Classification-Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1140825600\n",
      "1    1140825600\n",
      "2    1140739200\n",
      "3    1140912000\n",
      "4    1140566400\n",
      "Name: film_date, dtype: int64\n",
      "0    1151367060\n",
      "1    1151367060\n",
      "2    1151367060\n",
      "3    1151367060\n",
      "4    1151440680\n",
      "Name: published_date, dtype: int64\n",
      "0    24-02-2006\n",
      "1    24-02-2006\n",
      "2    23-02-2006\n",
      "3    25-02-2006\n",
      "4    21-02-2006\n",
      "Name: film_date, dtype: object\n",
      "0    26-06-2006\n",
      "1    26-06-2006\n",
      "2    26-06-2006\n",
      "3    26-06-2006\n",
      "4    27-06-2006\n",
      "Name: published_date, dtype: object\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comments</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>event</th>\n",
       "      <th>film_date</th>\n",
       "      <th>languages</th>\n",
       "      <th>main_speaker</th>\n",
       "      <th>name</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>published_date</th>\n",
       "      <th>ratings</th>\n",
       "      <th>related_talks</th>\n",
       "      <th>speaker_occupation</th>\n",
       "      <th>tags</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4553</td>\n",
       "      <td>Sir Ken Robinson makes an entertaining and pro...</td>\n",
       "      <td>1164</td>\n",
       "      <td>TED2006</td>\n",
       "      <td>24-02-2006</td>\n",
       "      <td>60</td>\n",
       "      <td>Ken Robinson</td>\n",
       "      <td>Ken Robinson: Do schools kill creativity?</td>\n",
       "      <td>1</td>\n",
       "      <td>26-06-2006</td>\n",
       "      <td>[{'id': 7, 'name': 'Funny', 'count': 19645}, {...</td>\n",
       "      <td>[{'id': 865, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Author/educator</td>\n",
       "      <td>['children', 'creativity', 'culture', 'dance',...</td>\n",
       "      <td>Do schools kill creativity?</td>\n",
       "      <td>https://www.ted.com/talks/ken_robinson_says_sc...</td>\n",
       "      <td>47227110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>265</td>\n",
       "      <td>With the same humor and humanity he exuded in ...</td>\n",
       "      <td>977</td>\n",
       "      <td>TED2006</td>\n",
       "      <td>24-02-2006</td>\n",
       "      <td>43</td>\n",
       "      <td>Al Gore</td>\n",
       "      <td>Al Gore: Averting the climate crisis</td>\n",
       "      <td>1</td>\n",
       "      <td>26-06-2006</td>\n",
       "      <td>[{'id': 7, 'name': 'Funny', 'count': 544}, {'i...</td>\n",
       "      <td>[{'id': 243, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Climate advocate</td>\n",
       "      <td>['alternative energy', 'cars', 'climate change...</td>\n",
       "      <td>Averting the climate crisis</td>\n",
       "      <td>https://www.ted.com/talks/al_gore_on_averting_...</td>\n",
       "      <td>3200520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124</td>\n",
       "      <td>New York Times columnist David Pogue takes aim...</td>\n",
       "      <td>1286</td>\n",
       "      <td>TED2006</td>\n",
       "      <td>23-02-2006</td>\n",
       "      <td>26</td>\n",
       "      <td>David Pogue</td>\n",
       "      <td>David Pogue: Simplicity sells</td>\n",
       "      <td>1</td>\n",
       "      <td>26-06-2006</td>\n",
       "      <td>[{'id': 7, 'name': 'Funny', 'count': 964}, {'i...</td>\n",
       "      <td>[{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Technology columnist</td>\n",
       "      <td>['computers', 'entertainment', 'interface desi...</td>\n",
       "      <td>Simplicity sells</td>\n",
       "      <td>https://www.ted.com/talks/david_pogue_says_sim...</td>\n",
       "      <td>1636292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>In an emotionally charged talk, MacArthur-winn...</td>\n",
       "      <td>1116</td>\n",
       "      <td>TED2006</td>\n",
       "      <td>25-02-2006</td>\n",
       "      <td>35</td>\n",
       "      <td>Majora Carter</td>\n",
       "      <td>Majora Carter: Greening the ghetto</td>\n",
       "      <td>1</td>\n",
       "      <td>26-06-2006</td>\n",
       "      <td>[{'id': 3, 'name': 'Courageous', 'count': 760}...</td>\n",
       "      <td>[{'id': 1041, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Activist for environmental justice</td>\n",
       "      <td>['MacArthur grant', 'activism', 'business', 'c...</td>\n",
       "      <td>Greening the ghetto</td>\n",
       "      <td>https://www.ted.com/talks/majora_carter_s_tale...</td>\n",
       "      <td>1697550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>593</td>\n",
       "      <td>You've never seen data presented like this. Wi...</td>\n",
