03_Data_Cleaning_EDA_Models1&2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning, EDA, and Model 1 & 2 Building\n",
    "#### Credit to Aiden Curley for assistance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"contents\"></a>\n",
    "- [Contents](#contents)  \n",
    "    - [Imports](#imports)  \n",
    "    - [Data Cleaning](#cleaning)  \n",
    "    - [EDA](#eda)  \n",
    "    - [Model 1: Logistic Regression on Title](#model1)  \n",
    "    - [Model 1 Scores](#model1_scores) \n",
    "    - [Model 1 Confusion Matrix](#model1_visuals) \n",
    "    - [Model 2: Logistic Regression on Title and Content](#model2) \n",
    "    - [Model 2 Scores](#model2_scores) \n",
    "    - [Model 2 Confusion Matrix](#model2_visuals) \n",
    "    - [Interpreting Coefficients](#coefs) \n",
    "    - [Save to File](#save)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"imports\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:18.276803Z",
     "iopub.status.busy": "2020-08-26T03:03:18.276580Z",
     "iopub.status.idle": "2020-08-26T03:03:19.434385Z",
     "shell.execute_reply": "2020-08-26T03:03:19.433865Z",
     "shell.execute_reply.started": "2020-08-26T03:03:18.276780Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:19.435266Z",
     "iopub.status.busy": "2020-08-26T03:03:19.435129Z",
     "iopub.status.idle": "2020-08-26T03:03:20.200590Z",
     "shell.execute_reply": "2020-08-26T03:03:20.200166Z",
     "shell.execute_reply.started": "2020-08-26T03:03:19.435248Z"
    }
   },
   "outputs": [],
   "source": [
    "amd = pd.read_csv('./Data/amd_big.csv')\n",
    "buildpc = pd.read_csv('./Data/build_pc_big.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"cleaning\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:20.201319Z",
     "iopub.status.busy": "2020-08-26T03:03:20.201222Z",
     "iopub.status.idle": "2020-08-26T03:03:20.214848Z",
     "shell.execute_reply": "2020-08-26T03:03:20.214489Z",
     "shell.execute_reply.started": "2020-08-26T03:03:20.201307Z"
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td>[removed]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 subreddit             title   selftext\n",
       "0           0       Amd  Low VRAM Issues?  [removed]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:20.215364Z",
     "iopub.status.busy": "2020-08-26T03:03:20.215278Z",
     "iopub.status.idle": "2020-08-26T03:03:20.225670Z",
     "shell.execute_reply": "2020-08-26T03:03:20.225241Z",
     "shell.execute_reply.started": "2020-08-26T03:03:20.215353Z"
    }
   },
   "outputs": [],
   "source": [
    "# Drop unneccesary columns\n",
    "amd.drop(columns='Unnamed: 0', inplace=True)\n",
    "buildpc.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:20.226263Z",
     "iopub.status.busy": "2020-08-26T03:03:20.226172Z",
     "iopub.status.idle": "2020-08-26T03:03:20.231388Z",
     "shell.execute_reply": "2020-08-26T03:03:20.230999Z",
     "shell.execute_reply.started": "2020-08-26T03:03:20.226251Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>Hey guys just finished my build not able to ge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Is there a somewhat reasonably priced GPU that...</td>\n",
       "      <td>I have been rocking a Vega blower for a couple...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit                                              title  \\\n",
       "0  buildapc                                         Windows 10   \n",
       "1  buildapc  Is there a somewhat reasonably priced GPU that...   \n",
       "\n",
       "                                            selftext  \n",
       "0  Hey guys just finished my build not able to ge...  \n",
       "1  I have been rocking a Vega blower for a couple...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildpc.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:20.900344Z",
     "iopub.status.busy": "2020-08-26T03:03:20.900156Z",
     "iopub.status.idle": "2020-08-26T03:03:20.903329Z",
     "shell.execute_reply": "2020-08-26T03:03:20.902999Z",
     "shell.execute_reply.started": "2020-08-26T03:03:20.900322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109740"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resize the data to have the same number of posts for each category\n",
    "amd.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:21.412307Z",
     "iopub.status.busy": "2020-08-26T03:03:21.412196Z",
     "iopub.status.idle": "2020-08-26T03:03:21.414779Z",
     "shell.execute_reply": "2020-08-26T03:03:21.414499Z",
     "shell.execute_reply.started": "2020-08-26T03:03:21.412293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-585"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.shape[0] - buildpc.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:21.600949Z",
     "iopub.status.busy": "2020-08-26T03:03:21.600761Z",
     "iopub.status.idle": "2020-08-26T03:03:21.604165Z",
     "shell.execute_reply": "2020-08-26T03:03:21.603884Z",
     "shell.execute_reply.started": "2020-08-26T03:03:21.600931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109740, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buildpc = buildpc[:-585]\n",
    "buildpc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:21.796514Z",
     "iopub.status.busy": "2020-08-26T03:03:21.796371Z",
     "iopub.status.idle": "2020-08-26T03:03:21.802543Z",
     "shell.execute_reply": "2020-08-26T03:03:21.802191Z",
     "shell.execute_reply.started": "2020-08-26T03:03:21.796501Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td>[removed]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amd</td>\n",
       "      <td>[Level1Techs] 32 Core Threadripper Workstation...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Decent cheap AMD prebuilt systems? Canada</td>\n",
       "      <td>Trying to see if there's anything worth recomm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amd</td>\n",
       "      <td>The new drivers don't wanna download</td>\n",
       "      <td>I have been trying to update my drivers from t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amd</td>\n",
       "      <td>aorus redemption I on b550m</td>\n",
       "      <td>last time, i said only gigabyte has not used d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit                                              title  \\\n",
       "0       Amd                                   Low VRAM Issues?   \n",
       "1       Amd  [Level1Techs] 32 Core Threadripper Workstation...   \n",
       "2       Amd          Decent cheap AMD prebuilt systems? Canada   \n",
       "3       Amd               The new drivers don't wanna download   \n",
       "4       Amd                        aorus redemption I on b550m   \n",
       "\n",
       "                                            selftext  \n",
       "0                                          [removed]  \n",
       "1                                                NaN  \n",
       "2  Trying to see if there's anything worth recomm...  \n",
       "3  I have been trying to update my drivers from t...  \n",
       "4  last time, i said only gigabyte has not used d...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:21.954774Z",
     "iopub.status.busy": "2020-08-26T03:03:21.954538Z",
     "iopub.status.idle": "2020-08-26T03:03:21.973723Z",
     "shell.execute_reply": "2020-08-26T03:03:21.973281Z",
     "shell.execute_reply.started": "2020-08-26T03:03:21.954751Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109740 entries, 0 to 109739\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   subreddit  109740 non-null  object\n",
      " 1   title      109740 non-null  object\n",
      " 2   selftext   78415 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check nan values\n",
    "amd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:22.159158Z",
     "iopub.status.busy": "2020-08-26T03:03:22.158934Z",
     "iopub.status.idle": "2020-08-26T03:03:22.183871Z",
     "shell.execute_reply": "2020-08-26T03:03:22.183398Z",
     "shell.execute_reply.started": "2020-08-26T03:03:22.159135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109740 entries, 0 to 109739\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   subreddit  109740 non-null  object\n",
      " 1   title      109740 non-null  object\n",
      " 2   selftext   106497 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "buildpc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:22.342595Z",
     "iopub.status.busy": "2020-08-26T03:03:22.342382Z",
     "iopub.status.idle": "2020-08-26T03:03:22.359394Z",
     "shell.execute_reply": "2020-08-26T03:03:22.358932Z",
     "shell.execute_reply.started": "2020-08-26T03:03:22.342573Z"
    }
   },
   "outputs": [],
   "source": [
    "# Replace nan values\n",
    "amd.fillna(\"\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:22.534431Z",
     "iopub.status.busy": "2020-08-26T03:03:22.534219Z",
     "iopub.status.idle": "2020-08-26T03:03:22.559117Z",
     "shell.execute_reply": "2020-08-26T03:03:22.558653Z",
     "shell.execute_reply.started": "2020-08-26T03:03:22.534409Z"
    }
   },
   "outputs": [],
   "source": [
    "buildpc.fillna(\"\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:22.936438Z",
     "iopub.status.busy": "2020-08-26T03:03:22.936211Z",
     "iopub.status.idle": "2020-08-26T03:03:22.959490Z",
     "shell.execute_reply": "2020-08-26T03:03:22.959043Z",
     "shell.execute_reply.started": "2020-08-26T03:03:22.936415Z"
    }
   },
   "outputs": [],
   "source": [
    "# Combine the Dataframes\n",
    "df = amd.append(buildpc, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:23.241262Z",
     "iopub.status.busy": "2020-08-26T03:03:23.241165Z",
     "iopub.status.idle": "2020-08-26T03:03:23.246771Z",
     "shell.execute_reply": "2020-08-26T03:03:23.246294Z",
     "shell.execute_reply.started": "2020-08-26T03:03:23.241250Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td>[removed]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amd</td>\n",
       "      <td>[Level1Techs] 32 Core Threadripper Workstation...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Decent cheap AMD prebuilt systems? Canada</td>\n",
       "      <td>Trying to see if there's anything worth recomm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amd</td>\n",
       "      <td>The new drivers don't wanna download</td>\n",
       "      <td>I have been trying to update my drivers from t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amd</td>\n",
       "      <td>aorus redemption I on b550m</td>\n",
       "      <td>last time, i said only gigabyte has not used d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit                                              title  \\\n",
       "0       Amd                                   Low VRAM Issues?   \n",
       "1       Amd  [Level1Techs] 32 Core Threadripper Workstation...   \n",
       "2       Amd          Decent cheap AMD prebuilt systems? Canada   \n",
       "3       Amd               The new drivers don't wanna download   \n",
       "4       Amd                        aorus redemption I on b550m   \n",
       "\n",
       "                                            selftext  \n",
       "0                                          [removed]  \n",
       "1                                                     \n",
       "2  Trying to see if there's anything worth recomm...  \n",
       "3  I have been trying to update my drivers from t...  \n",
       "4  last time, i said only gigabyte has not used d...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:23.638628Z",
     "iopub.status.busy": "2020-08-26T03:03:23.638419Z",
     "iopub.status.idle": "2020-08-26T03:03:23.642277Z",
     "shell.execute_reply": "2020-08-26T03:03:23.641782Z",
     "shell.execute_reply.started": "2020-08-26T03:03:23.638604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:24.321147Z",
     "iopub.status.busy": "2020-08-26T03:03:24.321042Z",
     "iopub.status.idle": "2020-08-26T03:03:24.327472Z",
     "shell.execute_reply": "2020-08-26T03:03:24.327114Z",
     "shell.execute_reply.started": "2020-08-26T03:03:24.321135Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td>[removed]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amd</td>\n",
       "      <td>[Level1Techs] 32 Core Threadripper Workstation...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Decent cheap AMD prebuilt systems? Canada</td>\n",
       "      <td>Trying to see if there's anything worth recomm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amd</td>\n",
       "      <td>The new drivers don't wanna download</td>\n",
       "      <td>I have been trying to update my drivers from t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amd</td>\n",
       "      <td>aorus redemption I on b550m</td>\n",
       "      <td>last time, i said only gigabyte has not used d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219475</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Installing Hyper 212 Evo</td>\n",
       "      <td>I’m trying to install a Hyper 212 Evo on a Ryz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219476</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Loud pop 1 day after replacing PSU now won’t t...</td>\n",
