Subtask A/ml_techniques_A.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Importing Libraries"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Handling pre-processed data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "df = pd.read_csv('../preprocess_data.csv')\n",
    "df.drop(['task_2','Unnamed: 0','text'], axis=1, inplace=True)\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Unnamed: 0.1                       _id task_1  \\\n",
       "0          4986  60c5d6bf5659ea5e55defa2c    HOF   \n",
       "1          3394  60c5d6bf5659ea5e55def461    HOF   \n",
       "2          1310  60c5d6bf5659ea5e55defaad    NOT   \n",
       "3          3390  60c5d6bf5659ea5e55def419    HOF   \n",
       "4          4626  60c5d6bf5659ea5e55def7fa    HOF   \n",
       "\n",
       "                                          text_clean  \n",
       "0  made amp amp onli abl start make money sustain...  \n",
       "1            technic still turn back clock dick head  \n",
       "2  govt stop think world media liber gang ani opt...  \n",
       "3                            soldier japan dick head  \n",
       "4         would better ask think sleazi shitbag lmao  "
      ],
      "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: 0.1</th>\n",
       "      <th>_id</th>\n",
       "      <th>task_1</th>\n",
       "      <th>text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4986</td>\n",
       "      <td>60c5d6bf5659ea5e55defa2c</td>\n",
       "      <td>HOF</td>\n",
       "      <td>made amp amp onli abl start make money sustain...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3394</td>\n",
       "      <td>60c5d6bf5659ea5e55def461</td>\n",
       "      <td>HOF</td>\n",
       "      <td>technic still turn back clock dick head</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1310</td>\n",
       "      <td>60c5d6bf5659ea5e55defaad</td>\n",
       "      <td>NOT</td>\n",
       "      <td>govt stop think world media liber gang ani opt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3390</td>\n",
       "      <td>60c5d6bf5659ea5e55def419</td>\n",
       "      <td>HOF</td>\n",
       "      <td>soldier japan dick head</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4626</td>\n",
       "      <td>60c5d6bf5659ea5e55def7fa</td>\n",
       "      <td>HOF</td>\n",
       "      <td>would better ask think sleazi shitbag lmao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "fig, ax = plt.subplots(figsize=(3,3))\n",
    "\n",
    "ax.hist(df['task_1'])\n",
    "\n",
    "ax.set_title('Tweet Hates')\n",
    "ax.set_xlabel('Tweets')\n",
    "ax.set_ylabel('Frequency')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "metadata": {},
     "execution_count": 29
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "print(df.isnull().values.any())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "True\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidfconverter = TfidfVectorizer(max_features=2000, min_df=5)\n",
    "X = tfidfconverter.fit_transform(df['text_clean'].values.astype('U')).toarray()\n",
    "y = df['task_1']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, random_state = 0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Using various ML techniques to find the accuracy"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)\n",
    "print(metrics.classification_report(y_test,y_pred))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[153 227]\n",
      " [ 43 154]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         HOF       0.78      0.40      0.53       380\n",
      "         NOT       0.40      0.78      0.53       197\n",
      "\n",
      "    accuracy                           0.53       577\n",
      "   macro avg       0.59      0.59      0.53       577\n",
      "weighted avg       0.65      0.53      0.53       577\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)\n",
    "print(metrics.classification_report(y_test,y_pred))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[358  22]\n",
      " [ 96 101]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         HOF       0.79      0.94      0.86       380\n",
      "         NOT       0.82      0.51      0.63       197\n",
      "\n",
      "    accuracy                           0.80       577\n",
      "   macro avg       0.80      0.73      0.74       577\n",
      "weighted avg       0.80      0.80      0.78       577\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 18, metric = 'minkowski', p = 2)\n",
    "classifier.fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "accuracy_score(y_test, y_pred)\n",
    "print(metrics.classification_report(y_test,y_pred))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[335  45]\n",
      " [102  95]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         HOF       0.77      0.88      0.82       380\n",
      "         NOT       0.68      0.48      0.56       197\n",
      "\n",
      "    accuracy                           0.75       577\n",
      "   macro avg       0.72      0.68      0.69       577\n",
      "weighted avg       0.74      0.75      0.73       577\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "from sklearn.model_selection import RepeatedKFold"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "sv = LinearSVC()\n",
    "\n",
    "kf = RepeatedKFold(n_splits=10, n_repeats=10, random_state=None) \n",
    "\n",
    "bet_acc = 0.0\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "      x_train, x_test = X[train_index], X[test_index] \n",
    "      y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "      sv.fit(x_train,y_train)\n",
    "      predictions = sv.predict(x_test)\n",
    "\n",
    "      score = accuracy_score(y_test,predictions)\n",
    "\n",
    "      if  score > bet_acc :\n",
    "        print(score)\n",
    "        bet_acc = score\n",
    "\n",
    "      "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.7636363636363637\n",
      "0.796875\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "print(metrics.classification_report(y_test,predictions))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         HOF       0.86      0.84      0.85       264\n",
      "         NOT       0.67      0.69      0.68       120\n",
      "\n",
      "    accuracy                           0.80       384\n",
      "   macro avg       0.76      0.77      0.77       384\n",
      "weighted avg       0.80      0.80      0.80       384\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "kf = RepeatedKFold(n_splits=10, n_repeats=10, random_state=None) \n",
    "\n",
    "bet_acc = 0.0\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "      x_train, x_test = X[train_index], X[test_index] \n",
    "      y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "      rf.fit(x_train,y_train)\n",
    "      predictions = rf.predict(x_test)\n",
    "\n",
    "      score = accuracy_score(y_test,predictions)\n",
    "\n",
    "      if  score > bet_acc :\n",
    "        print(score)\n",
    "        bet_acc = score"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.7454545454545455\n",
      "0.7922077922077922\n",
      "0.7948051948051948\n",
      "0.8177083333333334\n",
      "0.8207792207792208\n",
      "0.8307291666666666\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "print(metrics.classification_report(y_test,predictions))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         HOF       0.80      0.89      0.84       237\n",
      "         NOT       0.78      0.64      0.70       147\n",
      "\n",
      "    accuracy                           0.79       384\n",
      "   macro avg       0.79      0.76      0.77       384\n",
      "weighted avg       0.79      0.79      0.79       384\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  }
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