comparing_classification_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Can We Predict Left-Handedness from Psychological Factors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![A left-hand with 'I'm a lefty' written on it](https://imgur.com/BhrAgSx.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, we're going to use a survey of psychological factors to see if we can!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Define The Problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the context of the left-handedness and personality, what are three specific and conclusively answerable problems that you could answer using data science? \n",
    "\n",
    "1. As one's response to `Q1` increases, are people more or less likely to be left-handed?\n",
    "2. Does the distribution of handedness vary based one's response to Q1?\n",
    "3. Which question (`Q1`-`Q44`) has the highest association with left-handedness?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))#to expand the size of my container so it can take fill up my entire monitor\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "%config InlineBackend.figure_format = 'retina' #to enable higher quality plots on retina displays\n",
    "pd.options.display.max_rows = 200 #increasing maximum viewable rows and columns\n",
    "pd.options.display.max_columns = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Obtain the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lefty = pd.read_csv('data.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "   Q1  Q2  Q3  Q4  Q5  Q6  Q7  Q8  Q9  Q10  Q11  Q12  Q13  Q14  Q15  Q16  Q17  \\\n",
       "0   4   1   5   1   5   1   5   1   4    1    1    1    5    5    5    1    5   \n",
       "1   1   5   1   4   2   5   5   4   1    5    2    5    3    4    1    4    1   \n",
       "\n",
       "   Q18  Q19  Q20  Q21  Q22  Q23  Q24  Q25  Q26  Q27  Q28  Q29  Q30  Q31  Q32  \\\n",
       "0    1    5    1    5    1    1    1    5    5    5    1    5    1    1    1   \n",
       "1    1    1    5    2    4    4    4    1    2    1    2    1    3    1    5   \n",
       "\n",
       "   Q33  Q34  Q35  Q36  Q37  Q38  Q39  Q40  Q41  Q42  Q43  Q44  introelapse  \\\n",
       "0    1    5    5    1    1    1    5    5    5    1    5    1           91   \n",
       "1    2    4    4    4    4    4    1    3    1    4    4    5           17   \n",
       "\n",
       "   testelapse country  fromgoogle  engnat  age  education  gender  \\\n",
       "0         232      US           2       1   22          3       1   \n",
       "1         247      CA           2       1   14          1       2   \n",
       "\n",
       "   orientation  race  religion  hand  \n",
       "0            1     3         2     3  \n",
       "1            2     6         1     1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If we were actually conducting a survey to gather this data ourselves, from an ethics/privacy point of view, what are some things we might consider when attempting to gather this data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Is it necessary?\n",
    "2. How can we ensure the providers of the data remain anonymous?\n",
    "3. How can we ensure all sexual orenitations or gender identities are represented in a way that adequately reflects their proportionality in the general population? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4184, 56)"
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     "execution_count": 254,
     "metadata": {},
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   ],
   "source": [
    "lefty.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4184 entries, 0 to 4183\n",
      "Data columns (total 56 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
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      " 55  hand         4184 non-null   int64 \n",
      "dtypes: int64(55), object(1)\n",
      "memory usage: 1.8+ MB\n"
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   ],
   "source": [
    "lefty.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
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    "lefty.isnull().sum().sort_values(ascending=False)"
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  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
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       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "      <td>4184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.962715</td>\n",
       "      <td>3.829589</td>\n",
       "      <td>2.846558</td>\n",
       "      <td>3.186902</td>\n",
       "      <td>2.865440</td>\n",
       "      <td>3.672084</td>\n",
