docs/Capstone_Final_Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Python Imports](#pythonimports)\n",
    "* [Executive Summary](#executivesummary)\n",
    "* [Audience](#audience)\n",
    "* [Objective](#objective)\n",
    "* [The Process](#theprocess)\n",
    "  * [Data Collection](#datacollection)\n",
    "  * [Pre-processing](#pre-processing)\n",
    "  * [Data Cleaning](#datacleaning)\n",
    "  * [Exploratory Data Analysis (EDA)](#eda)\n",
    "  * [Model Selection & Evaluation](#modelselection)\n",
    "* [Results](#results)\n",
    "  * [NLP Data](#nlpdata)\n",
    "  * [Linear Regression](#linearregrssion)\n",
    "  * [Decision Tree Regressor](#decisiontreeregressor)\n",
    "  * [Decision Tree Feature Importances](#randomforestfeatureimportances)\n",
    "* [Challenges](#challenges)\n",
    "* [Risks](#risks)\n",
    "* [Assumptions](#assumptions)\n",
    "* [Key Takeaways](#keytakeaways)\n",
    "* [Next Steps](#nextsteps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"pythonimports\"></a>\n",
    "## Python Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML\n",
    "import pickle\n",
    "\n",
    "#Import Plotting Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from yellowbrick.regressor import ResidualsPlot\n",
    "\n",
    "#Importing Dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import nan\n",
    "import ast\n",
    "import requests\n",
    "from urllib.parse import urljoin, urlunsplit, urlparse\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "from bs4.element import Comment\n",
    "\n",
    "from collections import Counter\n",
    "from string import punctuation\n",
    "import csv\n",
    "\n",
    "import newspaper\n",
    "from newspaper import Article\n",
    "from newspaper import fulltext\n",
    "\n",
    "# Tokenization Of Sentences\n",
    "import nltk\n",
    "# nltk.download('punkt')\n",
    "\n",
    "#Readability Scores\n",
    "import textstat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, ElasticNetCV, RidgeCV, LassoCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# GridSearch\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Scipy Integration for Sparse Matrixes\n",
    "from scipy import sparse\n",
    "\n",
    "# Additional Feature Engineering - NLP Text Data Import\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df = pd.read_pickle('Pickled_Data_Files/final_df.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"executivesummary\"></a>\n",
    "## Executive Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary aim was to <strong> predict the number of shares an article would earn </strong>after being published on the internet for 1 year. \n",
    "\n",
    "The selected topics were:\n",
    "    \n",
    "- Affiliate Marketing\n",
    "- Content Marketing\n",
    "- Copy-writing\n",
    "- Display Advertising\n",
    "- Email Marketing\n",
    "- Growth Hacking\n",
    "- Influencer Marketing\n",
    "- Link Building\n",
    "- Marketing Automation\n",
    "- Performance Marketing\n",
    "- Podcast Marketing\n",
    "- Search Engine Marketing\n",
    "- Social Media Marketing\n",
    "- Video Marketing\n",
    "- Website Design   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evaluation metrics were MeanSquaredError (MSE) and the mean $R^2$ score from 5 cross validated train-test splits of either the standalone models or grid searched best_estimators_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong> Error Function: Minimizing The Residual Sum of Squares is the error function which will be used to evaluate the regression models. </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$RSS = \\sum_{i=1}^n(y_i-(\\beta_0+\\beta_1 X_{i1}+\\beta_2 X_{i2}+\\ldots+\\beta_p X_{ip}))^2 \n",
    "= \\sum_{i=1}^n\\left(y_i-\\sum_{j=1}^p X_{ij}\\beta_j\\right)^2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a variety of regression models, the best predictive score was 0.85 $R^2$ from 100 random forest regressor estimators as an ensemble method. \n",
    "\n",
    "Taking the logarithm + 1 - <strong> $log(y)+1$ </strong> of the target variable ('Total Shares') improved the model's $R^2$ score from 0.35 $R^2$ to 0.85 $R^2$. \n",
    "\n",
    "The most important coefficients that impact the shareability of an article are:\n",
    "\n",
    "- Evergreen Score (0 - 100)\n",
    "- Does the article have article amplifiers? (True / False)\n",
    "- Does The Article Page Have Referring Domains (True / False)\n",
    "\n",
    "Negative coefficients that decreased the chance of an article being shared are:\n",
    "\n",
    "- Number of Linking Pages\n",
    "- SSL Encryption (True / False)\n",
    "- Meta Description Length"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictive power that we have gained is only applicable to models that are 1 year or older as this is where our sampling was applied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"audience\"></a>\n",
    "## Audience ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Content creation is a time consuming and valuable activity. The time that marketers spend producing content must be classified as a treasured resource and needs to be effectively optimized. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong>Audience - Digital marketers: </strong> who wish to understand what are the <strong> key factors/variables that can make an article more shareable than another</strong>. \n",
    "\n",
    "- Discovering what are the core components of a successful article will empower and help marketers to create more impactful and shareable content."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"objective\"></a>\n",
    "## Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the number of shares an article will accumulate after 1 year. This allows us to avoid the additional bias as some articles accumulate a large amount of shares quickly after being published due to the news cycle.\n",
    "\n",
    "The predictive features used are:\n",
    "    \n",
    "- Technical On-Page Metrics (Page Load Speed, No SSL/SSL etc).\n",
    "- On-Page Word Metrics (Word Count, Unique Word Count, Number of Sentences).\n",
    "- Unique, Impactful Words via TF-IDF Vectorization (Every word is counted and assigned a score via the TF-IDF algorithim).\n",
    "- Link Metrics: The number of backlinks pointing to the HTML page.\n",
    "- Buzzsumo Metrics: Evergreen Score + Article Amplifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"theprocess\"></a>\n",
    "## The Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"datacollection\"></a>\n",
    "### 1. Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 30,000 Article URL's were collected via the <a href ='https://buzzsumo.com'> BuzzSumo's Pro Plan. </a>\n",
    "- 15 topics were selected within the digital marketing niche to see if the topic of an article could influence it's shareability. \n",
    "- A custom web crawler was created that allowed for the extraction of 61 metrics including the article text, HTML content.\n",
    "- <a href= 'https://developers.google.com/speed/docs/insights/v5/get-started' >Google's Page Speed API was utilised to extract page speed metrics for every article. </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"pre-processing\"></a>\n",
    "### 2. Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly 15 topics were downloaded from BuzzSumo's pro plan, these topics were selected by performing keyword research in <a href= 'https://ahrefs.com'> Ahrefs </a>, the keywords with the highest monthly search volume were chosen to be topics.\n",
    "\n",
    "Every row within each spreadsheet was labelled with it's appropriate topic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Topics To Analyse.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly I created a data structure for the Python Web Crawler to capture a range of features from the HTML web page. This included article information, NLP features, readability scores, meta data and technical features such as whether the web page was secure or not (HTTPS vs HTTP)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/data-structure.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"datacleaning\"></a>\n",
    "### Cleaning The BuzzSumo Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/BuzzSumo_Data.png'  >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our target variable is defined as the total_shares in column D, also there were additional columns that I decided to drop such as:\n",
    "    \n",
    "- Thumbnail \n",
    "- Video\n",
    "- twitter_shares, pinterest_shares, total_reddit_engagement (These metrics are directly correlated with our target variable and therefore we cannot use them within our machine learning models) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummifying Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally I decided to 'dummify' the non-numerical columns so that these variables could be used for predictive modeling. \n",
    "\n",
    "You can see an example of creating dummy variables below based on the different article types:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/article_types.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/article_type_second.png' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging Datasets (BuzzSumo + Web Scraped Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then both of these datasets were merged enabling us to not only use BuzzSumo's metrics for prediction, but also any useful features from the web crawled articles. \n",
    "\n",
