{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "remove-input" ] }, "outputs": [], "source": [ "from datascience import *\n", "import matplotlib\n", "path_data = '../../assets/data/'\n", "matplotlib.use('Agg')\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": [ "Here is a historical dataset used for the prediction of the heights of adults based on the heights of their parents. We have studied this dataset in an earlier section. The table `heights` contains data on the midparent height and child's height (all in inches) for a population of 934 adult \"children\". Recall that the midparent height is an average of the heights of the two parents." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Data on heights of parents and their adult children\n", "original = Table.read_table(path_data + 'family_heights.csv')\n", "heights = Table().with_columns(\n", " 'MidParent', original.column('midparentHeight'),\n", " 'Child', original.column('childHeight')\n", " )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "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", "
MidParent Child
75.43 73.2
75.43 69.2
75.43 69
75.43 69
73.66 73.5
73.66 72.5
73.66 65.5
73.66 65.5
72.06 71
72.06 68
\n", "

... (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": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "heights.scatter('MidParent')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A primary reason for studying the data was to be able to predict the adult height of a child born to parents who were similar to those in the dataset. We made these predictions in Section 8.1, 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": {}, "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, and visualized the result." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "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": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "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 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": 1 }