       "      <td>3.216539</td>\n",
       "      <td>3.184512</td>\n",
       "      <td>2.761233</td>\n",
       "      <td>3.522945</td>\n",
       "      <td>2.748805</td>\n",
       "      <td>2.852772</td>\n",
       "      <td>2.657505</td>\n",
       "      <td>3.334130</td>\n",
       "      <td>3.168021</td>\n",
       "      <td>2.930210</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>3.424952</td>\n",
       "      <td>2.928537</td>\n",
       "      <td>3.639818</td>\n",
       "      <td>2.867591</td>\n",
       "      <td>3.595124</td>\n",
       "      <td>3.861138</td>\n",
       "      <td>3.337237</td>\n",
       "      <td>1.999761</td>\n",
       "      <td>3.001434</td>\n",
       "      <td>2.730641</td>\n",
       "      <td>2.624044</td>\n",
       "      <td>2.543738</td>\n",
       "      <td>2.894359</td>\n",
       "      <td>3.002151</td>\n",
       "      <td>2.869503</td>\n",
       "      <td>2.741874</td>\n",
       "      <td>3.022228</td>\n",
       "      <td>3.074092</td>\n",
       "      <td>2.610660</td>\n",
       "      <td>3.465344</td>\n",
       "      <td>2.798757</td>\n",
       "      <td>2.569312</td>\n",
       "      <td>2.984226</td>\n",
       "      <td>3.385277</td>\n",
       "      <td>2.704828</td>\n",
       "      <td>2.676386</td>\n",
       "      <td>2.736616</td>\n",
       "      <td>347.808556</td>\n",
       "      <td>479.994503</td>\n",
       "      <td>1.576243</td>\n",
       "      <td>1.239962</td>\n",
       "      <td>30.370698</td>\n",
       "      <td>2.317878</td>\n",
       "      <td>1.654398</td>\n",
       "      <td>1.833413</td>\n",
       "      <td>5.013623</td>\n",
       "      <td>2.394359</td>\n",
       "      <td>1.190966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.360291</td>\n",
       "      <td>1.551683</td>\n",
       "      <td>1.664804</td>\n",
       "      <td>1.476879</td>\n",
       "      <td>1.545798</td>\n",
       "      <td>1.342238</td>\n",
       "      <td>1.490733</td>\n",
       "      <td>1.387382</td>\n",
       "      <td>1.511805</td>\n",
       "      <td>1.242890</td>\n",
       "      <td>1.443078</td>\n",
       "      <td>1.556284</td>\n",
       "      <td>1.559575</td>\n",
       "      <td>1.522866</td>\n",
       "      <td>1.501683</td>\n",
       "      <td>1.575544</td>\n",
       "      <td>1.619010</td>\n",
       "      <td>1.413236</td>\n",
       "      <td>1.493122</td>\n",
       "      <td>1.414569</td>\n",
       "      <td>1.360858</td>\n",
       "      <td>1.354475</td>\n",
       "      <td>1.291425</td>\n",
       "      <td>1.426095</td>\n",
       "      <td>1.290747</td>\n",
       "      <td>1.480610</td>\n",
       "      <td>1.485883</td>\n",
       "      <td>1.481709</td>\n",
       "      <td>1.611428</td>\n",
       "      <td>1.477968</td>\n",
       "      <td>1.420032</td>\n",
       "      <td>1.659141</td>\n",
       "      <td>1.405670</td>\n",
       "      <td>1.562694</td>\n",
       "      <td>1.546400</td>\n",
       "      <td>1.409707</td>\n",
       "      <td>1.521460</td>\n",
       "      <td>1.413584</td>\n",
       "      <td>1.621772</td>\n",
       "      <td>1.483752</td>\n",
       "      <td>1.423055</td>\n",
       "      <td>1.544345</td>\n",
       "      <td>1.523097</td>\n",
       "      <td>1.471845</td>\n",
       "      <td>5908.901681</td>\n",
       "      <td>3142.178542</td>\n",
       "      <td>0.494212</td>\n",
       "      <td>0.440882</td>\n",
       "      <td>367.201726</td>\n",
       "      <td>0.874264</td>\n",
       "      <td>0.640915</td>\n",
       "      <td>1.303454</td>\n",
       "      <td>1.970996</td>\n",
       "      <td>2.184164</td>\n",
       "      <td>0.495357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>324.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>252063.000000</td>\n",
       "      <td>119834.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23763.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Q1           Q2           Q3           Q4           Q5  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      1.962715     3.829589     2.846558     3.186902     2.865440   \n",
       "std       1.360291     1.551683     1.664804     1.476879     1.545798   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     3.000000     1.000000     2.000000     1.000000   \n",
       "50%       1.000000     5.000000     3.000000     3.000000     3.000000   \n",
       "75%       3.000000     5.000000     5.000000     5.000000     4.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "                Q6           Q7           Q8           Q9          Q10  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      3.672084     3.216539     3.184512     2.761233     3.522945   \n",
