Attendance Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attendance Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see how we can use bayes to solve an actual problem\n",
    "\n",
    "*I have a list of 7 events with attendance and registered people for each event, Can we guess what will be the attendance of the event given that the number of people registered are 100?*\n",
    "\n",
    "so given the data of the 7 events we want to predict how many people will attend the 8th event given that there are 100 registrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from thinkbayes2 import Suite\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.stats import binom, beta\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dataset\n",
    "data = pd.read_csv('attendance.csv', index_col='Event')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, lets think about the problem for a bit, how would you approach it ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AttendanceModel(Suite):\n",
    "    def Likelihood(self, data, hypo):\n",
    "        ## define the method\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try not to look at the data while developing the prior. I am using a beta prior, the beta prior has two params alpha(a) and beta(b) which are known as the shape parameters of the prior.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd57cd0e438>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5b0745e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Play around a bit with the beta distribution\n",
    "\n",
    "plt.plot(beta.pdf(range(101), a=2, b=5, loc=10, scale=80))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My beleif is that atleast 20% of the registered people would attend the event. I am representing it by \n",
    "\n",
    "        beta.pdf(range(101), a=2, b=5, loc=20, scale=100)\n",
    "\n",
    "notice the loc, and scale params are somewhat defining the range of my belief, and the alpha beta are actually choosen \n",
    "by looking at the picture from the wikipedia page depicting how the distribution looks like for different alpha, beta params.\n",
    "        \n",
    "        https://en.wikipedia.org/wiki/File:Beta_distribution_pdf.svg\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8Ulk68JqxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd57cd0ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attendance = AttendanceModel(range(101))\n",
    "# Feel free to change this to represent your own belief. \n",
    "prior = beta.pdf(range(101), a=2, b=5, loc=20, scale=100)\n",
    "for index in range(101):\n",
    "    attendance[index] = prior[index]\n",
    "attendance.Normalize()\n",
    "thinkplot.Pmf(attendance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD8CAYAAAB3u9PLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8Ulk68JqxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5b0745080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for attended, registered in zip(data.attended.tolist(), data.registered.tolist()):\n",
    "    attendance.UpdateSet([1] * attended + [0] * (registered - attended))\n",
    "attendance.Normalize()\n",
    "thinkplot.Pmf(attendance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Discribe the posterior\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should have an approximate on what percentage of people will turn up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To predict the number of people who show up for the next event, we can generate a predictive distribution by drawing samples from the posterior that we just calculated, and then drawing samples from a Binomial distribution with parameters x and n, where n is the number of people who signed up and x is the probability of them showing up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_registered = 100\n",
    "p = attendance.Sample(10000).mean() / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictive_prior = binom(number_of_registered, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd57cb38470>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/ycKslxYthbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd57cb56198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(predictive_prior.pmf(range(101)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used the binom method from scipy.stats the way to get descriptors is slightly different but not much\n",
    "\n",
    "1. predictive_prior.mean()\n",
    "2. predictive_prior.interval(.90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'There will be around 39.0 to 58.0 people attending the event.'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'There will be around {0} to {1} people attending the event.'.format(*predictive_prior.interval(0.95))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving the same problem in the frequentist paradigm, how should we approach ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do a simple regression to compare our results and see the differences in the insights gained from the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd578fd3278>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njjHIiYiI6tj/A1hGY1SJpNVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd579482470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data.attended, data.registered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "reg.fit(X=data.registered.values.reshape(7,1), y=data.attended.values.reshape(7,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 23.79428044]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1. By how much does the predictions differ ? \n",
    "\n",
    "Q2. If you had to bet on the result, which one would you bet the range that we got above or the result from regression ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}