       "      <td>Literally blew my old PSU 3 days ago, fixed it...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219477</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Error code 4d</td>\n",
       "      <td>So I started my build last night and after com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219478</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Someone help me!</td>\n",
       "      <td>My PC screen turned green and then black after...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219479</th>\n",
       "      <td>buildapc</td>\n",
       "      <td>Clean case design with wood?</td>\n",
       "      <td>Looking for an atx case that's basic / clean a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219480 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subreddit                                              title  \\\n",
       "0            Amd                                   Low VRAM Issues?   \n",
       "1            Amd  [Level1Techs] 32 Core Threadripper Workstation...   \n",
       "2            Amd          Decent cheap AMD prebuilt systems? Canada   \n",
       "3            Amd               The new drivers don't wanna download   \n",
       "4            Amd                        aorus redemption I on b550m   \n",
       "...          ...                                                ...   \n",
       "219475  buildapc                           Installing Hyper 212 Evo   \n",
       "219476  buildapc  Loud pop 1 day after replacing PSU now won’t t...   \n",
       "219477  buildapc                                      Error code 4d   \n",
       "219478  buildapc                                   Someone help me!   \n",
       "219479  buildapc                       Clean case design with wood?   \n",
       "\n",
       "                                                 selftext  \n",
       "0                                               [removed]  \n",
       "1                                                          \n",
       "2       Trying to see if there's anything worth recomm...  \n",
       "3       I have been trying to update my drivers from t...  \n",
       "4       last time, i said only gigabyte has not used d...  \n",
       "...                                                   ...  \n",
       "219475  I’m trying to install a Hyper 212 Evo on a Ryz...  \n",
       "219476  Literally blew my old PSU 3 days ago, fixed it...  \n",
       "219477  So I started my build last night and after com...  \n",
       "219478  My PC screen turned green and then black after...  \n",
       "219479  Looking for an atx case that's basic / clean a...  \n",
       "\n",
       "[219480 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:24.813181Z",
     "iopub.status.busy": "2020-08-26T03:03:24.812970Z",
     "iopub.status.idle": "2020-08-26T03:03:24.838214Z",
     "shell.execute_reply": "2020-08-26T03:03:24.837780Z",
     "shell.execute_reply.started": "2020-08-26T03:03:24.813158Z"
    }
   },
   "outputs": [],
   "source": [
    "# Replace [removed]\n",
    "df.replace(to_replace='[removed]', value='', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:25.496774Z",
     "iopub.status.busy": "2020-08-26T03:03:25.496609Z",
     "iopub.status.idle": "2020-08-26T03:03:25.502203Z",
     "shell.execute_reply": "2020-08-26T03:03:25.501847Z",
     "shell.execute_reply.started": "2020-08-26T03:03:25.496759Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amd</td>\n",
       "      <td>[Level1Techs] 32 Core Threadripper Workstation...</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Decent cheap AMD prebuilt systems? Canada</td>\n",
       "      <td>Trying to see if there's anything worth recomm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amd</td>\n",
       "      <td>The new drivers don't wanna download</td>\n",
       "      <td>I have been trying to update my drivers from t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amd</td>\n",
       "      <td>aorus redemption I on b550m</td>\n",
       "      <td>last time, i said only gigabyte has not used d...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit                                              title  \\\n",
       "0       Amd                                   Low VRAM Issues?   \n",
       "1       Amd  [Level1Techs] 32 Core Threadripper Workstation...   \n",
       "2       Amd          Decent cheap AMD prebuilt systems? Canada   \n",
       "3       Amd               The new drivers don't wanna download   \n",
       "4       Amd                        aorus redemption I on b550m   \n",
       "\n",
       "                                            selftext  \n",
       "0                                                     \n",
       "1                                                     \n",
       "2  Trying to see if there's anything worth recomm...  \n",
       "3  I have been trying to update my drivers from t...  \n",
       "4  last time, i said only gigabyte has not used d...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:27.982516Z",
     "iopub.status.busy": "2020-08-26T03:03:27.982305Z",
     "iopub.status.idle": "2020-08-26T03:03:27.997408Z",
     "shell.execute_reply": "2020-08-26T03:03:27.996976Z",
     "shell.execute_reply.started": "2020-08-26T03:03:27.982493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buildapc    109740\n",
       "Amd         109740\n",
       "Name: subreddit, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subreddit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:28.133885Z",
     "iopub.status.busy": "2020-08-26T03:03:28.133706Z",
     "iopub.status.idle": "2020-08-26T03:03:28.138343Z",
     "shell.execute_reply": "2020-08-26T03:03:28.137929Z",
     "shell.execute_reply.started": "2020-08-26T03:03:28.133863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subreddit    object\n",
       "title        object\n",
       "selftext     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:28.302914Z",
     "iopub.status.busy": "2020-08-26T03:03:28.302652Z",
     "iopub.status.idle": "2020-08-26T03:03:28.349505Z",
     "shell.execute_reply": "2020-08-26T03:03:28.349128Z",
     "shell.execute_reply.started": "2020-08-26T03:03:28.302880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subreddit    0\n",
       "title        0\n",
       "selftext     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:28.452756Z",
     "iopub.status.busy": "2020-08-26T03:03:28.452651Z",
     "iopub.status.idle": "2020-08-26T03:03:28.553507Z",
     "shell.execute_reply": "2020-08-26T03:03:28.553032Z",
     "shell.execute_reply.started": "2020-08-26T03:03:28.452745Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a column that includes both Title and Selftext \n",
    "df['title_text_combined'] = df['title'] + ' ' + df['selftext']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:28.630044Z",
     "iopub.status.busy": "2020-08-26T03:03:28.629946Z",
     "iopub.status.idle": "2020-08-26T03:03:28.636069Z",
     "shell.execute_reply": "2020-08-26T03:03:28.635709Z",
     "shell.execute_reply.started": "2020-08-26T03:03:28.630032Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>title_text_combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amd</td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "      <td></td>\n",
       "      <td>Low VRAM Issues?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subreddit             title selftext title_text_combined\n",
       "0       Amd  Low VRAM Issues?            Low VRAM Issues? "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-25T01:57:36.151985Z",
     "iopub.status.busy": "2020-08-25T01:57:36.151763Z",
     "iopub.status.idle": "2020-08-25T01:57:36.155033Z",
     "shell.execute_reply": "2020-08-25T01:57:36.154465Z",
     "shell.execute_reply.started": "2020-08-25T01:57:36.151961Z"
    }
   },
   "source": [
    "### Calculate percentage of selftext that has no content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:33.590927Z",
     "iopub.status.busy": "2020-08-26T03:03:33.590641Z",
     "iopub.status.idle": "2020-08-26T03:03:33.615096Z",
     "shell.execute_reply": "2020-08-26T03:03:33.614708Z",
     "shell.execute_reply.started": "2020-08-26T03:03:33.590898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47059"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows in selftext that have no input\n",
    "(df['selftext'] == '').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:33.783906Z",
     "iopub.status.busy": "2020-08-26T03:03:33.783709Z",
     "iopub.status.idle": "2020-08-26T03:03:33.787762Z",
     "shell.execute_reply": "2020-08-26T03:03:33.787170Z",
     "shell.execute_reply.started": "2020-08-26T03:03:33.783884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.44"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percent of data in selftext that has no input in selftext column\n",
    "round((47059 / df.shape[0]) *100, 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:33.929545Z",
     "iopub.status.busy": "2020-08-26T03:03:33.929346Z",
     "iopub.status.idle": "2020-08-26T03:03:33.958042Z",
     "shell.execute_reply": "2020-08-26T03:03:33.957656Z",
     "shell.execute_reply.started": "2020-08-26T03:03:33.929520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['title_text_combined'] == '').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:34.090353Z",
     "iopub.status.busy": "2020-08-26T03:03:34.090199Z",
     "iopub.status.idle": "2020-08-26T03:03:34.093299Z",
     "shell.execute_reply": "2020-08-26T03:03:34.092916Z",
     "shell.execute_reply.started": "2020-08-26T03:03:34.090335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:34.258263Z",
     "iopub.status.busy": "2020-08-26T03:03:34.258132Z",
     "iopub.status.idle": "2020-08-26T03:03:34.266566Z",
     "shell.execute_reply": "2020-08-26T03:03:34.266251Z",
     "shell.execute_reply.started": "2020-08-26T03:03:34.258247Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Low VRAM Issues?'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying that the title is pulled correctly\n",
    "df['title'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:03:34.425480Z",
     "iopub.status.busy": "2020-08-26T03:03:34.425270Z",
     "iopub.status.idle": "2020-08-26T03:03:43.218147Z",
     "shell.execute_reply": "2020-08-26T03:03:43.217723Z",
     "shell.execute_reply.started": "2020-08-26T03:03:34.425454Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a funcion that removes special character, symbols, \n",
    "# and stopwords that may hurt the performance of my models\n",
    "REPLACE_BY_SPACE_RE = re.compile('[/(){}\\[\\]\\|@,;]')   \n",
    "BAD_SYMBOLS_RE = re.compile('[^0-9a-z #+_]')\n",
    "STOPWORDS = set(stopwords.words('english'))\n",
    "\n",
    "def clean_text(text):\n",
    "    \"\"\"\n",
    "        text: a string\n",
    "        \n",
    "        return: modified initial string\n",
    "    \"\"\"\n",
    "    text = text.lower() # lowercase text\n",
    "    text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text. substitute the matched string in REPLACE_BY_SPACE_RE with space.\n",
    "    text = BAD_SYMBOLS_RE.sub('', text) # remove symbols which are in BAD_SYMBOLS_RE from text. substitute the matched string in BAD_SYMBOLS_RE with nothing. \n",
    "    text = re.sub(r'\\d+', '', text) # This gets rid of xthe integers\n",
    "    text = text.replace('x', '') # Credit - https://stackoverflow.com/questions/30315035/strip-numbers-from-string-in-python\n",
    "    text = ' '.join(word for word in text.split() if word not in STOPWORDS) # remove stopwors from text\n",
    "    \n",
    "    return text\n",
    "\n",
    "df['title'] = df['title'].apply(clean_text)\n",
    "df['title_text_combined'] = df['title_text_combined'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"eda\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-24T23:48:00.020797Z",
     "iopub.status.busy": "2020-08-24T23:48:00.020641Z",
     "iopub.status.idle": "2020-08-24T23:48:00.022707Z",
     "shell.execute_reply": "2020-08-24T23:48:00.022193Z",
     "shell.execute_reply.started": "2020-08-24T23:48:00.020780Z"
    }
   },
   "source": [
    "### Max word count for each subreddit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:03:56.084847Z",
     "iopub.status.busy": "2020-08-26T01:03:56.084756Z",
     "iopub.status.idle": "2020-08-26T01:03:57.062625Z",
     "shell.execute_reply": "2020-08-26T01:03:57.062118Z",
     "shell.execute_reply.started": "2020-08-26T01:03:56.084834Z"
    }
   },
   "outputs": [],
   "source": [
    "# Vecortrize up to 25,000 features with at least 5 instances \n",
    "eda_cvec = CountVectorizer(analyzer = \"word\",\n",
    "                             tokenizer = None,\n",
    "                             preprocessor = None,\n",
    "                             stop_words = None,\n",
    "                             max_features = 25000,\n",
    "                             min_df = 5\n",
    "                             )\n",