       "std       1.342238     1.490733     1.387382     1.511805     1.242890   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       3.000000     2.000000     2.000000     1.000000     3.000000   \n",
       "50%       4.000000     3.000000     3.000000     3.000000     4.000000   \n",
       "75%       5.000000     5.000000     4.000000     4.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q11          Q12          Q13          Q14          Q15  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      2.748805     2.852772     2.657505     3.334130     3.168021   \n",
       "std       1.443078     1.556284     1.559575     1.522866     1.501683   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     1.000000     1.000000     2.000000     2.000000   \n",
       "50%       3.000000     3.000000     3.000000     4.000000     3.000000   \n",
       "75%       4.000000     4.000000     4.000000     5.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q16          Q17          Q18          Q19          Q20  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      2.930210     2.564771     3.424952     2.928537     3.639818   \n",
       "std       1.575544     1.619010     1.413236     1.493122     1.414569   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     1.000000     2.000000     2.000000     3.000000   \n",
       "50%       3.000000     2.000000     4.000000     3.000000     4.000000   \n",
       "75%       4.000000     4.000000     5.000000     4.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q21          Q22          Q23          Q24          Q25  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      2.867591     3.595124     3.861138     3.337237     1.999761   \n",
       "std       1.360858     1.354475     1.291425     1.426095     1.290747   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000     3.000000     3.000000     2.000000     1.000000   \n",
       "50%       3.000000     4.000000     4.000000     3.000000     1.000000   \n",
       "75%       4.000000     5.000000     5.000000     5.000000     3.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q26          Q27          Q28          Q29          Q30  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      3.001434     2.730641     2.624044     2.543738     2.894359   \n",
       "std       1.480610     1.485883     1.481709     1.611428     1.477968   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "50%       3.000000     3.000000     3.000000     2.000000     3.000000   \n",
       "75%       4.000000     4.000000     4.000000     4.000000     4.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q31          Q32          Q33          Q34          Q35  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      3.002151     2.869503     2.741874     3.022228     3.074092   \n",
       "std       1.420032     1.659141     1.405670     1.562694     1.546400   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "50%       3.000000     3.000000     3.000000     3.000000     3.000000   \n",
       "75%       4.000000     5.000000     4.000000     4.000000     5.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q36          Q37          Q38          Q39          Q40  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000   \n",
       "mean      2.610660     3.465344     2.798757     2.569312     2.984226   \n",
       "std       1.409707     1.521460     1.413584     1.621772     1.483752   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       1.000000     2.000000     1.000000     1.000000     2.000000   \n",
       "50%       2.000000     4.000000     3.000000     2.000000     3.000000   \n",
       "75%       4.000000     5.000000     4.000000     4.000000     4.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000     5.000000   \n",
       "\n",
       "               Q41          Q42          Q43          Q44    introelapse  \\\n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000    4184.000000   \n",
       "mean      3.385277     2.704828     2.676386     2.736616     347.808556   \n",
       "std       1.423055     1.544345     1.523097     1.471845    5908.901681   \n",
       "min       0.000000     0.000000     0.000000     0.000000       1.000000   \n",
       "25%       2.000000     1.000000     1.000000     1.000000       6.000000   \n",
       "50%       4.000000     3.000000     3.000000     3.000000      12.000000   \n",
       "75%       5.000000     4.000000     4.000000     4.000000      35.000000   \n",
       "max       5.000000     5.000000     5.000000     5.000000  252063.000000   \n",