    "The combined dataset size was 15,963 articles with 67 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/cleaned_data.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging Datasets (Cleaned DataFrame + WebPageSpeed Data From Google Page Speed Insights API)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Web_Page_Speed_Data.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During a 1 week period the <a href='https://developers.google.com/speed/docs/insights/v5/get-started' > Google Page Speed Insights API was queried for 1 week.</a> The web page speed data was obtained for 14469/15743 URL's. Again the two datasets were merged by the URL as the foreign key for both dataframes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"eda\"></a>\n",
    "### 3. EDA - Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "      <td>https://www.entrepreneur.com/article/319017</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14465</td>\n",
       "      <td>https://edlio.com/apps/pages/Eastern-Lancaster...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>This week we are featuring the school website ...</td>\n",
       "      <td>506</td>\n",
       "      <td>1</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>4.0</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14466</td>\n",
       "      <td>https://purplecowagency.com/content-curation-h...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Content curation involves searching and gather...</td>\n",
       "      <td>3399</td>\n",
       "      <td>1</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>4.3</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14467</td>\n",
       "      <td>https://purplecowagency.com/easy-email-marketi...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>1024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>When it comes to communication, email is still...</td>\n",
       "      <td>6079</td>\n",
       "      <td>1</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>4.5</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14468</td>\n",
       "      <td>https://kwsmdesign.com/services/</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Every client has a story. Our job is to tell i...</td>\n",
       "      <td>515</td>\n",
       "      <td>1</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>4.6</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14469 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Url  Evergreen_Score  \\\n",
       "0      https://gatheringdreams.com/affiliate-marketin...             1.54   \n",
       "1      https://itsclaudiag.com/2018/09/how-to-use-aff...             1.44   \n",
       "2            https://www.entrepreneur.com/article/319017             5.68   \n",
       "3      https://onlinemediamasters.com/how-to-make-mon...             1.30   \n",
       "4      https://www.finsavvypanda.com/how-to-make-mone...             1.53   \n",
       "...                                                  ...              ...   \n",
       "14464  https://purplecowagency.com/what-kind-of-busin...             0.00   \n",
       "14465  https://edlio.com/apps/pages/Eastern-Lancaster...             0.00   \n",
       "14466  https://purplecowagency.com/content-curation-h...             0.00   \n",
       "14467  https://purplecowagency.com/easy-email-marketi...             0.00   \n",
       "14468                   https://kwsmdesign.com/services/             0.00   \n",
       "\n",
       "       Total_Shares Published_Date  Word_Count  num_linking_domains  \\\n",
       "0              8021     2018-08-23        4767                  1.0   \n",
       "1              2569     2018-09-16        1181                  2.0   \n",
       "2               844     2018-09-12         996                 12.0   \n",
       "3               775     2018-09-22        8996                  3.0   \n",
       "4               614     2018-10-09        2226                  1.0   \n",
       "...             ...            ...         ...                  ...   \n",
       "14464             0     2018-10-02         454                  0.0   \n",
       "14465             0     2018-09-28          82                  0.0   \n",
       "14466             0     2018-10-02         565                  0.0   \n",
       "14467             0     2018-10-02        1024                  0.0   \n",
       "14468             0     2018-08-30          91                  0.0   \n",
       "\n",
       "                                            Article_Text  Article_Text_Length  \\\n",
       "0      Some of the links below are affiliate links, s...                27301   \n",
       "1      Would you like to make money while you sleep?\\...                 6519   \n",
       "2      Learn three simple strategies to help you stac...                 5916   \n",
       "3      I never thought this would happen to me.\\n\\nIn...                39589   \n",
       "4      This post may contain affiliate links. Please ...                12657   \n",
       "...                                                  ...                  ...   \n",
       "14464  Relationships matter more than ever in busines...                 2742   \n",
       "14465  This week we are featuring the school website ...                  506   \n",
       "14466  Content curation involves searching and gather...                 3399   \n",
       "14467  When it comes to communication, email is still...                 6079   \n",
       "14468  Every client has a story. Our job is to tell i...                  515   \n",
       "\n",
       "       Has_Top_Image  Number_of_Movies  ...  \\\n",
       "0                  1                 0  ...   \n",
       "1                  1                 0  ...   \n",
       "2                  1                 0  ...   \n",
       "3                  1                 0  ...   \n",
       "4                  1                 0  ...   \n",
       "...              ...               ...  ...   \n",
       "14464              1                 0  ...   \n",
       "14465              1                 0  ...   \n",
       "14466              1                 0  ...   \n",
       "14467              1                 0  ...   \n",
       "14468              1                 0  ...   \n",
       "\n",
       "       article_types_['list', 'infographic', 'general_article']  \\\n",
       "0                                                      0          \n",
       "1                                                      0          \n",
       "2                                                      0          \n",
       "3                                                      0          \n",
       "4                                                      0          \n",
       "...                                                  ...          \n",
       "14464                                                  0          \n",
       "14465                                                  0          \n",
       "14466                                                  0          \n",
       "14467                                                  0          \n",
       "14468                                                  0          \n",
       "\n",
       "       article_types_['list', 'newsletter', 'general_article']  \\\n",
       "0                                                      0         \n",
       "1                                                      0         \n",
       "2                                                      0         \n",
       "3                                                      0         \n",
       "4                                                      0         \n",
       "...                                                  ...         \n",
       "14464                                                  0         \n",
       "14465                                                  0         \n",
       "14466                                                  0         \n",
       "14467                                                  0         \n",
       "14468                                                  0         \n",
       "\n",
       "       article_types_['newsletter', 'general_article']  \\\n",
       "0                                                    0   \n",
       "1                                                    0   \n",
       "2                                                    0   \n",
       "3                                                    0   \n",
       "4                                                    0   \n",
       "...                                                ...   \n",
       "14464                                                0   \n",
       "14465                                                0   \n",
       "14466                                                0   \n",
       "14467                                                0   \n",
       "14468                                                0   \n",
       "\n",
       "      article_types_['what_post', 'general_article']  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "...                                              ...   \n",
       "14464                                              0   \n",
       "14465                                              0   \n",
       "14466                                              0   \n",
       "14467                                              0   \n",
       "14468                                              0   \n",
       "\n",
       "       article_types_['what_post', 'infographic', 'general_article']  \\\n",
       "0                                                      0               \n",
       "1                                                      0               \n",
       "2                                                      0               \n",
       "3                                                      0               \n",
       "4                                                      0               \n",
       "...                                                  ...               \n",
       "14464                                                  0               \n",