    "eda_cvec_data = eda_cvec.fit_transform(df['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:03:57.063219Z",
     "iopub.status.busy": "2020-08-26T01:03:57.063127Z",
     "iopub.status.idle": "2020-08-26T01:03:57.654267Z",
     "shell.execute_reply": "2020-08-26T01:03:57.653774Z",
     "shell.execute_reply.started": "2020-08-26T01:03:57.063207Z"
    }
   },
   "outputs": [],
   "source": [
    "cleaned_df = pd.DataFrame(eda_cvec_data.toarray(), columns= eda_cvec.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:03:57.654868Z",
     "iopub.status.busy": "2020-08-26T01:03:57.654775Z",
     "iopub.status.idle": "2020-08-26T01:03:57.664461Z",
     "shell.execute_reply": "2020-08-26T01:03:57.664050Z",
     "shell.execute_reply.started": "2020-08-26T01:03:57.654856Z"
    }
   },
   "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>_rogame</th>\n",
       "      <th>aa</th>\n",
       "      <th>aaa</th>\n",
       "      <th>ab</th>\n",
       "      <th>abandoned</th>\n",
       "      <th>abb</th>\n",
       "      <th>abba</th>\n",
       "      <th>abgaming</th>\n",
       "      <th>abilities</th>\n",
       "      <th>ability</th>\n",
       "      <th>...</th>\n",
       "      <th>zh</th>\n",
       "      <th>zhaoin</th>\n",
       "      <th>zi</th>\n",
       "      <th>zip</th>\n",
       "      <th>zm</th>\n",
       "      <th>zone</th>\n",
       "      <th>zoom</th>\n",
       "      <th>zotac</th>\n",
       "      <th>zowie</th>\n",
       "      <th>zp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <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>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <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>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8397 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   _rogame  aa  aaa  ab  abandoned  abb  abba  abgaming  abilities  ability  \\\n",
       "0        0   0    0   0          0    0     0         0          0        0   \n",
       "1        0   0    0   0          0    0     0         0          0        0   \n",
       "2        0   0    0   0          0    0     0         0          0        0   \n",
       "3        0   0    0   0          0    0     0         0          0        0   \n",
       "4        0   0    0   0          0    0     0         0          0        0   \n",
       "\n",
       "   ...  zh  zhaoin  zi  zip  zm  zone  zoom  zotac  zowie  zp  \n",
       "0  ...   0       0   0    0   0     0     0      0      0   0  \n",
       "1  ...   0       0   0    0   0     0     0      0      0   0  \n",
       "2  ...   0       0   0    0   0     0     0      0      0   0  \n",
       "3  ...   0       0   0    0   0     0     0      0      0   0  \n",
       "4  ...   0       0   0    0   0     0     0      0      0   0  \n",
       "\n",
       "[5 rows x 8397 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:03:57.664972Z",
     "iopub.status.busy": "2020-08-26T01:03:57.664881Z",
     "iopub.status.idle": "2020-08-26T01:05:02.513725Z",
     "shell.execute_reply": "2020-08-26T01:05:02.513283Z",
     "shell.execute_reply.started": "2020-08-26T01:03:57.664961Z"
    }
   },
   "outputs": [],
   "source": [
    "split_these = pd.concat([cleaned_df,df['subreddit']], axis=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:02.514343Z",
     "iopub.status.busy": "2020-08-26T01:05:02.514249Z",
     "iopub.status.idle": "2020-08-26T01:05:02.524919Z",
     "shell.execute_reply": "2020-08-26T01:05:02.524471Z",
     "shell.execute_reply.started": "2020-08-26T01:05:02.514331Z"
    }
   },
   "outputs": [],
   "source": [
    "subs = split_these['subreddit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:02.525432Z",
     "iopub.status.busy": "2020-08-26T01:05:02.525346Z",
     "iopub.status.idle": "2020-08-26T01:05:02.531520Z",
     "shell.execute_reply": "2020-08-26T01:05:02.531163Z",
     "shell.execute_reply.started": "2020-08-26T01:05:02.525421Z"
    }
   },
   "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>subreddit</th>\n",
       "      <th>subreddit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Amd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219475</th>\n",
       "      <td>0</td>\n",
       "      <td>buildapc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219476</th>\n",
       "      <td>0</td>\n",
       "      <td>buildapc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219477</th>\n",
       "      <td>0</td>\n",
       "      <td>buildapc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219478</th>\n",
       "      <td>0</td>\n",
       "      <td>buildapc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219479</th>\n",
       "      <td>0</td>\n",
       "      <td>buildapc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219480 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subreddit subreddit\n",
       "0              0       Amd\n",
       "1              0       Amd\n",
       "2              0       Amd\n",
       "3              0       Amd\n",
       "4              0       Amd\n",
       "...          ...       ...\n",
       "219475         0  buildapc\n",
       "219476         0  buildapc\n",
       "219477         0  buildapc\n",
       "219478         0  buildapc\n",
       "219479         0  buildapc\n",
       "\n",
       "[219480 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:02.532019Z",
     "iopub.status.busy": "2020-08-26T01:05:02.531932Z",
     "iopub.status.idle": "2020-08-26T01:05:04.246299Z",
     "shell.execute_reply": "2020-08-26T01:05:04.245813Z",
     "shell.execute_reply.started": "2020-08-26T01:05:02.532008Z"
    }
   },
   "outputs": [],
   "source": [
    "# Credit https://stackoverflow.com/questions/32041245/fast-method-for-removing-duplicate-columns-in-pandas-dataframe\n",
    "split_these = split_these.loc[:,~split_these.columns.duplicated(keep='last')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:04.246943Z",
     "iopub.status.busy": "2020-08-26T01:05:04.246848Z",
     "iopub.status.idle": "2020-08-26T01:05:04.261445Z",
     "shell.execute_reply": "2020-08-26T01:05:04.261086Z",
     "shell.execute_reply.started": "2020-08-26T01:05:04.246931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amd         109740\n",
       "buildapc    109740\n",
       "Name: subreddit, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_these['subreddit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:04.261944Z",
     "iopub.status.busy": "2020-08-26T01:05:04.261857Z",
     "iopub.status.idle": "2020-08-26T01:05:06.428909Z",
     "shell.execute_reply": "2020-08-26T01:05:06.428407Z",
     "shell.execute_reply.started": "2020-08-26T01:05:04.261933Z"
    }
   },
   "outputs": [],
   "source": [
    "amd = split_these[split_these['subreddit'] == 'Amd']\n",
    "build_pc = split_these[split_these['subreddit'] == 'buildapc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:06.429508Z",
     "iopub.status.busy": "2020-08-26T01:05:06.429416Z",
     "iopub.status.idle": "2020-08-26T01:05:06.432453Z",
     "shell.execute_reply": "2020-08-26T01:05:06.431939Z",
     "shell.execute_reply.started": "2020-08-26T01:05:06.429495Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(109740, 8397)\n",
      "(109740, 8397)\n"
     ]
    }
   ],
   "source": [
    "print(amd.shape)\n",
    "print(build_pc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:06.433158Z",
     "iopub.status.busy": "2020-08-26T01:05:06.433030Z",
     "iopub.status.idle": "2020-08-26T01:05:07.537606Z",
     "shell.execute_reply": "2020-08-26T01:05:07.537108Z",
     "shell.execute_reply.started": "2020-08-26T01:05:06.433142Z"
    }
   },
   "outputs": [],
   "source": [
    "# Credit to Chris :)\n",
    "top_words_amd = {}\n",
    "for i in amd.columns[:-1]:\n",
    "    top_words_amd[i] =  amd[i].sum()\n",
    "# top_words to dataframe sorted by highest occurance\n",
    "most_freq_amd = pd.DataFrame(sorted(top_words_amd.items(), key = lambda x: x[1], reverse = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:07.538193Z",
     "iopub.status.busy": "2020-08-26T01:05:07.538102Z",
     "iopub.status.idle": "2020-08-26T01:05:07.540650Z",
     "shell.execute_reply": "2020-08-26T01:05:07.540155Z",
     "shell.execute_reply.started": "2020-08-26T01:05:07.538181Z"
    }
   },
   "outputs": [],
   "source": [
    "most_freq_amd = most_freq_amd[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:07.541420Z",
     "iopub.status.busy": "2020-08-26T01:05:07.541290Z",
     "iopub.status.idle": "2020-08-26T01:05:08.660125Z",
     "shell.execute_reply": "2020-08-26T01:05:08.659700Z",
     "shell.execute_reply.started": "2020-08-26T01:05:07.541403Z"
    }
   },
   "outputs": [],
   "source": [
    "# Credit to Chris :)\n",
    "top_words_build_pc = {}\n",
    "for i in amd.columns[:-1]:\n",
    "    top_words_build_pc[i] =  build_pc[i].sum()\n",
    "# top_words to dataframe sorted by highest occurance\n",
    "most_freq_build_pc = pd.DataFrame(sorted(top_words_build_pc.items(), key = lambda x: x[1], reverse = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:08.660903Z",
     "iopub.status.busy": "2020-08-26T01:05:08.660768Z",
     "iopub.status.idle": "2020-08-26T01:05:08.663284Z",
     "shell.execute_reply": "2020-08-26T01:05:08.662964Z",
     "shell.execute_reply.started": "2020-08-26T01:05:08.660886Z"
    }
   },
   "outputs": [],
   "source": [
    "most_freq_build_pc = most_freq_build_pc[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:08.663781Z",
     "iopub.status.busy": "2020-08-26T01:05:08.663695Z",
     "iopub.status.idle": "2020-08-26T01:05:08.845750Z",
     "shell.execute_reply": "2020-08-26T01:05:08.845361Z",
     "shell.execute_reply.started": "2020-08-26T01:05:08.663770Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "chart = sns.barplot(x= most_freq_amd[0], y = most_freq_amd[1], palette='YlGnBu_r')\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=45);\n",
    "plt.title('Most Common Words in AMD')\n",
    "\n",
    "plt.xlabel('Common Words')\n",
    "plt.ylabel('Word Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:08.846357Z",
     "iopub.status.busy": "2020-08-26T01:05:08.846261Z",
     "iopub.status.idle": "2020-08-26T01:05:08.992965Z",
     "shell.execute_reply": "2020-08-26T01:05:08.992577Z",
     "shell.execute_reply.started": "2020-08-26T01:05:08.846344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAIbCAYAAACnlVuUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd5hkVZnH8e8LQ5IMM6QZYEgiQUkjIqgoiAQDoKKDKEFkiAKKusKuigEXDKCooIOgYAIUVBRREAQMLDooCgius2AYYQUDiK6i4Lt/nFP2naa7p0NVz+3p7+d56umqU1X3nFt1w++ee251ZCaSJEmSFq+lFncDJEmSJBnMJUmSpFYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JGncRcTMiMiImDLC950SER/vVbsWUfdVEXFIvX9oRHxniNdeHxGvGb/WSVoSGMwltV5E/CIi/h4RU/uV31rD3cwxTj8jYtNFvGbdiDg/Iu6LiIcj4q6IeHtErDiWutuizl9GxNqNsn8fpOzri6eVkJnvzsxRBd6I+GRdjv5cv8NbImLXEdS9d2ZeOJq6+7Xj1Ij4R23HgxHxvYh4euP5JXpZkzQ4g7mkieIe4MDOg4h4MrDCeFQcEWsAN9X6np6ZKwN7AKsBm4xHG3otM+8D5gPPahQ/C7hrgLIbRzLtkfaK99h7MnMlYFXgXODyiFh6MbTjktqOacB3ajtiMixrkgZnMJc0UXwKOLjx+BDgouYLImLViLgoIh6IiF9GxH9ExFL1uU0j4oaIeCgifhcRl9TyTsj8ce3BfPkAdb8eeBh4ZWb+AiAzf52ZJ2TmT+p0do6IH9Tp/yAidm606/qIeFftGf1zRHwlItaMiM9ExJ/q62c2Xp8RcUxE/Lz2mL4zIjaJiJvq6y+NiGUbrz8iIuZHxB8i4oqIWK/ftI6q0/pjRHwkImKQz/hGagivYXU74IP9yp4O3BgRS9XP95cRcX/93Fetr+sMUzk8In4FXBcRS0fE++pnfzfw/H7f3aERcXed33si4qCBGlh7mz/dr55DIuJXddr/Psi8LSQz/wl8FlgDWLv/tPtNf0p9POjwlIjYo/ZsPxQRHwYG+4z7t+MfwIXAOsCaDGNZk7TkMphLmij+C1glIraoAfHlwKf7veZDlJ7QjYFdKUH+sPrcO4GrgdWBGfW1ZGanN3ibzFwpMy8ZoO7nApfXMPc4tZfzSuBsSrg6E7gyItZsvGw28CpgOqXn8ybgE5RgeCfwtn6T3QvYAdgJeBMwFzgIWB/Ymnr2ICJ2A/4TeBmwLvBL4OJ+03oB8FRgm/q6PQeaDxrBnBLK7wKu7Ve2DPB94NB6ew7l814J+HC/6e0KbFHrO6K2YztgFvDSzovqEI2zgb1rD/HOwK2DtHEgzwA2B3YH3hoRWyzqDXUZOphyJua3I6hroGlNBS4D/gOYCvwPsMsw37sc5XNckJm/YxHLmqQlm8Fc0kTS6TXfgxIaf9N5ohHWT87Mh2tv4/spYRjgH8CGwHqZ+bfMHPTCvQGsCdw3xPPPB36emZ/KzEcz83O1fS9svOYTmfk/mfkQcBXwP5n5zcx8FPg8JbA2nZGZf8rMO4Dbgasz8+7G+zuvPwi4IDN/mJmPACcDT4+Fx92fnpkPZuavgG8B2w4yHzcAW0fE6sAzgW9n5s+BqY2y/8rMv9d6z6xt+nOtd3a/YSunZuZfMvOvlAOCD9Te3z9QDiaa/lnrXiEz76vzPVxvz8y/ZuaPgR9TDkAG84aIeBD4C/AB4C2Z+dgI6hrIPsBPM/MLtQf8A8D/LuI9L6vt+DXlAGy/Wr6oZU3SEsxgLmki+RTwCkoP40X9npsKLEvpMe74JaWHGkqvcwDfj4g7IuLVI6j395Te6MGs16/e/nXDwr2yfx3g8Ur93j/c1y9Udw3Jv+9XdzMk/t8AdXXe+wtgAaUH+lnAt+tTNzXKOkN/+s/zL4Ep1GEh1a8b99fr97jZ5r9QDqqOAu6LiCsj4kkDtXEQw5q/6n2ZuRplDPcs4L0RsfcI6hrIQvOWmcnC8zqQSzNztcxcKzN3y8xbavmiljVJSzCDuaQJIzN/SRl6sA9web+nf0dfr3jHBtRe9cz838w8IjPXA44EzolF/BJLwzeB/Tvj1Qdwb796F6q7xxaquw4LWXMMdX+bEsCfDnyvX9kz6Avm/ed5A+BRFj6AyMb9+yjDcJqv73th5jcycw9KKL0LOG+U7R+WLG4HvkvfePe/AE9ovGydYU5uoXmrY/jXH/zlQ1rUsiZpCeaKL2miORzYrfay/ksdjnApcFpErBwRG1IupOtcKHhARMyoL/8jJTR2hjD8ljJOejBnAqsAF9bpEhHTI+LMiHgK8DXgiRHxioiYUi8g3RL4ahfmd1E+CxwWEdvW8crvBm7uXDg4CjdShgvdm5l/qmXfqWWrUnrPAT4HvC4iNoqIlWq9l9ShOQO5FDg+ImbUYTFv7jwREWtHxIvqQcUjwJ/p+256pvbKPwPoDJu5FXhWRGxQL2Q9eZiTuhLYKiJeXIfyHM/wQ31/i1rWJC3BDOaSJpQ6TnveIE+/ltLreTclTH4WuKA+91Tg5oj4M3AFcEJm3lOfO5UShB6MiJcNUOcfKBck/qNO42HKRZEPAfMz8/eUCxtPogxFeBPwgnoxX09l5rXAWygXH95HubB09hgmeQOwFuXz67iVMvTjlsz8v1p2AWVo0Y2Usxh/o3z+gzkP+AZlDPgPWfiMx1KUz+5e4A+Ui0aPGcM8DOVNUX4Z5y+Ui4E/AXwMIDOvAS4BfgLcwjAPrOr3fABwOuX734zSEz9ii1rWRjNNSRNHlKFwkiRJkhYne8wlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWqBKYt+yZJl6tSpOXPmzMXdDEmSJC3hbrnllt9l5rThvn7SBfOZM2cyb95gP4EsSZIkdUdE/HIkr3coiyRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgtMWdwNWJxmbHVI16e54I4Luz5NSZIkLfnsMZckSZJaoGfBPCLWj4hvRcSdEXFHRJxQy0+NiN9ExK31tk/jPSdHxPyI+FlE7Nko3yEibqvPnR0RUcuXi4hLavnNETGzV/MjSZIk9VIve8wfBU7KzC2AnYBjI2LL+txZmbltvX0NoD43G9gK2As4JyKWrq8/F5gDbFZve9Xyw4E/ZuamwFnAGT2cH0mSJKlnehbMM/O+zPxhvf8wcCcwfYi37AtcnJmPZOY9wHxgx4hYF1glM2/KzAQuAvZrvKczqPsLwO6d3nRJkiRpIhmXMeZ1iMl2wM216LiI+ElEXBARq9ey6cCvG29bUMum1/v9yxd6T2Y+CjwErNmDWZAkSZJ6qufBPCJWAi4DTszMP1GGpWwCbAvcB7y/89IB3p5DlA/1nv5tmBMR8yJi3gMPPDDCOZAkSZJ6r6fBPCKWoYTyz2Tm5QCZ+dvMfCwz/wmcB+xYX74AWL/x9hnAvbV8xgDlC70nIqYAqwJ/6N+OzJybmbMyc9a0adO6NXuSJElS1/TyV1kCOB+4MzPPbJSv23jZ/sDt9f4VwOz6SysbUS7y/H5m3gc8HBE71WkeDHy58Z7Oj5G/FLiujkOXJEmSJpRe/oOhXYBXAbdFxK217BTgwIjYljLk5BfAkQCZeUdEXAr8lPKLLsdm5mP1fUcDnwRWAK6qNyjB/1MRMZ/SUz67h/MjSZIk9UzPgnlmfoeBx4B/bYj3nAacNkD5PGDrAcr/BhwwhmZKkiRJreB//pQkSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAlMWdwMmi81nndj1af5s3ge6Pk1JkiQtHvaYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWqBngXziFg/Ir4VEXdGxB0RcUItXyMiromIn9e/qzfec3JEzI+In0XEno3yHSLitvrc2RERtXy5iLiklt8cETN7NT+SJElSL/Wyx/xR4KTM3ALYCTg2IrYE3gxcm5mbAdfWx9TnZgNbAXsB50TE0nVa5wJzgM3qba9afjjwx8zcFDgLOKOH8yNJkiT1TM+CeWbel5k/rPcfBu4EpgP7AhfWl10I7Ffv7wtcnJmPZOY9wHxgx4hYF1glM2/KzAQu6veezrS+AOze6U2XJEmSJpJxGWNeh5hsB9wMrJ2Z90EJ78Ba9WXTgV833raglk2v9/uXL/SezHwUeAhYc4D650TEvIiY98ADD3RnpiRJkqQu6nkwj4iVgMuAEzPzT0O9dICyHKJ8qPcsXJA5NzNnZeasadOmLarJkiRJ0rjraTCPiGUoofwzmXl5Lf5tHZ5C/Xt/LV8ArN94+wzg3lo+Y4Dyhd4TEVOAVYE/dH9OJEmSpN7q5a+yBHA+cGdmntl46grgkHr/EODLjfLZ9ZdWNqJc5Pn9Otzl4YjYqU7z4H7v6UzrpcB1dRy6JEmSNKFM6eG0dwFeBdwWEbfWslOA04FLI+Jw4FfAAQCZeUdEXAr8lPKLLsdm5mP1fUcDnwRWAK6qNyjB/1MRMZ/SUz67h/MjSZIk9UzPgnlmfoeBx4AD7D7Ie04DThugfB6w9QDlf6MGe0mSJGki8z9/SpIkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFuhZMI+ICyLi/oi4vVF2akT8JiJurbd9Gs+dHBHzI+JnEbFno3yHiLitPnd2REQtXy4iLqnlN0fEzF7NiyRJktRrvewx/ySw1wDlZ2XmtvX2NYCI2BKYDWxV33NORCxdX38uMAfYrN460zwc+GNmbgqcBZzRqxmRJEmSeq1nwTwzbwT+MMyX7wtcnJmPZOY9wHxgx4hYF1glM2/KzAQuAvZrvOfCev8LwO6d3nRJkiRpollkMI+I5YZTNgLHRcRP6lCX1WvZdODXjdcsqGXT6/3+5Qu9JzMfBR4C1hxDuyRJkqTFZjg95jcNs2w4zgU2AbYF7gPeX8sH6unOIcqHes/jRMSciJgXEfMeeOCBkbVYkiRJGgdTBnsiItah9EqvEBHb0ReEVwGeMJrKMvO3jemfB3y1PlwArN946Qzg3lo+Y4Dy5nsWRMQUYFUGGTqTmXOBuQCzZs0aMLxLkiRJi9OgwRzYEziUEobPbJQ/DJwymsoiYt3MvK8+3B/o/GLLFcBnI+JMYD3KRZ7fz8zHIuLhiNgJuBk4GPhQ4z2HUHrvXwpcV8ehS5IkSRPOoME8My8ELoyIl2TmZSOdcER8Dng2MDUiFgBvA54dEdtShpz8Ajiy1nVHRFwK/BR4FDg2Mx+rkzqa8gsvKwBX1RvA+cCnImI+pad89kjbKEmSJLXFUD3mHV+NiFcAM5uvz8x3DPWmzDxwgOLzh3j9acBpA5TPA7YeoPxvwAFDtUGSJEmaKIYTzL9M+cWTW4BHetscSZIkaXIaTjCfkZkD/aMgSZIkSV0ynJ9L/F5EPLnnLZEkSZImseH0mD8DODQi7qEMZQkgM/MpPW2ZJEmSNIkMJ5jv3fNWSJIkSZPccIK5vw0uSZIk9dhwgvmVlHAewPLARsDPgK162C5JkiRpUllkMM/MhS78jIjtqf8YSJIkSVJ3DOdXWRaSmT8EntqDtkiSJEmT1iJ7zCPi9Y2HSwHbAw/0rEWSJEnSJDScMeYrN+4/ShlzfllvmiNJkiRNTsMZY/52gIhYuTzMP/e8VZIkSdIks8gx5hGxdUT8CLgduCMibomIrXvfNEmSJGnyGM7Fn3OB12fmhpm5IXBSLZMkSZLUJcMJ5itm5rc6DzLzemDFnrVIkiRJmoSGc/Hn3RHxFuBT9fErgXt61yRJkiRp8hlOj/mrgWnA5fU2FTisl42SJEmSJptBe8wjYnlg5cx8ADi+Ub428NdxaJskSZI0aQzVY3428MwByp8LnNWb5kiSJEmT01DB/BmZeXn/wsz8DPCs3jVJkiRJmnyGCuYxyvdJkiRJGqGhAvb9EbFj/8KIeCrwQO+aJEmSJE0+Q/1c4huBSyPik8AttWwWcDAwu8ftkiRJkiaVQXvMM/P7wI6UIS2H1lsAT8vMm8ejcZIkSdJkMeQ/GMrM+4G3jVNbJEmSpEnLizglSZKkFhiyx1wTz3bPfEdPpvujb7+1J9OVJElSYY+5JEmS1AKD9phHxFeAHOz5zHxRT1okSZIkTUJDDWV5X/37YmAd4NP18YHAL3rYJkmSJGnSGTSYZ+YNABHxzsx8VuOpr0TEjT1vmSRJkjSJDGeM+bSI2LjzICI2Aqb1rkmSJEnS5DOcX2U5Ebg+Iu6uj2cCc3rWIkmSJGkSGjKYR8RSwKrAZsCTavFdmflIrxsmSZIkTSZDDmXJzH8Cx2XmI5n543ozlEuSJEldNpwx5tdExBsiYv2IWKNz63nLJEmSpElkOGPMX13/HtsoS2DjAV4rSZIkaRQWGcwzc6PxaIgkSZI0mS0ymEfEMsDRQOe3zK8HPpaZ/+hhuyRJkqRJZThDWc4FlgHOqY9fVcte06tGSZIkSZPNcIL5UzNzm8bj6yLix71qkCRJkjQZDedXWR6LiE06D+p/AX2sd02SJEmSJp/h9Ji/EfhW/c+fAWwIHNbTVkmSJEmTzKDBPCJOBL4L3ED5z5+bU4K5//lTkiRJ6rKhhrLMAD4I3A98A5hdy1Ych3ZJkiRJk8qgPeaZ+QaAiFgWmAXsTPlnQ+dFxIOZueX4NFGSJEla8g1njPkKwCrAqvV2L3BbLxslSZIkTTZDjTGfC2wFPAzcDHwPODMz/zhObZMkSZImjaHGmG8ALAf8L/AbYAHw4Hg0SpIkSZpshhpjvldEBKXXfGfgJGDriPgDcFNmvm2c2ihJkiQt8YYcY56ZCdweEQ8CD9XbC4AdAYO5JEmS1CVDjTE/ntJTvgvwD8pvmt8EXIAXf0qSJEldNVSP+UzgC8DrMvO+8WmOJEmSNDkNNcb89ePZEEmSJGkyG+pXWSRJkiSNE4O5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFehbMI+KCiLg/Im5vlK0REddExM/r39Ubz50cEfMj4mcRsWejfIeIuK0+d3ZERC1fLiIuqeU3R8TMXs2LJEmS1GtTejjtTwIfBi5qlL0ZuDYzT4+IN9fH/xYRWwKzga2A9YBvRsQTM/Mx4FxgDvBfwNeAvYCrgMOBP2bmphExGzgDeHkP50f9zNrzvT2Z7rxvvLEn05UkSWqznvWYZ+aNwB/6Fe8LXFjvXwjs1yi/ODMfycx7gPnAjhGxLrBKZt6UmUkJ+fsNMK0vALt3etMlSZKkiWa8x5ivnZn3AdS/a9Xy6cCvG69bUMum1/v9yxd6T2Y+CjwErNmzlkuSJEk91JaLPwfq6c4hyod6z+MnHjEnIuZFxLwHHnhglE2UJEmSeme8g/lv6/AU6t/7a/kCYP3G62YA99byGQOUL/SeiJgCrMrjh84AkJlzM3NWZs6aNm1al2ZFkiRJ6p7xDuZXAIfU+4cAX26Uz66/tLIRsBnw/Trc5eGI2KmOHz+433s603opcF0dhy5JkiRNOD37VZaI+BzwbGBqRCwA3gacDlwaEYcDvwIOAMjMOyLiUuCnwKPAsfUXWQCOpvzCywqUX2O5qpafD3wqIuZTespn92peJEmSpF7rWTDPzAMHeWr3QV5/GnDaAOXzgK0HKP8bNdhLkiRJE11bLv6UJEmSJjWDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWmLK4GyANx6z9P9D1ac774oldn6YkSdJo2WMuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktYDBXJIkSWoBg7kkSZLUAgZzSZIkqQUM5pIkSVILGMwlSZKkFjCYS5IkSS1gMJckSZJawGAuSZIktcCUxd0AqW1mvfKcrk9z3qeP6fo0JUnSksUec0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEkt4MWf0mIya855PZnuvLlH9GS6kiSpt+wxlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1gMFckiRJagGDuSRJktQCBnNJkiSpBQzmkiRJUgsYzCVJkqQWMJhLkiRJLWAwlyRJklrAYC5JkiS1wJTF3QBJvbfDSRf2ZLq3vP+Qx9d16sXdr+fU2V2fpiRJbbNYeswj4hcRcVtE3BoR82rZGhFxTUT8vP5dvfH6kyNifkT8LCL2bJTvUKczPyLOjohYHPMjSZIkjdXiHMrynMzcNjNn1cdvBq7NzM2Aa+tjImJLYDawFbAXcE5ELF3fcy4wB9is3vYax/ZLkiRJXdOmMeb7Ap3z7RcC+zXKL87MRzLzHmA+sGNErAuskpk3ZWYCFzXeI0mSJE0oiyuYJ3B1RNwSEXNq2dqZeR9A/btWLZ8O/Lrx3gW1bHq9379ckiRJmnAW18Wfu2TmvRGxFnBNRNw1xGsHGjeeQ5Q/fgIl/M8B2GCDDUbaVkkt9dT3Xdb1af7gDS8ZsHzXc77U9bpuOMaTfJKkPoulxzwz761/7we+COwI/LYOT6H+vb++fAGwfuPtM4B7a/mMAcoHqm9uZs7KzFnTpk3r5qxIkiRJXTHuwTwiVoyIlTv3gecBtwNXAJ3fXjsE+HK9fwUwOyKWi4iNKBd5fr8Od3k4Inaqv8ZycOM9kiRJ0oSyOIayrA18sf6y4RTgs5n59Yj4AXBpRBwO/Ao4ACAz74iIS4GfAo8Cx2bmY3VaRwOfBFYArqo3SZqw9v7kFT2Z7lWHvqgn05Ukdc+4B/PMvBvYZoDy3wO7D/Ke04DTBiifB2zd7TZKkiRJ461NP5coSZIkTVqL61dZJEmL2UsuvbIn073sZc/vyXQlaUlnj7kkSZLUAvaYS5J67tVf7n7v/AX72jMvaclij7kkSZLUAgZzSZIkqQUM5pIkSVILOMZckrREecM3uj+e/X17Op5dUu/ZYy5JkiS1gMFckiRJagGDuSRJktQCjjGXJGmUTrvxa12f5r8/a5+uT1PSxGCPuSRJktQCBnNJkiSpBQzmkiRJUgs4xlySpJabO+/rPZnunFl7Pa7si3de05O69t9ij55MV1qSGMwlSdJiceMvvtn1aT5r5nO7Pk1pvDiURZIkSWoBe8wlSdIS74e/6X7v/PbT7Z1Xd9ljLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsB/MCRJktQld/32mp5M90lr7/G4sl/+vjd1bbjm4+vS+LDHXJIkSWoBg7kkSV1gZRsAACAASURBVJLUAgZzSZIkqQUM5pIkSVILePGnJEmShnT/Q9d1fZprrbrbgOUP/uXmrte12opP6/o0e8FgLkmSpEnp//52R9en+YTltxr1ex3KIkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktYDCXJEmSWsBgLkmSJLWAwVySJElqAYO5JEmS1AIGc0mSJKkFDOaSJElSCxjMJUmSpBYwmEuSJEktMOGDeUTsFRE/i4j5EfHmxd0eSZIkaTQmdDCPiKWBjwB7A1sCB0bElou3VZIkSdLITehgDuwIzM/MuzPz78DFwL6LuU2SJEnSiE30YD4d+HXj8YJaJkmSJE0okZmLuw2jFhEHAHtm5mvq41cBO2bma/u9bg4wpz7cHPjZCKuaCvxujM21riWvriVxnqxr4tRjXROnHuuaWHUtifO0pNY1EeZpw8ycNtwXTxlFBW2yAFi/8XgGcG//F2XmXGDuaCuJiHmZOWu077euJbOuJXGerGvi1GNdE6ce65pYdS2J87Sk1rUkztNEH8ryA2CziNgoIpYFZgNXLOY2SZIkSSM2oXvMM/PRiDgO+AawNHBBZt6xmJslSZIkjdiEDuYAmfk14Gs9rmbUw2Csa4mua0mcJ+uaOPVY18Spx7omVl1L4jwtqXUtcfM0oS/+lCRJkpYUE32MuSRJkrREMJhrUoiIWNxtkCRJGorBvMUiYqeI2HBxt2Mii4hnRMSO6ZgtNUTElPp3id4GtvmAtM1tk9rAdWRyWqJ3St0UEVuNc30B7AVcGRHrL+r1Xairf1lXl42IWKlxf81uTnsRtge+HBHb17rHbUPXy7oiYrvxmJdOgF2S1OXvyojYODP/uaSG84iIzgFpRBweETu0oE07R8RnATIzJ3vwWNzz36v6F/d8dVtnfiJi7XGscwVg43p/i4hYfbzq7pZx2kdFRCxf76/Q6/qGayzzvkTukLotIvaihLttx6m+pbI4Ffgm8JmImN6r+uoOcq+IOCUi3hkRU2pg6cpKVVeaF0bEfhHxCuCIiFiuG9Meos6lADLzbOBzwEWdnvPx2mnUunaJiMO6Nc26EVoJ+BhwQremO0hd2wOvjog1ellPrWvLzsZ1HPyR8j8QLoyIGYsjnDd29MvX/8HQdY1Q/gLgJcD/9qKeEboZmBURH4exh/OI2DQidu9a6/qm29MD0ohYMyKWW5wHJxGxKfCciFi6y9NtHhA+KSI26+b0G/VsGxGbNDt9elRPdPaRlG3G+uP0nW0GHBgR76L88tyog/niWMb6LQdLRcQynfvdrAN4NmWbchBwZq+zxTDbBLDyaKdhMF+EiNgceC9wWGbeOk7Vdhbm19THKwGXRJeHtTTCwbbAGZR/NfsU4JqIWKWLO41/AHcC7wH+EzgnMx/p9g6hKTP/CRARRwErAvcBX4+Ip/V6Z9j4XLcHXgmcHhEHd2nyT87MPwOvAF7Ui1DSsCZwILBfr3pr6oHGUsDZwEd7Hc7rzuKfwGeBPwGfWhzhvC6D+wIfBy6NiD17sUxGxNbAccCPMvM3dQe5WHbSEbF0Zj4GHAK8JCI+AiMP5431ayfgNOCYiHh6F9v6QuDzEfGOiHhGt6bbmP6bKMvfjRGx0XiG88ZntwvwUcrn97RuHog0wtgbKev1RyLi7Ohi51Kd9nuB04F3R8TG3Zp2f/X72RX4MPCOzPw10PPwl5k/oYS7NwJzM/PuMUyr852cEBFzI+IN3fw+FlHn8ZRl7aKIeE5n39zFOpaiLAvvAr6RmY90a/qjbVNE7A18MSLOiIjtR7pvMZgv2j+B72bmt/sd9fViJ7oq/OuLfTbwb8D7gYMo/0Tpkm6sTBGxRkRsWOvZETgRODcz52bmvsD/UP+D6ljGZnc+o7oz/j3wIHAX8MJGec/UUHI88K7M3AP4D8rKslMvx5zXz3UP4DPAtcCHgNdFxJGjnWZELB0RTwZujdLb+GTgA8BTImLlbi6PEbFxlN68aygbu/2Bl0bEit2qo2GFuqF+EbAWcFYvw3n9bl5A+T3a6ynh/At1fRi3cF4D38nAScCj9e+Ye84HWA4epPRS7xoRu2XmPxdHL209A/hYRJwEHAO8j3IW7dOd54fbpvra51GC0ncp4eUlEbHbWNsZpRf5eMr29o/A27sx3cb01wJ2oxywfwP4UkRsOV7fSa3nOZRtx9nA/cChwDO62VFSP7PnZObzgP8GNgDu7dK0dwZ2q9v0PwLrAvd08+Ci1rN0/bsMZUjkGcAdEXE4cH1EvLeb9TXqbS4Hn6JsJzaMiP0jYrVGm0Y0rYjYDtiPss5MBd4fvR8mewRl2/52yvb90C5Ou5MvrgV+CtwD/KOuY4tNzVTHU9av1Sj5bfcR7Vsy09sAN2AX4GBKz+T9wN6N5/YA3trl+jalrPjPqI+fCny83l+GsvO5EvgxMH0M9awAvLPepgM71mnOBdZovO6LwLpjqCca948DdqoL6U7Al4Bj6nOzgI269BlGv8drAJ+mbLin1LK5wN+Ap/R4+TkGOK7eXxp4DuWg5KBRTm+p+vftlLMOnweuqt/Ttl1s91TgI8A7gOVq2R7AfOANwJpdrGtz4BRgi8ay+U3KAc1yPfxuPgoc0KjzVOBbwPo9rHM9YE7j8SHASyk7yu8BM2v5amOoo7nO7QO8GNiBcubjeMrwp117NY/DaNPylAPV7evjKcDtlE6BYU+vbg8/BLyylm1cl9eLgKePoa3bU4Y4ddbblSlB4hrgeV34LA6u28KzG2VvAX5EORM2Lt8F5czlKfX+0sC76/eyC7D0WL/n+vjplIPNdwJfB5at5U/rwnzsTOnpf1vdBi7XqHOpbnxOtY6ZlFB5cN0G/g64um4H96zLyjY9+o72At4E/HttzyuBT9R27EvZDywz3O+Est99LfCy+nj9us58trPt6dEydyJl//s6ynCcKZQOiLW69Dk9iXLQtw7wNMqw1eZ2Yb1ezdsg7VoPuAE4oz5eFXgrpYN17+Eun/aYD6CeIj0XeB6wHbAAeEdEvLaefn4P0LVhLRHxBOCvlDMYe0XELMrR324RcURm/iMzHwa+A/yQsmMalcz8K/Btyk7yEMqR5qspIWnfiNgsykViT2YMPXjZWXsiXkfZuT2cmQ/W9p8L7B0RlwNnUoLymEQsNJ5t/SjDE/4APIFyxNo5fXY15QDn4bHW2b/+fkXLUoaBkOXMwLx6Oz4inj+S6UbEM4EfR7kA+U7gt5Tv7gbKgdVH6+u60eP2B+DLlA3KSRHxhCw951cCL6C7p3DXATakDMnZvC6bB1N66N/di97r+hktA2xdi/5OOTu0JmVIyfLd7rms09sMOCAiXluLHwKOAF5P2ZH8IiJeBnx4tG1oLP9HUbZR2wOXUnbmV1PW9aOiB8MzBtJvnZwN7EpZdpeu7X2UEjyOjIizhjPNLP4BPEDZVq6W5RT/5yjbrD0jYt1RNvlHlE6YA2rbH6YcAH8eeEuUceGjWjYiYh9KONkC2KFz9iwz30npOf9o9Ohag47OdwHcBmwcERtk5mOZeQql02R/yno/Iv2+586wt78Dz6Vsn16cmX+PiKOB90TEKqNpf5TroKZQet63BnanHGA/EhHHUDosujHefCnKsjQX+CBwX90GPouyrr6Psi4F8Ocu1Pcvmf8aBvEuypmulwIfzsxPU/Yf+1PW7R/X9eBx6q5gqcZ38hpKZ8QRwCsiYvUsw3HmUoZ4vrUbZxsGWTemU/dTmblPXecPBw4byxmaxuf0ecq+8HrKd/Il4Hn1bMb3gRmjrWOU/o/SyfPiiHhmZj5EyTh/pwTz4Q0JHc+jiYlwo2xIrqUe2VOOuk4ALga+Qjk98fzOMt+F+jahr/diI0pPwHtr+TaUXtZ3UnbgNzCGI00aR2uUCybOpvRYrkjpLfkOpXfonM48jqKO5lH6GsBXKUfnqwAHAEdTdk5Popwh2KLL39+JlBDydUpP0EbATZRe4LnAfwEzerTsPBM4DNi0Pr6IEvqWofRYfJzS03H8KKZ9JOXU/ZuAnwCvq+VbABt0oe17UXqg/qN+V8+nHOV/kBLsvkwXertqXVtRDgRXqe0/izJsa/263J8PPLNLdXV6VrahBNUNaj13AEfV555W17Gu9n4N0JYD6rJ5IOXA7St13jemnFG5DdhnLPNKOcC4DnhSLduVstPai7JTOJoxnAkbZbt2o/SWRf2e/wdYuz63X11nNxvGd7gV8Nx6f1PKcJijKL1wG9d17Xpg/xG2byfKQefW9fFXKGfaOvWuOJZtBuVg85PAlvXxyyhB6cjGa6b26LPvzMNTKWdRNgKm1XXs1cAT62f5Rcr+5eQx1HVMXa9m1sen1Pk+hTIc4zZgq1FO+1hKp8QG9fEJlP3XByljsEc97X71dM5Mbk/Zzl5OOQhYofGaA2pbRrScjeC7ei/lQH5fyj55ZuM1K1PP7DFI/qCeHa73X0DZdgdle/tlSuhfvT6/HjCty/PxSkpn2JZ13fke5eACSii/A9h8jHXMpATvTSijGm6nnm2s6/NhwO7dnK9FfGdPoRyIbkI5yD2Csj3apT6/EkNs4x433V43fKLdKCHkMfrC8jKU01nvHegL6UJ9G1F2mNtSwupalHD+nvp4k3r/TMZwurOxAG1RV8aV6/Q/RF84347Sm37UWOqo919RV45LKIH485Rg+l3gtT367vajBJ+lKDvtG2r5OvU7PH6sG4QhPtedKAdRX6LsjA6uK+NFlNOtd9QN1XGUg4RFLj/1tR+on986lDB7AOVA44FubXgoO+3bapuvo5zR2JKyc/oIcCPwgi7VtTvwK8rYybvrBm2b+n1dQxkys1v/5WmMdT6f0ht6Ua3jREoY+Tnl9PCvgD17sUw22rAPpff6q5Qd1UGUHeV59bP4GqM44KcE8U5Y2Z6yUziPenajlr8K+Hy9P+Tp7x7M97PrfJ/WKDuD0vt3Qf0OBh3K1li/9qScRbyhLo/rUYbqnFvXh9sp29KTGMGBb90u/AB4M2Xb96JafiXwpS59BnMowyAOq49XpYTzzwCv7uay3q/eTsjcjdLLfF7dbjyRsr85i9IJdWstO5By4DSstgArN+5vSQlK6/d7zWzK0I93MsptL6XD44fUTinKPmzjuk6dRAn9T+rC59VZ1nahnIHZgnJG58PAs+tzq1B6zp/bfE+Xvq9V6t8PUIaYfJO+Tp6XUnqGh6yPctB1BSWIL03pbHmI0mMNpWPiy/W7H/WwuYE+t3p/P0rP9ccoB38vp2yjrqYc7F5HPUAdQ33r1nX9qLoefb/xOe0JrDRY+7p5ow77ovSE313n91bKCIGnUDrTrmMUnUxdb+yScKMcqc4HDqyPn1W//LW79SX3W5iXppyGO58SxNemhPPT6eLYw7oA/YQy5um/KTvxXSmh6O2UsP4cSoh5+WjntX5eX6v3d64L6hPr4zmUgLRst1cYSgjYk7KTvZoaQoDterCMLN+4vxMlcG1VH7+K0ptzUH28AuXg6zl1o7XIDROlZ/OblB3QrSw8LnUb4AuM4VqDxrS2pPTendgoOwe4pPG407sypu+L0uP5YWDn+vhIysHME+k7fbxDl7+nlSlhrnPtxqaUYLd//U6eRBd62hbRhql1ndqGchZpL8rO88WN16w5ms+YvjMxH6IE4KmUsaPvoW/s/kGUA5Axj78dRnv6jzXelBIIL6VxRoJyZnJ7BhnfysI9lJvX9z+5Pj6/Lv/rUQLIdvX+bpTt2iIDIH09iF+iBOVXUgL+9MZrrmYM129Qzsx0rm05iLJPeWZ9vDrlwGKdHnwH/QPzucCz6uNjKQcJWzTauDYl5N4+3HWhrsvH0Td2fHPgq43nOweFK9S/I1726AvKT6fso15H6U2+i7K93W600x6izlmUA8dX1MfLU/bFH6RcDzCfLpyhHGAen0Q5eFm7rhcP0ndGb5c6z7sOc5pPoHQwdnqQT6vL+Tb18UzKCIAx95SzcI6ZXr+jztm6l1HW/f0br1l5LPXQN8TojZQzb79qrGM7UYaRbNKt72eQtqzduL8q5axKZ73eh7I93osy7PMEYNaI6+jlDEzkG+WXQ/5E6fH9BF3qMRygnufXBXoa5ej8o5Sd2VqUI+e30dhJjaGeTSnDOJqnfjqB65mUwNQ56twN2HAE096ps/DVjciXgA8N8LrDar1jHr7C4wNA5/dMFwBXNMpfQ+mtX7n/e8ZQ9+p1ZVy5Pt6fMob9yPp4ecqO+OOUgB2U4HAq9XT5ouarfu9TKT1CV1LO3CxP38Z22S7Mx86UXsurKL0ZGzWe+3ZjeejGkK2NKb3V8+pGq9Pb8BZKj/GoLjobRr0r1s9vZqPsQOD0XtQ3SBvWofSSd8L3apRhVndQzx4xwnBBOW06g3JAcxnlV49e3pj+eZQe2cvqZ97Ti537LyeUMyNbU3rL1qD0oL2dYXQ01PXrg3U+lqMEpduA/Rqv+RhlJ7xefbwh5WBnuMFyjfr3orpe3thY3p/PGIf71PX2K5QAtE8texUlYHX1jFC/elel7EfWqcvG6ZTe5sPoCzDHUH4JqDNcc2XKUJZhdwLV73Va/Y63q3V9i3qmub5mTl0OlxrNvNLXQ75cnY+P0ReA5gJHdOtzpG+7ewLl4O4U+g4qlqXsR06nnlHp5vpCuZbtS5SQ+b66