       "\n",
       "          testelapse   fromgoogle       engnat           age    education  \\\n",
       "count    4184.000000  4184.000000  4184.000000   4184.000000  4184.000000   \n",
       "mean      479.994503     1.576243     1.239962     30.370698     2.317878   \n",
       "std      3142.178542     0.494212     0.440882    367.201726     0.874264   \n",
       "min         7.000000     1.000000     0.000000     13.000000     0.000000   \n",
       "25%       186.000000     1.000000     1.000000     18.000000     2.000000   \n",
       "50%       242.000000     2.000000     1.000000     21.000000     2.000000   \n",
       "75%       324.250000     2.000000     1.000000     27.000000     3.000000   \n",
       "max    119834.000000     2.000000     2.000000  23763.000000     4.000000   \n",
       "\n",
       "            gender  orientation         race     religion         hand  \n",
       "count  4184.000000  4184.000000  4184.000000  4184.000000  4184.000000  \n",
       "mean      1.654398     1.833413     5.013623     2.394359     1.190966  \n",
       "std       0.640915     1.303454     1.970996     2.184164     0.495357  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     1.000000     5.000000     1.000000     1.000000  \n",
       "50%       2.000000     1.000000     6.000000     2.000000     1.000000  \n",
       "75%       2.000000     2.000000     6.000000     2.000000     1.000000  \n",
       "max       3.000000     5.000000     7.000000     7.000000     3.000000  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 933,
       "width": 613
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 15))\n",
    "heatmap = sns.heatmap(lefty.corr()[['hand']].sort_values(by='hand', ascending=False), vmin=-1, vmax=1, annot=True, cmap='coolwarm')\n",
    "heatmap.set_title('Correlation with Handedness', fontdict={'fontsize':15}, pad=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4: Model the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we want to predict handedness, this is a classification, not a regressions problem (the output is discrete).\n",
    "\n",
    "While it's often a good idea to standardize our variables when using KNN, we won't here.\n",
    "\n",
    "Why?\n",
    "\n",
    "Since we're only using Q1-Q44 as predictors, we don't need to because they're already on the same scale (1 to 5). \n",
    "\n",
    "We want the interpretability of a one-unit increase in the response to an individual question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# But first, we need to do some cleaning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whether a person is left-handed is a yes or no question, but hand currently has 4 values instead of 2. \n",
    "\n",
    "According to the codebook, 1=Right, 2=Left, 3=Both.\n",
    "\n",
    "I'm going to mape 1 on left and 0 for right and both. Before doing that however, I will drop every observation that has 0 listed for hand since those are missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2, 0])"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3542\n",
       "2     452\n",
       "3     179\n",
       "0      11\n",
       "Name: hand, dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4184,)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "lefty.drop(lefty[lefty.hand == 0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4173,)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "lefty['hand'] = lefty['hand'].map({1 : 0, 2 : 1, 3 : 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3721\n",
       "1     452\n",
       "Name: hand, dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lefty['hand'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's *(finally)* use $k$-nearest neighbors to predict whether or not a person is left-handed!\n",
    "\n",
    "- We'll four separate models, one with $k = 3$, one with $k = 5$, one with $k = 15$, and one with $k = 25$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = lefty.drop(columns=['introelapse', 'testelapse', 'country',\n",
    "                        'fromgoogle', 'engnat', 'age', 'education', 'gender', 'orientation',\n",
    "                        'race', 'religion', 'hand'], axis = 1)\n",
    "y = lefty['hand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_knn_3 = Pipeline([\n",
    "    ('poly', PolynomialFeatures(include_bias=False)), \n",
    "    ('knn', KNeighborsClassifier(n_neighbors=3))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8529069297686485"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipe_knn_3, X_train, y_train, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly', PolynomialFeatures(include_bias=False)),\n",
