RandomForestClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "errors='coerce'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./analysis/BTC.csv')\n",
    "df.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(index=str, columns={\"votes_disliked\": \"dislike\",\n",
    "                              \"votes_important\": \"important\",\n",
    "                              \"votes_liked\": \"liked\",\n",
    "                              \"votes_lol\": \"lol\",\n",
    "                              \"votes_negative\": \"bearish\",\n",
    "                              \"votes_positive\": \"bullish\",\n",
    "                              \"votes_saved\": \"saved\",\n",
    "                              \"votes_toxic\": \"toxic\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_sent_opinions'] = df['bearish'] + df['bullish']\n",
    "\n",
    "df['bullishness'] = ((df['bullish'] / df['user_sent_opinions']) * 100)\n",
    "\n",
    "df['bearishness'] = -((df['bearish'] / df['user_sent_opinions']) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(list(df.filter(regex = 'isUp_')), axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace([np.inf, -np.inf], np.nan)\n",
    "df = df.fillna(0)\n",
    "df = df.replace(np.nan, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list of target cols for predictions\n",
    "\n",
    "target_cols = ['dislike', 'important',\n",
    "       'liked', 'lol', 'bearish', 'bullish',\n",
    "       'saved', 'toxic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and seperate the dataframes \n",
    "\n",
    "targets = df[target_cols]\n",
    "\n",
    "X_features = df.drop(columns=target_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_features.drop(columns=['date_time','bullishness', 'bearishness', 'user_sent_opinions'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>title_smog_score</th>\n",
       "      <th>description_smog_score</th>\n",
       "      <th>title_coleman_liau_score</th>\n",
       "      <th>description_coleman_liau_score</th>\n",
       "      <th>title_polarity</th>\n",
       "      <th>title_subjectivity</th>\n",
       "      <th>description_polarity</th>\n",
       "      <th>description_subjectivity</th>\n",
       "      <th>domain__@Bitcoin</th>\n",
       "      <th>domain__@ErikVoorhees</th>\n",
       "      <th>...</th>\n",
       "      <th>close_12</th>\n",
       "      <th>close_24</th>\n",
       "      <th>is_Friday</th>\n",
       "      <th>is_Monday</th>\n",
       "      <th>is_Saturday</th>\n",
       "      <th>is_Sunday</th>\n",
       "      <th>is_Thursday</th>\n",
       "      <th>is_Tuesday</th>\n",
       "      <th>is_Wednesday</th>\n",
       "      <th>is_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.208143</td>\n",
       "      <td>11.602472</td>\n",
       "      <td>20.663000</td>\n",
       "      <td>13.085088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.372222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6968.36</td>\n",
       "      <td>6883.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.841846</td>\n",
       "      <td>9.945662</td>\n",
       "      <td>13.456761</td>\n",
       "      <td>9.003663</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.092676</td>\n",
       "      <td>0.474950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6981.57</td>\n",
       "      <td>6988.87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.129100</td>\n",
       "      <td>14.554593</td>\n",
       "      <td>5.998571</td>\n",
       "      <td>12.545714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6710.39</td>\n",
       "      <td>6804.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.208143</td>\n",
       "      <td>12.457976</td>\n",
       "      <td>26.498000</td>\n",
       "      <td>10.168793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.591667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6789.53</td>\n",
       "      <td>6764.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.129100</td>\n",
       "      <td>10.686353</td>\n",
       "      <td>0.108571</td>\n",
       "      <td>9.854655</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.019481</td>\n",
       "      <td>0.176500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6779.03</td>\n",
       "      <td>6764.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   title_smog_score  description_smog_score  title_coleman_liau_score  \\\n",
       "0         11.208143               11.602472                 20.663000   \n",
       "1          8.841846                9.945662                 13.456761   \n",
       "2          3.129100               14.554593                  5.998571   \n",
       "3         11.208143               12.457976                 26.498000   \n",
       "4          3.129100               10.686353                  0.108571   \n",
       "\n",
       "   description_coleman_liau_score  title_polarity  title_subjectivity  \\\n",
       "0                       13.085088             0.0                 0.1   \n",
       "1                        9.003663            -0.1                 0.5   \n",
       "2                       12.545714             0.0                 0.0   \n",
       "3                       10.168793             0.0                 0.0   \n",
       "4                        9.854655             0.5                 0.5   \n",
       "\n",
       "   description_polarity  description_subjectivity  domain__@Bitcoin  \\\n",
       "0              0.100000                  0.372222               0.0   \n",
       "1              0.092676                  0.474950               0.0   \n",
       "2              0.400000                  0.366667               0.0   \n",
       "3              0.000000                  0.591667               0.0   \n",
       "4              0.019481                  0.176500               0.0   \n",
       "\n",
       "   domain__@ErikVoorhees   ...    close_12  close_24  is_Friday  is_Monday  \\\n",
       "0                    0.0   ...     6968.36   6883.74          0          0   \n",
       "1                    0.0   ...     6981.57   6988.87          0          1   \n",
