Regression_Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "# SK-learn libraries for learning.\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.utils import shuffle\n",
    "# SK-learn libraries for evaluation.\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.feature_extraction.text import *\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from matplotlib.colors import LogNorm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from scipy import stats\n",
    "from gensim.summarization import keywords\n",
    "import datetime\n",
    "from mlxtend.plotting import scatterplotmatrix\n",
    "from mlxtend.plotting import heatmap\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import RANSACRegressor\n",
    "from sklearn.linear_model import PoissonRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = pd.read_csv(\"Data/data.csv\", index_col = 0)\n",
    "feature = feature.reset_index().drop(['index'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>link</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>view</th>\n",
       "      <th>topic</th>\n",
       "      <th>time</th>\n",
       "      <th>month</th>\n",
       "      <th>Innovation</th>\n",
       "      <th>Leadership</th>\n",
       "      <th>Lifestyle</th>\n",
       "      <th>...</th>\n",
       "      <th>num_keywords</th>\n",
       "      <th>kw_min_min</th>\n",
       "      <th>kw_min_max</th>\n",
       "      <th>kw_min_avg</th>\n",
       "      <th>kw_max_min</th>\n",
       "      <th>kw_max_max</th>\n",
       "      <th>kw_max_avg</th>\n",
       "      <th>kw_avg_min</th>\n",
       "      <th>kw_avg_max</th>\n",
       "      <th>kw_avg_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.forbes.com/sites/billybambrough/20...</td>\n",
       "      <td>$50 Billion Crash—What Next For Bitcoin, Ether...</td>\n",
       "      <td>Bitcoin, ethereum, Ripple\\s XRP, bitcoin cash,...</td>\n",
       "      <td>47,503</td>\n",
       "      <td>Crypto &amp; Blockchain</td>\n",
       "      <td>Nov 27, 2020, 07:12am</td>\n",
       "      <td>Nov</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>316</td>\n",
       "      <td>33.0</td>\n",
       "      <td>47503.0</td>\n",
       "      <td>7025.750000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11524072.0</td>\n",
       "      <td>972514.750000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1859311.0</td>\n",
       "      <td>27658.245020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.forbes.com/sites/abigailabesamis/2...</td>\n",
       "      <td>12 Bakers Share What They’re Whipping Up Durin...</td>\n",
       "      <td>Sourdough loaves (plus creative uses for disca...</td>\n",
       "      <td>2,826</td>\n",
       "      <td>Dining</td>\n",
       "      <td>May 22, 2020, 11:31am</td>\n",
       "      <td>May</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1069</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>964.800000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12490200.0</td>\n",
       "      <td>222607.118110</td>\n",
       "      <td>63.0</td>\n",
       "      <td>41856.0</td>\n",
       "      <td>5524.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.forbes.com/sites/abigailabesamis/2...</td>\n",
       "      <td>15 Chefs Share What They’re Cooking During The...</td>\n",
       "      <td>In addition to offering delivery and curbside ...</td>\n",
       "      <td>4,064</td>\n",
       "      <td>Dining</td>\n",
       "      <td>Apr 16, 2020, 04:56pm</td>\n",
       "      <td>Apr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1137</td>\n",
       "      <td>137.0</td>\n",
       "      <td>4064.0</td>\n",
       "      <td>1397.833333</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8418600.0</td>\n",
       "      <td>177826.712121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>70403.0</td>\n",
       "      <td>7310.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.forbes.com/sites/kyleedward/2020/1...</td>\n",
       "      <td>2021 Genesis GV80 First Drive: The Flagship SU...</td>\n",
       "      <td>Hyundai\\s luxury brand Genesis has had a stron...</td>\n",
       "      <td>4,264</td>\n",
       "      <td>Cars &amp; Bikes</td>\n",
       "      <td>Nov 26, 2020, 07:03pm</td>\n",
       "      <td>Nov</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>455</td>\n",
       "      <td>738.0</td>\n",
       "      <td>4264.0</td>\n",
       "      <td>1983.333333</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12490200.0</td>\n",
       "      <td>105478.694805</td>\n",
       "      <td>2926.0</td>\n",
       "      <td>19966.0</td>\n",
       "      <td>8524.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.forbes.com/sites/jasonfogelson/202...</td>\n",
       "      <td>2021 Genesis GV80 Test Drive And Review: Serio...</td>\n",
       "      <td>Meet the 2021 Genesis GV80.The US automotive m...</td>\n",
       "      <td>738</td>\n",
       "      <td>Cars &amp; Bikes</td>\n",
       "      <td>Nov 29, 2020, 03:05pm</td>\n",
       "      <td>Nov</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>815</td>\n",
       "      <td>738.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1859311.0</td>\n",
       "      <td>304880.250000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>551511.0</td>\n",
       "      <td>11766.080460</td>\n",
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       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
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      "text/plain": [
       "                                                link  \\\n",
       "0  https://www.forbes.com/sites/billybambrough/20...   \n",
       "1  https://www.forbes.com/sites/abigailabesamis/2...   \n",
       "2  https://www.forbes.com/sites/abigailabesamis/2...   \n",
       "3  https://www.forbes.com/sites/kyleedward/2020/1...   \n",
       "4  https://www.forbes.com/sites/jasonfogelson/202...   \n",
       "\n",
       "                                               title  \\\n",
       "0  $50 Billion Crash—What Next For Bitcoin, Ether...   \n",
       "1  12 Bakers Share What They’re Whipping Up Durin...   \n",
       "2  15 Chefs Share What They’re Cooking During The...   \n",
       "3  2021 Genesis GV80 First Drive: The Flagship SU...   \n",
       "4  2021 Genesis GV80 Test Drive And Review: Serio...   \n",
       "\n",
       "                                                text    view  \\\n",
       "0  Bitcoin, ethereum, Ripple\\s XRP, bitcoin cash,...  47,503   \n",
       "1  Sourdough loaves (plus creative uses for disca...   2,826   \n",
       "2  In addition to offering delivery and curbside ...   4,064   \n",
       "3  Hyundai\\s luxury brand Genesis has had a stron...   4,264   \n",
       "4  Meet the 2021 Genesis GV80.The US automotive m...     738   \n",