       "      <td>1190</td>\n",
       "      <td>TED2006</td>\n",
       "      <td>21-02-2006</td>\n",
       "      <td>48</td>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Hans Rosling: The best stats you've ever seen</td>\n",
       "      <td>1</td>\n",
       "      <td>27-06-2006</td>\n",
       "      <td>[{'id': 9, 'name': 'Ingenious', 'count': 3202}...</td>\n",
       "      <td>[{'id': 2056, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>['Africa', 'Asia', 'Google', 'demo', 'economic...</td>\n",
       "      <td>The best stats you've ever seen</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_shows_t...</td>\n",
       "      <td>12005869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   comments                                        description  duration  \\\n",
       "0      4553  Sir Ken Robinson makes an entertaining and pro...      1164   \n",
       "1       265  With the same humor and humanity he exuded in ...       977   \n",
       "2       124  New York Times columnist David Pogue takes aim...      1286   \n",
       "3       200  In an emotionally charged talk, MacArthur-winn...      1116   \n",
       "4       593  You've never seen data presented like this. Wi...      1190   \n",
       "\n",
       "     event   film_date  languages   main_speaker  \\\n",
       "0  TED2006  24-02-2006         60   Ken Robinson   \n",
       "1  TED2006  24-02-2006         43        Al Gore   \n",
       "2  TED2006  23-02-2006         26    David Pogue   \n",
       "3  TED2006  25-02-2006         35  Majora Carter   \n",
       "4  TED2006  21-02-2006         48   Hans Rosling   \n",
       "\n",
       "                                            name  num_speaker published_date  \\\n",
       "0      Ken Robinson: Do schools kill creativity?            1     26-06-2006   \n",
       "1           Al Gore: Averting the climate crisis            1     26-06-2006   \n",
       "2                  David Pogue: Simplicity sells            1     26-06-2006   \n",
       "3             Majora Carter: Greening the ghetto            1     26-06-2006   \n",
       "4  Hans Rosling: The best stats you've ever seen            1     27-06-2006   \n",
       "\n",
       "                                             ratings  \\\n",
       "0  [{'id': 7, 'name': 'Funny', 'count': 19645}, {...   \n",
       "1  [{'id': 7, 'name': 'Funny', 'count': 544}, {'i...   \n",
       "2  [{'id': 7, 'name': 'Funny', 'count': 964}, {'i...   \n",
       "3  [{'id': 3, 'name': 'Courageous', 'count': 760}...   \n",
       "4  [{'id': 9, 'name': 'Ingenious', 'count': 3202}...   \n",
       "\n",
       "                                       related_talks  \\\n",
       "0  [{'id': 865, 'hero': 'https://pe.tedcdn.com/im...   \n",
       "1  [{'id': 243, 'hero': 'https://pe.tedcdn.com/im...   \n",
       "2  [{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...   \n",
       "3  [{'id': 1041, 'hero': 'https://pe.tedcdn.com/i...   \n",
       "4  [{'id': 2056, 'hero': 'https://pe.tedcdn.com/i...   \n",
       "\n",
       "                     speaker_occupation  \\\n",
       "0                       Author/educator   \n",
       "1                      Climate advocate   \n",
       "2                  Technology columnist   \n",
       "3    Activist for environmental justice   \n",
       "4  Global health expert; data visionary   \n",
       "\n",
       "                                                tags  \\\n",
       "0  ['children', 'creativity', 'culture', 'dance',...   \n",
       "1  ['alternative energy', 'cars', 'climate change...   \n",
       "2  ['computers', 'entertainment', 'interface desi...   \n",
       "3  ['MacArthur grant', 'activism', 'business', 'c...   \n",
       "4  ['Africa', 'Asia', 'Google', 'demo', 'economic...   \n",
       "\n",
       "                             title  \\\n",
       "0      Do schools kill creativity?   \n",
       "1      Averting the climate crisis   \n",
       "2                 Simplicity sells   \n",
       "3              Greening the ghetto   \n",
       "4  The best stats you've ever seen   \n",
       "\n",
       "                                                 url     views  \n",
       "0  https://www.ted.com/talks/ken_robinson_says_sc...  47227110  \n",
       "1  https://www.ted.com/talks/al_gore_on_averting_...   3200520  \n",
       "2  https://www.ted.com/talks/david_pogue_says_sim...   1636292  \n",
       "3  https://www.ted.com/talks/majora_carter_s_tale...   1697550  \n",
       "4  https://www.ted.com/talks/hans_rosling_shows_t...  12005869  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "data = pd.read_csv ('C:/Users/garim/Documents/Projects/Data Mining Project/ted_main.csv')\n",
    "#print (data.columns)\n",
    "print (data['film_date'].head())\n",
    "print (data['published_date'].head())\n",