       "14465                                                  0               \n",
       "14466                                                  0               \n",
       "14467                                                  0               \n",
       "14468                                                  0               \n",
       "\n",
       "       article_types_['what_post', 'newsletter', 'general_article']  \\\n",
       "0                                                      0              \n",
       "1                                                      0              \n",
       "2                                                      0              \n",
       "3                                                      0              \n",
       "4                                                      0              \n",
       "...                                                  ...              \n",
       "14464                                                  0              \n",
       "14465                                                  0              \n",
       "14466                                                  0              \n",
       "14467                                                  0              \n",
       "14468                                                  0              \n",
       "\n",
       "       article_types_['why_post', 'general_article']  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "...                                              ...   \n",
       "14464                                              0   \n",
       "14465                                              0   \n",
       "14466                                              0   \n",
       "14467                                              0   \n",
       "14468                                              0   \n",
       "\n",
       "       article_types_['why_post', 'newsletter', 'general_article']  \\\n",
       "0                                                      0             \n",
       "1                                                      0             \n",
       "2                                                      0             \n",
       "3                                                      0             \n",
       "4                                                      0             \n",
       "...                                                  ...             \n",
       "14464                                                  0             \n",
       "14465                                                  0             \n",
       "14466                                                  0             \n",
       "14467                                                  0             \n",
       "14468                                                  0             \n",
       "\n",
       "       First_Contentful_Paint  First_Interactive  \n",
       "0                         3.5               16.1  \n",
       "1                         5.2                9.2  \n",
       "2                         3.2               13.0  \n",
       "3                         2.5               18.3  \n",
       "4                         4.7               21.9  \n",
       "...                       ...                ...  \n",
       "14464                     4.4                7.2  \n",
       "14465                     4.0               11.7  \n",
       "14466                     4.3                7.3  \n",
       "14467                     4.5                7.1  \n",
       "14468                     4.6               11.3  \n",
       "\n",
       "[14469 rows x 69 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14469 entries, 0 to 14468\n",
      "Data columns (total 69 columns):\n",
      "Url                                                                   14469 non-null object\n",
      "Evergreen_Score                                                       14469 non-null float64\n",
      "Total_Shares                                                          14469 non-null int64\n",
      "Published_Date                                                        14469 non-null datetime64[ns]\n",
      "Word_Count                                                            14469 non-null int64\n",
      "num_linking_domains                                                   14469 non-null float64\n",
      "Article_Text                                                          14469 non-null object\n",
      "Article_Text_Length                                                   14469 non-null int64\n",
      "Has_Top_Image                                                         14469 non-null int64\n",
      "Number_of_Movies                                                      14469 non-null int64\n",
      "Article_Is_Media_News                                                 14469 non-null int64\n",
      "Number_Of_Images                                                      14469 non-null int64\n",
      "Is_Valid_Body                                                         14469 non-null int64\n",
      "Setences_Text                                                         14469 non-null object\n",
      "Number_Of_Sentences                                                   14469 non-null int64\n",
      "Lexicon_Count                                                         14469 non-null int64\n",
      "Flesch_Reading_Ease_formula                                           14469 non-null float64\n",
      "Flesch_Kincaid_Grade_Level                                            14469 non-null float64\n",
      "FOG_Scale                                                             14469 non-null float64\n",
      "SMOG_Index                                                            14469 non-null float64\n",
      "ARI_Index                                                             14469 non-null float64\n",
      "Title_Text                                                            14469 non-null object\n",
      "Title_Tag_Length                                                      14469 non-null int64\n",
      "Meta_Description                                                      14469 non-null object\n",
      "Meta_Description_Length                                               14469 non-null int64\n",
      "SSL                                                                   14469 non-null int64\n",
      "Page_Size_In_Bytes                                                    14469 non-null int64\n",
      "Plain_Text_Size                                                       14469 non-null int64\n",
      "Plain_Text_Rate                                                       14469 non-null float64\n",
      "Has_Referring_Domains                                                 14469 non-null int64\n",
      "Has_Article_Amplifiers                                                14469 non-null int64\n",
      "Has_Author_Name                                                       14469 non-null int64\n",
      "Topic_Content Marketing                                               14469 non-null uint8\n",
      "Topic_Copywriting                                                     14469 non-null uint8\n",
      "Topic_Display Advertising                                             14469 non-null uint8\n",
      "Topic_Email Marketing                                                 14469 non-null uint8\n",
      "Topic_Growth Marketing                                                14469 non-null uint8\n",
      "Topic_Influencer Marketing                                            14469 non-null uint8\n",
      "Topic_Link Building                                                   14469 non-null uint8\n",
      "Topic_Marketing Automation                                            14469 non-null uint8\n",
      "Topic_Podcast Marketing                                               14469 non-null uint8\n",
      "Topic_Search Engine Marketing                                         14469 non-null uint8\n",
      "Topic_Social Media Marketing                                          14469 non-null uint8\n",
      "Topic_Video Marketing                                                 14469 non-null uint8\n",
      "Topic_Website Design                                                  14469 non-null uint8\n",
      "Encoding_ISO-8859-1                                                   14469 non-null uint8\n",
      "Encoding_UTF-8                                                        14469 non-null uint8\n",
      "Encoding_iso-8859-1                                                   14469 non-null uint8\n",
      "Encoding_utf-8                                                        14469 non-null uint8\n",
      "Encoding_windows-1252                                                 14469 non-null uint8\n",
      "Number_Of_Article_Amplifiers                                          14469 non-null int64\n",
      "article_types_['how_to_article', 'general_article']                   14469 non-null uint8\n",
      "article_types_['how_to_article', 'infographic', 'general_article']    14469 non-null uint8\n",
      "article_types_['how_to_article', 'list', 'general_article']           14469 non-null uint8\n",
      "article_types_['how_to_article', 'newsletter', 'general_article']     14469 non-null uint8\n",
      "article_types_['how_to_article', 'what_post', 'general_article']      14469 non-null uint8\n",
      "article_types_['how_to_article', 'why_post', 'general_article']       14469 non-null uint8\n",
      "article_types_['infographic', 'general_article']                      14469 non-null uint8\n",
      "article_types_['list', 'general_article']                             14469 non-null uint8\n",
      "article_types_['list', 'infographic', 'general_article']              14469 non-null uint8\n",
      "article_types_['list', 'newsletter', 'general_article']               14469 non-null uint8\n",
