upvotes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# Import Data from /r/politics The post title and the number of upvotes\n",
    "data = pd.read_csv('data.csv',escapechar=\"\\\\\")\n",
    "data_sub = data[['title', 'ups']]\n",
    "data_sub.is_copy = False\n",
    "data_sub['log_ups'] = np.log1p(data_sub['ups'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Description:\n",
    "------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ups</th>\n",
       "      <th>log_ups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35018.000000</td>\n",
       "      <td>35018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>589.979239</td>\n",
       "      <td>3.775398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2723.521822</td>\n",
       "      <td>2.264599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.484907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>3.663562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>166.000000</td>\n",
       "      <td>5.117994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52230.000000</td>\n",
       "      <td>10.863431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ups       log_ups\n",
       "count  35018.000000  35018.000000\n",
       "mean     589.979239      3.775398\n",
       "std     2723.521822      2.264599\n",
       "min        0.000000      0.000000\n",
       "25%       11.000000      2.484907\n",
       "50%       38.000000      3.663562\n",
       "75%      166.000000      5.117994\n",
       "max    52230.000000     10.863431"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stats for Upvotes\n",
    "data_sub.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Titles: \t35018\n",
      "Max Length of Title: \t299 characters\n",
      "Avg Length of Title: \t67 characters\n",
      "Max words in Title: \t53 words\n",
      "Avg words in Title: \t10 words\n"
     ]
    }
   ],
   "source": [
    "# Stats for Titles\n",
    "num_of_titles = len(data_sub['title'])\n",
    "max_len_title = max([len(x) for x in data_sub['title']])\n",
    "avg_len_title = int(np.mean([len(x) for x in data_sub['title']]))\n",
    "max_word_count = max([len(x.split()) for x in data_sub['title']])\n",
    "avg_word_count = int(np.mean([len(x.split()) for x in data_sub['title']]))\n",
    "\n",
    "print('Number of Titles: \\t{0}'.format(num_of_titles))\n",
    "print('Max Length of Title: \\t{0} characters'.format(max_len_title))\n",
    "print('Avg Length of Title: \\t{0} characters'.format(avg_len_title))\n",
    "print('Max words in Title: \\t{0} words'.format(max_word_count))\n",
    "print('Avg words in Title: \\t{0} words'.format(avg_word_count))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UczjJUZShg09Sei/n9pLl0Xkeehq4khKMTe5hDPlmlN/Pk5Tzxg6UcxeUOr8KeErS7f2o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\nM7Ma/H/BBZccwFLz0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab70858c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Upvotes\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.title('Distribution of Upvotes')\n",
    "plt.hist(data_sub['ups'])\n",
    "plt.xlabel('Upvotes per Post')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('Log Transformed Distribution of Upvotes')\n",
    "plt.hist(data_sub['log_ups'])\n",
    "plt.xlabel('Upvotes per Post(log)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply Model Flow\n",
    "========"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split Data in to test and training sets\n",
    "train_titles, test_titles, train_labels, test_labels = train_test_split(data_sub.title, \n",
    "                                                                        data_sub.log_ups, \n",
    "                                                                        test_size=0.20,\n",
    "                                                                        random_state=42)\n",
    "# Tokenize with specified ngram then apply tf-idf\n",
    "tfidf = TfidfVectorizer(ngram_range=(1,3)).fit(train_titles, train_labels)\n",
    "train_titles_tfidf = tfidf.transform(train_titles)\n",
    "test_titles_tfidf = tfidf.transform(test_titles)\n",
    "\n",
    "# Apply Linear Regression & Prediction\n",
    "reg = Ridge()\n",
    "reg.fit(train_titles_tfidf, train_labels)\n",
    "test_predictions = reg.predict(test_titles_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  627.313849349\n",
      "Mean Train 601.92946384\n",
      "Mean Test 542.181753284\n"
     ]
    },
    {
     "data": {
      "image/png": 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3T8UBzmqmT+thCknXVa0FZuMNDp6pk021cYfxjkeYFYVbKm3ujoc3s/7xFxge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\ny42DipmZ5cZBxczMcvP/AeDkuA4eDxfHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9b5b04c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('MAE: ', mean_absolute_error(test_predictions, test_labels))\n",
    "print('Mean Train', np.mean(train_labels))\n",
    "print('Mean Test', np.mean(test_labels))\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Upvotes')\n",
    "plt.ylabel('Predicted Upvotes')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}