    "data['film_date'] =data['film_date'].apply(lambda x: dt.datetime.fromtimestamp( int(x)).strftime('%d-%m-%Y'))\n",
    "data['published_date'] = data['published_date'].apply(lambda x: dt.datetime.fromtimestamp( int(x)).strftime('%d-%m-%Y'))\n",
    "print (data['film_date'].head())\n",
    "print (data['published_date'].head())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "data['ratings'] = data['ratings'].apply(lambda x: ast.literal_eval(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add Topic column in the dataset\n",
    "talk_topic = \"\"\n",
    "def topic_function(data_topic):\n",
    "    data_topic = sorted(data_topic, key=lambda k: k['count'])\n",
    "    return data_topic[0]['name']\n",
    "data['Topics']= data['ratings'].apply(topic_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_or_neg = \"\"\n",
    "def pos_neg_function(rating):\n",
    "    i=rating\n",
    "    if (i=='Beautiful' or i=='Courageous' or i=='Funny' or i=='Informative' or i=='Ingenious' or i=='Inspiring' or i=='Jaw-dropping' or i=='Persuasive' ):\n",
    "        pos_or_neg=1\n",
    "    elif (i=='OK'):\n",
    "        pos_or_neg=0\n",
    "    else :\n",
    "        pos_or_neg=-1\n",
    "    return pos_or_neg\n",
    "data[\"sentiment\"] = data['Topics'].apply(pos_neg_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "755399.0\n",
      "1124523.5\n",
      "1701743.0\n",
      "946344.0\n"
     ]
    }
   ],
   "source": [
    "#Calculating quartile ranges and means\n",
    "data = data.sort_values('views')\n",
    "mid = int(len(data)/2)\n",
    "k=list(data['views'])\n",
    "lowerQ = np.median(sorted(k[:mid]))\n",
    "median = np.median(sorted(k))\n",
    "upperQ = np.median(sorted(k[mid:]))\n",
    "iq_range = upperQ-lowerQ\n",
    "print(lowerQ)\n",
    "print(median)\n",
    "print(upperQ)\n",
    "print(iq_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47227110\n",
      "2543750\n"
     ]
    }
   ],
   "source": [
    "print(max(data['views']))\n",
    "def is_outlier(data):\n",
    "    if data > (median + (1.5* iq_range)) or data < (median - (1.5* iq_range)):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "#apply the function to the original df:\n",
    "data.loc[:, 'outlier'] = data['views'].apply(is_outlier)\n",
    "#filter to only non-outliers:\n",
    "data = data[~(data.outlier)]\n",
    "data.head()\n",
    "len(data)\n",
    "print(max(data['views']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "707788.0\n",
      "1099370.98648\n",
      "1428575.0\n",
      "720787.0\n"
     ]
    }
   ],
   "source": [
    "data = data.sort_values('views')\n",
    "mid = int(len(data)/2)\n",
    "k=list(data['views'])\n",
    "lowerQ = np.median(sorted(k[:mid]))\n",
    "mean = np.mean(sorted(k))\n",
    "upperQ = np.median(sorted(k[mid:]))\n",
    "iq_range = upperQ-lowerQ\n",
    "print(lowerQ)\n",
    "print(mean)\n",
    "print(upperQ)\n",
    "print(iq_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1    1232\n",
      " 1     987\n",
      "Name: views_based, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['speaker_occupation'], dtype='object')"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def views_function(data_views):\n",
    "    if(data_views<=mean):\n",
    "            return(-1)\n",
    "    else:\n",
    "            return(1)\n",
    "data[\"views_based\"] = data['views'].apply(views_function)\n",
    "print (data[\"views_based\"].value_counts())\n",
    "data.columns[data.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(max(data['comments']))\n",
    "#print(max(data['duration']))\n",
    "#data['comments']= (data['comments'] - data['comments'].mean()) / (data['comments'].max() - data['comments'].min())\n",
    "#data['comments'] = data['comments'].apply(lambda x : float(x)/len(data['comments']))\n",
    "#data['duration']= (data['duration'] - data['duration'].mean()) / (data['duration'].max() - data['duration'].min())\n",
    "#data[\"views\"]=(data['views'] - data['views'].mean()) / (data['views'].max() - data['views'].min())\n",
    "#data[\"languages\"]=(data['languages'] - data['languages'].mean()) / (data['languages'].max() - data['languages'].min())\n",
    "#data['duration'] =  data['duration'].apply(lambda x : float(x)/len(data['duration']))\n",
    "#print(max(data['comments']))\n",
    "#print(max(data['duration']))\n",
    "#data['views_based'].corr(data['comments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('ted_main_1.csv', sep=',',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>comments</th>\n",
       "      <th>duration</th>\n",
       "      <th>languages</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>comments</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104326</td>\n",
       "      <td>0.235007</td>\n",