       "\n",
       "                 topic                    time month  Innovation  Leadership  \\\n",
       "0  Crypto & Blockchain  Nov 27, 2020, 07:12am    Nov           0           0   \n",
       "1               Dining  May 22, 2020, 11:31am    May           0           0   \n",
       "2               Dining  Apr 16, 2020, 04:56pm    Apr           0           0   \n",
       "3         Cars & Bikes  Nov 26, 2020, 07:03pm    Nov           0           0   \n",
       "4         Cars & Bikes  Nov 29, 2020, 03:05pm    Nov           0           0   \n",
       "\n",
       "   Lifestyle  ...  num_keywords  kw_min_min  kw_min_max   kw_min_avg  \\\n",
       "0          0  ...           316        33.0     47503.0  7025.750000   \n",
       "1          1  ...          1069        60.0      2826.0   964.800000   \n",
       "2          1  ...          1137       137.0      4064.0  1397.833333   \n",
       "3          1  ...           455       738.0      4264.0  1983.333333   \n",
       "4          1  ...           815       738.0       738.0   738.000000   \n",
       "\n",
       "   kw_max_min  kw_max_max     kw_max_avg  kw_avg_min  kw_avg_max    kw_avg_avg  \n",
       "0        33.0  11524072.0  972514.750000        41.0   1859311.0  27658.245020  \n",
       "1        30.0  12490200.0  222607.118110        63.0     41856.0   5524.631579  \n",
       "2        84.0   8418600.0  177826.712121       162.0     70403.0   7310.030303  \n",
       "3        30.0  12490200.0  105478.694805      2926.0     19966.0   8524.333333  \n",
       "4       738.0   1859311.0  304880.250000        24.0    551511.0  11766.080460  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The following two cells are for preprocessing. The function takes in a list of column names that will be dropped. The function gets rid of December, filter outlier and re set index in the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process(lst):\n",
    "    data = feature.drop(lst, axis = 1)\n",
    "    data['view'] = pd.Series([int(v.replace(\",\", \"\").replace(\" \", \"\").replace(\".\", \"\")) for v in data['view']])\n",
    "    data = data[data['Dec'] == 0]\n",
    "    data = data.drop(['Dec'], axis = 1)\n",
    "    data.drop(data[data['view'] > 250000].index, inplace = True) \n",
    "    data.drop(data[data['view'] < 200].index, inplace = True)\n",
    "    data = data.reset_index().drop(['index'], axis = 1)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = feature.drop(['link', 'title', 'text', 'Unnamed: 0.1'], axis = 1)\n",
    "data = pre_process(['link', 'title', 'text', 'topic', 'time', 'month', 'weekend_or_weekday'])\n",
    "train = pre_process(['link', 'title', 'text', 'time', 'topic', 'weekend_or_weekday'])\n",
    "EDA = pre_process([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>view</th>\n",
       "      <th>Innovation</th>\n",
       "      <th>Leadership</th>\n",
       "      <th>Lifestyle</th>\n",
       "      <th>Money</th>\n",
       "      <th>Apr</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Jan</th>\n",
       "      <th>Jul</th>\n",
       "      <th>...</th>\n",
       "      <th>num_keywords</th>\n",
       "      <th>kw_min_min</th>\n",
       "      <th>kw_min_max</th>\n",
       "      <th>kw_min_avg</th>\n",
       "      <th>kw_max_min</th>\n",
       "      <th>kw_max_max</th>\n",
       "      <th>kw_max_avg</th>\n",
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       "      <th>kw_avg_max</th>\n",
       "      <th>kw_avg_avg</th>\n",
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       "      <th>0</th>\n",
       "      <td>47503</td>\n",
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       "      <th>1</th>\n",
       "      <td>2826</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1069</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>964.800000</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>137.0</td>\n",
       "      <td>4064.0</td>\n",
       "      <td>1397.833333</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8418600.0</td>\n",
       "      <td>177826.712121</td>\n",
       "      <td>162.0</td>\n",
       "      <td>70403.0</td>\n",
       "      <td>7310.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>455</td>\n",
       "      <td>738.0</td>\n",
       "      <td>4264.0</td>\n",
       "      <td>1983.333333</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12490200.0</td>\n",
       "      <td>105478.694805</td>\n",
       "      <td>2926.0</td>\n",
       "      <td>19966.0</td>\n",
       "      <td>8524.333333</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>738</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>815</td>\n",
       "      <td>738.0</td>\n",
       "      <td>738.0</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>738.0</td>\n",
       "      <td>1859311.0</td>\n",
       "      <td>304880.250000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>551511.0</td>\n",
       "      <td>11766.080460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    view  Innovation  Leadership  Lifestyle  Money  Apr  Aug  Feb  Jan  Jul  \\\n",
       "0  47503           0           0          0      1    0    0    0    0    0   \n",
       "1   2826           0           0          1      0    0    0    0    0    0   \n",
       "2   4064           0           0          1      0    1    0    0    0    0   \n",
       "3   4264           0           0          1      0    0    0    0    0    0   \n",
       "4    738           0           0          1      0    0    0    0    0    0   \n",
       "\n",
       "   ...  num_keywords  kw_min_min  kw_min_max   kw_min_avg  kw_max_min  \\\n",
       "0  ...           316        33.0     47503.0  7025.750000        33.0   \n",
       "1  ...          1069        60.0      2826.0   964.800000        30.0   \n",
       "2  ...          1137       137.0      4064.0  1397.833333        84.0   \n",
       "3  ...           455       738.0      4264.0  1983.333333        30.0   \n",
       "4  ...           815       738.0       738.0   738.000000       738.0   \n",
       "\n",
       "   kw_max_max     kw_max_avg  kw_avg_min  kw_avg_max    kw_avg_avg  \n",
       "0  11524072.0  972514.750000        41.0   1859311.0  27658.245020  \n",
       "1  12490200.0  222607.118110        63.0     41856.0   5524.631579  \n",
       "2   8418600.0  177826.712121       162.0     70403.0   7310.030303  \n",
       "3  12490200.0  105478.694805      2926.0     19966.0   8524.333333  \n",
       "4   1859311.0  304880.250000        24.0    551511.0  11766.080460  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.855000e+03\n",
       "mean     3.776093e+04\n",
       "std      3.741388e+05\n",
       "min      2.000000e+02\n",
       "25%      1.227500e+03\n",
       "50%      3.563000e+03\n",
       "75%      1.271200e+04\n",