       "                ('knn', KNeighborsClassifier(n_neighbors=3))])"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9038346315158777"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_3.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8634730538922155"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_3.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_knn_5 = Pipeline([\n",
    "    ('poly', PolynomialFeatures(include_bias=False)), \n",
    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.876272342870481"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipe_knn_5, X_train, y_train, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly', PolynomialFeatures(include_bias=False)),\n",
       "                ('knn', KNeighborsClassifier())])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_5.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.890653085680048"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_5.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8838323353293414"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_5.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_knn_15 = Pipeline([\n",
    "    ('poly', PolynomialFeatures(include_bias=False)), \n",
    "    ('knn', KNeighborsClassifier(n_neighbors=15))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8900542243848136"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipe_knn_15, X_train, y_train, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly', PolynomialFeatures(include_bias=False)),\n",
       "                ('knn', KNeighborsClassifier(n_neighbors=15))])"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_15.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.889754343918514"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_15.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8982035928143712"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_15.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_knn_25 = Pipeline([\n",
    "    ('poly', PolynomialFeatures(include_bias=False)), \n",
    "    ('knn', KNeighborsClassifier(n_neighbors=25))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8900542243848136"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(pipe_knn_25, X_train, y_train, cv = 5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly', PolynomialFeatures(include_bias=False)),\n",
       "                ('knn', KNeighborsClassifier(n_neighbors=25))])"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_25.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.890053924505692"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_25.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8982035928143712"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_knn_25.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Being good data scientists, we know that we might not run just one type of model. We might run many different models and see which is best."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. Let's use logistic regression to predict whether or not the person is left-handed.\n",
    "\n",
    "- We'll create four separate models, one with LASSO and $\\alpha = 1$, one with LASSO and $\\alpha = 10$, one with Ridge and $\\alpha = 1$, and one with Ridge and $\\alpha = 10$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_1 = LogisticRegression(C = 1.0)\n",
    "lasso_1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.890053924505692\n",
      "Testing Score: 0.8982035928143712\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Score: {lasso_1.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {lasso_1.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1)"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_10 = LogisticRegression(C = 0.1)\n",
    "lasso_10.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.890053924505692\n",
      "Testing Score: 0.8982035928143712\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Score: {lasso_10.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {lasso_10.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_1 = LogisticRegression(penalty = 'l2', C = 1.0)\n",
    "ridge_1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.890053924505692\n",
      "Testing Score: 0.8982035928143712\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Score: {ridge_1.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {ridge_1.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_10 = LogisticRegression(penalty = 'l2', C = 0.1)\n",
    "ridge_10.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.890053924505692\n",
      "Testing Score: 0.8982035928143712\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Score: {ridge_10.score(X_train, y_train)}')\n",