      "article_types_['newsletter', 'general_article']                       14469 non-null uint8\n",
      "article_types_['what_post', 'general_article']                        14469 non-null uint8\n",
      "article_types_['what_post', 'infographic', 'general_article']         14469 non-null uint8\n",
      "article_types_['what_post', 'newsletter', 'general_article']          14469 non-null uint8\n",
      "article_types_['why_post', 'general_article']                         14469 non-null uint8\n",
      "article_types_['why_post', 'newsletter', 'general_article']           14469 non-null uint8\n",
      "First_Contentful_Paint                                                14469 non-null float64\n",
      "First_Interactive                                                     14469 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(10), int64(19), object(5), uint8(34)\n",
      "memory usage: 4.4+ MB\n"
     ]
    }
   ],
   "source": [
    "cleaned_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Evergreen_Score</th>\n",
       "      <th>Total_Shares</th>\n",
       "      <th>Word_Count</th>\n",
       "      <th>num_linking_domains</th>\n",
       "      <th>Article_Text_Length</th>\n",
       "      <th>Has_Top_Image</th>\n",
       "      <th>Number_of_Movies</th>\n",
       "      <th>Article_Is_Media_News</th>\n",
       "      <th>Number_Of_Images</th>\n",
       "      <th>Is_Valid_Body</th>\n",
       "      <th>...</th>\n",
       "      <th>article_types_['list', 'infographic', 'general_article']</th>\n",
       "      <th>article_types_['list', 'newsletter', 'general_article']</th>\n",
       "      <th>article_types_['newsletter', 'general_article']</th>\n",
       "      <th>article_types_['what_post', 'general_article']</th>\n",
       "      <th>article_types_['what_post', 'infographic', 'general_article']</th>\n",
       "      <th>article_types_['what_post', 'newsletter', 'general_article']</th>\n",
       "      <th>article_types_['why_post', 'general_article']</th>\n",
       "      <th>article_types_['why_post', 'newsletter', 'general_article']</th>\n",
       "      <th>First_Contentful_Paint</th>\n",
       "      <th>First_Interactive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.0</td>\n",
       "      <td>14469.000000</td>\n",
       "      <td>14469.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.527305</td>\n",
       "      <td>62.537563</td>\n",
       "      <td>661.707167</td>\n",
       "      <td>1.055913</td>\n",
       "      <td>3876.142512</td>\n",
       "      <td>0.968484</td>\n",
       "      <td>0.076992</td>\n",
       "      <td>0.011404</td>\n",
       "      <td>13.324694</td>\n",
       "      <td>0.582763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.007948</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.020734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.741634</td>\n",
       "      <td>14.512461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.625998</td>\n",
       "      <td>411.285889</td>\n",
       "      <td>881.566752</td>\n",
       "      <td>7.094390</td>\n",
       "      <td>4671.986863</td>\n",
       "      <td>0.174713</td>\n",
       "      <td>0.589057</td>\n",
       "      <td>0.106181</td>\n",
       "      <td>17.520206</td>\n",
       "      <td>0.493120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037154</td>\n",
       "      <td>0.018587</td>\n",
       "      <td>0.088800</td>\n",
       "      <td>0.145485</td>\n",
       "      <td>0.011757</td>\n",
       "      <td>0.008313</td>\n",
       "      <td>0.142497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.046454</td>\n",
       "      <td>9.820514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>8.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2824.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>12.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>901.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5441.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>18.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.140000</td>\n",
       "      <td>31499.000000</td>\n",
       "      <td>25219.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>96995.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.300000</td>\n",
       "      <td>179.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Evergreen_Score  Total_Shares    Word_Count  num_linking_domains  \\\n",
       "count     14469.000000  14469.000000  14469.000000         14469.000000   \n",
       "mean          0.527305     62.537563    661.707167             1.055913   \n",
       "std           1.625998    411.285889    881.566752             7.094390   \n",
       "min           0.000000      0.000000      0.000000             0.000000   \n",
       "25%           0.000000      1.000000    143.000000             0.000000   \n",
       "50%           0.170000      2.000000    476.000000             0.000000   \n",
       "75%           0.360000     12.000000    901.000000             0.000000   \n",
       "max          49.140000  31499.000000  25219.000000           398.000000   \n",
       "\n",
       "       Article_Text_Length  Has_Top_Image  Number_of_Movies  \\\n",
       "count         14469.000000   14469.000000      14469.000000   \n",
       "mean           3876.142512       0.968484          0.076992   \n",
       "std            4671.986863       0.174713          0.589057   \n",
       "min              38.000000       0.000000          0.000000   \n",
       "25%             773.000000       1.000000          0.000000   \n",
       "50%            2824.000000       1.000000          0.000000   \n",
       "75%            5441.000000       1.000000          0.000000   \n",
       "max           96995.000000       1.000000         26.000000   \n",
       "\n",
       "       Article_Is_Media_News  Number_Of_Images  Is_Valid_Body  ...  \\\n",
       "count           14469.000000      14469.000000   14469.000000  ...   \n",
       "mean                0.011404         13.324694       0.582763  ...   \n",
       "std                 0.106181         17.520206       0.493120  ...   \n",
       "min                 0.000000          0.000000       0.000000  ...   \n",
       "25%                 0.000000          5.000000       0.000000  ...   \n",
       "50%                 0.000000         10.000000       1.000000  ...   \n",
       "75%                 0.000000         17.000000       1.000000  ...   \n",
       "max                 1.000000        821.000000       1.000000  ...   \n",
       "\n",
       "       article_types_['list', 'infographic', 'general_article']  \\\n",
       "count                                       14469.000000          \n",
       "mean                                            0.001382          \n",
       "std                                             0.037154          \n",
       "min                                             0.000000          \n",
       "25%                                             0.000000          \n",
       "50%                                             0.000000          \n",
       "75%                                             0.000000          \n",
       "max                                             1.000000          \n",
       "\n",
       "       article_types_['list', 'newsletter', 'general_article']  \\\n",
       "count                                       14469.000000         \n",
       "mean                                            0.000346         \n",
       "std                                             0.018587         \n",
       "min                                             0.000000         \n",
       "25%                                             0.000000         \n",
       "50%                                             0.000000         \n",
       "75%                                             0.000000         \n",
       "max                                             1.000000         \n",
       "\n",
       "       article_types_['newsletter', 'general_article']  \\\n",
       "count                                     14469.000000   \n",
       "mean                                          0.007948   \n",
       "std                                           0.088800   \n",
       "min                                           0.000000   \n",
       "25%                                           0.000000   \n",
       "50%                                           0.000000   \n",
       "75%                                           0.000000   \n",
       "max                                           1.000000   \n",
       "\n",
       "       article_types_['what_post', 'general_article']  \\\n",
       "count                                    14469.000000   \n",
       "mean                                         0.021632   \n",
       "std                                          0.145485   \n",
       "min                                          0.000000   \n",
       "25%                                          0.000000   \n",
       "50%                                          0.000000   \n",
       "75%                                          0.000000   \n",
       "max                                          1.000000   \n",
       "\n",
       "       article_types_['what_post', 'infographic', 'general_article']  \\\n",
       "count                                       14469.000000               \n",
       "mean                                            0.000138               \n",
       "std                                             0.011757               \n",
       "min                                             0.000000               \n",
       "25%                                             0.000000               \n",
       "50%                                             0.000000               \n",