       "max      1.801222e+07\n",
       "Name: view, dtype: float64"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['view'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of views')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['view']).set_title(\"Distribution of views\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot of Views')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 10))\n",
    "sns.boxplot(x=data['view']).set_title(\"Boxplot of Views\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['view', 'num_keywords']\n",
    "scatterplotmatrix(data[features].values, names=features, figsize=(18,10), alpha=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "innovation = [\"Tech\", \"Enterprise & Cloud\", \"Big Data\", \"Healthcare, Healthcare\", \"Cloud\", \"AI\", \"Amazon Web Services BrandVoice | Paid Program\",\n",
    "                  \"AWS Infrastructure Solutions BrandVoice | Paid Program\", \"Big Data,Cloud\",\"Cloud 100\",\n",
    "                  \"Consumer Tech\",\"Cybersecurity\",\"Enterprise Tech\",\"Games\",\"Green Tech\",\"Healthcare\",\n",
    "                  \"Innovation Rules\",\"Japan BrandVoice | Paid Program\",\"Jumio BrandVoice | Paid Program\",\n",
    "                  \"SAP BrandVoice | Paid Program\",\"Science\",\"ServiceNow BrandVoice | Paid Program\",\"Social Media\",\n",
    "                  \"Tableau BrandVoice | Paid Program\",\"T-Mobile For Business BrandVoice | Paid Program\",\n",
    "                  \"Venture Capital\"]\n",
    "leadership = [\"ForbesWomen, ForbesWomen\", \"Billionaires\", \"Asia\", \"World's Billionaires\", \"Forbes 400\",\"America's Richest\", \"Self-Made Women\", \"China's Richest\", \n",
    "                  \"India's Richest\",\"Indonesia's Richest\",\"Korea's Richest\",\"Thailand's Richest\",\"Japan's Richest\",\n",
    "                  \"Australia'sc Richest\",\"Taiwan's Richest\",\"Singapore's Richest\",\"Philippines' Richest\", \n",
    "                  \"Hong Kong's Richest\", \"Malaysia's Richest\", \"Money & Politics\", \"2020 Money\", \"Careers\",\"CEO Network\",\"CFO Network\",\"CIO Network\",\"CMO Network\",\n",
    "                  \"Deloitte BrandVoice | Paid Program\",\"Diversity & Inclusion\",\"Education\",\"Forbes The Culture\",\n",
    "                  \"ForbesWomen\",\"Google Cloud BrandVoice | Paid Program\",\"Leadership Strategy\",\"Under 30\",\n",
    "                  \"Working Remote\"]\n",
    "money = [\"Hedge Funds & Private Equity\", \"Fintech\", \"Banking & Insurance\",\"Crypto & Blockchain\",\"ETFs & Mutual Funds\",\"Fintech\"\"Hedge Funds & Private Equity\",\n",
    "         \"Investing\",\"Markets\",\"New York Life Investments BrandVoice | Paid Program\",\"Personal Finance\",\n",
    "         \"Premium Investing Newsletters\",\"Retirement\",\"Taxes\",\"Tax-Smart Investing\",\"Top Advisor | SHOOK\",\n",
    "         \"Wealth Management\",\"Election 2020\", \"Newsletters\"]\n",
    "business = [\"Consumer\", \"Business, Business\", \"Small Business\", \"Aerospace & Defense\",\"Energy\",\"Hollywood & Entertainment\",\n",
    "            \"Honeywell BrandVoice | Paid Program\",\"Manufacturing\",\"Media\",\n",
    "            \"Mitsubishi Heavy Industries BrandVoice | Paid Program\",\"Policy\",\"Real Estate\",\"Retail\",\n",
    "            \"Salesforce BrandVoice | Paid Program\",\"SportsMoney\",\"Transportation\", \"Business As (Un)usual\",\"Entrepreneurs\",\"Franchises\",\"Small Business Strategy\",\"Square BrandVoice | Paid Program\"]\n",
    "lifestyle = [\"Dining & Drinking\", \"Food & Drink\", \"Watches & Jewelry\", \"Arts\",\"Boats & Planes\",\"Cars & Bikes\",\"Dining\",\"ForbesLife\",\"Forbes Travel Guide\",\"Spirits\",\n",
    "             \"Style & Beauty\",\"Travel\",\"Vices\",\"Watches\"]\n",
    "EDA['topic'] = EDA['topic'].replace(innovation, \"Innovation\")\n",
    "EDA['topic'] = EDA['topic'].replace(leadership, \"Leadership\")\n",
    "EDA['topic'] = EDA['topic'].replace(money, \"Money\")\n",
    "EDA['topic'] = EDA['topic'].replace(business, \"Business\")\n",
    "EDA['topic'] = EDA['topic'].replace(lifestyle, \"Lifestyle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhang\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Views by Month')"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon_lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "data_lst = []\n",
    "for i in mon_lst:\n",
    "    month = EDA[EDA.month.isin([i])]\n",
    "    month_views = np.sum(month.view)\n",
    "    data_lst.append(month_views)\n",
    "sns.barplot(mon_lst, data_lst).set_title(\"Total Views by Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhang\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Articles by Month')"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mon_lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "data_lst = []\n",
    "for i in mon_lst:\n",
    "    month = EDA[EDA.month.isin([i])]\n",
    "    month_counts = np.sum(month.shape[0])\n",
    "    data_lst.append(month_counts)\n",
    "sns.barplot(mon_lst, data_lst).set_title(\"Articles by Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhang\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Articles by Topic')"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_lst = ['Business', 'Innovation', 'Leadership', 'Lifestyle', 'Money']\n",
    "data_lst = []\n",
    "for i in topic_lst:\n",
    "    topic = EDA[EDA.topic.isin([i])]\n",
    "    topic_counts = np.sum(topic.shape[0])\n",
    "    data_lst.append(topic_counts)\n",
    "sns.barplot(topic_lst, data_lst).set_title(\"Articles by Topic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zhang\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6]), <a list of 7 Text xticklabel objects>)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_lst = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]\n",
    "data_lst = []\n",
    "for i in weekday_lst:\n",
    "    day = EDA[EDA.day_of_week.isin([i])]\n",
    "    day_counts = np.sum(day.shape[0])\n",
    "    data_lst.append(day_counts)\n",
    "sns.barplot(['Sunday', 'Monday', 'Tuesday', \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"], data_lst).set_title(\"Articles by Day of Week\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor_pear = []\n",
    "cor_ken = []\n",
    "cor_spe = []\n",
    "for i in data.columns:\n",
    "    views = np.array([x for x in data['view']])\n",
    "    cor, _ = stats.pearsonr(data[i], views)\n",
    "    cor2, _ = stats.kendalltau(data[i], views)\n",
    "    cor3, _ = stats.spearmanr(data[i], views)\n",
    "    cor_pear.append(cor)\n",
    "    cor_ken.append(cor2)\n",
    "    cor_spe.append(cor3)\n",
    "dic = {'pears_cor': cor_pear,\n",
    "      'ken_cor': cor_ken,\n",
    "      'spe_cor': cor_spe}\n",
    "cor_tbl = pd.DataFrame(dic, data.columns)\n",