Xj+bcobogvr9jSh/UHquF9TlIer8XEbt+KAx3GUM7W8OjX095ezLvfTtB1ejhPOLgUPG+l1RzgJ+nXIA/WlK58P9lDMyx1E6P/bt1vcz2HdGOeOwWaPs0yw8LO0oSqfa0qP9nHs2A0vCjXKa84fA6xtfSjdPXXV2OldTTs9MpQSVj1B6IKYxhqNaGmGnbkhfy8CnfpYGVu3M4wjreCnl9OiGlKP95Sm9Gl+kcaRIOTCYyxhPYw1Q/3GUsdDn1Xl8I6VXek/KKc6f0IMeUcrB1BPpO0X4ylrvCxvf7cHN+WUYvzRSpzkFuJAyXvZiFt6hHs0od3T96nlhXbafSjlr8mlKD/aTKb1s/02XeoYop/VOp+wcvkIZx94ZJ7kvcH4Xv5fOzm49+nrt3kq5kGr5+ng2pQe262dt+rVhlUbZBymnxzunqw+o68mozhBQxgfPrMtC5/qAy4E9Gq95KuVAecNuz+Mi2vY6ypC191IOurat6+Y5lOAx6LAD+g7YNqzztx1lB3865WDm6Y3Xng88tfF4WL1xlNBzE+UA4N+A39AX+HaljB8e9fUGdfm6vn4336Ec5B5an5tT17sVerTsrUbZl8ygBJkplAOisyjXUnSWzeNo/NIMoxziRNmmn03p8d2+LoOfp1wLcwujPONL6dm/ui4vr+j33P71Mxxzz2jj85jWKDuk1r0LjQ4Q+oYGdTMDbF2Xt80peeMsSs/5avRdHL/5aOqt24Wf0xfO30X9xSu6e5Zhb8oZrFXq8n0XfWcou3JmiJItvtP4LI6ry/U7KWeE3tBZnnuxXvVryxTKNQBz6+NXU/8fQH28HWUo0qh/WaxnjV9SbpSj2QU0Tjt3efpTKcHhq3WDOo2yEzpzDBvL5qnM51CO4J5HGQf1K/qGeOxE2YFsPIa2X1kXxM5PE+5dV/wzKTvm7RuvH3PPf7/6m8M9fgycVctPqhuhc+jC2MN+dfbvJfhfajigHPT8EHhJfRz937OIaR9XN6Tvp/Ti/Z6+05mHUoL/sC8gGaKelSgHTjs3yp5fy66vG5W9m/Mwhro6vRz/RQmmb6GMu7ukLvffpsu9HJQd0k2U3tDPUMZVvpWyA3wDZcexVzfrbNTd+c5fQDmo+iDl9PimdZn8UV0f/xvYaYx1bUH5NaeN6+MT6nZkG+AZ1DHN43mj9ORfU+9/mHKm4GJKaFuLEjwG7GyghPevU8/c1Pm5oc7PNMpwv3fRb8zmcNevxnJ+B+U0+KaUAHsqZZz5W+o6Nuqzo5Rt0oV1uidSzkbNoWyfOuvyKqOd/iLq7gTHKZQe8osp2+NlKb2L76vLRfPMRjT/jqCuAymh6AmUM7un1e94DcoB+LGM8swoZVv3XcqB2aWUoXxvr889j7KN6uYQzxdQfhnnfdRhF5SL975G2X+OuXe5UdcGwPsaj/cArm483omyrTyTOvxzjPXtQ9nudc60jvnnbilDizo/KTqTciB2bb914E66eGaIEvC/R1/gX4ZyRvpL1P1tL2+UM6+dDrKN63L/AOV/pEDpNLiEsr+5jTHmxZ7OzJJyqyvPqMJrYxrNQPdy4D8bj9eoG7YvUI4MpzLKq/TrAnMjpSd7M8pO6PI6/TMp4wtfR+k9H9OpH8op0K/XFfOzlB7d8yk75xUowfxcujTGm347ER4/3GN5Su9/58Cjq2NqG/Wu0yibQwlZnZ7zg+pGaa2R1E/pLZlLGWp0NKUn7xOU05mdf6TSlbMNdSPzbWrvKn079MPpG37TjeFTA/VynErpTfsqJcCM+nenB6lz4/p9PJNy9uFN9bNbidJL/VJ6/FvelGEcP6YEvxsoO/0D6vL5GkpoGfHvYjPADo4Sjn5DXzg/jtJTeSdd2LEPt02Nv9tSdtZHUA7AZtZtw42UHvxB1wlKD/Np9bXr1rJj67rdCednUjouVhvo81hEW7ehDKXbvG47Ome3lqGEsxd1lseRTru+5wWUM3cb1PZ9qfHc9ZTrAFbtwXewTOP+7pTt8FqUoTPnU8LZsvT9U6ERX/TX/3ujHHhcUed1bUonyLsZ4zBFykHsSyhB7DjK/mVXSih7c31N135FhHL2pLOuXkbZXh1fnzuOMjxj9S7W9wTKWdwP1MerUPb7/7qmq67TnwQOHu2y2K/O/eo2oVtDOTegdCLOrI+fRzkTelLjNa+rdXbtzBClI+wt9P160nMp2eOTdLnTb4C696AcBLyobp+Wp+S2eyj/DZa6XXk5fcOFRj9sp5cz423AL3gtylCBX9P4dZK68fkh5YhrTONtKaf6bqb01nQu+HgVZQjBRyjh60T6gtlYxn29kfJbrp1fsXkNJVDuXjdC72KM/0ygfxsZerjHcXRpuMcg7diHMhToK9SARwkhP6XvosYRnbajDIv5FXBBfbwc5TqAf6MElbXo8g6dcmB2Kn0XgT29fo5HUna4BzPGniIG7+W4jPILOVdQDt668k+LKDuLJwPn1MedA46PMMp/7DTS5bMu8/9B6T3cuy4r76CMv30JjYuGRzP9en936oXp9fHJlN8H36Q+3oIe/rOkQdq0aZ33zvChfw3TqduA0xniYuXG57clZbt1HX3h/LWUcZ2dXvcR/7pHXS6Oou8C7Y9QgwRlqNVYfymisw5fWB+vXJf9t9Tv/au9+E4o4eBC+sLKQfT1zE+lDMs4jzJkbNlRfnbN4N/5nlag9DB3wuP02o63McpT+JTt9hfrsjSNEoSm1ucupxygrTGaaQ+x3B5V15fnU67FOJpyIH1Cfb5r3xl9HUY71mWl8w8EX0M54Hw3JWx+l75rFLryj9bo90slo5zGWo3lbD9KJ18nlO5NuQbg9Y3Xd+VXXxrTm1G3JVdR9ot3UP6B2lfo8U/d1vq/Qbk2Y/dG2eqUs9wf7WpdvZ6ZyX6jXGA3u94/lnJ0fgYlnNwPHFufO6AubF35T1WUI7w/Am+sj6dQwt7pnY1Ol+rZsG5M7qobuSdQxlx9gfoTU12oo/9/Ee35cI+B6qf2glF6/t5AOVLvnPo8hjJUaFVG19v2YsqFM51lZan6Of5ntzdwdfrTKT0zN9Y67qZv+MqeXVwOB+rluJwS3DanHIiO5bf5m1frf4zSQ/4bGsM4KMH4pNHWMYI27FnregIlMF1B3wWf36dcyDXWiwqPpPSGf4MSJGbU8jdRfua1573kA7TpaMrQoQspp3NXpwwd+hmlA+BHDCPgUHqcv0M52L+iTrMzfydRek9H/KsO9ft4Kwv/OsTBlOtCnkjp2dv5/9s772i7qmqN/74UUUIRpQpSpMijg48i8ogISBENIEgAgxHJQ3EoiAoI4iP09vBhAQY1iIDSMTTLoyqE/ghdDYgBAalKCybke3/Mdbg7lxtyyz73nHvv/I2xx71nn332WvucXeaa65tz9nS/XbSzI3FPb1zDHy/3i5uYR9B3H9pcopxzFxMzpHsAh1TeX5hwHJxDLzzN5fs5ntA7r0cYQ5uW83s94Ak6snAsQS+1xIQnciolHoIYZF9PPD/HETLJ2nK9E5KeLxDe3qUJj3+j7WsJGdzyTfi9tivn9oHEYPqnxAzmaMJpcRFhbH6CMDhHNeO86WXfVya09+cR9/BNCIfDD8r7WxH384bMpRmOsYUIqeKBxD1/o3LeLFF3W9VjIGZc9yXstnuB+SvbvJ+YpV2TmmbpW/5jD/aFGIk/TgQqnEtMtW9FPMxuJvRI55Zt6tZDb19upI20j8MJ47wZwZDrEQbzV8qNZhw1FzKhH+UeXbS9efnuzq6s24cwzt8uc1zDuTKVOY3zXqWX6mZ7o4ip3F2oqXhQF23MzctxbXmvDv1hVcd+HjFlP53Quu5IaFQ/2azvsfRhI+Ih2wgiXKCcj+OJAcjVVOItetnGxoRHsRFA+mMixqJhvO5HPxvmhJE2lcgrvBIx2Lu6vPddwgvYpR6Y8OhWA8RPJyo5QnhkDyeMs7czr/Shn99mTi3vGGIAN5UaM26Vc3EqsFNlXe0D605tLklkYvkl4Xk9pFxbKxDG1Kr0IrUqETC4Sbmejij3i73L66sIA+lwufMCRwAAFX1JREFUwiHTJ4Ok7OPt2iGV3+xCYuahzx5ROoyshjF3LvH8uAz4C+G5Xo3IHlVrFiPi2TsfYWyPaxwnYeSdUdluPuI5cDd9SNXZxHPtRCJbXSOb1OrlPndoeb0lTUgBOpe+bEbc85viLa+cL2PKOd/IEjQJeKj8vzYxi15v6udW/9BDYSkn6wPA+eX1fMT02fHlveWoyUPZRdvblov8S/1wnGsTHuza26IFco9K2xsQ3r/jCC/HkZX39iM0tLWM2IkpwelUHuz9udR9gyn7nJuXo883cN6pY/86YSwcWX6Xoyha4iZ+Z8MJLe8LVIywcu3dSMyS9bgPlQeDCA/lSeU+MqayzcmE57zPOe17c34Qg4VGVb+R5bo8jw6ZV5eyPGLQeTChed6orDsLOKzyna5HOC5upRdT+kQWkmqKxVPpMIrWJqSDtVcHrFzDOzf7d6Aji83CxEDoSeBFYkbqpnLu9ThQkjlnKRspZw8tv+9w4rl1I5HX+zb6Lnvbhhisf7SybjtiYFubfpiuK3t/g5jhvYcw9Gqt6FnaaaRePJ7ieCmv1yRS7Z5cWbcHNTvpajyOlQin2z101OlYjBjYfK2f+7IUTc44Vc7ze+gUC0Wktb6fcDT1qkr6u7bb6h96qCzEqOslSq7hsq6/Ioo/R0g8PkSTi4wQ6Z9WatK++1XuUdpYi5BINGYdViMMwYmVbWorOFH21+dg43ZdqNnLwdx17JdRMYxowoCj7LeRdnR+Ql5SDfhrFLHp8W/JnIZRI83jKGKgMUdmkvKwr/UcfJd+NQzBhqHxIULOUNW8n8m7aPoJD28j3ehEQl7XKA3/CB15kDckPLU9PleIAcNLxADwDCI96CFUqoLSUfSsGYPRpl/DdASbXl6Odw06YkSWLNv0SaNMOB5+XX7Ta8q9trHvJQgJRh2ZohYq5/XR5bi+SGTKqfVZQteVvceU62oBOryidaZEXJGY0fsQEW9wKx3xIGsSM1+jm3muNOHc+2y5trYr58AFdZwH7baU+8+3ym+3OxGH9Z3y3hbUmB2oujRG3Uk/IGk7wkN0HqE5PYIwHqb1Q9uL2X6u2e00G0mfIR4OR9v+haRhhA7vlZrbkW1LGktIVu4iMuk8J+nfiCnWa2wfXGe7gx1JSxG5gZ+ocZ/7E0br5bYfkLQFYaC8Rnhx3qirrdJe49xYmzBWbrK9m6T5CR05tsfW1NY3CRnVQoQO9hYizmIY8Dvb19fRTjf6sTEwxfZsSfsSHs5phOZ0GmEcng28Qng5d+nqviZpJOFxu9X2I5KWIAKB30N4zOcjBlW3EIXOJtj+dQ/7ugHhjT/K9p2Svl32vw9FZmf76h5+BW2FpH8nniMTCANhacITfAsh81uLMCTesD2rl218gJDH7Gz7ZUmjCcPyJeCntv/e5wOZs72lCCP5c0QZ+WNsT62zjdLOGCJG6VDbF0ralJiN2tr2801ob1EiJmkVwhs+npCZTiekQnvZvqlxX6m7/WYhaWvCKfAv4pp6uMVd6jOVe/tI2zMlfZ5wCK5OPPOHETKxQ22/2LSOtHpEMtQW4oJ8ixh5Ld/q/gzEhSbKPeiYIl6msm4LwiOwGx0VA1en5lR/ufT6N+v3aP1yDl5JyAemA5PK+lFl/WU1tLEjkV1pFUIKNI0oXrMgkQ/8UJqcJqzSlwsJOddowljbnEg9eQeRM3vl0qcTmHd12wUIb+vphP55ifK7HU/oohcu++tVwCQR0Ff1ijay82xNaGT3r64fiAth3J1Zeb0rISlZrCw9Lr5Dp1kaQrJyO5XZDyID0L1E9pVmVet9DzVUNp5HG02v7E3F20/k6J9Y7g0LEPLVj1MpkDUQF0JCWlv6ynZYyn3iaMJhsEa5Fy1b3luHkLA0dXag5V/CUFzKw225VvdjIC80caqYCL75A6Ep/y4x3bkNEak/nprS++VS62/WL9H6hDylEcTV0FiOpKQ6La9HUQlq7MG+O+eK7lzvYANCQ9wItGz6A5E56y+cXoyZqhxkPWKafp7XBB2D3vmIwM+jiYDZlejILvIjaghEJjyvj1LR85b16xMDuPn72kZ/LrxT278B4SyoFnD7BfCJvu6fmJE5hZjCH1f+/0zlnDyRGlLgtnqhCZW96ZB6DSPiQf6r8t5ixEDgunz+t+dCJENoOHamE86CEeUev0W5p9SuKe+8jCDpd2zf1Oo+DHRs/7YZ+5W0EWGQjyGyAjSmiQ8iLs7dien7pI2w/U/igXedpM0IudM428/W3I6BNyU9ArxZ1s2UtAcwRdKxtg8C7urp1LTt2QCStgdeJwIUR0l6HzDD9h2SJhNG5YN1HldXlP43+rQrYUiPJK6FH5XN7iUkQ4sSwa9zxbYlrUhoaicQcoJ9iGn+EwgDcF/g5b723faVkmYCR0gaYfvn5a35CS/9KOI7bnsq0+ujiZm6N4k4l+eAzSWtTGTE+hi9/O4a56mkfYjUvbsT8r0pRKrC4yXtTBTu2tY1y1hage1fSZoBnC3pL7Yv6+2+JC1o+xXbb0n6D+L8Ohk4WNLztn/qkEHeScQDLEbEZiRtgKQRRBDu5sQ9aCRR1ftHtmdJWoDIFLWX7Vua3p8ePDeSZFAjaVlixHwrMX11BJErfQJxEz2QKNQw4LX6g5m6dewVw2h1wMDTRBDr/sB423+WtBaRveLTRB7pS3u6//L/WEIScgaRVnVVIvj4RuDDxENjK9vT6zi2bvZvK8KAHm/7JUmXEFO8exGyk/8mipXNs08Vve2qhBZ6JhEPsDIhZ3nC9r9q7PsYYpD2KdvPlPbfb/vPdbXRTMqgYlYZbJ5DePDGE/mu/0bMNGxMDDhOt31lH9paiNBaH0rk+N6GMP7nI/I3vwD80fZfe31AbYikLYFpth/r5efnJ5wCJxMyh0vK3+lEOtEViGv6GeKa+Vp/DKyT7iNplO3XJH2JkBAuRcRWPCFpHHENnNUTR0uf+pOGeTKUqRhdaxEG+P3EdOPhRGrG+ySdQ2guj7D9UAu7m7QISZsThtFNhDZ0GyIjwXbEoG00kR5xe+ABdzO4sJNRvhwhwbnL9jRJnyW08zOIwh1bAgf25zkoaRXCO3u67e9X1v+MyJxxKHDhvIwaSSs1jGFJHyTkEusTWZVEGOmX2H6gCcewWPFWDmvMALQ7klYAXrT9D0nvJWbxpto+qwTMHgT8w/ZhZftFbT/f1wBCSfMRg6b/sb1ZCa5/jjAsj69z0DSYkLQD8Zu8Anzf9hRJKxHyug0JCdhCwKW2r2hdT5POlGvt14SEdSRxnz+ZGPyuTswYHWD72v7qU0pZkiFNMcq3IwyFBYliMCI0nH8rWSSWJ6qZpVE+BJG0GuFFGWv7VklfJYK4ti5/lyQM6OUJCcBO3dxv1Sj/OqHnXQg4SdJTtidLGk5IP+4ETnEvM2x0l86Gne0/SjoK+L6kybZvL+v3kPQ8cPHcjHJJw8vU/jDgCkkX255o+wVJp5Tj+hlRNv4o2zObcUyNGa6BYpQXVgTukfSRMkvxJLCOpA/aflbSccBVks6yPd0lm0hfPXq235T0OjBC0prELM21wM/SKJ87ti+X9CrhLd+CkAA9QdTeWN72dypOoAGVfWWwY/txSecTufq3JWbKd6Lk0CcGWtf25++WhnkypKl4n75i+9GisVySSD22M5HO6pScehyaFI/xTwipwORibJ4maXHiBv5l248Vr8vhwBdt/7E7+64Y5WOAdQnDfAIleFXS721fUTymz/anUS5pJyLbzb1EhcRXgNMl7W17Sun//nPZT3f1tncRpceXcc2xAAMd278ruv67Ja1LVI7dD/ikpP8lnAgzylI3fyUqe55ESGW+MNjkK83A9m8ljQdOkDTNkYrxZWB0uV80BohplLcB5Z79hu1nbE+U9C/Cc76l7auKtFVFztKvg6mUsiRDGkmLEA+9A2z/vnjITyMefJOBq4rHKr0cQ4yiVZ8AvJ+o1HsL8HPbfy3G9Bjbe5ZtBSzoCELtSRtLE2nufmN7r2KEH1La/BVwQ7MN8i769A0i+8YFRGaOQ2xfJGlPIuXbjrbvnMtnU29bI5K2JQzk9Yi87mOIapWjgBN6EsvQw3ZHEg6K2bafakYbg5UiQTuXiAt5mUidelVLO5XMgSII/SzgZiIn/7Nl/STCGbdtdx0szSA95smQphLMtpmklx0Fai4EvkoE+F3c2h4m/UmnAdjfCYNyFmFcfhpYS9LDxHT1iY3Plc/0yCgvn3tK0n7ATyTtWrxsE4kgv62ItJ39ZphLWpLw3o8mpnL/AlyqyFpwDpEffK7ZV2y/LumHdOht/7Oit32S0NsuR8wKnJRG+btj+5oy6LuLyHl9laQ1gDdt/6lZDoMiK+q3AOPBRJGg7UUkDviKo8hVOnZaTEVKtBZRD+J6InD6dUmTbD9DOBUWJwalLTPM02OeDHkkLUMY4h8j8tpuT1RoO4zQl93Xut4l/Y2iyuUKts8vGu9dCC/lCMKwfJoIhpxSY5uNirbHFON8BLCIm5wBqHNAZDECjyNSNRrYpjzMJgC3uZvBmYpMF5cQXt0jiwd2G2DT1Nv2HEnbEAOj1dzMioNJbUj6QP5W7UWJJ/sGMSM5DXiYmMm7HXiVKDw1wfZDrbw3DWtFo0nSTth+kvBQnkx4PccS0dnLEVPuydBiESL/9VjbbxGzJvMTRvlUIgfx5xXZRWrBkcXlQEq+aNuz+sEoH+mOPOUrSlqxPIgeJ473qGI870bom1/r7r4ddQbGA+PLTMBMYlq/obdtbJdGeTdwZIT4CjFgSgYAaZS3F+W+cxCwr+0NidnI4URdkleJ1K/HuyR5aOW9KaUsSUL/FahJ2h/bV0uaDRxbPMoXSLqOyJRxIiHnOIWaHRsl8n9PwpPTVIocYhXgMkn7EmkLZ0m6iBikLgt8R9K3CV34LrYf70kbjkwVs4BzFcVpXgYmehAUp2kFZfD2jsw5SZJ0i5mEIb448AhRyfg0ooDgL4GTbb9RAvzfal03U8qSJO9ANReoSQYmkrYm0vldROjL97Z9Q3lvRH8HZdaJpK8RxbSuJ46tUSzoNkJ+cpKkDxPp8h63/XQf2tqR1NsmSdJiJO0PLEAE5D4gaQvgy0SxrseASbbfaGUfIQ3zJEmSuVI8yxsDD5WsPQPaqJT0Xtszyv/7EEGeI4A9HcVsliUKCl1s+9s1tpt62yRJWkqJJ9ubqFNyF7ADUcNkBaL68DG2/9G6HgZpmCdJkgwBFCXX1yc05OsCzxNFkXYgAgtvsf1iye97NWG0Pz+QByJJkiRVJC1IVG9eA7jX9g0lyH9B2y+3tndBGuZJkiRDgBKsOpYonLUssLbtV4qsZUOiiuktjtLuA1qqkyRJ0h3aQVPemczKkiRJMogpKRCx/QKho1yVqHC3TFl/KpGhYDei4ugwIsA1SZJkUNNuRjmkxzxJkmTQUtXEl6qiw4gcvl8qfycX7fyiRL72y/oS6JkkSZL0jTTMkyRJBjmSvkmUdF8Y+DGRfeXrRPqwRYhKd+Nsv9KyTiZJkiQpZUmSJBnMlHSFuwMHEDnKTyACO48DHiXSh/0gjfIkSZLWkx7zJEmSQUQpijS78noXYB3b3yuvNwAuA7aw/Ug7Bj8lSZIMVbLyZ5IkySCiYZRL2h54nSjjPkrS+4AZtu+QNJkyY5pGeZIkSfuQhnmSJMkgoFOg51jgh8AZwOZEJpYZwI2loudo4OhW9TVJkiTpmjTMkyRJBjidjPLlAAOb2J4m6U7gSMIYfxLYEtjJ9vSWdThJkiTpkjTMkyRJBjCdjPKvA+OAhYCTJD1le3KpbPdj4E7glCwelCRJ0p6kYZ4kSTKAqRjlY4B1CcN8ArAmUTDo97avKHnMn02jPEmSpH3JrCxJkiQDHElLE7nJf2N7r2KEH0IUEfoVcEMa5EmSJO1P5jFPkiQZ4Nh+CtgP2FbSrrZnABOBmcBWwHta2b8kSZKke6SUJUmSZBBg+zJJbwLHSML2hZIOABax/Xqr+5ckSZLMmzTMkyRJBgm2r5Y0Gzhd0izbFwPPtbpfSZIkSfdIjXmSJMkgQ9KWwDTbj7W6L0mSJEn3ScM8SZIkSZIkSdqADP5MkiRJkiRJkjYgDfMkSZIkSZIkaQPSME+SJEmSJEmSNiAN8yRJkiRJkiRpA9IwT5IkaSMkLSnpF5KmSXpI0jWSVml1v+aFguclLVJeLyXJkjapbPOcpA/2cv+TJO1UV3+TJEnakTTMkyRJ2gRJAi4HbrS9ou3VgIOBJVrbs3njSPF1O/Dxsmpj4N7yF0kfBZ63/cK89iVpeLP6mSRJ0s6kYZ4kSdI+bAbMtH1aY4Xt/7N9S/FInyDpAUn3S9oFQNInJd0k6SJJf5R0rKTdJd1RtluxbDdJ0qmSbpD0mKTRks6W9LCkSY32JO1aPveApOMq61+VdJSk+yRNkdTVYOEPFEO8/D2JOQ31W+dxHDdIugC4v2z3kzJrcDWweKUvx5b1UyWd2OdvPUmSpE1IwzxJkqR9WAO4ey7v7QisA6wNbAGcIGmp8t7awL7AmsA4YBXbGwBnAt+o7GMR4FPAt4DJwA+B1YE1Ja0j6UPAcWWbdYD1JW1fPjsKmGJ7beBmYEIXfbyVDsN8A+AK4MPl9caE4f5ux7EBcEiZKdgB+Gg5pgl0eN4/UN5b3fZawJFz+b6SJEkGHGmYJ0mSDAw2AS60/ZbtZ4GbgPXLe3faftr2m8A04Ddl/f3A8pV9TC6Sk/uBZ23fb3s28GDZbn1CRvOc7VnA+cCm5bP/Aq4q/9/dab8N7gDWlTQKGGn7VeAxSStRPObzOI47bD9e/t+0st3fgOvL+n8CM4AzJe0IvN6dLy9JkmQgkIZ5kiRJ+/Ag8LG5vKd3+dyblf9nV17PBkZ0sd3sLj4zYh5tzHRHqei3Ou0XANuvA38G9gTuKaunANsSUpRH59HGa5132UUbswjP+qXA9sB177K/JEmSAUUa5kmSJO3D9cB8kt6WiUhaX9JoQj6yi6ThkhYjPMp31Nz+7cBoSYuWAMxdCY92T/gDsB9wW3l9GyGzmVIM++4ex83A2LLdUoT+HkkLAAvbvqa0s04P+5ckSdK2pGGeJEnSJhTDdQdgy5Iu8UHgMOBvRLaWqcB9hAF/gO1nam7/aeB7wA2lnXtsX9nD3fwB+Agdhvk9wDKEjAW6fxyXA38iZDen0jFAWBC4StLUsu5bPexfkiRJ26KOmckkSZIkSZIkSVpFesyTJEmSJEmSpA1IwzxJkiRJkiRJ2oA0zJMkSZIkSZKkDUjDPEmSJEmSJEnagDTMkyRJkiRJkqQNSMM8SZIkSZIkSdqANMyTJEmSJEmSpA1IwzxJkiRJkiRJ2oD/B1YyrBpg8jn5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "chartbuildpc = sns.barplot(x= most_freq_build_pc[0], y = most_freq_build_pc[1], palette='YlGnBu_r')\n",
    "chartbuildpc.set_xticklabels(chartbuildpc.get_xticklabels(), rotation=45)\n",
    "plt.title('Most Common Words in Build PC')\n",
    "plt.xlabel('Common Words')\n",
    "plt.ylabel('Word Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:08.993619Z",
     "iopub.status.busy": "2020-08-26T01:05:08.993520Z",
     "iopub.status.idle": "2020-08-26T01:05:08.998065Z",
     "shell.execute_reply": "2020-08-26T01:05:08.997638Z",
     "shell.execute_reply.started": "2020-08-26T01:05:08.993605Z"
    }
   },
   "outputs": [],
   "source": [
    "amd_list = [i for i in most_freq_amd[0]]\n",
    "build_pc_list = [i for i in most_freq_build_pc[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:08.998622Z",
     "iopub.status.busy": "2020-08-26T01:05:08.998529Z",
     "iopub.status.idle": "2020-08-26T01:05:09.001495Z",
     "shell.execute_reply": "2020-08-26T01:05:09.001147Z",
     "shell.execute_reply.started": "2020-08-26T01:05:08.998609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'amd',\n",
       " 'asus',\n",
       " 'bios',\n",
       " 'cooler',\n",
       " 'driver',\n",
       " 'drivers',\n",
       " 'gb',\n",
       " 'intel',\n",
       " 'msi',\n",
       " 'radeon',\n",
       " 'vega',\n",
       " 'vs'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Words in Amd list not in build pc list\n",
    "set(amd_list) - set(build_pc_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:09.002038Z",
     "iopub.status.busy": "2020-08-26T01:05:09.001949Z",
     "iopub.status.idle": "2020-08-26T01:05:09.004981Z",
     "shell.execute_reply": "2020-08-26T01:05:09.004639Z",