       "75%                                             0.000000               \n",
       "max                                             1.000000               \n",
       "\n",
       "       article_types_['what_post', 'newsletter', 'general_article']  \\\n",
       "count                                       14469.000000              \n",
       "mean                                            0.000069              \n",
       "std                                             0.008313              \n",
       "min                                             0.000000              \n",
       "25%                                             0.000000              \n",
       "50%                                             0.000000              \n",
       "75%                                             0.000000              \n",
       "max                                             1.000000              \n",
       "\n",
       "       article_types_['why_post', 'general_article']  \\\n",
       "count                                   14469.000000   \n",
       "mean                                        0.020734   \n",
       "std                                         0.142497   \n",
       "min                                         0.000000   \n",
       "25%                                         0.000000   \n",
       "50%                                         0.000000   \n",
       "75%                                         0.000000   \n",
       "max                                         1.000000   \n",
       "\n",
       "       article_types_['why_post', 'newsletter', 'general_article']  \\\n",
       "count                                            14469.0             \n",
       "mean                                                 0.0             \n",
       "std                                                  0.0             \n",
       "min                                                  0.0             \n",
       "25%                                                  0.0             \n",
       "50%                                                  0.0             \n",
       "75%                                                  0.0             \n",
       "max                                                  0.0             \n",
       "\n",
       "       First_Contentful_Paint  First_Interactive  \n",
       "count            14469.000000       14469.000000  \n",
       "mean                 3.741634          14.512461  \n",
       "std                  2.046454           9.820514  \n",
       "min                  0.600000           0.600000  \n",
       "25%                  2.500000           8.500000  \n",
       "50%                  3.300000          12.100000  \n",
       "75%                  4.400000          18.300000  \n",
       "max                 30.300000         179.700000  \n",
       "\n",
       "[8 rows x 63 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Evergreen_Score</td>\n",
       "      <td>0.529737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>num_linking_domains</td>\n",
       "      <td>0.323450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Article_Amplifiers</td>\n",
       "      <td>0.271738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Referring_Domains</td>\n",
       "      <td>0.244202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Number_Of_Article_Amplifiers</td>\n",
       "      <td>0.242462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VERB</td>\n",
       "      <td>0.132810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>VERB</td>\n",
       "      <td>0.132810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SPACE</td>\n",
       "      <td>0.122645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SPACE</td>\n",
       "      <td>0.122645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Number_Of_Sentences</td>\n",
       "      <td>0.122163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TIME</td>\n",
       "      <td>0.120133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TIME</td>\n",
       "      <td>0.120133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Total_Shares\n",
       "Evergreen_Score                   0.529737\n",
       "num_linking_domains               0.323450\n",
       "Has_Article_Amplifiers            0.271738\n",
       "Has_Referring_Domains             0.244202\n",
       "Number_Of_Article_Amplifiers      0.242462\n",
       "VERB                              0.132810\n",
       "VERB                              0.132810\n",
       "SPACE                             0.122645\n",
       "SPACE                             0.122645\n",
       "Number_Of_Sentences               0.122163\n",
       "TIME                              0.120133\n",
       "TIME                              0.120133"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.corr()[['Total_Shares']].sort_values(by='Total_Shares', ascending=False)[1:].head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>article_types_['newsletter', 'general_article']</td>\n",
       "      <td>-0.012763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Encoding_ISO-8859-1</td>\n",
       "      <td>-0.013549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>First_Contentful_Paint</td>\n",
       "      <td>-0.015268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Topic_Podcast Marketing</td>\n",
       "      <td>-0.018183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Topic_Influencer Marketing</td>\n",
       "      <td>-0.022908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Topic_Marketing Automation</td>\n",
       "      <td>-0.029547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Topic_Email Marketing</td>\n",
       "      <td>-0.036242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Title_Tag_Length</td>\n",
       "      <td>-0.041045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Topic_Website Design</td>\n",
       "      <td>-0.044245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Encoding_utf-8</td>\n",
       "      <td>-0.051097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Encoding_windows-1252</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>article_types_['why_post', 'newsletter', 'general_article']</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Total_Shares\n",
       "article_types_['newsletter', 'general_article']        -0.012763\n",
       "Encoding_ISO-8859-1                                    -0.013549\n",
       "First_Contentful_Paint                                 -0.015268\n",
       "Topic_Podcast Marketing                                -0.018183\n",
       "Topic_Influencer Marketing                             -0.022908\n",
       "Topic_Marketing Automation                             -0.029547\n",
       "Topic_Email Marketing                                  -0.036242\n",
       "Title_Tag_Length                                       -0.041045\n",
       "Topic_Website Design                                   -0.044245\n",
       "Encoding_utf-8                                         -0.051097\n",
       "Encoding_windows-1252                                        NaN\n",
       "article_types_['why_post', 'newsletter', 'gener...           NaN"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.corr()[['Total_Shares']].sort_values(by='Total_Shares', ascending=False).tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "sns.distplot(cleaned_df['Total_Shares'], color='purple', bins=90)\n",
    "plt.title('The Distribution Of Article Shares', pad=30, fontsize='21')\n",
    "plt.xlabel('Number of Article Shares', fontsize='16', labelpad=20)\n",
    "plt.ylabel('Counts',  fontsize='16', labelpad=20)\n",
    "plt.savefig('Article_Share_Distribution',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the above graph our target variable is highly skewed and is not normally distributed. This means that majority of articles only receive a small number of shares. \n",
    "\n",
    "Additionally the target variable is more likely to be from an exponential distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Article Data EDA - Distributions For Positively Correlated Predictor Variables In Relation To The Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2,  ax3, ax4) = plt.subplots(figsize=(20, 6), ncols=4)\n",
    "sns.distplot(cleaned_df['Evergreen_Score'].sort_values(ascending=False), ax = ax1)\n",
    "sns.distplot(cleaned_df['Word_Count'].sort_values(ascending=False), ax = ax2)\n",
    "sns.distplot(cleaned_df['num_linking_domains'].sort_values(ascending=False), ax = ax3)\n",
    "sns.distplot(cleaned_df['Number_Of_Sentences'].sort_values(ascending=False), ax = ax4)\n",
    "plt.savefig('X_Predictor_Variables_1',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2,  ax3, ax4) = plt.subplots(figsize=(20, 6), ncols=4)\n",
    "\n",
    "sns.distplot(cleaned_df['Lexicon_Count'].sort_values(ascending=False), ax = ax1)\n",
    "sns.distplot(cleaned_df['Plain_Text_Size'].sort_values(ascending=False), ax = ax2)\n",
    "sns.distplot(cleaned_df['Article_Text_Length'].sort_values(ascending=False), ax = ax3)\n",
    "sns.distplot(cleaned_df['Has_Author_Name'].sort_values(ascending=False), ax = ax4)\n",
    "plt.savefig('X_Predictor_Variables_2',dpi=200)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Article Types EDA - What Article Type Is Shared Mostly Frequently?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "article_types = [(x, cleaned_df.groupby(x)) for x in cleaned_df.columns if x.startswith('article')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "article_data = [(item[0], item[1].Total_Shares.mean().values) for item in article_types]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "article_types = [(x, cleaned_df.groupby(x)) for x in cleaned_df.columns if x.startswith('article')]\n",
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "article_data = [(item[0], item[1].Total_Shares.mean().values[-1]) for item in article_types]\n",