    "pos_cor = cor_tbl[(cor_tbl['pears_cor'] > 0) & (cor_tbl['ken_cor'] > 0) & (cor_tbl['spe_cor'] > 0)].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlations between Features and Shares')"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,6))\n",
    "plt.scatter(pos_cor['index'],pos_cor['pears_cor'], label='Pearson')\n",
    "plt.scatter(pos_cor['index'],pos_cor['ken_cor'], label='Kendall')\n",
    "plt.scatter(pos_cor['index'],pos_cor['spe_cor'], label='Spearman')\n",
    "plt.xticks(rotation=90, fontsize = 20)\n",
    "plt.legend()\n",
    "plt.title(\"Correlations between Features and Shares\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An OLS demo on Jan, Feb, Mar, Apr window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                   view   R-squared (uncentered):                   0.376\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.366\n",
      "Method:                 Least Squares   F-statistic:                              37.00\n",
      "Date:                Wed, 09 Dec 2020   Prob (F-statistic):                   3.93e-143\n",
      "Time:                        23:54:38   Log-Likelihood:                         -18854.\n",
      "No. Observations:                1623   AIC:                                  3.776e+04\n",
      "Df Residuals:                    1597   BIC:                                  3.790e+04\n",
      "Df Model:                          26                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "================================================================================================\n",
      "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------\n",
      "Innovation                    5534.4751   1756.946      3.150      0.002    2088.313    8980.637\n",
      "Money                         3296.9548   2408.030      1.369      0.171   -1426.276    8020.186\n",
      "Apr                              0.0002      0.001      0.392      0.695      -0.001       0.001\n",
      "Aug                              0.0002      0.001      0.345      0.730      -0.001       0.001\n",
      "Feb                           1730.5782   1763.734      0.981      0.327   -1728.899    5190.055\n",
      "Jul                              0.0002      0.000      0.331      0.740      -0.001       0.001\n",
      "Jun                           8.423e-06   2.38e-05      0.353      0.724   -3.83e-05    5.52e-05\n",
      "Mar                           3346.6549   2000.537      1.673      0.095    -577.300    7270.610\n",
      "May                           3.258e-07   1.04e-06      0.314      0.754   -1.71e-06    2.36e-06\n",
      "n_tokens_title:                456.7496    219.950      2.077      0.038      25.329     888.170\n",
      "n_unique_tokens              -8.636e+04   3.56e+04     -2.426      0.015   -1.56e+05   -1.65e+04\n",
      "n_non_stop_words             -1.824e+04   1.74e+04     -1.051      0.294   -5.23e+04    1.58e+04\n",
      "n_non_stop_unique_tokens      8.757e+04   5.12e+04      1.710      0.087   -1.29e+04    1.88e+05\n",
      "day_of_week                    771.7325    732.591      1.053      0.292    -665.209    2208.674\n",
      "friday                        -344.0938   2610.788     -0.132      0.895   -5465.025    4776.837\n",
      "saturday                      -345.2807   2990.240     -0.115      0.908   -6210.489    5519.928\n",
      "sunday                        3916.2419   2822.295      1.388      0.165   -1619.551    9452.035\n",
      "weekend                       3570.9612   1347.706      2.650      0.008     927.503    6214.420\n",
      "abs_title_sentiment_polarity  8554.8745   4108.314      2.082      0.037     496.619    1.66e+04\n",
      "title_subjectivity             283.6200   3001.620      0.094      0.925   -5603.910    6171.150\n",
      "global_rate_negative_words     5.52e+04   1.48e+05      0.372      0.710   -2.36e+05    3.46e+05\n",
      "rate_negative_words           1.962e+04   1.02e+04      1.925      0.054    -368.113    3.96e+04\n",
      "min_positive_polarity        -2.081e+04   2.08e+04     -1.000      0.317   -6.16e+04       2e+04\n",
      "LDA_00                        2490.9085   2154.957      1.156      0.248   -1735.932    6717.749\n",
      "timedelta                        4.1800     20.090      0.208      0.835     -35.225      43.585\n",
      "kw_min_max                      -0.0166      0.002     -6.731      0.000      -0.021      -0.012\n",
      "kw_min_avg                       3.7419      0.224     16.691      0.000       3.302       4.182\n",
      "kw_max_min                      -0.0003      0.004     -0.067      0.947      -0.009       0.008\n",
      "kw_max_avg                      -0.0015      0.002     -0.808      0.419      -0.005       0.002\n",
      "kw_avg_min                      -0.1095      0.135     -0.813      0.417      -0.374       0.155\n",
      "kw_avg_max                       0.0011      0.001      1.062      0.288      -0.001       0.003\n",
      "kw_avg_avg                      -0.0051      0.046     -0.112      0.911      -0.095       0.084\n",
      "==============================================================================\n",
      "Omnibus:                     1187.033   Durbin-Watson:                   1.933\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            21255.403\n",
      "Skew:                           3.282   Prob(JB):                         0.00\n",
      "Kurtosis:                      19.469   Cond. No.                     1.08e+16\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[3] The smallest eigenvalue is 1.74e-17. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "# mobility\n",
    "train_months = ['Jan', 'Feb', 'Mar']\n",
    "# variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "variable_list = list(pos_cor['index'])[1:]\n",
    "train_data = train[train.month.isin(train_months)]\n",
    "train_label = train_data.view\n",
    "# train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "train_data = train_data[variable_list]\n",
    "\n",
    "test_data = train[train.month.isin(['Apr'])]\n",
    "test_label = test_data.view\n",
    "# test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "test_data = test_data[variable_list]\n",
    "\n",
    "\n",
    "model = sm.OLS(train_label, train_data).fit() \n",
    "print(model.summary())\n",
    "\n",
    "y_hat = model.predict(test_data)\n",
    "pred = pd.DataFrame(y_hat)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred['true_view'] = test_label\n",
    "pred['predict_view'] = pred[0]\n",
    "pred['pct_error'] = ((pred.predict_view - pred.true_view)/pred.true_view)*100\n",
    "pred = pred.drop([0], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>true_view</th>\n",
       "      <th>predict_view</th>\n",
       "      <th>pct_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4064</td>\n",