     "shell.execute_reply.started": "2020-08-26T01:05:09.002027Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'advice',\n",
       " 'budget',\n",
       " 'building',\n",
       " 'case',\n",
       " 'get',\n",
       " 'good',\n",
       " 'im',\n",
       " 'looking',\n",
       " 'monitor',\n",
       " 'parts',\n",
       " 'question',\n",
       " 'time'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Words in build pc list not in Amd list\n",
    "\n",
    "set(build_pc_list) - set(amd_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:09.005496Z",
     "iopub.status.busy": "2020-08-26T01:05:09.005405Z",
     "iopub.status.idle": "2020-08-26T01:05:09.007290Z",
     "shell.execute_reply": "2020-08-26T01:05:09.006956Z",
     "shell.execute_reply.started": "2020-08-26T01:05:09.005485Z"
    }
   },
   "outputs": [],
   "source": [
    "# There seems to be enough variation in words, so I believe lemmatizing and stemming is not required for my topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model1\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "# Model 1: Logistic Regression Model with just Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:19.722598Z",
     "iopub.status.busy": "2020-08-26T03:11:19.722382Z",
     "iopub.status.idle": "2020-08-26T03:11:19.730698Z",
     "shell.execute_reply": "2020-08-26T03:11:19.730171Z",
     "shell.execute_reply.started": "2020-08-26T03:11:19.722575Z"
    }
   },
   "outputs": [],
   "source": [
    "# First test on just the title\n",
    "X = df[['title']]\n",
    "y = df['subreddit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:20.408856Z",
     "iopub.status.busy": "2020-08-26T03:11:20.408621Z",
     "iopub.status.idle": "2020-08-26T03:11:20.412514Z",
     "shell.execute_reply": "2020-08-26T03:11:20.412011Z",
     "shell.execute_reply.started": "2020-08-26T03:11:20.408832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480, 1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:20.999814Z",
     "iopub.status.busy": "2020-08-26T03:11:20.999595Z",
     "iopub.status.idle": "2020-08-26T03:11:21.002950Z",
     "shell.execute_reply": "2020-08-26T03:11:21.002689Z",
     "shell.execute_reply.started": "2020-08-26T03:11:20.999788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480,)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:22.222430Z",
     "iopub.status.busy": "2020-08-26T03:11:22.222287Z",
     "iopub.status.idle": "2020-08-26T03:11:22.355649Z",
     "shell.execute_reply": "2020-08-26T03:11:22.355219Z",
     "shell.execute_reply.started": "2020-08-26T03:11:22.222413Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:23.705379Z",
     "iopub.status.busy": "2020-08-26T03:11:23.705251Z",
     "iopub.status.idle": "2020-08-26T03:11:23.718249Z",
     "shell.execute_reply": "2020-08-26T03:11:23.717888Z",
     "shell.execute_reply.started": "2020-08-26T03:11:23.705364Z"
    }
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(ngram_range=(1,2),\n",
    "                             min_df=2,   \n",
    "                            max_features=25000\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:24.470930Z",
     "iopub.status.busy": "2020-08-26T03:11:24.470814Z",
     "iopub.status.idle": "2020-08-26T03:11:26.276382Z",
     "shell.execute_reply": "2020-08-26T03:11:26.275956Z",
     "shell.execute_reply.started": "2020-08-26T03:11:24.470915Z"
    }
   },
   "outputs": [],
   "source": [
    "train_features = vectorizer.fit_transform(X_train['title'])\n",
    "test_features = vectorizer.transform(X_test['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:29.443427Z",
     "iopub.status.busy": "2020-08-26T03:11:29.443221Z",
     "iopub.status.idle": "2020-08-26T03:11:29.941351Z",
     "shell.execute_reply": "2020-08-26T03:11:29.940936Z",
     "shell.execute_reply.started": "2020-08-26T03:11:29.443404Z"
    }
   },
   "outputs": [],
   "source": [
    "train_features = train_features.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:31.374361Z",
     "iopub.status.busy": "2020-08-26T03:11:31.374238Z",
     "iopub.status.idle": "2020-08-26T03:11:31.377247Z",
     "shell.execute_reply": "2020-08-26T03:11:31.376819Z",
     "shell.execute_reply.started": "2020-08-26T03:11:31.374346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164610, 25000)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:32.004416Z",
     "iopub.status.busy": "2020-08-26T03:11:32.004241Z",
     "iopub.status.idle": "2020-08-26T03:11:32.007465Z",
     "shell.execute_reply": "2020-08-26T03:11:32.007121Z",
     "shell.execute_reply.started": "2020-08-26T03:11:32.004394Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54870, 25000)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:32.705286Z",
     "iopub.status.busy": "2020-08-26T03:11:32.705196Z",
     "iopub.status.idle": "2020-08-26T03:11:32.717434Z",
     "shell.execute_reply": "2020-08-26T03:11:32.717079Z",
     "shell.execute_reply.started": "2020-08-26T03:11:32.705275Z"
    }
   },
   "outputs": [],
   "source": [
    "vocab = vectorizer.get_feature_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:33.629269Z",
     "iopub.status.busy": "2020-08-26T03:11:33.629071Z",
     "iopub.status.idle": "2020-08-26T03:11:33.632728Z",
     "shell.execute_reply": "2020-08-26T03:11:33.632437Z",
     "shell.execute_reply.started": "2020-08-26T03:11:33.629246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:34.714353Z",
     "iopub.status.busy": "2020-08-26T03:11:34.714255Z",
     "iopub.status.idle": "2020-08-26T03:11:34.716192Z",
     "shell.execute_reply": "2020-08-26T03:11:34.715914Z",
     "shell.execute_reply.started": "2020-08-26T03:11:34.714342Z"
    }
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression(max_iter=15000,\n",
    "                       solver='liblinear'\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:36.516198Z",
     "iopub.status.busy": "2020-08-26T03:11:36.515991Z",
     "iopub.status.idle": "2020-08-26T03:11:55.100837Z",
     "shell.execute_reply": "2020-08-26T03:11:55.100381Z",
     "shell.execute_reply.started": "2020-08-26T03:11:36.516175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=15000, solver='liblinear')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_features, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:11:56.968491Z",
     "iopub.status.busy": "2020-08-26T03:11:56.968270Z",
     "iopub.status.idle": "2020-08-26T03:12:01.609811Z",
     "shell.execute_reply": "2020-08-26T03:12:01.609340Z",
     "shell.execute_reply.started": "2020-08-26T03:11:56.968467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8764655853228844"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_features, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:12:05.054964Z",
     "iopub.status.busy": "2020-08-26T03:12:05.054745Z",
     "iopub.status.idle": "2020-08-26T03:12:05.122257Z",
     "shell.execute_reply": "2020-08-26T03:12:05.121806Z",
     "shell.execute_reply.started": "2020-08-26T03:12:05.054940Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8442682704574449"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_features, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model1_scores\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "## Model 1 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:52.471504Z",
     "iopub.status.busy": "2020-08-26T01:05:52.471284Z",
     "iopub.status.idle": "2020-08-26T01:05:52.475461Z",
     "shell.execute_reply": "2020-08-26T01:05:52.474989Z",
     "shell.execute_reply.started": "2020-08-26T01:05:52.471485Z"
    }
   },
   "outputs": [],
   "source": [
    "# Model 1: NLP Logistic Regression Model for Title only\n",
    "\n",
    "# With ngram_range=(1,1): # max features 25000   min_df was 2 in this model\n",
    "# train score: .8575481440981715\n",
    "# test score: .84117003827228\n",
    "\n",
    "# With ngram_range=(1,2): # max features 25000   min_df was 2 in this model\n",
    "# train score: .8764655853228844\n",
    "# test score: .8442682704574449\n",
    "\n",
    "# With ngram_range=(1,3): # max features 25000   min_df was 2 in this model\n",
    "# train score: .8816839803171131\n",
    "# test score: .8528704209950793\n",
    "\n",
    "# With ngram_range=(1,2): # max features 25000   min_df was 5 in this model\n",
    "# train score: .8765749346941255\n",
    "# test score: .8452524147986149\n",
    "\n",
    "# It seems the best ngram range is (1,3), min_df 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:52.476693Z",
     "iopub.status.busy": "2020-08-26T01:05:52.476473Z",
     "iopub.status.idle": "2020-08-26T01:05:52.628689Z",
     "shell.execute_reply": "2020-08-26T01:05:52.628243Z",
     "shell.execute_reply.started": "2020-08-26T01:05:52.476668Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get predictions\n",
    "preds = lr.predict(test_features)\n",
    "\n",
    "# Save confusion matrix values\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model1_visuals\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "### Model 1 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:40:48.270860Z",
     "iopub.status.busy": "2020-08-26T02:40:48.270618Z",
     "iopub.status.idle": "2020-08-26T02:40:48.495579Z",
     "shell.execute_reply": "2020-08-26T02:40:48.495181Z",
     "shell.execute_reply.started": "2020-08-26T02:40:48.270836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View confusion matrix\n",
    "plot_confusion_matrix(lr, test_features, y_test, cmap='Blues', values_format='d')\n",
    "plt.title('Model 1: Title only Logistic Regression Model');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:52.983763Z",
     "iopub.status.busy": "2020-08-26T01:05:52.983602Z",
     "iopub.status.idle": "2020-08-26T01:05:52.986960Z",
     "shell.execute_reply": "2020-08-26T01:05:52.986408Z",
     "shell.execute_reply.started": "2020-08-26T01:05:52.983743Z"
    }
   },
   "outputs": [],
   "source": [
    "TN = 22860\n",
    "TP = 23519\n",
    "FN = 3916\n",
    "FP = 4575"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:05:52.988797Z",
     "iopub.status.busy": "2020-08-26T01:05:52.988594Z",
     "iopub.status.idle": "2020-08-26T01:05:52.994669Z",
     "shell.execute_reply": "2020-08-26T01:05:52.994011Z",
     "shell.execute_reply.started": "2020-08-26T01:05:52.988773Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy score is: 84.5%\n",
      "The Missclassification rate is: 15.5%\n",
      "The Sensitivity is: 85.7%\n",
      "The Specificity is: 83.3%\n",
      "The Precision is 83.7%\n"
     ]
    }
   ],
   "source": [
    "# Credit to Aiden\n",
    "# Accuracy:  # What percentage of observation did I correctly predict?\n",
    "print(f\"The Accuracy score is: {round((TP + TN) / (TP + TN + FP + FN), 3) * 100}%\")\n",
    "# Misclassification: # What percentage of observation did I incorrectly predict?\n",
    "print(f\"The Missclassification rate is: {round((FP + FN) / (TP + TN + FP + FN), 3) * 100}%\")\n",
    "# Sensitivity:  # Among Positives, how many did I get correctly? Same as recall\n",
    "print(f\"The Sensitivity is: {round(TP / (TP + FN), 3) * 100}%\")\n",
    "# Specificity:  # Among Negatives, how many did I get correctly?\n",
    "print(f\"The Specificity is: {round(TN / (TN + FP), 3) * 100}%\")\n",
    "# Precision: # Among Positives, how many did i Predict correct\n",
    "print(f\"The Precision is {round(TP / (TP + FP), 3) * 100}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model2\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "## Model 2: Logistic Regression Model with  Title and Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:33.047849Z",
     "iopub.status.busy": "2020-08-26T03:04:33.047625Z",
     "iopub.status.idle": "2020-08-26T03:04:33.080863Z",
     "shell.execute_reply": "2020-08-26T03:04:33.080470Z",
     "shell.execute_reply.started": "2020-08-26T03:04:33.047825Z"
    }
   },
   "outputs": [],
   "source": [
    "# First test on just the title\n",
    "X = df[['title_text_combined']]\n",
    "y = df['subreddit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:34.156854Z",
     "iopub.status.busy": "2020-08-26T03:04:34.156613Z",
     "iopub.status.idle": "2020-08-26T03:04:34.160619Z",
     "shell.execute_reply": "2020-08-26T03:04:34.160038Z",
     "shell.execute_reply.started": "2020-08-26T03:04:34.156830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:34.878141Z",
     "iopub.status.busy": "2020-08-26T03:04:34.877932Z",
     "iopub.status.idle": "2020-08-26T03:04:34.881181Z",
     "shell.execute_reply": "2020-08-26T03:04:34.880830Z",
     "shell.execute_reply.started": "2020-08-26T03:04:34.878118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219480,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:40.620264Z",
     "iopub.status.busy": "2020-08-26T03:04:40.620046Z",
     "iopub.status.idle": "2020-08-26T03:04:40.757167Z",
     "shell.execute_reply": "2020-08-26T03:04:40.756807Z",
     "shell.execute_reply.started": "2020-08-26T03:04:40.620241Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:41.813214Z",
     "iopub.status.busy": "2020-08-26T03:04:41.813081Z",
     "iopub.status.idle": "2020-08-26T03:04:41.817839Z",
     "shell.execute_reply": "2020-08-26T03:04:41.817464Z",
