will', \"mightn't\", 'your', 'which', 'am', 're', \"wasn't\", \"don't\", 'was', 'ourselves', 'weren', 'that', 'just', \"mustn't\", 'be', \"that'll\", 'from', 'no', 'below', 'down', 'and', 'when', 'on', 'against', 'very', 'm', 'to', 'after', 't', 'wasn', \"wouldn't\", 'aren', 'should', 'above', \"couldn't\", 'won', 'y', 'more', 'are', 'own', \"haven't\", 'over', 'needn', 'me', 'didn', 'isn', 'mightn', 'll', 'her', \"isn't\", 'not', 'both', 'those', \"doesn't\", 'its', 'these', 'hadn', 'he', 'before', 'had', 'up', 'most', 'couldn', 'during', 'yours', \"hasn't\", 'why', 'haven', 'or', 'with', 'each', 'some', 'myself', 'nor', 'themselves', 'hers', 'we', 'who', 'a', 'do', 'their', 'theirs', 'again', 'same', 'now', 'of', 'what', 'in', 'at', 'than', 'd', \"she's\", 'don', 'but', \"aren't\", 'his', 'yourselves', 'is', 'out', 'how', 'further', \"shan't\", 'they', 'for', 'so', \"should've\"}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
    }
   ],
   "source": [
    "#checking for stopwords\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = set(stopwords.words('English'))\n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc325422",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = list()\n",
    "for i in range(len(data)):\n",
    "    lip = data.Text[i].split()\n",
    "    for k in lip:\n",
    "        word_list.append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "90b5e250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 1796),\n",
       " ('to', 1668),\n",
       " ('a', 1280),\n",
       " ('on', 1032),\n",
       " ('of', 944),\n",
       " ('in', 891),\n",
       " ('AAP', 884),\n",
       " ('for', 868),\n",
       " ('and', 850),\n",
       " ('is', 811),\n",
       " ('-', 728),\n",
       " ('at', 541),\n",
       " ('this', 461),\n",
       " ('it', 454),\n",
       " ('I', 453),\n",
       " ('up', 357),\n",
       " ('user:', 340),\n",
       " ('from', 331),\n",
       " ('will', 330),\n",
       " ('be', 324)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "wordCounter = Counter(word_list)\n",
    "countedWordDict = dict(wordCounter)\n",
    "sortedWordDict = sorted(countedWordDict.items(),key = lambda x : x[1],reverse=True)\n",
    "sortedWordDict[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04b18e9",
   "metadata": {},
   "source": [
    "# Data Preprocesing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b04e3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the value of -1 to 0 in the output column so that the model can predict well\n",
    "data['Sentiment'] = data['Sentiment'].replace(-1,0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5942aeb4",
   "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>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>Industry body CII said #discoms are likely to ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5787</th>\n",
       "      <td>#Gold prices slip below Rs 46,000 as #investor...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5788</th>\n",
       "      <td>Workers at Bajaj Auto have agreed to a 10% wag...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5789</th>\n",
       "      <td>#Sharemarket LIVE: Sensex off day’s high, up 6...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5790</th>\n",
       "      <td>#Sensex, #Nifty climb off day's highs, still u...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Text  Sentiment\n",
       "5786  Industry body CII said #discoms are likely to ...          0\n",
       "5787  #Gold prices slip below Rs 46,000 as #investor...          0\n",
       "5788  Workers at Bajaj Auto have agreed to a 10% wag...          1\n",
       "5789  #Sharemarket LIVE: Sensex off day’s high, up 6...          1\n",
       "5790  #Sensex, #Nifty climb off day's highs, still u...          1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ffdeedd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3685\n",
       "0    2106\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aa4e2eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5791, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49043eed",
   "metadata": {},
   "source": [
    "# NLP processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5fffb8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = PorterStemmer()\n",
    "lemma = WordNetLemmatizer()\n",
    "stopwordSet = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d4a65ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Puja\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#cleaning the text \n",
    "nltk.download('punkt')\n",
    "text_reviews = list()\n",
    "for i in range(len(data)):\n",
    "    text = re.sub('[^a-zA-Z]', \" \", data['Text'][i])\n",
    "    text = text.lower()\n",
    "    text = word_tokenize(text,language=\"english\")\n",
    "    text = [lemma.lemmatize(word) for word in text if(word) not in stopwordSet]\n",
    "    text = \" \".join(text)\n",
    "    text_reviews.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "203b2674",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b2d8130b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create the (B.O.W) bag of word model\n",
    "cv = CountVectorizer(max_features = 1500)\n",
    "X = cv.fit_transform(text_reviews).toarray()\n",
    "y= data['Sentiment']\n",
    "\n",
    "## Split the dataset into Training and Test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68435256",
   "metadata": {},
   "source": [
    "# Modelling and Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6b40e0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Logistic Regression model\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "Y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fc7f5171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.64      0.68       417\n",
      "           1       0.81      0.86      0.84       742\n",
      "\n",
      "    accuracy                           0.78      1159\n",
      "   macro avg       0.77      0.75      0.76      1159\n",
      "weighted avg       0.78      0.78      0.78      1159\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cf421c14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[267 150]\n",
      " [102 640]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ec06f7c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Naives baye multinomial model\n",
    "clf = MultinomialNB()\n",
    "clf.fit(X_train, y_train)\n",
    "Y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f7faca43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.70      0.70       417\n",
      "           1       0.83      0.84      0.83       742\n",
      "\n",
      "    accuracy                           0.79      1159\n",
      "   macro avg       0.77      0.77      0.77      1159\n",
      "weighted avg       0.79      0.79      0.79      1159\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "62e8b9a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[291 126]\n",
      " [120 622]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0b0a907f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Random Forest model\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "Y_pred = random_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7e8925e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.72      0.71       417\n",
      "           1       0.84      0.82      0.83       742\n",
      "\n",
      "    accuracy                           0.79      1159\n",
      "   macro avg       0.77      0.77      0.77      1159\n",
      "weighted avg       0.79      0.79      0.79      1159\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "719d9137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[302 115]\n",
      " [134 608]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d16533",
   "metadata": {},
   "source": [
    "# conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e28b424",
   "metadata": {},
   "source": [
    "Random forest and MultinomialNB produced the predictions with the\n",
    "highest accuracy, at 79%, while logistic regression scored 78%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87491835",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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