       "      <td>-0.037601</td>\n",
       "      <td>0.062477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.104326</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.332363</td>\n",
       "      <td>0.021472</td>\n",
       "      <td>0.022377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>languages</th>\n",
       "      <td>0.235007</td>\n",
       "      <td>-0.332363</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061661</td>\n",
       "      <td>-0.009331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_speaker</th>\n",
       "      <td>-0.037601</td>\n",
       "      <td>0.021472</td>\n",
       "      <td>-0.061661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment</th>\n",
       "      <td>0.062477</td>\n",
       "      <td>0.022377</td>\n",
       "      <td>-0.009331</td>\n",
       "      <td>0.058196</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             comments  duration  languages  num_speaker  sentiment\n",
       "comments     1.000000  0.104326   0.235007    -0.037601   0.062477\n",
       "duration     0.104326  1.000000  -0.332363     0.021472   0.022377\n",
       "languages    0.235007 -0.332363   1.000000    -0.061661  -0.009331\n",
       "num_speaker -0.037601  0.021472  -0.061661     1.000000   0.058196\n",
       "sentiment    0.062477  0.022377  -0.009331     0.058196   1.000000"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#classification for views_based\n",
    "from sklearn.model_selection import train_test_split\n",
    "predictors = data.drop(['views_based','views','outlier','Topics','published_date','film_date','description','event','main_speaker','name','ratings','related_talks','speaker_occupation','tags','title','url'], axis=1)\n",
    "target = data[\"views_based\"]\n",
    "x_train, x_val, y_train, y_val = train_test_split(predictors, target, test_size = 0.22, random_state = 1)\n",
    "predictors.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.62\n",
      "Confusion matrix\n",
      "[[153 132]\n",
      " [ 41 163]]\n",
      "Precision : 0.552542372881\n",
      "Recall : 0.799019607843\n",
      "F measure : 0.653306613226\n"
     ]
    },
    {
     "data": {
      "image/png": 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bbjoMgLUG9WPJJboz9aOZDB6wIt26ZT+tQav0YdiQlZn0zgdFhlrVWjG8W+Gq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\nzcxycsI0M8vJCdPMLCcnTDOznP4ffAzsQDojFOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803cfb358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a804d75f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(x_train, y_train)\n",
    "y_pred = gaussian.predict(x_val)\n",
    "acc_gaussian = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_gaussian)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=['-1','1'],\n",
    "                      title='Confusion matrix for views Naive Bayes')\n",
    "plt.figure()\n",
    "#y_val = label_binarize(y_val, classes=[-1,1])\n",
    "precision = precision_score(y_val,y_pred)\n",
    "recall=recall_score(y_val,y_pred)\n",
    "fscore = f1_score(y_val,y_pred)\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.08\n",
      "Confusion matrix\n",
      "[[280   5]\n",
      " [200   4]]\n",
      "Precision : 0.444444444444\n",
      "Recall : 0.0196078431373\n",
      "F measure : 0.037558685446\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803be7a90>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Whw9cNzPLR/iwIjOz/OooMR2YZlYob8M0M8vJ2zDNzHJyYJqZ5eRVcjOzHOrt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a804d752e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803be7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(x_train, y_train)\n",
    "y_pred = svc.predict(x_val)\n",
    "acc_svc = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_svc)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=['-1','1'],\n",
    "                      title='Confusion matrix for views SVM')\n",
    "precision = precision_score(y_val,y_pred)\n",
    "recall=recall_score(y_val,y_pred)\n",
    "fscore = f1_score(y_val,y_pred)\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.26\n",
      "Confusion matrix\n",
      "[[206  79]\n",
      " [ 86 118]]\n",
      "Precision : 0.598984771574\n",
      "Recall : 0.578431372549\n",
      "F measure : 0.588528678304\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803aa70f0>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NxIMz36JGcMCwfsx5eznLVq1t67CrQjXVMJ0w29Dq1atZvmwZ3bp1o7a2li23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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8039aa5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803aa70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "y_pred = logreg.predict(x_val)\n",
    "acc_logreg = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_logreg)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=['-1','1'],\n",