       "2                    0.0   ...     6710.39   6804.50          0          0   \n",
       "3                    0.0   ...     6789.53   6764.20          0          0   \n",
       "4                    0.0   ...     6779.03   6764.38          0          0   \n",
       "\n",
       "   is_Saturday  is_Sunday  is_Thursday  is_Tuesday  is_Wednesday  is_nan  \n",
       "0            0          1            0           0             0       0  \n",
       "1            0          0            0           0             0       0  \n",
       "2            0          0            0           1             0       0  \n",
       "3            0          0            0           1             0       0  \n",
       "4            0          0            0           1             0       0  \n",
       "\n",
       "[5 rows x 4114 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/roseporter/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "targets['user_sent_opinions'] = targets['bearish'] + targets['bullish']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/roseporter/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "targets['bullishness'] = ((targets['bullish'] / targets['user_sent_opinions']) * 100)\n",
    "\n",
    "# targets['bearishness'] = -((targets['bearish'] / targets['user_sent_opinions']) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/roseporter/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "# I want to to decide if the post was flagged as primarly bullish or bearish\n",
    "\n",
    "targets['is_bullish'] = np.where(targets['bullishness'] >= 60, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/roseporter/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "targets.drop(columns=['bullishness', 'bullish', 'bearish'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>dislike</th>\n",
       "      <th>important</th>\n",
       "      <th>liked</th>\n",
       "      <th>lol</th>\n",
       "      <th>saved</th>\n",
       "      <th>toxic</th>\n",
       "      <th>user_sent_opinions</th>\n",
       "      <th>is_bullish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dislike  important  liked  lol  saved  toxic  user_sent_opinions  \\\n",
       "0      2.0       19.0   18.0  1.0    3.0    0.0                26.0   \n",
       "1      0.0       15.5   14.5  1.0    3.0    0.0                26.0   \n",
       "2      2.0       12.0   16.0  4.0    4.0    0.0                21.0   \n",
       "3      0.0        5.0    6.0  0.0    3.0    0.0                 7.0   \n",
       "4      0.0        3.0    3.0  0.0    3.0    0.0                 3.0   \n",
       "\n",
       "   is_bullish  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__abstractmethods__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_abc_cache', '_abc_negative_cache', '_abc_negative_cache_version', '_abc_registry', '_estimator_type', '_get_param_names', '_make_estimator', '_set_oob_score', '_validate_X_predict', '_validate_estimator', '_validate_y_class_weight', 'apply', 'base_estimator', 'bootstrap', 'class_weight', 'criterion', 'decision_path', 'estimator_params', 'feature_importances_', 'fit', 'get_params', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'predict', 'predict_log_proba', 'predict_proba', 'random_state', 'score', 'set_params', 'verbose', 'warm_start']\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(dir(RandomForestClassifier()))\n",
    "print(RandomForestClassifier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on '***is_bullish***'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForestClassifier with Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67256637, 0.66371681, 0.68141593, 0.6875    , 0.76785714])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "k_fold = KFold(n_splits=5)\n",
    "\n",
    "cross_val_score(rf, X_features, targets['is_bullish'], cv=k_fold, scoring='accuracy', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForestClassifier with Fscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =  train_test_split(X_features, targets['is_bullish'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)\n",
    "rf_model = rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_imp_feats = pd.DataFrame(sorted(zip(rf_model.feature_importances_, X_train.columns), reverse=True)[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision, recall, fscore, support = score(y_test, y_pred, average='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.786 / Recall: 0.88 / Accuracy 0.761\n"
     ]
    }
   ],
   "source": [
    "print('Precision: {} / Recall: {} / Accuracy {}'.format(round(precision, 3),\n",
    "                                                   round(recall, 3),\n",
    "                                                   round((y_pred==y_test).sum() / len(y_pred),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision show that when the Random Forest Classifier identified an article as Bullish it was Bullish 78.6% of the time.\n",
      "Recall shows that of all of the bullish articles 88.0% where identified by the Random Forest Classifier.\n",
      "Accuracy show that of all the articles the Random Forest Classifier predicted them to be bullish correctly 76.0% of the time.\n"
     ]
    }
   ],
   "source": [
    "print('Precision show that when the Random Forest Classifier identified an article as Bullish it was Bullish {}% of the time.'\n",
    "      .format(round((precision*100), 1)))\n",
    "\n",
    "print('Recall shows that of all of the bullish articles {}% where identified by the Random Forest Classifier.'\n",
    "      .format(round((recall)*100, 1)))\n",
    "\n",
    "print('Accuracy show that of all the articles the Random Forest Classifier predicted them to be bullish correctly {}% of the time.'\n",
    "      .format(round(round((y_pred==y_test).sum() / len(y_pred),3)*100),1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_imp_feats = most_imp_feats.rename(index=str, columns={0: \"Importance\",\n",
    "                                                           1: \"Feature\"})\n",
    "\n",
    "#most_imp_feats.set_index('Feature', inplace=True)\n",
    "\n",
    "most_imp_feats.plot.barh(x='Feature', y=\"Importance\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}