Descriptive Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "merged.pkl\r\n",
      "movie_urls.pkl\r\n",
      "series_deltas.pkl\r\n"
     ]
    }
   ],
   "source": [
    "!ls | grep pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('merged.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Remove the first item of each sequel\n",
    "df = df[df.part > 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fundamental relationships"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box Office totals are pretty strongly related to total runtimes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ioXn2qUt48sFzANhUhqgTx62fAazPApSyNQBGKQLmZ9J45J75li2j41YdtB0PN7bKLdW/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\nAMAEl4Q/mR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa544e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6));\n",
    "sns.regplot('runtime', 'total_gross', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_cols = ['budget', 'total_gross', 'runtime', 'masses', 'critics', 'estimated_roi',\n",
    "           'thumb_rule_roi']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining a good Metric of ROI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conventional wisdom is that a firm needs to make twice their production budget in order to break even (achieve a 0% ROI). This rule of thumb, however, has been around for decades and may no longer be accurate due to rising marketing costs. In 1980, average marketing costs were $12.4MM in 2015 dollars. Today it is at 40MM. The biggest cost is TV advertising.  Below is a table of market costs for various movies. \n",
    "\n",
    "Movie | Year | Cost | Produciton BG\n",
    "-- | -- | -- | --\n",
    "Average | 1980 | 12.4MM |\n",
    "Average | 2015 | 40MM |\n",
    "Average | 2007 | 36 MM |70MM\n",
    "Transformers 1 | 2007 | 150 MM | 147 MM\n",
    "Transformers 4 | 2014 | 200MM | 210MM\n",
    "\n",
    "In additional to marketing costs, studios share about 45% of their revenues with movie theatres.\n",
    "\n",
    "Given this data, I estimate ROIs using the following formulas:\n",
    "\n",
    "$$ estimated\\_roi = \\frac{Gross * .55 - Production\\_Budget * 1.5}{Production\\_Budget} $$\n",
    "\n",
    "I multiply the production budget by 1.5 to account for the fact that marketing costs about as much as 50% of the production budget, on average. Gross is multiplide by .55 to reflect that studios give 45% of revenues to the movie threatres.\n",
    "\n",
    "$$ thumb\\_rule\\_ROI = \\frac{Gross - Production\\_Budget * 2}{Production\\_Budget} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimated_roi = (df.total_gross * .55 - df.budget * 1.5) / df.budget\n",
    "thumb_rule_roi = (df.total_gross - df.budget * 2) / df.budget\n",
    "naive_roi = df.total_gross / df.budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.7809822155272386, 2.780982215527239, 1.1295402185399814)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_roi.mean(), thumb_rule_roi.mean(), estimated_roi.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The degree to which the rule of thumb roi diverges from my estimated roi is staggering. According to the rule of thumb, sequels are earning a 137% return on investment. When estimating the ROI directly, we see that the ROI is only about 110%. \n",
    "\n",
    "If anything my estimated ROI is optimistic. My marketing budget is based off of proportions obsreved in 2007. Since then, marketing [expenses have skyrocketed](http://www.hollywoodreporter.com/news/200-million-rising-hollywood-struggles-721818)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['estimated_roi'] = estimated_roi\n",
    "df['thumb_rule_roi'] = thumb_rule_roi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://dl.dropboxusercontent.com/u/97258109/Screens/S3494.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7sYqnwnrIZLo5Pg2OY9T4OEaNjePT+DhGjY3j0/g4Ro0vk+le8WOWat3skfSwpC3Bv+F/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\nkiT1mf8HzLPRP+Ekwk8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d16c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6));\n",
    "sns.regplot('budget', 'estimated_roi', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8Wh/HqPVxjDaXasKfK+mybduOpKBvnuc4zofW88CO43zatu1RSS9K6opc1ScpvdL9U6ma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\nNVqh0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5b8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6));\n",
    "sns.regplot('budget', 'thumb_rule_roi', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.estimated_roi < -1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 39 movies in our dataset that managed to lose more than their entire production budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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az0kAgCFaaFbMX8kCxS7Jea21uxY5D8CqdOONb0+i2AEAR2beYtdaO3cJcwCsWpOT29Pa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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d039fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6));\n",
    "sns.boxplot(x='rating', y='total_gross', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df76b38>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uxwAAgKFx6xkAbGJp6WASZxQBADA9hCIAGKDXW06tK8fXYhEMz803vyW33fah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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d90b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12));\n",
    "sns.boxplot(x='rating', y='estimated_roi', data=df);\n",
    "# sns.stripplot('rating', 'estimated_roi', data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating\n",
       "G        0.747900\n",
       "PG       1.626363\n",
       "PG-13    0.917033\n",
       "R        1.132656\n",
       "Name: estimated_roi, dtype: float64"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('rating').estimated_roi.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that ROIs that are negative are quite common for every single genre. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.delta = df.delta.apply(lambda x: x / np.timedelta64(1, 'D'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['month'] = pd.DatetimeIndex(df.release_date).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Doing our Basic Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from patsy import dmatrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "y, X = dmatrices('thumb_rule_roi ~ C(rating) + np.log(budget)'\n",