     "shell.execute_reply.started": "2020-08-26T03:04:41.813201Z"
    }
   },
   "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_text_combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152141</th>\n",
       "      <td>want retrofit computer best best usb ports ava...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132626</th>\n",
       "      <td>high allow temperatures different components g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115181</th>\n",
       "      <td>drivers nowadays know amds series rough patch ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94517</th>\n",
       "      <td>prices make sense</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208593</th>\n",
       "      <td>socket long term support plan upgrade cpu sock...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      title_text_combined\n",
       "152141  want retrofit computer best best usb ports ava...\n",
       "132626  high allow temperatures different components g...\n",
       "115181  drivers nowadays know amds series rough patch ...\n",
       "94517                                   prices make sense\n",
       "208593  socket long term support plan upgrade cpu sock..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:42.889200Z",
     "iopub.status.busy": "2020-08-26T03:04:42.888985Z",
     "iopub.status.idle": "2020-08-26T03:04:42.892453Z",
     "shell.execute_reply": "2020-08-26T03:04:42.891916Z",
     "shell.execute_reply.started": "2020-08-26T03:04:42.889177Z"
    }
   },
   "outputs": [],
   "source": [
    "# I will test with the same parameter options\n",
    "vectorizer = CountVectorizer(ngram_range=(1,2),\n",
    "                             min_df=2,   \n",
    "                            max_features=25000\n",
    "                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:04:48.600860Z",
     "iopub.status.busy": "2020-08-26T03:04:48.600614Z",
     "iopub.status.idle": "2020-08-26T03:05:03.334666Z",
     "shell.execute_reply": "2020-08-26T03:05:03.334149Z",
     "shell.execute_reply.started": "2020-08-26T03:04:48.600836Z"
    }
   },
   "outputs": [],
   "source": [
    "train_features = vectorizer.fit_transform(X_train['title_text_combined'])\n",
    "test_features = vectorizer.transform(X_test['title_text_combined'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:09.464585Z",
     "iopub.status.busy": "2020-08-26T03:05:09.464362Z",
     "iopub.status.idle": "2020-08-26T03:05:11.646373Z",
     "shell.execute_reply": "2020-08-26T03:05:11.645846Z",
     "shell.execute_reply.started": "2020-08-26T03:05:09.464561Z"
    }
   },
   "outputs": [],
   "source": [
    "train_features = train_features.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:20.830722Z",
     "iopub.status.busy": "2020-08-26T03:05:20.830488Z",
     "iopub.status.idle": "2020-08-26T03:05:20.834097Z",
     "shell.execute_reply": "2020-08-26T03:05:20.833665Z",
     "shell.execute_reply.started": "2020-08-26T03:05:20.830698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(164610, 25000)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:23.794467Z",
     "iopub.status.busy": "2020-08-26T03:05:23.794238Z",
     "iopub.status.idle": "2020-08-26T03:05:23.797895Z",
     "shell.execute_reply": "2020-08-26T03:05:23.797490Z",
     "shell.execute_reply.started": "2020-08-26T03:05:23.794442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54870, 25000)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:30.547702Z",
     "iopub.status.busy": "2020-08-26T03:05:30.547462Z",
     "iopub.status.idle": "2020-08-26T03:05:30.564837Z",
     "shell.execute_reply": "2020-08-26T03:05:30.564413Z",
     "shell.execute_reply.started": "2020-08-26T03:05:30.547663Z"
    }
   },
   "outputs": [],
   "source": [
    "vocab = vectorizer.get_feature_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:31.334504Z",
     "iopub.status.busy": "2020-08-26T03:05:31.334348Z",
     "iopub.status.idle": "2020-08-26T03:05:31.337640Z",
     "shell.execute_reply": "2020-08-26T03:05:31.337274Z",
     "shell.execute_reply.started": "2020-08-26T03:05:31.334488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features[0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:35.901741Z",
     "iopub.status.busy": "2020-08-26T03:05:35.901499Z",
     "iopub.status.idle": "2020-08-26T03:05:35.904719Z",
     "shell.execute_reply": "2020-08-26T03:05:35.904152Z",
     "shell.execute_reply.started": "2020-08-26T03:05:35.901717Z"
    }
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression(max_iter=15000,\n",
    "                       solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:05:38.259848Z",
     "iopub.status.busy": "2020-08-26T03:05:38.259617Z",
     "iopub.status.idle": "2020-08-26T03:07:49.650621Z",
     "shell.execute_reply": "2020-08-26T03:07:49.650181Z",
     "shell.execute_reply.started": "2020-08-26T03:05:38.259824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=15000, solver='liblinear')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_features, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:07:55.901421Z",
     "iopub.status.busy": "2020-08-26T03:07:55.901203Z",
     "iopub.status.idle": "2020-08-26T03:08:00.838176Z",
     "shell.execute_reply": "2020-08-26T03:08:00.837709Z",
     "shell.execute_reply.started": "2020-08-26T03:07:55.901398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9332604337525059"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(train_features, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T03:08:01.862529Z",
     "iopub.status.busy": "2020-08-26T03:08:01.862335Z",
     "iopub.status.idle": "2020-08-26T03:08:01.934561Z",
     "shell.execute_reply": "2020-08-26T03:08:01.934191Z",
     "shell.execute_reply.started": "2020-08-26T03:08:01.862506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8841443411700383"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(test_features, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model2_scores\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "## Model 2 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:30.760451Z",
     "iopub.status.busy": "2020-08-26T01:08:30.760356Z",
     "iopub.status.idle": "2020-08-26T01:08:30.762536Z",
     "shell.execute_reply": "2020-08-26T01:08:30.762079Z",
     "shell.execute_reply.started": "2020-08-26T01:08:30.760438Z"
    }
   },
   "outputs": [],
   "source": [
    "# Model 2: NLP Logistic Regression Model with Title and Selftext\n",
    "\n",
    "# With ngram_range=(1,1): # max features 25000   min_df was 2 in this model\n",
    "# train score: .9072049085717757\n",
    "# test score: .879861490796428\n",
    "\n",
    "# With ngram_range=(1,2): # max features 25000   min_df was 2 in this model\n",
    "# train score: .9332604337525059\n",
    "# test score: .8841443411700383\n",
    "\n",
    "# With ngram_range=(1,3): # max features 25000   min_df was 2 in this model\n",
    "# train score: .9319482412976126\n",
    "# test score: .8877164206305814\n",
    "\n",
    "# With ngram_range=(1,2): # max features 25000   min_df was 5 in this model\n",
    "# train score: .9332118340319543\n",
    "# test score: .8839074175323491\n",
    "\n",
    "\n",
    "# It seen the best ngram range is (1,2) min_df 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:30.763841Z",
     "iopub.status.busy": "2020-08-26T01:08:30.763619Z",
     "iopub.status.idle": "2020-08-26T01:08:30.917176Z",
     "shell.execute_reply": "2020-08-26T01:08:30.916715Z",
     "shell.execute_reply.started": "2020-08-26T01:08:30.763823Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get predictions\n",
    "preds = lr.predict(test_features)\n",
    "\n",
    "# Save confusion matrix values\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model2_visuals\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "### Model 2 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:39:52.343815Z",
     "iopub.status.busy": "2020-08-26T02:39:52.343555Z",
     "iopub.status.idle": "2020-08-26T02:39:52.572162Z",
     "shell.execute_reply": "2020-08-26T02:39:52.571692Z",
     "shell.execute_reply.started": "2020-08-26T02:39:52.343788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View confusion matrix\n",
    "plot_confusion_matrix(lr, test_features, y_test, cmap='Blues', values_format='d')\n",
    "plt.title('Model 2: Title and Contents \\nLogistic Regression Model');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:31.170391Z",
     "iopub.status.busy": "2020-08-26T01:08:31.170191Z",
     "iopub.status.idle": "2020-08-26T01:08:31.173095Z",
     "shell.execute_reply": "2020-08-26T01:08:31.172708Z",
     "shell.execute_reply.started": "2020-08-26T01:08:31.170366Z"
    }
   },
   "outputs": [],
   "source": [
    "TN = 24727\n",
    "TP = 23773\n",
    "FN = 3662\n",
    "FP = 2708"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:31.175341Z",
     "iopub.status.busy": "2020-08-26T01:08:31.175082Z",
     "iopub.status.idle": "2020-08-26T01:08:31.179973Z",
     "shell.execute_reply": "2020-08-26T01:08:31.179152Z",
     "shell.execute_reply.started": "2020-08-26T01:08:31.175315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy score is: 88.39%\n",
      "The Missclassification rate is: 11.61%\n",
      "The Sensitivity is: 86.65%\n",
      "The Specificity is: 90.13%\n",
      "The Precision is 89.8%\n"
     ]
    }
   ],
   "source": [
    "# Accuracy:  # What percentage of observation did I correctly predict?\n",
    "print(f\"The Accuracy score is: {round((TP + TN) / (TP + TN + FP + FN), 4) * 100}%\")\n",
    "# Misclassification: # What percentage of observation did I incorrectly predict?\n",
    "print(f\"The Missclassification rate is: {round((FP + FN) / (TP + TN + FP + FN), 4) * 100}%\")\n",
    "# Sensitivity:  # Among Positives, how many did I get correctly? Same as recall\n",
    "print(f\"The Sensitivity is: {round(TP / (TP + FN), 4) * 100}%\")\n",
    "# Specificity:  # Among Negatives, how many did I get correctly?\n",
    "print(f\"The Specificity is: {round(TN / (TN + FP), 4) * 100}%\")\n",
    "# Precision: # Among Positives, how many did i Predict correct\n",
    "print(f\"The Precision is {round(TP / (TP + FP), 3) * 100}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:31.180742Z",
     "iopub.status.busy": "2020-08-26T01:08:31.180637Z",
     "iopub.status.idle": "2020-08-26T01:08:31.182572Z",
     "shell.execute_reply": "2020-08-26T01:08:31.182221Z",
     "shell.execute_reply.started": "2020-08-26T01:08:31.180730Z"
    }
   },
   "outputs": [],
   "source": [
    "# So far my best models are ones that include both title and the selftext columns with n_grams (1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:31.184983Z",
     "iopub.status.busy": "2020-08-26T01:08:31.184878Z",
     "iopub.status.idle": "2020-08-26T01:08:31.191550Z",
     "shell.execute_reply": "2020-08-26T01:08:31.190955Z",
     "shell.execute_reply.started": "2020-08-26T01:08:31.184971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[25000]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(n) for n in lr.coef_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:31.192415Z",
     "iopub.status.busy": "2020-08-26T01:08:31.192262Z",
     "iopub.status.idle": "2020-08-26T01:08:32.944393Z",
     "shell.execute_reply": "2020-08-26T01:08:32.943919Z",
     "shell.execute_reply.started": "2020-08-26T01:08:31.192395Z"
    }
   },
   "outputs": [],
   "source": [
    "coef_df = pd.DataFrame([n for n in lr.coef_], columns=vectorizer.get_feature_names()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:59:45.913210Z",
     "iopub.status.busy": "2020-08-26T01:59:45.912988Z",
     "iopub.status.idle": "2020-08-26T01:59:45.923446Z",
     "shell.execute_reply": "2020-08-26T01:59:45.923071Z",
     "shell.execute_reply.started": "2020-08-26T01:59:45.913187Z"
    }
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cl memory</th>\n",
       "      <td>2.969861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amd wraith</th>\n",
       "      <td>2.423745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temps low</th>\n",
       "      <td>2.378933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zi</th>\n",
       "      <td>2.312894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supers</th>\n",
       "      <td>2.311313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zp</th>\n",
       "      <td>2.247580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free time</th>\n",
       "      <td>2.222362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sdd</th>\n",
       "      <td>2.172693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>havent even</th>\n",
       "      <td>2.146657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bought gt</th>\n",
       "      <td>2.131466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vgaq</th>\n",
       "      <td>2.052682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rt series</th>\n",
       "      <td>2.032773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seated properly</th>\n",
       "      <td>2.013155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinking switching</th>\n",