    "print(f'Testing Score: {ridge_10.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5: evaluate our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll evaulate all eight of them using accuracy as our metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Training Score with K = 3 : 0.9038346315158777\n",
      "KNN Testing Score with K = 3 : 0.8634730538922155\n",
      "KNN Training Score with K = 5: 0.890653085680048\n",
      "KNN Testing Score with K = 5: 0.8838323353293414\n",
      "KNN Training Score with K = 15: 0.889754343918514\n",
      "KNN Testing Score with K = 15: 0.8982035928143712\n",
      "KNN Training Score with K = 25: 0.890053924505692\n",
      "KNN Testing Score with K = 25: 0.8982035928143712\n",
      "Logistic Regression Training Score with LASSO penalty and alpha = 1: 0.890053924505692\n",
      "Logistic Regression Testing Score with LASSO penalty and alpha = 1: 0.8982035928143712\n",
      "Logistic Regression Training Score with LASSO penalty and alpha = 10: 0.890053924505692\n",
      "Logistic Regression Testing Score with LASSO penalty and alpha = 10: 0.8982035928143712\n",
      "Logistic Regression Training Score with Ridge penalty and alpha = 1: 0.890053924505692\n",
      "Logistic Regression Testing Score with Ridge penalty and alpha = 1: 0.8982035928143712\n",
      "Logistic Regression Training Score with Ridge penalty and alpha = 10: 0.890053924505692\n",
      "Logistic Regression Testing Score with Ridge penalty and alpha = 10: 0.8982035928143712\n"
     ]
    }
   ],
   "source": [
    "print(f'KNN Training Score with K = 3 : {pipe_knn_3.score(X_train, y_train)}')\n",
    "print(f'KNN Testing Score with K = 3 : {pipe_knn_3.score(X_test, y_test)}')\n",
    "\n",
    "print(f'KNN Training Score with K = 5: {pipe_knn_5.score(X_train, y_train)}')\n",
    "print(f'KNN Testing Score with K = 5: {pipe_knn_5.score(X_test, y_test)}')\n",
    "\n",
    "print(f'KNN Training Score with K = 15: {pipe_knn_15.score(X_train, y_train)}')\n",
    "print(f'KNN Testing Score with K = 15: {pipe_knn_15.score(X_test, y_test)}')\n",
    "\n",
    "print(f'KNN Training Score with K = 25: {pipe_knn_25.score(X_train, y_train)}')\n",
    "print(f'KNN Testing Score with K = 25: {pipe_knn_25.score(X_test, y_test)}')\n",
    "\n",
    "print(f'Logistic Regression Training Score with LASSO penalty and alpha = 1: {lasso_1.score(X_train, y_train)}')\n",
    "print(f'Logistic Regression Testing Score with LASSO penalty and alpha = 1: {lasso_1.score(X_test, y_test)}')\n",
    "\n",
    "print(f'Logistic Regression Training Score with LASSO penalty and alpha = 10: {lasso_10.score(X_train, y_train)}')\n",
    "print(f'Logistic Regression Testing Score with LASSO penalty and alpha = 10: {lasso_10.score(X_test, y_test)}')\n",
    "\n",
    "print(f'Logistic Regression Training Score with Ridge penalty and alpha = 1: {ridge_1.score(X_train, y_train)}')\n",
    "print(f'Logistic Regression Testing Score with Ridge penalty and alpha = 1: {ridge_1.score(X_test, y_test)}')\n",
    "\n",
    "print(f'Logistic Regression Training Score with Ridge penalty and alpha = 10: {ridge_10.score(X_train, y_train)}')\n",
    "print(f'Logistic Regression Testing Score with Ridge penalty and alpha = 10: {ridge_10.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that are models are overfit for $k = 3$ and $k = 5$ because the training scores are higher than the testing scores. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we also see is that: \n",
    "- As $k$ increases, our bias increases and our variance decreases.\n",
    "- As $k$ decreases, our bias decreases and our variance increases.\n",
    "\n",
    "> There's a helpful visualization that demonstrates that [here](http://scott.fortmann-roe.com/docs/BiasVariance.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What can we do for a KNN model if it's overfit?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Increase K to decrease variance.\n",
    "\n",
    "2. Simplifying our model by using less questions.\n",
    "\n",
    "3. Use a different model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In regards to our logistical regression models..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there's no overfitting because our training scores are less than our test scores.\n",
    "\n",
    "If they were overfit though we could:\n",
    "1. Reduce the number of variables.\n",
    "2. Increase regularization strength.\n",
    "3. Get better data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As for the bias-variance tradeoff with log reg:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $C$ is the the inverse of regularization strength in Logistic Regression.\n",
