Stress Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03bf26f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import nltk\n",
    "import pickle\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import pandas as pd\n",
    "import string\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from nltk import SnowballStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db0fb73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"dreaddit-train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf81ff8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2838, 116)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b6dae7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words_list = stopwords.words(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "655b51b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"\\n\", \" \", text)\n",
    "    text = re.sub(r\"\\d\", \" \", text)\n",
    "    text = re.sub(r\"<.*?>+\", \"\", text)\n",
    "    text = re.sub(r\"\\[.*?\\]\", \"\", text)\n",
    "    text = re.sub(r\"https?://S+|www.\\.\\S+\", \"\", text)\n",
    "    text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
    "    words = text.split()\n",
    "    words = [word for word in words if not word in stop_words_list]\n",
    "    words = [re.sub(r\"(.)\\1{1,}\", r\"\\1\\1\", word) for word in words]\n",
    "    words = [word.strip() for word in words if len(word.strip()) > 1]\n",
    "    text = \" \".join(words)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72b76bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"text\"] = df[\"text\"].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e401e0df",
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aed39f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stemming(text):\n",
    "    stemmed_text = \"\"\n",
    "    for word in text.split():\n",
    "        stem = stemmer.stem(word)\n",
    "        stemmed_text += stem\n",
    "        stemmed_text += \" \"\n",
    "        \n",
    "    stemmed_text = stemmed_text.strip()\n",
    "    return stemmed_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b27fc97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"text\"] = df[\"text\"].apply(stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f1bd0e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"label\"] = df[\"label\"].map({\n",
    "    0: \"No Stress\", 1: \"Stress\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "523ee72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"text\"]\n",
    "X = np.array(X)\n",
    "\n",
    "y = df[\"label\"]\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "437892ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "X_scaled = cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d333767a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e8610e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BernoulliNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BernoulliNB</label><div class=\"sk-toggleable__content\"><pre>BernoulliNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = BernoulliNB()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55e85451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7312206572769953"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e0143bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No Stress'], dtype='<U9')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = \"I'm so happy now.\"\n",
    "test = cv.transform([text]).toarray()\n",
    "model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7970250e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model, open(\"model.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e8f3d172",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(cv, open(\"cv.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dce6bb9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}