       "      <td>2.013054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>problem https</th>\n",
       "      <td>2.010111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>didnt go</th>\n",
       "      <td>1.996502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amd cooler</th>\n",
       "      <td>1.995802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>refmp_s_a__dchildamp</th>\n",
       "      <td>1.988375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amd athlon</th>\n",
       "      <td>1.980840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intel processor</th>\n",
       "      <td>1.967976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0\n",
       "cl memory             2.969861\n",
       "amd wraith            2.423745\n",
       "temps low             2.378933\n",
       "zi                    2.312894\n",
       "supers                2.311313\n",
       "zp                    2.247580\n",
       "free time             2.222362\n",
       "sdd                   2.172693\n",
       "havent even           2.146657\n",
       "bought gt             2.131466\n",
       "vgaq                  2.052682\n",
       "rt series             2.032773\n",
       "seated properly       2.013155\n",
       "thinking switching    2.013054\n",
       "problem https         2.010111\n",
       "didnt go              1.996502\n",
       "amd cooler            1.995802\n",
       "refmp_s_a__dchildamp  1.988375\n",
       "amd athlon            1.980840\n",
       "intel processor       1.967976"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient highs and lows\n",
    "# Top features for indicating post is for subreddit AMD\n",
    "amd_coefs = coef_df.sort_values(by=0, ascending=False).head(20)\n",
    "amd_coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:00:10.679335Z",
     "iopub.status.busy": "2020-08-26T02:00:10.679112Z",
     "iopub.status.idle": "2020-08-26T02:00:10.689155Z",
     "shell.execute_reply": "2020-08-26T02:00:10.688796Z",
     "shell.execute_reply.started": "2020-08-26T02:00:10.679311Z"
    }
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intel nvidia</th>\n",
       "      <td>-2.150911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tried install</th>\n",
       "      <td>-2.152822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>full amd</th>\n",
       "      <td>-2.162299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>since build</th>\n",
       "      <td>-2.177977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need information</th>\n",
       "      <td>-2.224457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fans put</th>\n",
       "      <td>-2.242616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everything think</th>\n",
       "      <td>-2.270910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>never go</th>\n",
       "      <td>-2.281564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>division</th>\n",
       "      <td>-2.297085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navi</th>\n",
       "      <td>-2.331819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zenith</th>\n",
       "      <td>-2.344793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rdna</th>\n",
       "      <td>-2.357906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgb gaming</th>\n",
       "      <td>-2.406608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abba</th>\n",
       "      <td>-2.458437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ryzen laptop</th>\n",
       "      <td>-2.586339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ireddit</th>\n",
       "      <td>-2.642952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flair</th>\n",
       "      <td>-2.704776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relive</th>\n",
       "      <td>-2.864133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aib</th>\n",
       "      <td>-3.105778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>storemi</th>\n",
       "      <td>-3.377340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0\n",
       "intel nvidia     -2.150911\n",
       "tried install    -2.152822\n",
       "full amd         -2.162299\n",
       "since build      -2.177977\n",
       "need information -2.224457\n",
       "fans put         -2.242616\n",
       "everything think -2.270910\n",
       "never go         -2.281564\n",
       "division         -2.297085\n",
       "navi             -2.331819\n",
       "zenith           -2.344793\n",
       "rdna             -2.357906\n",
       "rgb gaming       -2.406608\n",
       "abba             -2.458437\n",
       "ryzen laptop     -2.586339\n",
       "ireddit          -2.642952\n",
       "flair            -2.704776\n",
       "relive           -2.864133\n",
       "aib              -3.105778\n",
       "storemi          -3.377340"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficient highs and lows\n",
    "# Top features for indicating post is for subreddit Build PC\n",
    "\n",
    "build_pc_coefs = coef_df.sort_values(by=0, ascending=False).tail(20)\n",
    "build_pc_coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"coefs\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "## Interpretting Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:38:38.613137Z",
     "iopub.status.busy": "2020-08-26T02:38:38.612913Z",
     "iopub.status.idle": "2020-08-26T02:38:38.615973Z",
     "shell.execute_reply": "2020-08-26T02:38:38.615614Z",
     "shell.execute_reply.started": "2020-08-26T02:38:38.613113Z"
    }
   },
   "outputs": [],
   "source": [
    "# Credit to: https://towardsdatascience.com/interpreting-coefficients-in-linear-and-logistic-regression-6ddf1295f6f1\n",
    "odds_amd = np.exp(amd_coefs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:29:01.732424Z",
     "iopub.status.busy": "2020-08-26T02:29:01.732203Z",
     "iopub.status.idle": "2020-08-26T02:29:01.735616Z",
     "shell.execute_reply": "2020-08-26T02:29:01.735384Z",
     "shell.execute_reply.started": "2020-08-26T02:29:01.732400Z"
    }
   },
   "outputs": [],
   "source": [
    "odds_amd.rename(columns={0: 'Highest Amd Odds'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:35:38.107803Z",
     "iopub.status.busy": "2020-08-26T02:35:38.107572Z",
     "iopub.status.idle": "2020-08-26T02:35:38.115081Z",
     "shell.execute_reply": "2020-08-26T02:35:38.114665Z",
     "shell.execute_reply.started": "2020-08-26T02:35:38.107780Z"
    }
   },
   "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>Highest Amd Odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cl memory</th>\n",
       "      <td>19.489206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amd wraith</th>\n",
       "      <td>11.288051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temps low</th>\n",
       "      <td>10.793375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zi</th>\n",
       "      <td>10.103625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>supers</th>\n",
       "      <td>10.087663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zp</th>\n",
       "      <td>9.464808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free time</th>\n",
       "      <td>9.229105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sdd</th>\n",
       "      <td>8.781899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>havent even</th>\n",
       "      <td>8.556207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bought gt</th>\n",
       "      <td>8.427214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vgaq</th>\n",
       "      <td>7.788766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rt series</th>\n",
       "      <td>7.635230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seated properly</th>\n",
       "      <td>7.486901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thinking switching</th>\n",
       "      <td>7.486148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>problem https</th>\n",
       "      <td>7.464145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Highest Amd Odds\n",
       "cl memory                  19.489206\n",
       "amd wraith                 11.288051\n",
       "temps low                  10.793375\n",
       "zi                         10.103625\n",
       "supers                     10.087663\n",
       "zp                          9.464808\n",
       "free time                   9.229105\n",
       "sdd                         8.781899\n",
       "havent even                 8.556207\n",
       "bought gt                   8.427214\n",
       "vgaq                        7.788766\n",
       "rt series                   7.635230\n",
       "seated properly             7.486901\n",
       "thinking switching          7.486148\n",
       "problem https               7.464145"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds_amd[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:37:13.020409Z",
     "iopub.status.busy": "2020-08-26T02:37:13.020267Z",
     "iopub.status.idle": "2020-08-26T02:37:13.023591Z",
     "shell.execute_reply": "2020-08-26T02:37:13.023089Z",
     "shell.execute_reply.started": "2020-08-26T02:37:13.020396Z"
    }
   },
   "outputs": [],
   "source": [
    "odds_least_amd = np.exp(build_pc_coefs)\n",
    "odds_least_amd.rename(columns={0: 'Lowest Amd Odds'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T02:37:14.657531Z",
     "iopub.status.busy": "2020-08-26T02:37:14.657312Z",
     "iopub.status.idle": "2020-08-26T02:37:14.664551Z",
     "shell.execute_reply": "2020-08-26T02:37:14.664238Z",
     "shell.execute_reply.started": "2020-08-26T02:37:14.657507Z"
    }
   },
   "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>Lowest Amd Odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fans put</th>\n",
       "      <td>0.106180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>everything think</th>\n",
       "      <td>0.103218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>never go</th>\n",
       "      <td>0.102124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>division</th>\n",
       "      <td>0.100552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>navi</th>\n",
       "      <td>0.097119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zenith</th>\n",
       "      <td>0.095867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rdna</th>\n",
       "      <td>0.094618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rgb gaming</th>\n",
       "      <td>0.090120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abba</th>\n",
       "      <td>0.085569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ryzen laptop</th>\n",
       "      <td>0.075295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ireddit</th>\n",
       "      <td>0.071151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flair</th>\n",
       "      <td>0.066885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relive</th>\n",
       "      <td>0.057033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aib</th>\n",
       "      <td>0.044790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>storemi</th>\n",
       "      <td>0.034138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Lowest Amd Odds\n",
       "fans put                 0.106180\n",
       "everything think         0.103218\n",
       "never go                 0.102124\n",
       "division                 0.100552\n",
       "navi                     0.097119\n",
       "zenith                   0.095867\n",
       "rdna                     0.094618\n",
       "rgb gaming               0.090120\n",
       "abba                     0.085569\n",
       "ryzen laptop             0.075295\n",
       "ireddit                  0.071151\n",
       "flair                    0.066885\n",
       "relive                   0.057033\n",
       "aib                      0.044790\n",
       "storemi                  0.034138"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Credit to: https://towardsdatascience.com/interpreting-coefficients-in-linear-and-logistic-regression-6ddf1295f6f1\n",
    "odds_least_amd[5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T04:28:57.858450Z",
     "iopub.status.busy": "2020-08-26T04:28:57.858233Z",
     "iopub.status.idle": "2020-08-26T04:28:57.861925Z",
     "shell.execute_reply": "2020-08-26T04:28:57.861276Z",
     "shell.execute_reply.started": "2020-08-26T04:28:57.858425Z"
    }
   },
   "source": [
    "<a name=\"save\"></a>\n",
    "- [Back to Contents](#contents)\n",
    "# Save to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-26T01:08:32.965480Z",
     "iopub.status.busy": "2020-08-26T01:08:32.965392Z",
     "iopub.status.idle": "2020-08-26T01:08:36.262303Z",
     "shell.execute_reply": "2020-08-26T01:08:36.261833Z",
     "shell.execute_reply.started": "2020-08-26T01:08:32.965470Z"
    }
   },
   "outputs": [],
   "source": [
    "# Save to file for TensorFlow models:\n",
    "try:\n",
    "    df.to_csv('./Data/tf_df.csv') \n",
    "except:\n",
    "    pass\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}