    "\n",
    "- As $C$  increases, there's less regularization, meaning variance increases while bias decreases and vice versa.\n",
    "\n",
    "With respect to our models, either regularization has no effect or it has such a big effect that it cancels out the variables. This likely means that the variables we're using are essentially useless."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: Answer the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to understand which psychological features are most important in determining left-handedness, of the two types of models we used, we'd probably lean towards logistic regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A logistic regression model would allow us to understand the effect of psychological features on left-handedness. \n",
    "\n",
    "Specifically, we can estimate coefficients that tell us the impact of a one-unit change in psychological factors on whether or not people are left-handed. \n",
    "\n",
    "$K$-nearest neighbors does not provide us with a similar way to understaand how individual features affect left-handedness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case in point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.06421386, -0.02060742,  0.00444628, -0.05209546,  0.0627217 ,\n",
       "        -0.00692477,  0.01826786, -0.15702048, -0.04669025,  0.04181673,\n",
       "         0.01843316,  0.0043401 , -0.06040932, -0.00401882, -0.03606262,\n",
       "         0.05252417,  0.04481475, -0.01822204, -0.05226153, -0.04108601,\n",
       "        -0.04004829, -0.08371763, -0.05702724,  0.00673017,  0.04419278,\n",
       "         0.07871443,  0.07578701,  0.019684  ,  0.04566003,  0.03049151,\n",
       "         0.05846479, -0.01687587, -0.01412198, -0.01361486,  0.02832475,\n",
       "        -0.02973448, -0.02165608,  0.08120201, -0.05444295, -0.07177668,\n",
       "        -0.05012084, -0.03360265, -0.14776591, -0.04054722]])"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_10.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see:\n",
    "- as Q1 increases by 1, a person is 6.4% less likely to be left-handed.\n",
    "- as Q2 increases by 1, a person is 2.1% less likely to be left-handed.\n",
    "- as Q3 increases by 1, a person is 0.0% less likely to be left-handed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Circling back to the the questions we came up with at the beginning...\n",
    "\n",
    "1. As one's response to `Q1` increases, are people more or less likely to be left-handed?\n",
    "2. Does the distribution of handedness vary based one's response to Q1?\n",
    "3. Which question (`Q1`-`Q44`) has the highest association with left-handedness?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. As one's response to `Q1` increases, are people more or less likely to be left-handed?\n",
    "\n",
    "According to my lasso_10, no. As Q1 increases by 1, a person is 6.4% less likely to be left-handed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Does the distribution of handedness vary based one's response to Q1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">religion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     religion                                    \n",
       "Q1          0       1      2      3      4      5\n",
       "hand                                             \n",
       "1         2.0  2159.0  332.0  408.0  377.0  264.0\n",
       "2         2.0   277.0   37.0   50.0   58.0   28.0\n",
       "3         NaN    79.0   14.0   35.0   26.0   25.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(lefty[['Q1', 'hand', 'religion']], index = 'hand', columns = 'Q1', aggfunc = 'count').drop(labels=0, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that `hand = 1` is the most common category across all values of `Q1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Which question (`Q1`-`Q44`) has the highest association with left-handedness?\n",
    "\n",
    "Since our $Y$ is binary (left-handed vs. not left-handed), it makes more sense for us to use [Spearman correlation](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) instead of the standard [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's iterate through columns `Q1` through `Q44`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = list(range(1,45))\n",
    "\n",
    "for i in range(len(lst)):\n",
    "    lst[i] = \"Q\" + str(lst[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q1',\n",
       " 'Q2',\n",
       " 'Q3',\n",
       " 'Q4',\n",
       " 'Q5',\n",
       " 'Q6',\n",
       " 'Q7',\n",