    "article_data.sort(key= lambda x: x[1])\n",
    "plt.barh([item[0] for item in article_data], width = [item[1] for item in article_data] )\n",
    "plt.xticks(rotation = 90)\n",
    "plt.xlabel('Mean Shares - μ ', fontsize='16', labelpad=20)\n",
    "plt.ylabel('Article Types',  fontsize='16', labelpad=20)\n",
    "plt.title('Articles Grouped By Type Of Content', pad=30, fontsize='21')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Article_Types',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Articles that are tagged \"how to articles\" & \"why post\" on average received the most amount of shares, this was followed by infographics and list articles.\n",
    "\n",
    "Therefore the insight that we can gain from this is that <strong> people reading digital marketing topics actively share content that is more educational (how to), visual (infographic) and easily digestible (list articles). </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topic Type EDA - What Topic Is Shared Mostly Frequently?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_by_objects = []\n",
    "\n",
    "for x in cleaned_df.columns:\n",
    "    if x.startswith('Topic_'):\n",
    "        group_by_objects.append((x , cleaned_df.groupby(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "topic_data = [(item[0], item[1].Total_Shares.mean().values[1]) for item in group_by_objects]\n",
    "topic_data.sort(key= lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "plt.bar([item[0] for item in topic_data], height = [item[1] for item in topic_data])\n",
    "plt.xticks(rotation = 45)\n",
    "plt.xlabel('Topics', fontsize='16', labelpad=20)\n",
    "plt.ylabel('Mean Shares - μ',  fontsize='16', labelpad=20)\n",
    "plt.title('Articles Grouped By Topic - What Topic Is Shared Mostly Frequently?', pad=30, fontsize='21')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Article_Topic_Types',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<strong> Search Engine Marketing was the most shared topic </strong> and was closely followed by Growth Marketing & Social Media Marketing.\n",
    "\n",
    "The mean shares was considerably lower for topics such as website design and display advertising. Hence carefully selecting topics that are shared more on average could be a useful way to:\n",
    "\n",
    "- Earn more backlinks, social mentions and publicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"modelselection\"></a>\n",
    "### 4. Model Selection + Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we will be using TF-IDF, the matrices are often sparse in shape and large in terms of dimensionality (~3,000 columns by 15,000 rows).\n",
    "\n",
    "Therefore a custom pipeline and grid search was created, allowing for optimization of:\n",
    "* The Model hyperparameters \n",
    "* The TFID pre-processing stage\n",
    "\n",
    "The following models were trialled:\n",
    "\n",
    "- Linear Regression\n",
    "- Ridge (Linear Regression)\n",
    "- Lasso (Linear Regression)\n",
    "- Decision Tree Regressor\n",
    "- RandomForest Regressor (100 RandomForest Models)\n",
    "- ADA GradientBoostingRegressor with a RandomForest Ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"results\"></a>\n",
    "### 5. Results / Findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data_Used</th>\n",
       "      <th>Data_Type</th>\n",
       "      <th>Model_Name</th>\n",
       "      <th>Model_Training_Score</th>\n",
       "      <th>Model_Test_Score</th>\n",
       "      <th>Mean_Squared_Error</th>\n",
       "      <th>Model_Cross_Val_Score</th>\n",
       "      <th>Coefficients/Feature_Importances</th>\n",
       "      <th>Grid_Search_Best_Params / Regularization_Params</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
       "      <td>0.533955</td>\n",
       "      <td>0.155411</td>\n",
       "      <td>460883.697684</td>\n",
       "      <td>-3.677588e+19</td>\n",
       "      <td>{'coefficient_values': [223.7456730565131, -8....</td>\n",
       "      <td>No Grid Search Used</td>\n",
       "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "      <td>0.533842</td>\n",
       "      <td>0.155194</td>\n",
       "      <td>461001.734275</td>\n",
       "      <td>5.400575e-01</td>\n",
       "      <td>{'coefficient_values': [220.37598710911834, -8...</td>\n",
       "      <td>58.5702</td>\n",
       "      <td>RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>LassoCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "      <td>0.533555</td>\n",
       "      <td>0.155288</td>\n",
       "      <td>460950.479850</td>\n",
       "      <td>5.404661e-01</td>\n",
       "      <td>{'coefficient_values': [222.03808027737844, -6...</td>\n",
       "      <td>0.559081</td>\n",
       "      <td>LassoCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>DecisionTreeRegressor(criterion='mse', max_dep...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701080</td>\n",
       "      <td>0.900788</td>\n",
       "      <td>7.018761e-01</td>\n",
       "      <td>{'feature_importance_values': [0.3173539791750...</td>\n",
       "      <td>No Grid Search Used / Regularization Used</td>\n",
       "      <td>DecisionTreeRegressor(criterion='mse', max_dep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>RandomForestRegressor(bootstrap=True, criterio...</td>\n",
       "      <td>0.980080</td>\n",
       "      <td>0.854972</td>\n",
       "      <td>0.437038</td>\n",
       "      <td>8.547309e-01</td>\n",
       "      <td>{'feature_importance_values': [0.3591549644615...</td>\n",
       "      <td>No Grid Search Used / Regularization Used</td>\n",
       "      <td>RandomForestRegressor(bootstrap=True, criterio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "      <td>0.970121</td>\n",
       "      <td>0.804518</td>\n",
       "      <td>0.589082</td>\n",
       "      <td>8.013276e-01</td>\n",
       "      <td>{'feature_importance_values': [0.7574930508186...</td>\n",
       "      <td>No Grid Search Used / Regularization Used</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "      <td>0.979795</td>\n",
       "      <td>0.859630</td>\n",
       "      <td>0.439756</td>\n",
       "      <td>8.568030e-01</td>\n",
       "      <td>{'feature_importance_values': [0.5520762574707...</td>\n",
       "      <td>No Grid Search Used / Regularization Used</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data_Used        Data_Type  \\\n",
       "0  Numerical  Non-Logged Data   \n",
       "1  Numerical  Non-Logged Data   \n",
       "2  Numerical  Non-Logged Data   \n",
       "3  Numerical           Logged   \n",
       "4  Numerical           Logged   \n",
       "5  Numerical           Logged   \n",
       "6  Numerical           Logged   \n",
       "\n",
       "                                          Model_Name  Model_Training_Score  \\\n",
       "0  LinearRegression(copy_X=True, fit_intercept=Tr...              0.533955   \n",
       "1  RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...              0.533842   \n",
       "2  LassoCV(alphas=array([1.00000e-05, 1.26186e-05...              0.533555   \n",
       "3  DecisionTreeRegressor(criterion='mse', max_dep...              1.000000   \n",
       "4  RandomForestRegressor(bootstrap=True, criterio...              0.980080   \n",
       "5  AdaBoostRegressor(base_estimator=RandomForestR...              0.970121   \n",
       "6  AdaBoostRegressor(base_estimator=RandomForestR...              0.979795   \n",
       "\n",
       "   Model_Test_Score  Mean_Squared_Error  Model_Cross_Val_Score  \\\n",
       "0          0.155411       460883.697684          -3.677588e+19   \n",
       "1          0.155194       461001.734275           5.400575e-01   \n",
       "2          0.155288       460950.479850           5.404661e-01   \n",
       "3          0.701080            0.900788           7.018761e-01   \n",
       "4          0.854972            0.437038           8.547309e-01   \n",
       "5          0.804518            0.589082           8.013276e-01   \n",
       "6          0.859630            0.439756           8.568030e-01   \n",
       "\n",
       "                    Coefficients/Feature_Importances  \\\n",
       "0  {'coefficient_values': [223.7456730565131, -8....   \n",
       "1  {'coefficient_values': [220.37598710911834, -8...   \n",
       "2  {'coefficient_values': [222.03808027737844, -6...   \n",
       "3  {'feature_importance_values': [0.3173539791750...   \n",
       "4  {'feature_importance_values': [0.3591549644615...   \n",
       "5  {'feature_importance_values': [0.7574930508186...   \n",
       "6  {'feature_importance_values': [0.5520762574707...   \n",
       "\n",
       "  Grid_Search_Best_Params / Regularization_Params  \\\n",
       "0                             No Grid Search Used   \n",
       "1                                         58.5702   \n",
       "2                                        0.559081   \n",
       "3       No Grid Search Used / Regularization Used   \n",
       "4       No Grid Search Used / Regularization Used   \n",
       "5       No Grid Search Used / Regularization Used   \n",
       "6       No Grid Search Used / Regularization Used   \n",
       "\n",
       "                                               Notes  \n",
       "0  LinearRegression(copy_X=True, fit_intercept=Tr...  \n",
       "1  RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...  \n",
       "2  LassoCV(alphas=array([1.00000e-05, 1.26186e-05...  \n",
       "3  DecisionTreeRegressor(criterion='mse', max_dep...  \n",
       "4  RandomForestRegressor(bootstrap=True, criterio...  \n",
       "5  AdaBoostRegressor(base_estimator=RandomForestR...  \n",
       "6  AdaBoostRegressor(base_estimator=RandomForestR...  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pickle.load(open('Pickled_Data_Files/results.pkl', 'rb')).reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data_Used</th>\n",
