{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "tags": [ "remove_input" ] }, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'datascience'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdatascience\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpath_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'../../../data/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Agg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'datascience'" ] } ], "source": [ "from datascience import *\n", "import matplotlib\n", "path_data = '../../../data/'\n", "matplotlib.use('Agg', warn=False)\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction ###\n", "\n", "An important aspect of data science is to find out what data can tell us about the future. What do data about climate and pollution say about temperatures a few decades from now? Based on a person's internet profile, which websites are likely to interest them? How can a patient's medical history be used to judge how well he or she will respond to a treatment?\n", "\n", "To answer such questions, data scientists have developed methods for making *predictions*. In this chapter we will study one of the most commonly used ways of predicting the value of one variable based on the value of another." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The foundations of the method were laid by [Sir Francis Galton](https://en.wikipedia.org/wiki/Francis_Galton). As we saw in Section 7.1, Galton studied how physical characteristics are passed down from one generation to the next. Among his best known work is the prediction of the heights of adults based on the heights of their parents. We have studied the dataset that Galton collected for this. The table `heights` contains his data on the midparent height and child's height (all in inches) for a population of 934 adult \"children\"." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Galton's data on heights of parents and their adult children\n", "galton = Table.read_table(path_data + 'galton.csv')\n", "heights = Table().with_columns(\n", " 'MidParent', galton.column('midparentHeight'),\n", " 'Child', galton.column('childHeight')\n", " )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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75.43 73.2
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... (924 rows omitted)

" ], "text/plain": [ "MidParent | Child\n", "75.43 | 73.2\n", "75.43 | 69.2\n", "75.43 | 69\n", "75.43 | 69\n", "73.66 | 73.5\n", "73.66 | 72.5\n", "73.66 | 65.5\n", "73.66 | 65.5\n", "72.06 | 71\n", "72.06 | 68\n", "... (924 rows omitted)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "heights" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/15/Prediction_5_0.png" } }, "output_type": "display_data" } ], "source": [ "heights.scatter('MidParent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The primary reason for collecting the data was to be able to predict the adult height of a child born to parents similar to those in the dataset. We made these predictions in Section 7.1, after noticing the positive association between the two variables. \n", "\n", "Our approach was to base the prediction on all the points that correspond to a midparent height of around the midparent height of the new person. To do this, we wrote a function called `predict_child` which takes a midparent height as its argument and returns the average height of all the children who had midparent heights within half an inch of the argument." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_child(mpht):\n", " \"\"\"Return a prediction of the height of a child \n", " whose parents have a midparent height of mpht.\n", " \n", " The prediction is the average height of the children \n", " whose midparent height is in the range mpht plus or minus 0.5 inches.\n", " \"\"\"\n", " \n", " close_points = heights.where('MidParent', are.between(mpht-0.5, mpht + 0.5))\n", " return close_points.column('Child').mean() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We applied the function to the column of `Midparent` heights, visualized our results." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Apply predict_child to all the midparent heights\n", "\n", "heights_with_predictions = heights.with_column(\n", " 'Prediction', heights.apply(predict_child, 'MidParent')\n", " )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/Users/rob/DataScience/textbook-gh-pages/_build/jupyter_execute/content/chapters/15/Prediction_10_0.png" } }, "output_type": "display_data" } ], "source": [ "# Draw the original scatter plot along with the predicted values\n", "\n", "heights_with_predictions.scatter('MidParent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The prediction at a given midparent height lies roughly at the center of the vertical strip of points at the given height. This method of prediction is called *regression.* Later in this chapter we will see where this term came from. We will also see whether we can avoid our arbitrary definitions of \"closeness\" being \"within 0.5 inches\". But first we will develop a measure that can be used in many settings to decide how good one variable will be as a predictor of another." ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 0 }