    "                      title='Confusion matrix for views Logistic regression')\n",
    "precision = precision_score(y_val,y_pred)\n",
    "recall=recall_score(y_val,y_pred)\n",
    "fscore = f1_score(y_val,y_pred)\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.85\n",
      "Confusion matrix\n",
      "[[166 119]\n",
      " [ 92 112]]\n",
      "Precision : 0.484848484848\n",
      "Recall : 0.549019607843\n",
      "F measure : 0.514942528736\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803d1bb00>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t6THAEcDktEwHvt9T5Q5MMyuUlG/JIyJuBxZ1Kj4a+En6+SfAMWXlV0bmbmC0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\nHJhmZjk5MM3Mcvo/TUg+qBOYr4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a80398b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803d1bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN or k-Nearest Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)\n",
    "y_pred = knn.predict(x_val)\n",
    "acc_knn = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_knn)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=['-1','1'],\n",
    "                      title='Confusion matrix for views KNN')\n",
    "precision = precision_score(y_val,y_pred)\n",
    "recall=recall_score(y_val,y_pred)\n",
    "fscore = f1_score(y_val,y_pred)\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.37\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "randomforest = RandomForestClassifier()\n",
    "randomforest.fit(x_train, y_train)\n",
    "y_pred = randomforest.predict(x_val)\n",
    "acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_randomforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>66.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>64.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>58.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>56.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Score\n",
       "2      Logistic Regression  66.26\n",
       "3              Naive Bayes  64.62\n",
       "0  Support Vector Machines  58.08\n",
       "1                      KNN  56.85"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',  'Naive Bayes'],\n",
    "    'Score': [acc_svc, acc_knn, acc_logreg, acc_gaussian]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "#classification for comments\n",
    "from sklearn.model_selection import train_test_split\n",
    "predictors = data.drop(['sentiment','Topics','published_date','outlier','film_date','description','event','main_speaker','name','ratings','related_talks','speaker_occupation','tags','title','url'], axis=1)\n",
    "target = data[\"sentiment\"]\n",
    "\n",
    "x_train, x_val, y_train, y_val = train_test_split(predictors, target, test_size = 0.22, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.87\n",
      "Confusion matrix\n",
      "[[260   2  16]\n",
      " [  1   0   0]\n",
      " [187   0  23]]\n",
      "Precision : 0.583201307895\n",
      "Recall : 0.578732106339\n",
      "F measure : 0.486530957103\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803bac358>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a87b88e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803bac358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(x_train, y_train)\n",
    "y_pred = gaussian.predict(x_val)\n",
    "acc_gaussian = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_gaussian)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=[\"Neagtive\",\"Neutral\",\"Positive\"],\n",
    "                      title='Confusion matrix for rated as Naive Bayes')\n",
    "#y_val = label_binarize(y_val, classes=[-1,0,1])\n",
    "precision = precision_score(y_val,y_pred,average='weighted')\n",
    "recall=recall_score(y_val,y_pred,average='weighted')\n",
    "fscore = f1_score(y_val,y_pred,average='weighted')\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.85\n",
      "[[278   0   0]\n",
      " [  1   0   0]\n",
      " [210   0   0]]\n",
      "Confusion matrix\n",
      "[[278   0   0]\n",
      " [  1   0   0]\n",
      " [210   0   0]]\n",
      "Precision : 0.323200388088\n",
      "Recall : 0.568507157464\n",
      "F measure : 0.412112098501\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8016a06a0>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a80398bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8016a06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(x_train, y_train)\n",
    "y_pred = svc.predict(x_val)\n",
    "acc_svc = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_svc)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "print(cnf_matrix)\n",
    "plot_confusion_matrix(cnf_matrix,classes=[\"Neagtive\",\"Neutral\",\"Positive\"],\n",