    "                 '+ delta + np.log(masses) + critics + C(month) +'\n",
    "                'runtime', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = OLS(y, X).fit_regularized(alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>thumb_rule_roi</td>  <th>  R-squared:         </th> <td>   0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 08 Oct 2015</td> <th>  Prob (F-statistic):</th> <td>7.63e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:58:42</td>     <th>  Log-Likelihood:    </th> <td> -975.95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   310</td>      <th>  AIC:               </th> <td>   1992.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   290</td>      <th>  BIC:               </th> <td>   2067.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    19</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(rating)[T.PG]</th>    <td>    0.0452</td> <td>    0.932</td> <td>    0.048</td> <td> 0.961</td> <td>   -1.790     1.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(rating)[T.PG-13]</th> <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(rating)[T.R]</th>     <td>   -0.0198</td> <td>    0.757</td> <td>   -0.026</td> <td> 0.979</td> <td>   -1.511     1.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.2]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.3]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.4]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.5]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.6]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.7]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.8]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.9]</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.10]</th>     <td>    4.9611</td> <td>    1.303</td> <td>    3.806</td> <td> 0.000</td> <td>    2.396     7.527</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.11]</th>     <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(month)[T.12]</th>     <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(budget)</th>     <td>   -0.5198</td> <td>    0.179</td> <td>   -2.906</td> <td> 0.004</td> <td>   -0.872    -0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delta</th>              <td>   -0.0004</td> <td>    0.000</td> <td>   -1.726</td> <td> 0.085</td> <td>   -0.001  5.52e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(masses)</th>     <td>    6.3473</td> <td>    1.856</td> <td>    3.420</td> <td> 0.001</td> <td>    2.694    10.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>critics</th>            <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0         0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>runtime</th>            <td>    0.0064</td> <td>    0.020</td> <td>    0.321</td> <td> 0.749</td> <td>   -0.033     0.045</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>313.800</td> <th>  Durbin-Watson:     </th> <td>   1.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9903.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 4.300</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>29.320</td>  <th>  Cond. No.          </th> <td>5.31e+04</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         thumb_rule_roi   R-squared:                       0.159\n",
       "Model:                            OLS   Adj. R-squared:                  0.104\n",
       "Method:                 Least Squares   F-statistic:                     2.881\n",
       "Date:                Thu, 08 Oct 2015   Prob (F-statistic):           7.63e-05\n",
       "Time:                        15:58:42   Log-Likelihood:                -975.95\n",
       "No. Observations:                 310   AIC:                             1992.\n",
       "Df Residuals:                     290   BIC:                             2067.\n",
       "Df Model:                          19                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept                   0          0        nan        nan             0         0\n",
       "C(rating)[T.PG]        0.0452      0.932      0.048      0.961        -1.790     1.880\n",
       "C(rating)[T.PG-13]          0          0        nan        nan             0         0\n",
       "C(rating)[T.R]        -0.0198      0.757     -0.026      0.979        -1.511     1.471\n",
       "C(month)[T.2]               0          0        nan        nan             0         0\n",
       "C(month)[T.3]               0          0        nan        nan             0         0\n",
       "C(month)[T.4]               0          0        nan        nan             0         0\n",
       "C(month)[T.5]               0          0        nan        nan             0         0\n",
       "C(month)[T.6]               0          0        nan        nan             0         0\n",
       "C(month)[T.7]               0          0        nan        nan             0         0\n",
       "C(month)[T.8]               0          0        nan        nan             0         0\n",
       "C(month)[T.9]               0          0        nan        nan             0         0\n",
       "C(month)[T.10]         4.9611      1.303      3.806      0.000         2.396     7.527\n",
       "C(month)[T.11]              0          0        nan        nan             0         0\n",
       "C(month)[T.12]              0          0        nan        nan             0         0\n",
       "np.log(budget)        -0.5198      0.179     -2.906      0.004        -0.872    -0.168\n",
       "delta                 -0.0004      0.000     -1.726      0.085        -0.001  5.52e-05\n",
       "np.log(masses)         6.3473      1.856      3.420      0.001         2.694    10.000\n",
       "critics                     0          0        nan        nan             0         0\n",
       "runtime                0.0064      0.020      0.321      0.749        -0.033     0.045\n",
       "==============================================================================\n",