       "      <td>2268.734393</td>\n",
       "      <td>-44.174843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3540</td>\n",
       "      <td>1361.347454</td>\n",
       "      <td>-61.543857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1645</td>\n",
       "      <td>-4532.256416</td>\n",
       "      <td>-375.517107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>662</td>\n",
       "      <td>-4162.322176</td>\n",
       "      <td>-728.749573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1684</td>\n",
       "      <td>-707.909079</td>\n",
       "      <td>-142.037356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>512</td>\n",
       "      <td>17320.722842</td>\n",
       "      <td>3282.953680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>3152</td>\n",
       "      <td>14174.035234</td>\n",
       "      <td>349.683859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>3097</td>\n",
       "      <td>17563.426713</td>\n",
       "      <td>467.110969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5720</th>\n",
       "      <td>6817</td>\n",
       "      <td>33285.688613</td>\n",
       "      <td>388.274734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5738</th>\n",
       "      <td>2054</td>\n",
       "      <td>25485.580065</td>\n",
       "      <td>1140.777997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      true_view  predict_view    pct_error\n",
       "2          4064   2268.734393   -44.174843\n",
       "22         3540   1361.347454   -61.543857\n",
       "82         1645  -4532.256416  -375.517107\n",
       "114         662  -4162.322176  -728.749573\n",
       "148        1684   -707.909079  -142.037356\n",
       "...         ...           ...          ...\n",
       "5694        512  17320.722842  3282.953680\n",
       "5695       3152  14174.035234   349.683859\n",
       "5697       3097  17563.426713   467.110969\n",
       "5720       6817  33285.688613   388.274734\n",
       "5738       2054  25485.580065  1140.777997\n",
       "\n",
       "[408 rows x 3 columns]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train OLS on rolling window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct','Nov']\n",
    "# variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "variable_list = list(pos_cor['index'])[1:]\n",
    "result = pd.DataFrame({'Train_month': [], 'Test_month': [], 'Average_pct_error': []})\n",
    "\n",
    "for i in range(len(lst) - 3):\n",
    "    \n",
    "    months = lst[i: i+3]\n",
    "    train_data = train[train.month.isin(months)]\n",
    "    train_label = train_data.view\n",
    "    \n",
    "    train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "#     train_data = train_data[variable_list]\n",
    "    \n",
    "    test_data = train[train.month.isin([lst[i+3]])]\n",
    "    test_label = test_data.view\n",
    "    test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "#     test_data = test_data[variable_list]\n",
    "\n",
    "    model = sm.OLS(train_label, train_data).fit()\n",
    "    \n",
    "    y_hat = model.predict(test_data)\n",
    "    pred = pd.DataFrame(y_hat)\n",
    "    pred['true_view'] = test_label\n",
    "    pred['predict_view'] = pred[0]\n",
    "    pred['pct_error'] = ((pred.predict_view - pred.true_view)/pred.true_view)*100\n",
    "    \n",
    "    pct_error = np.mean(pred['pct_error'])\n",
    "    row = [', '.join(months), lst[i+3], pct_error]\n",
    "    result.loc[len(result)] = row\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>Train_month</th>\n",
       "      <th>Test_month</th>\n",
       "      <th>Average_pct_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan, Feb, Mar</td>\n",
       "      <td>Apr</td>\n",
       "      <td>-4.078043e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb, Mar, Apr</td>\n",
       "      <td>May</td>\n",
       "      <td>-2.512117e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar, Apr, May</td>\n",
       "      <td>Jun</td>\n",
       "      <td>-2.077185e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr, May, Jun</td>\n",
       "      <td>Jul</td>\n",
       "      <td>-1.620805e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May, Jun, Jul</td>\n",
       "      <td>Aug</td>\n",
       "      <td>-2.158531e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun, Jul, Aug</td>\n",
       "      <td>Sep</td>\n",
       "      <td>2.129441e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul, Aug, Sep</td>\n",
       "      <td>Oct</td>\n",
       "      <td>-7.432516e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug, Sep, Oct</td>\n",
       "      <td>Nov</td>\n",
       "      <td>-6.090499e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Train_month Test_month  Average_pct_error\n",
       "0  Jan, Feb, Mar        Apr      -4.078043e+07\n",
       "1  Feb, Mar, Apr        May      -2.512117e+08\n",
       "2  Mar, Apr, May        Jun      -2.077185e+08\n",
       "3  Apr, May, Jun        Jul      -1.620805e+08\n",
       "4  May, Jun, Jul        Aug      -2.158531e+08\n",
       "5  Jun, Jul, Aug        Sep       2.129441e+07\n",
       "6  Jul, Aug, Sep        Oct      -7.432516e+07\n",
       "7  Aug, Sep, Oct        Nov      -6.090499e+08"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following two cells are for ridge model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.31326910e+03  5.12171198e+02  2.19846776e+04  3.29092530e+00\n",
      "  8.23386252e-05 -1.66742483e-01]\n"
     ]
    }
   ],
   "source": [
    "train_months = ['Jan', 'Feb', 'Mar']\n",
    "variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "train_data = train[train.month.isin(train_months)]\n",
    "train_label = train_data.view\n",
    "# train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "train_data = train_data[variable_list]\n",
    "\n",
    "test_data = train[train.month.isin(['Apr'])]\n",
    "test_label = test_data.view\n",
    "# test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "test_data = test_data[variable_list]\n",
    "\n",
    "\n",
    "model = Ridge(alpha=1.0).fit(train_data, train_label) \n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>view</th>\n",
       "      <th>predict_view</th>\n",
       "      <th>pct_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4064</td>\n",
       "      <td>7488.090793</td>\n",
       "      <td>84.254203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3540</td>\n",
       "      <td>11165.285493</td>\n",
       "      <td>215.403545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1645</td>\n",
       "      <td>7408.464853</td>\n",
       "      <td>350.362605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>662</td>\n",
       "      <td>1626.939274</td>\n",
       "      <td>145.761220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1684</td>\n",