    "                      title='Confusion matrix for rated as SVM')\n",
    "precision = precision_score(y_val,y_pred,average='weighted')\n",
    "recall=recall_score(y_val,y_pred,average='weighted')\n",
    "fscore = f1_score(y_val,y_pred,average='weighted')\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.24\n",
      "Confusion matrix\n",
      "[[268   0  10]\n",
      " [  1   0   0]\n",
      " [203   0   7]]\n",
      "Precision : 0.499627905649\n",
      "Recall : 0.562372188139\n",
      "F measure : 0.432778885856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8016d3fd0>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LOudc6gQl0w41n0JO/0cln0v6B2EkKOecK44S6jGVz/L0/V8PWCftQJxzLheV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\n80vgXWBfM5uTXZTpk7QycDQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a87b88e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a8016d3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "y_pred = logreg.predict(x_val)\n",
    "acc_logreg = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_logreg)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=[\"Neagtive\",\"Neutral\",\"Positive\"],\n",
    "                      title='Confusion matrix for rated as Logistic Regression')\n",
    "precision = precision_score(y_val,y_pred,average='weighted')\n",
    "recall=recall_score(y_val,y_pred,average='weighted')\n",
    "fscore = f1_score(y_val,y_pred,average='weighted')\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.62\n",
      "Confusion matrix\n",
      "[[203   0  75]\n",
      " [  1   0   0]\n",
      " [141   0  69]]\n",
      "Precision : 0.54029000326\n",
      "Recall : 0.556237218814\n",
      "F measure : 0.537899685287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\garim\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803d1def0>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a80397ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a803d1def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN or k-Nearest Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)\n",
    "y_pred = knn.predict(x_val)\n",
    "acc_knn = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_knn)\n",
    "cnf_matrix=confusion_matrix(y_val,y_pred)\n",
    "plot_confusion_matrix(cnf_matrix,classes=[\"Neagtive\",\"Neutral\",\"Positive\"],\n",
    "                      title='Confusion matrix for rated as KNN')\n",
    "precision = precision_score(y_val,y_pred,average='weighted')\n",
    "recall=recall_score(y_val,y_pred,average='weighted')\n",
    "fscore = f1_score(y_val,y_pred,average='weighted')\n",
    "print(\"Precision : \" + str(precision))\n",
    "print(\"Recall : \"+ str(recall))\n",
    "print(\"F measure : \" + str(fscore))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.06\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "randomforest = RandomForestClassifier()\n",
    "randomforest.fit(x_train, y_train)\n",
    "y_pred = randomforest.predict(x_val)\n",
    "acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_randomforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_tree(tree, feature_names):\n",
    "    \"\"\"Create tree png using graphviz.\n",
    "\n",
    "    Args\n",
    "    ----\n",
    "    tree -- scikit-learn DecsisionTree.\n",
    "    feature_names -- list of feature names.\n",
    "    \"\"\"\n",
    "    with open(\"dt.dot\", 'w') as f:\n",
    "        export_graphviz(tree, out_file=f,\n",
    "                        feature_names=feature_names)\n",
    "\n",
    "    command = [\"dot\", \"-Tpng\", \"dt.dot\", \"-o\", \"dt.png\"]\n",
    "    try:\n",
    "        subprocess.check_call(command)\n",
    "    except:\n",
    "        exit(\"Could not run dot, ie graphviz, to \"\n",
    "             \"produce visualization\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.3\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "\n",
    "decisiontree = DecisionTreeClassifier()\n",
    "decisiontree.fit(x_train, y_train)\n",
    "y_pred = decisiontree.predict(x_val)\n",
    "acc_decisiontree = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "print(acc_decisiontree)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>57.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>56.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>56.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>55.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Score\n",
       "3              Naive Bayes  57.87\n",
       "0  Support Vector Machines  56.85\n",
       "2      Logistic Regression  56.24\n",
       "1                      KNN  55.62"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',  'Naive Bayes'],\n",
    "    'Score': [acc_svc, acc_knn, acc_logreg, acc_gaussian]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  }
 ],
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