       "Omnibus:                      313.800   Durbin-Watson:                   1.386\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9903.108\n",
       "Skew:                           4.300   Prob(JB):                         0.00\n",
       "Kurtosis:                      29.320   Cond. No.                     5.31e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.31e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the most lucrative movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>stars</th>\n",
       "      <th>critics</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "      <th>year</th>\n",
       "      <th>budget</th>\n",
       "      <th>scope</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>part</th>\n",
       "      <th>ROI</th>\n",
       "      <th>masses</th>\n",
       "      <th>delta</th>\n",
       "      <th>ROI_delta</th>\n",
       "      <th>budget_delta</th>\n",
       "      <th>rt_delta</th>\n",
       "      <th>estimated_roi</th>\n",
       "      <th>thumb_rule_roi</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Saw II</td>\n",
       "      <td>[Donnie Wahlberg, Beverley Mitchell, Franky G,...</td>\n",
       "      <td>0.40</td>\n",
       "      <td>[Horror]</td>\n",
       "      <td>R</td>\n",
       "      <td>2005</td>\n",
       "      <td>4000000</td>\n",
       "      <td>World</td>\n",
       "      <td>147748505</td>\n",
       "      <td>2005-10-28</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>36.937126</td>\n",
       "      <td>6.6</td>\n",
       "      <td>364</td>\n",
       "      <td>-49.655931</td>\n",
       "      <td>2800000</td>\n",
       "      <td>-1 days +23:50:00</td>\n",
       "      <td>18.815419</td>\n",
       "      <td>34.937126</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Paranormal Activity 2</td>\n",
       "      <td>[Katie Featherston, Micah Sloat, Molly Ephraim...</td>\n",
       "      <td>0.53</td>\n",
       "      <td>[Horror]</td>\n",
       "      <td>R</td>\n",
       "      <td>2010</td>\n",
       "      <td>3000000</td>\n",
       "      <td>World</td>\n",
       "      <td>177512032</td>\n",
       "      <td>2010-10-22</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>59.170677</td>\n",
       "      <td>5.7</td>\n",
       "      <td>371</td>\n",
       "      <td>-12831.215989</td>\n",
       "      <td>2985000</td>\n",
       "      <td>00:05:00</td>\n",
       "      <td>31.043873</td>\n",
       "      <td>57.170677</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Paranormal Activity 3</td>\n",
       "      <td>[Chloe Csengery, Jessica Tyler Brown, Christop...</td>\n",
       "      <td>0.59</td>\n",
       "      <td>[Horror]</td>\n",
       "      <td>R</td>\n",
       "      <td>2011</td>\n",
       "      <td>5000000</td>\n",
       "      <td>World</td>\n",
       "      <td>207039844</td>\n",
       "      <td>2011-10-21</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>41.407969</td>\n",
       "      <td>5.9</td>\n",
       "      <td>364</td>\n",
       "      <td>-17.762709</td>\n",
       "      <td>2000000</td>\n",
       "      <td>-1 days +23:52:00</td>\n",
       "      <td>21.274383</td>\n",
       "      <td>39.407969</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Paranormal Activity 4</td>\n",
       "      <td>[Stephen Dunham, Katie Featherston, Matt Shive...</td>\n",
       "      <td>0.40</td>\n",
       "      <td>[Horror]</td>\n",
       "      <td>R</td>\n",
       "      <td>2012</td>\n",
       "      <td>5000000</td>\n",
       "      <td>World</td>\n",
       "      <td>142838957</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>28.567791</td>\n",
       "      <td>4.6</td>\n",
       "      <td>364</td>\n",
       "      <td>-12.840177</td>\n",
       "      <td>0</td>\n",
       "      <td>00:05:00</td>\n",
       "      <td>14.212285</td>\n",
       "      <td>26.567791</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     title                                              stars  \\\n",
       "55                  Saw II  [Donnie Wahlberg, Beverley Mitchell, Franky G,...   \n",
       "291  Paranormal Activity 2  [Katie Featherston, Micah Sloat, Molly Ephraim...   \n",
       "292  Paranormal Activity 3  [Chloe Csengery, Jessica Tyler Brown, Christop...   \n",
       "293  Paranormal Activity 4  [Stephen Dunham, Katie Featherston, Matt Shive...   \n",
       "\n",
       "     critics    genres rating  year   budget  scope  total_gross release_date  \\\n",
       "55      0.40  [Horror]      R  2005  4000000  World    147748505   2005-10-28   \n",
       "291     0.53  [Horror]      R  2010  3000000  World    177512032   2010-10-22   \n",
       "292     0.59  [Horror]      R  2011  5000000  World    207039844   2011-10-21   \n",
       "293     0.40  [Horror]      R  2012  5000000  World    142838957   2012-10-19   \n",
       "\n",
       "     ...    part        ROI  masses  delta     ROI_delta  budget_delta  \\\n",
       "55   ...       2  36.937126     6.6    364    -49.655931       2800000   \n",
       "291  ...       2  59.170677     5.7    371 -12831.215989       2985000   \n",
       "292  ...       3  41.407969     5.9    364    -17.762709       2000000   \n",
       "293  ...       4  28.567791     4.6    364    -12.840177             0   \n",
       "\n",
       "             rt_delta  estimated_roi  thumb_rule_roi  month  \n",
       "55  -1 days +23:50:00      18.815419       34.937126     10  \n",
       "291          00:05:00      31.043873       57.170677     10  \n",
       "292 -1 days +23:52:00      21.274383       39.407969     10  \n",
       "293          00:05:00      14.212285       26.567791     10  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df.month == 10) & (df.estimated_roi > 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_no_paranormal = df.drop(df.index[[291,292,293]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = OLS(y, X).fit_regularized(alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Misc Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.regression.linear_model as linear_model\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.linear_model import OLS"
   ]
  }
 ],
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