       "      <td>5968.376975</td>\n",
       "      <td>254.416685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>512</td>\n",
       "      <td>13727.970650</td>\n",
       "      <td>2581.244268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>3152</td>\n",
       "      <td>11893.077761</td>\n",
       "      <td>277.318457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>3097</td>\n",
       "      <td>20906.743453</td>\n",
       "      <td>575.064367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5720</th>\n",
       "      <td>6817</td>\n",
       "      <td>34880.783643</td>\n",
       "      <td>411.673517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5738</th>\n",
       "      <td>2054</td>\n",
       "      <td>26959.979708</td>\n",
       "      <td>1212.559869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      view  predict_view    pct_error\n",
       "2     4064   7488.090793    84.254203\n",
       "22    3540  11165.285493   215.403545\n",
       "82    1645   7408.464853   350.362605\n",
       "114    662   1626.939274   145.761220\n",
       "148   1684   5968.376975   254.416685\n",
       "...    ...           ...          ...\n",
       "5694   512  13727.970650  2581.244268\n",
       "5695  3152  11893.077761   277.318457\n",
       "5697  3097  20906.743453   575.064367\n",
       "5720  6817  34880.783643   411.673517\n",
       "5738  2054  26959.979708  1212.559869\n",
       "\n",
       "[408 rows x 3 columns]"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = model.predict(test_data)\n",
    "pred = pd.DataFrame(test_label)\n",
    "pred['predict_view'] =  y_hat\n",
    "pred['pct_error'] = ((pred.predict_view - pred.view)/pred.view)*100\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following two cells are for RANSAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_months = ['Jan', 'Feb', 'Mar']\n",
    "# variable_list = list(pos_cor['index'])[1:]\n",
    "variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "train_data = train[train.month.isin(train_months)]\n",
    "train_label = train_data.view\n",
    "# train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "train_data = train_data[variable_list]\n",
    "\n",
    "test_data = train[train.month.isin(['Apr'])]\n",
    "test_label = test_data.view\n",
    "# test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "test_data = test_data[variable_list]\n",
    "\n",
    "\n",
    "model = RANSACRegressor(random_state=1).fit(train_data, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.030875757531353853"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(train_data, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>view</th>\n",
       "      <th>predict_view</th>\n",
       "      <th>pct_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4064</td>\n",
       "      <td>3566.553747</td>\n",
       "      <td>-12.240311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3540</td>\n",
       "      <td>4393.431788</td>\n",
       "      <td>24.108243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1645</td>\n",
       "      <td>2013.180583</td>\n",
       "      <td>22.381798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>662</td>\n",
       "      <td>-1304.593132</td>\n",
       "      <td>-297.068449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1684</td>\n",
       "      <td>1699.216778</td>\n",
       "      <td>0.903609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>512</td>\n",
       "      <td>-735.160828</td>\n",
       "      <td>-243.586099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>3152</td>\n",
       "      <td>1266.868243</td>\n",
       "      <td>-59.807480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>3097</td>\n",
       "      <td>6319.645197</td>\n",
       "      <td>104.056997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5720</th>\n",
       "      <td>6817</td>\n",
       "      <td>13209.421860</td>\n",
       "      <td>93.771774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5738</th>\n",
       "      <td>2054</td>\n",
       "      <td>9444.519139</td>\n",
       "      <td>359.811058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      view  predict_view   pct_error\n",
       "2     4064   3566.553747  -12.240311\n",
       "22    3540   4393.431788   24.108243\n",
       "82    1645   2013.180583   22.381798\n",
       "114    662  -1304.593132 -297.068449\n",
       "148   1684   1699.216778    0.903609\n",
       "...    ...           ...         ...\n",
       "5694   512   -735.160828 -243.586099\n",
       "5695  3152   1266.868243  -59.807480\n",
       "5697  3097   6319.645197  104.056997\n",
       "5720  6817  13209.421860   93.771774\n",
       "5738  2054   9444.519139  359.811058\n",
       "\n",
       "[408 rows x 3 columns]"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = model.predict(test_data)\n",
    "pred = pd.DataFrame(test_label)\n",
    "pred['predict_view'] =  y_hat\n",
    "pred['pct_error'] = ((pred.predict_view - pred.view)/pred.view)*100\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following is for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "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>Train_month</th>\n",
       "      <th>Test_month</th>\n",
       "      <th>Average_pct_error_OLS</th>\n",
       "      <th>Average_pct_error_Ridge</th>\n",
       "      <th>Average_pct_error_RANSAC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan, Feb, Mar, Apr, May, Jun</td>\n",
       "      <td>Jul</td>\n",
       "      <td>357.296020</td>\n",
       "      <td>340.065529</td>\n",
       "      <td>12.765784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb, Mar, Apr, May, Jun, Jul</td>\n",
       "      <td>Aug</td>\n",
       "      <td>409.791899</td>\n",
       "      <td>386.235687</td>\n",
       "      <td>14.712310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar, Apr, May, Jun, Jul, Aug</td>\n",
       "      <td>Sep</td>\n",
       "      <td>666.568385</td>\n",
       "      <td>634.645327</td>\n",
       "      <td>89.812642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr, May, Jun, Jul, Aug, Sep</td>\n",
       "      <td>Oct</td>\n",
       "      <td>523.779937</td>\n",
       "      <td>498.377277</td>\n",
       "      <td>47.477806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May, Jun, Jul, Aug, Sep, Oct</td>\n",
       "      <td>Nov</td>\n",
       "      <td>765.292144</td>\n",
       "      <td>727.014411</td>\n",
       "      <td>94.771695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Train_month Test_month  Average_pct_error_OLS  \\\n",
       "0  Jan, Feb, Mar, Apr, May, Jun        Jul             357.296020   \n",
       "1  Feb, Mar, Apr, May, Jun, Jul        Aug             409.791899   \n",