       "      <th>Data_Type</th>\n",
       "      <th>Model_Name</th>\n",
       "      <th>Model_Training_Score</th>\n",
       "      <th>Model_Test_Score</th>\n",
       "      <th>Model_Cross_Val_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>LinearRegression(copy_X=True, fit_intercept=Tr...</td>\n",
       "      <td>0.533955</td>\n",
       "      <td>0.155411</td>\n",
       "      <td>-3.677588e+19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "      <td>0.533842</td>\n",
       "      <td>0.155194</td>\n",
       "      <td>5.400575e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Non-Logged Data</td>\n",
       "      <td>LassoCV(alphas=array([1.00000e-05, 1.26186e-05...</td>\n",
       "      <td>0.533555</td>\n",
       "      <td>0.155288</td>\n",
       "      <td>5.404661e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>DecisionTreeRegressor(criterion='mse', max_dep...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701080</td>\n",
       "      <td>7.018761e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>RandomForestRegressor(bootstrap=True, criterio...</td>\n",
       "      <td>0.980080</td>\n",
       "      <td>0.854972</td>\n",
       "      <td>8.547309e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "      <td>0.970121</td>\n",
       "      <td>0.804518</td>\n",
       "      <td>8.013276e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Numerical</td>\n",
       "      <td>Logged</td>\n",
       "      <td>AdaBoostRegressor(base_estimator=RandomForestR...</td>\n",
       "      <td>0.979795</td>\n",
       "      <td>0.859630</td>\n",
       "      <td>8.568030e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Data_Used        Data_Type  \\\n",
       "0  Numerical  Non-Logged Data   \n",
       "1  Numerical  Non-Logged Data   \n",
       "2  Numerical  Non-Logged Data   \n",
       "3  Numerical           Logged   \n",
       "4  Numerical           Logged   \n",
       "5  Numerical           Logged   \n",
       "6  Numerical           Logged   \n",
       "\n",
       "                                          Model_Name  Model_Training_Score  \\\n",
       "0  LinearRegression(copy_X=True, fit_intercept=Tr...              0.533955   \n",
       "1  RidgeCV(alphas=array([1.00000e-05, 1.26186e-05...              0.533842   \n",
       "2  LassoCV(alphas=array([1.00000e-05, 1.26186e-05...              0.533555   \n",
       "3  DecisionTreeRegressor(criterion='mse', max_dep...              1.000000   \n",
       "4  RandomForestRegressor(bootstrap=True, criterio...              0.980080   \n",
       "5  AdaBoostRegressor(base_estimator=RandomForestR...              0.970121   \n",
       "6  AdaBoostRegressor(base_estimator=RandomForestR...              0.979795   \n",
       "\n",
       "   Model_Test_Score  Model_Cross_Val_Score  \n",
       "0          0.155411          -3.677588e+19  \n",
       "1          0.155194           5.400575e-01  \n",
       "2          0.155288           5.404661e-01  \n",
       "3          0.701080           7.018761e-01  \n",
       "4          0.854972           8.547309e-01  \n",
       "5          0.804518           8.013276e-01  \n",
       "6          0.859630           8.568030e-01  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, [0,1, 2,3, 4, 6]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most impactful model utilised a logged + 1 target variable, with 5 bagged forest random regressor's serving as the base estimator for the AdaBoostRegressor model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally by adding the web page speed data from Google Page Speed Insights, <strong> the mean cross validation score increased on our best model by ~ 5%. </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"nlpdata\"></a>\n",
    "#### NLP Text Data\n",
    "\n",
    "Text data parsed through a TFID vectorizer resulted in poorer model performance for both linear regression and decision tree regressors, therefore it was excluded from the future modeling experiments.  \n",
    "\n",
    "- This is likely due to over-fitting on noisy text data\n",
    "- Also random forests struggle to use all of the correct features as the matrix increases in dimensional size and sparsity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"linearregrssion\"></a>\n",
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear regression model with non-logged data produced better scores than our baseline, however the mean cross validation scores were negative. This suggested that there was multi-collinearity inside of the data, which was reduced after applying regularization via the Lasso + Ridge regression models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_residuals = pickle.load(open('Pickled_Data_Files/lasso_residuals.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residual Plot\n",
    "fig, ax = plt.subplots(figsize=(20,9))\n",
    "sns.distplot(lasso_residuals)\n",
    "plt.xlabel('', fontsize='16', labelpad=20)\n",
    "plt.title('The Distribution Of Residuals From A LassoCV Model', pad=30, fontsize='25')\n",
    "plt.savefig('Residuals_Distribution',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After taking the logarithm of the target variable, applying LassoCV we can see that the shape of the residuals <strong> is normally distributed. </strong> This means we can reliably make inference from the coefficient values (linear regression models rely on the assumption that the residuals are normally distributed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lasso Coeficients ###\n",
    "lasso_coefficients = pickle.load(open('Pickled_Data_Files/lasso_coefficients.pkl', 'rb'))\n",
    "lasso_coefficients.columns = ['Coefficients']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,9))\n",
    "x_values =  lasso_coefficients.sort_values(by='Coefficients', ascending=False).head(10).index\n",
    "sns.barplot(x=x_values, y='Coefficients', \n",
    "            data=lasso_coefficients.sort_values(by='Coefficients', ascending=False).head(10))\n",
    "plt.xticks(rotation = 25)\n",
    "label = ax.set_title('The Top 10 Positive Coefficients From A LassoCV Model', fontsize = 24, pad=30)\n",
    "plt.savefig('Positive_Coefficients_Lasso_Model',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,9))\n",
    "x_values =  lasso_coefficients.sort_values(by='Coefficients', ascending=True).head(10).index\n",
    "sns.barplot(x=x_values, y='Coefficients', \n",
    "            data=lasso_coefficients.sort_values(by='Coefficients', ascending=True).head(10))\n",
    "label = ax.set_title('The Top 10 Negative Coefficients From A LassoCV Model', fontsize = 24, pad=30)\n",
    "plt.xticks(rotation = 25)\n",
    "plt.savefig('Negative_Coefficients_Lasso_Model',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Evergreen_Score</td>\n",
       "      <td>0.773039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Article_Amplifiers</td>\n",
       "      <td>0.741567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>num_linking_domains</td>\n",
       "      <td>0.300811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Referring_Domains</td>\n",
       "      <td>0.253278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Author_Name</td>\n",
       "      <td>0.099609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Is_Valid_Body</td>\n",
       "      <td>0.086709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Number_Of_Sentences</td>\n",
       "      <td>0.078869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SMOG_Index</td>\n",
       "      <td>0.036236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Plain_Text_Rate</td>\n",
       "      <td>0.033719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Page_Size_In_Bytes</td>\n",
       "      <td>0.021121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SSL</td>\n",
       "      <td>0.019273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Has_Top_Image</td>\n",
       "      <td>0.019202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARI_Index</td>\n",
       "      <td>0.017794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Word_Count</td>\n",
       "      <td>0.013042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Meta_Description_Length</td>\n",
       "      <td>0.012776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Coefficients\n",
       "Evergreen_Score              0.773039\n",
       "Has_Article_Amplifiers       0.741567\n",
       "num_linking_domains          0.300811\n",
       "Has_Referring_Domains        0.253278\n",
       "Has_Author_Name              0.099609\n",
       "Is_Valid_Body                0.086709\n",
       "Number_Of_Sentences          0.078869\n",
       "SMOG_Index                   0.036236\n",
       "Plain_Text_Rate              0.033719\n",
       "Page_Size_In_Bytes           0.021121\n",
       "SSL                          0.019273\n",
       "Has_Top_Image                0.019202\n",
       "ARI_Index                    0.017794\n",
       "Word_Count                   0.013042\n",
       "Meta_Description_Length      0.012776"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_coefficients.abs().sort_values(by='Coefficients', ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the coefficients above contain: \n",
    "- A logged target (y) variable.\n",
    "- All variables have been stadardized with Z scores.\n",
    "\n",
    "Therefore the current interpretation is:\n",
    "- For <strong> every 1 increase/decrease of a specific X variable there is a 1 standard deviation increase/decrease which will result in an increase/decrease of log(y). </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center > <h2> Linear Prediction  Plot + Cross Validation Training Scores </h2> <center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Lasso_Model_Images/Learning_Curve_Lasso_CV.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The learning curve shows us how many training observations we need to reduce the variability/standard deviation of the mean training score and the cross validation score. \n",