       "2  Mar, Apr, May, Jun, Jul, Aug        Sep             666.568385   \n",
       "3  Apr, May, Jun, Jul, Aug, Sep        Oct             523.779937   \n",
       "4  May, Jun, Jul, Aug, Sep, Oct        Nov             765.292144   \n",
       "\n",
       "   Average_pct_error_Ridge  Average_pct_error_RANSAC  \n",
       "0               340.065529                 12.765784  \n",
       "1               386.235687                 14.712310  \n",
       "2               634.645327                 89.812642  \n",
       "3               498.377277                 47.477806  \n",
       "4               727.014411                 94.771695  "
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct','Nov']\n",
    "variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "# + ['Jan', 'Sep', 'Oct', 'Nov']\n",
    "result = pd.DataFrame({'Train_month': [], 'Test_month': [], 'Average_pct_error_OLS': [], 'Average_pct_error_Ridge': [], 'Average_pct_error_RANSAC': []})\n",
    "\n",
    "for i in range(len(lst) - 6):\n",
    "    \n",
    "    months = lst[i: i+6]\n",
    "    train_data = train[train.month.isin(months)]\n",
    "    train_label = train_data.view\n",
    "    \n",
    "#     train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "    train_data = train_data[variable_list]\n",
    "    \n",
    "    test_data = train[train.month.isin([lst[i+6]])]\n",
    "    test_label = test_data.view\n",
    "#     test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "    test_data = test_data[variable_list]\n",
    "\n",
    "    model1 = sm.OLS(train_label, train_data).fit()\n",
    "    model2 = Ridge(alpha=10).fit(train_data, train_label)\n",
    "    model3 = RANSACRegressor(random_state=1).fit(train_data, train_label)\n",
    "    \n",
    "    y_hat_1 = model1.predict(test_data)\n",
    "    pred_1 = pd.DataFrame(test_label)\n",
    "    pred_1['predict_view'] =  y_hat_1\n",
    "    pred_1['pct_error'] = ((pred_1.predict_view - pred_1.view)/pred_1.view)*100\n",
    "    \n",
    "    y_hat_2 = model2.predict(test_data)\n",
    "    pred_2 = pd.DataFrame(test_label)\n",
    "    pred_2['predict_view'] =  y_hat_2\n",
    "    pred_2['pct_error'] = ((pred_2.predict_view - pred_2.view)/pred_2.view)*100\n",
    "    \n",
    "    y_hat_3 = model3.predict(test_data)\n",
    "    pred_3 = pd.DataFrame(test_label)\n",
    "    pred_3['predict_view'] =  y_hat_3\n",
    "    pred_3['pct_error'] = ((pred_3.predict_view - pred_3.view)/pred_3.view)*100\n",
    "    \n",
    "    pct_error_1 = np.mean(pred_1['pct_error'])\n",
    "    pct_error_2 = np.mean(pred_2['pct_error'])\n",
    "    pct_error_3 = np.mean(pred_3['pct_error'])\n",
    "    row = [', '.join(months), lst[i+6], pct_error_1, pct_error_2, pct_error_3]\n",
    "    result.loc[len(result)] = row\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "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>Train_month</th>\n",
       "      <th>Test_month</th>\n",
       "      <th>Average_pct_error_OLS</th>\n",
       "      <th>Average_pct_error_Ridge</th>\n",
       "      <th>Average_pct_error_RANSAC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan, Feb, Mar</td>\n",
       "      <td>Apr</td>\n",
       "      <td>319.729076</td>\n",
       "      <td>356.004239</td>\n",
       "      <td>1392.898791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb, Mar, Apr</td>\n",
       "      <td>May</td>\n",
       "      <td>401.005362</td>\n",
       "      <td>368.403006</td>\n",
       "      <td>398.307972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar, Apr, May</td>\n",
       "      <td>Jun</td>\n",
       "      <td>1193.665884</td>\n",
       "      <td>298.099140</td>\n",
       "      <td>217.026743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr, May, Jun</td>\n",
       "      <td>Jul</td>\n",
       "      <td>1125.715705</td>\n",
       "      <td>242.256164</td>\n",
       "      <td>396.768366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May, Jun, Jul</td>\n",
       "      <td>Aug</td>\n",
       "      <td>1713.486585</td>\n",
       "      <td>347.450707</td>\n",
       "      <td>171.833617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun, Jul, Aug</td>\n",
       "      <td>Sep</td>\n",
       "      <td>1587.295163</td>\n",
       "      <td>347.152479</td>\n",
       "      <td>107.398336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul, Aug, Sep</td>\n",
       "      <td>Oct</td>\n",
       "      <td>167.605017</td>\n",
       "      <td>127.312918</td>\n",
       "      <td>1353.532792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug, Sep, Oct</td>\n",
       "      <td>Nov</td>\n",
       "      <td>354.960497</td>\n",
       "      <td>305.893875</td>\n",
       "      <td>599.146119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Train_month Test_month  Average_pct_error_OLS  Average_pct_error_Ridge  \\\n",
       "0  Jan, Feb, Mar        Apr             319.729076               356.004239   \n",
       "1  Feb, Mar, Apr        May             401.005362               368.403006   \n",
       "2  Mar, Apr, May        Jun            1193.665884               298.099140   \n",
       "3  Apr, May, Jun        Jul            1125.715705               242.256164   \n",
       "4  May, Jun, Jul        Aug            1713.486585               347.450707   \n",
       "5  Jun, Jul, Aug        Sep            1587.295163               347.152479   \n",
       "6  Jul, Aug, Sep        Oct             167.605017               127.312918   \n",
       "7  Aug, Sep, Oct        Nov             354.960497               305.893875   \n",
       "\n",
       "   Average_pct_error_RANSAC  \n",
       "0               1392.898791  \n",
       "1                398.307972  \n",
       "2                217.026743  \n",
       "3                396.768366  \n",
       "4                171.833617  \n",
       "5                107.398336  \n",
       "6               1353.532792  \n",
       "7                599.146119  "
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct','Nov']\n",
    "# variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "# + ['Jan', 'Sep', 'Oct', 'Nov']\n",
    "variable_list = list(pos_cor['index'])[1:]\n",
    "result = pd.DataFrame({'Train_month': [], 'Test_month': [], 'Average_pct_error_OLS': [], 'Average_pct_error_Ridge': [], 'Average_pct_error_RANSAC': []})\n",
    "\n",
    "for i in range(len(lst) - 3):\n",
    "    \n",
    "    months = lst[i: i+3]\n",
    "    train_data = train[train.month.isin(months)]\n",
    "    train_label = train_data.view\n",
    "    \n",
    "#     train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "    train_data = train_data[variable_list]\n",
    "    \n",
    "    test_data = train[train.month.isin([lst[i+3]])]\n",
    "    test_label = test_data.view\n",
    "#     test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "    test_data = test_data[variable_list]\n",
    "\n",
    "    model1 = sm.OLS(train_label, train_data).fit()\n",
    "    model2 = Ridge(alpha=10).fit(train_data, train_label)\n",
    "    model3 = RANSACRegressor(random_state=4).fit(train_data, train_label)\n",
    "    \n",
    "    y_hat_1 = model1.predict(test_data)\n",
    "    pred_1 = pd.DataFrame(test_label)\n",
    "    pred_1['predict_view'] =  abs(y_hat_1)\n",
    "    pred_1['pct_error'] = ((pred_1.predict_view - pred_1.view)/pred_1.view)*100\n",
    "    \n",
    "    y_hat_2 = model2.predict(test_data)\n",
    "    pred_2 = pd.DataFrame(test_label)\n",
    "    pred_2['predict_view'] =  abs(y_hat_2)\n",
    "    pred_2['pct_error'] = ((pred_2.predict_view - pred_2.view)/pred_2.view)*100\n",
    "    \n",
    "    y_hat_3 = model3.predict(test_data)\n",
    "    pred_3 = pd.DataFrame(test_label)\n",
    "    pred_3['predict_view'] =  abs(y_hat_3)\n",
    "    pred_3['pct_error'] = ((pred_3.predict_view - pred_3.view)/pred_3.view)*100\n",
    "    \n",
    "    pct_error_1 = np.mean(pred_1['pct_error'])\n",
    "    pct_error_2 = np.mean(pred_2['pct_error'])\n",
    "    pct_error_3 = np.mean(pred_3['pct_error'])\n",
    "    row = [', '.join(months), lst[i+3], pct_error_1, pct_error_2, pct_error_3]\n",
    "    result.loc[len(result)] = row\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "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>Train_month</th>\n",
       "      <th>Test_month</th>\n",
       "      <th>Average_pct_error_OLS</th>\n",
       "      <th>Average_pct_error_Ridge</th>\n",
       "      <th>Average_pct_error_RANSAC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan, Feb, Mar</td>\n",
       "      <td>Apr</td>\n",
       "      <td>477.185388</td>\n",
       "      <td>466.504964</td>\n",
       "      <td>62.594194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb, Mar, Apr</td>\n",
       "      <td>May</td>\n",
       "      <td>487.871928</td>\n",
       "      <td>454.136389</td>\n",
       "      <td>163.364450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar, Apr, May</td>\n",
       "      <td>Jun</td>\n",
       "      <td>461.134148</td>\n",
       "      <td>422.646195</td>\n",
       "      <td>20.804344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr, May, Jun</td>\n",
       "      <td>Jul</td>\n",
       "      <td>374.704348</td>\n",
       "      <td>347.975590</td>\n",
       "      <td>31.416039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May, Jun, Jul</td>\n",
       "      <td>Aug</td>\n",
       "      <td>439.161979</td>\n",
       "      <td>415.720109</td>\n",
       "      <td>183.568721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun, Jul, Aug</td>\n",
       "      <td>Sep</td>\n",
       "      <td>696.576601</td>\n",
       "      <td>692.576029</td>\n",
       "      <td>246.302175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul, Aug, Sep</td>\n",
       "      <td>Oct</td>\n",
       "      <td>500.632742</td>\n",
       "      <td>497.333328</td>\n",
       "      <td>66.804967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug, Sep, Oct</td>\n",
       "      <td>Nov</td>\n",
       "      <td>515.204290</td>\n",
       "      <td>535.530223</td>\n",
       "      <td>134.833123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Train_month Test_month  Average_pct_error_OLS  Average_pct_error_Ridge  \\\n",
       "0  Jan, Feb, Mar        Apr             477.185388               466.504964   \n",
       "1  Feb, Mar, Apr        May             487.871928               454.136389   \n",
       "2  Mar, Apr, May        Jun             461.134148               422.646195   \n",
       "3  Apr, May, Jun        Jul             374.704348               347.975590   \n",
       "4  May, Jun, Jul        Aug             439.161979               415.720109   \n",
       "5  Jun, Jul, Aug        Sep             696.576601               692.576029   \n",
       "6  Jul, Aug, Sep        Oct             500.632742               497.333328   \n",
       "7  Aug, Sep, Oct        Nov             515.204290               535.530223   \n",
       "\n",
       "   Average_pct_error_RANSAC  \n",
       "0                 62.594194  \n",
       "1                163.364450  \n",
       "2                 20.804344  \n",
       "3                 31.416039  \n",
       "4                183.568721  \n",
       "5                246.302175  \n",
       "6                 66.804967  \n",
       "7                134.833123  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct','Nov']\n",
    "variable_list = ['LDA_00', 'n_tokens_title:', 'rate_negative_words','kw_min_avg','kw_avg_max', 'kw_avg_avg']\n",
    "# + ['Jan', 'Sep', 'Oct', 'Nov']\n",
    "result = pd.DataFrame({'Train_month': [], 'Test_month': [], 'Average_pct_error_OLS': [], 'Average_pct_error_Ridge': [], 'Average_pct_error_RANSAC': []})\n",
    "\n",
    "for i in range(len(lst) - 3):\n",
    "    \n",
    "    months = lst[i: i+3]\n",
    "    train_data = train[train.month.isin(months)]\n",
    "    train_label = train_data.view\n",
    "    \n",
    "#     train_data = train_data.drop(['month', 'view'], axis = 1)\n",
    "    train_data = train_data[variable_list]\n",
    "    \n",
    "    test_data = train[train.month.isin([lst[i+3]])]\n",
    "    test_label = test_data.view\n",
    "#     test_data = test_data.drop(['month', 'view'], axis = 1)\n",
    "    test_data = test_data[variable_list]\n",
    "\n",
    "    model1 = sm.OLS(train_label, train_data).fit()\n",
    "    model2 = Ridge(alpha=1).fit(train_data, train_label)\n",
    "    model3 = RANSACRegressor(random_state=4).fit(train_data, train_label)\n",
    "    \n",
    "    y_hat_1 = model1.predict(test_data)\n",
    "    pred_1 = pd.DataFrame(test_label)\n",
    "    pred_1['predict_view'] =  y_hat_1\n",
    "    pred_1['pct_error'] = ((pred_1.predict_view - pred_1.view)/pred_1.view)*100\n",
    "    \n",
    "    y_hat_2 = model2.predict(test_data)\n",
    "    pred_2 = pd.DataFrame(test_label)\n",
    "    pred_2['predict_view'] =  y_hat_2\n",
    "    pred_2['pct_error'] = ((pred_2.predict_view - pred_2.view)/pred_2.view)*100\n",
    "    \n",
    "    y_hat_3 = model3.predict(test_data)\n",
    "    pred_3 = pd.DataFrame(test_label)\n",
    "    pred_3['predict_view'] =  y_hat_3\n",
    "    pred_3['pct_error'] = ((pred_3.predict_view - pred_3.view)/pred_3.view)*100\n",
    "    \n",
    "    pct_error_1 = np.mean(pred_1['pct_error'])\n",
    "    pct_error_2 = np.mean(pred_2['pct_error'])\n",
    "    pct_error_3 = np.mean(pred_3['pct_error'])\n",
    "    row = [', '.join(months), lst[i+3], pct_error_1, pct_error_2, pct_error_3]\n",
    "    result.loc[len(result)] = row\n",
    "result"
   ]
  }
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