    "\n",
    "After ~ 4500 observations the training score and cross validation score start to converge which shows us that we only need ~ 4500 observations to start making stable predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center > <h2> Linear Prediction  Plot + Cross Validation Training Scores </h2> <center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Lasso_Model_Images/Lasso_CV_Alpha_Error.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center > <h2> Linear Prediction  Plot + Cross Validation Training Scores </h2> <center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Lasso_Model_Images/prediction_error_for_Lasso_CV.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center > <h2> Linear Prediction  Plot + Cross Validation Training Scores </h2> <center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Lasso_Model_Images/Cross_Val_Scores_For_Lasso_CV.png' >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"decisiontreeregressor\"></a>\n",
    "### Logging The Target Variable + Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the target variable 'Total Shares' distribution looked skewed and exponential, I decided to apply a log + 1 transform to the target variable. This combined with 5 ADA boosted bagged random forest regressors (100 estimators) led to a mean cross validated training score of 0.856 $R^2$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree - Max Depth: 5 To View The Tree Structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/Decision_Tree_Images/tree.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The primary features that are driving the decisions of a max_depth 5 decision tree regressor are:\n",
    "- Evergreen Score\n",
    "- Has Article Amplifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally for articles that had more than 0.5 article amplifiers and a higher evergreen score than 0.155:\n",
    "\n",
    "- 5852/12594 samples were divided by the Title_Tag_Length and an example of this can be seen below. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/title_tag_length.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/title_tag_example.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can infer from this that <strong> having a title_tag_length > 79.5 characters causes an article to become less shareable and also if the title_tag_length is less than 45.5 the article is also less likely to be shared. </strong>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes sense because we want a catchy, strong headline that entices someone to click and read the article, however if the article headline is too long then it will cause the title to be truncated within the Google SERPS (search engine reuslts pages) which often leads to a lower click through rate for the article. \n",
    "\n",
    "An example of a truncuated <'title'> tag can be seen below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Images/consider-modifying-truncated-title-tags.jpg'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"randomforestfeatureimportances\"></a>\n",
    "### Decision Tree Regressor Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance_values = df.iloc[5:,:]['Coefficients/Feature_Importances'].values[0]['feature_importance_values']\n",
    "indexes = df.iloc[5:,:]['Coefficients/Feature_Importances'].values[0]['indexes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75749305, 0.02477214, 0.03901546, 0.0240626 , 0.0341717 ,\n",
       "       0.03103255, 0.03088174, 0.05857078])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = list(zip(indexes, feature_importance_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = sorted(feature_importances, key=lambda x: x[1], reverse= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 9))\n",
    "sns.barplot(x = [item[1] for item in new_list]  , y = [item[0] for item in new_list])\n",
    "plt.xlabel('Importance of Feature', fontsize='16', labelpad=20)\n",
    "plt.ylabel('Types of Feature Importances',  fontsize='16', labelpad=20)\n",
    "label = ax.set_title('The Feature Importances From A Decision Tree Regressor - Max Depth 5', fontsize = 24, pad=30)\n",
    "plt.savefig('Feature_Importances_Decision_Tree_Regressor',dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Evergreen_Score: If an article is deemed to be evergreen it is meant to be non-seasonal and information that is repeatedly searched for.\n",
    "- Has_Article_Amplifiers: An article amplifier is defined as a key influencer who has an audience which is largest enough to amplify the publisher's article.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"challenges\"></a>\n",
    "### Challenges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 30,000 articles were downloaded, however I was only able to obtain the article text data for 15,000 URL's, this was due to NewsPaper3k only being able to extract the main content for $1/2$ of the article data.\n",
    "- Working with text data naturally creates sparse matrices, this can be problematic because it drastically increases the dimensionality of the feature space. In order to combat this challenge, a custom standard_scaler and TFID_vectorizer class were created in sci-kit learn for optimising the pipelines and grid_search process.\n",
    "- The target variable 'Total Shares' was not normally distributed and was exponentially distributed. By applying the logarithm and adding +1 to all of the values, the model captured a more linear relationship between our predictor features and our target variable.\n",
    "- Some of the articles had 404'ing pages, therefore whilst web scraping excessive exception handling was required to ensure that all of the features gathered were aligned to the correct URL.\n",
    "- The variables which were our best predictors are only available via BuzzSumo which means that our predictions are currently partially dependent on a 3rd party tool/data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"risks\"></a>\n",
    "### Risks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The sample chosen was 1 year old, this was selected to remove any bias of an article not being online enough to receive a significant part of it's online shares. However this type of sample could lead to omission bias within the model's predictions. \n",
    "* Therefore it would be advisable to study the natural share cycle of article's and the share velocity from when an article is originally published until it reaches a certain level of maturity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"assumptions\"></a>\n",
    "### Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have assumed that by taking 1 month's worth of content for 15 topics that our sample is representative of the population for every individual topic.\n",
    "- Taking a sample of articles for a 3 month snapshot might not be a representative sample, furthermore seasonality might have an influential factor on how article's are shared across different topics. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"keytakeaways\"></a>\n",
    "### Key Takeaways"
   ]
  },
  {
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    "- Focus on producing <strong> more evergreen content. </strong>\n",
    "- Prioritise creating <strong> how to guides </strong> over infographics and list posts.\n",
    "- <strong> Leverage relationships with key influencers </strong> to increase the number of  article shares.\n",
    "- Focus <strong> on producing long-form content </strong> because it was a positive coefficient within the LassoCV model (higher number of sentences)."
   ]
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    "------------------------------------------------------------------------------------------------------------------"
   ]
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    "<a class=\"anchor\" id=\"nextsteps\"></a>\n",
    "### Next Steps"
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    "- <strong> Sentiment analysis: </strong> could be performed on all of the articles as this could be influencing the shareability of an article.\n",
    "- <strong> Scrape additional topics </strong> and to expand the number of article's crawled to 100,000 + articles.\n",
    "- <strong> Implementing a neural network: </strong>, from having to apply a logarithm to the target variable we can clearly see that the relationship between the predictor matrix and the target variable is not linear. A neural network might be able to model effectively the non-linear relationships within our dataset.\n",
    "- To scrape additional link metrics from 3rd party providers including:\n",
    "    * Ahrefs\n",
    "    * SEMrush\n",
    "    * Majestic"
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    "* <strong> Time Series Analysis: </strong> It would also be good idea to track articles / topics for several months or years. This would allow us to perform time series analysis on individual topics. We would then be ble to see what topics are increasing or decreasing over time and would be able to make predictions on what the number of article shares a particular topic group would receive in the future.\n",
    "* <strong> Testing New Python Packages: </strong> Also it would be worth testing different scraping / article parsing libraries because NewsPaper3K was only able to scrape 50-55% of the original set of URL's. If we could improve the reliability of scraping the main content, then we would be able to acquire more data."
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   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------------"
   ]
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