       " 'Q8',\n",
       " 'Q9',\n",
       " 'Q10',\n",
       " 'Q11',\n",
       " 'Q12',\n",
       " 'Q13',\n",
       " 'Q14',\n",
       " 'Q15',\n",
       " 'Q16',\n",
       " 'Q17',\n",
       " 'Q18',\n",
       " 'Q19',\n",
       " 'Q20',\n",
       " 'Q21',\n",
       " 'Q22',\n",
       " 'Q23',\n",
       " 'Q24',\n",
       " 'Q25',\n",
       " 'Q26',\n",
       " 'Q27',\n",
       " 'Q28',\n",
       " 'Q29',\n",
       " 'Q30',\n",
       " 'Q31',\n",
       " 'Q32',\n",
       " 'Q33',\n",
       " 'Q34',\n",
       " 'Q35',\n",
       " 'Q36',\n",
       " 'Q37',\n",
       " 'Q38',\n",
       " 'Q39',\n",
       " 'Q40',\n",
       " 'Q41',\n",
       " 'Q42',\n",
       " 'Q43',\n",
       " 'Q44']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We'll instantiate correlation at 0, p_value at 1, and question at Q0.\n",
    "- We'll iterate through each question column and calculate the Spearman correlation between `hand` and that question column.\n",
    "- If the p-value is smaller than our current smallest p-value, we edit our correlation, p-value, and question number to refer to the one with the smaller p-value.\n",
    "    - This allows us to track, over time, which question has the most significant relationship with `hand`.\n",
    "- By the end of the list, we'll have iterated through each question column and can then identify the question column that was most strongly associated with `hand`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 SpearmanrResult(correlation=0.038818825729667314, pvalue=0.01203395243626251)\n",
      "Q2 SpearmanrResult(correlation=-0.019377941726195537, pvalue=0.21013913637830656)\n",
      "Q3 SpearmanrResult(correlation=0.04098019682176355, pvalue=0.008023523162547742)\n",
      "Q4 SpearmanrResult(correlation=0.011445449496240264, pvalue=0.45921582138201034)\n",
      "Q5 SpearmanrResult(correlation=0.03469254930878191, pvalue=0.024829024407076284)\n",
      "Q6 SpearmanrResult(correlation=-0.014194808288387744, pvalue=0.3586467734540134)\n",
      "Q7 SpearmanrResult(correlation=0.03095553249738592, pvalue=0.04526275088840356)\n",
      "Q8 SpearmanrResult(correlation=-0.03030929087216318, pvalue=0.049950681987500745)\n",
      "Q9 SpearmanrResult(correlation=0.011306728388149976, pvalue=0.4646768343685942)\n",
      "Q10 SpearmanrResult(correlation=-0.01815649593909648, pvalue=0.24032379255061073)\n",
      "Q11 SpearmanrResult(correlation=0.012486669610290177, pvalue=0.41939231798270693)\n",
      "Q12 SpearmanrResult(correlation=-0.008557336953483931, pvalue=0.5800126720396054)\n",
      "Q13 SpearmanrResult(correlation=0.013305911537371826, pvalue=0.38953637306085587)\n",
      "Q14 SpearmanrResult(correlation=-0.023100456736389944, pvalue=0.13518103781652677)\n",
      "Q15 SpearmanrResult(correlation=0.002207118932590285, pvalue=0.8865093792303868)\n",
      "Q16 SpearmanrResult(correlation=-0.0012066329144484003, pvalue=0.9378070802549321)\n",
      "Q17 SpearmanrResult(correlation=0.04088889589333849, pvalue=0.00816506171116326)\n",
      "Q18 SpearmanrResult(correlation=-0.022297431066543097, pvalue=0.1492933683635129)\n",
      "Q19 SpearmanrResult(correlation=0.00444474659335631, pvalue=0.7737916521602752)\n",
      "Q20 SpearmanrResult(correlation=-0.02402215539122291, pvalue=0.12027859416833062)\n",
      "Q21 SpearmanrResult(correlation=0.0027287654387508397, pvalue=0.859936786761291)\n",
      "Q22 SpearmanrResult(correlation=-0.02868804826218895, pvalue=0.06352761120483745)\n",
      "Q23 SpearmanrResult(correlation=-0.03221288251800338, pvalue=0.037199184791266535)\n",
      "Q24 SpearmanrResult(correlation=-0.02208771174529751, pvalue=0.15315791461936692)\n",
      "Q25 SpearmanrResult(correlation=0.04006503517347029, pvalue=0.009546780871994773)\n",
      "Q26 SpearmanrResult(correlation=0.02723069128769288, pvalue=0.07820690678156375)\n",
      "Q27 SpearmanrResult(correlation=0.025521746063153945, pvalue=0.0988161354227481)\n",
      "Q28 SpearmanrResult(correlation=0.0029995382321272466, pvalue=0.8462039657754934)\n",
      "Q29 SpearmanrResult(correlation=0.0318179912535997, pvalue=0.03958858377921857)\n",
      "Q30 SpearmanrResult(correlation=-0.016582306597952647, pvalue=0.2835580945751978)\n",
      "Q31 SpearmanrResult(correlation=0.018701007502726942, pvalue=0.22651020336490607)\n",
      "Q32 SpearmanrResult(correlation=-0.01309219148235458, pvalue=0.397197735869662)\n",
      "Q33 SpearmanrResult(correlation=0.022394554302195235, pvalue=0.14752905757828405)\n",
      "Q34 SpearmanrResult(correlation=0.0017622572276510193, pvalue=0.90927298648967)\n",
      "Q35 SpearmanrResult(correlation=0.0729262042820922, pvalue=2.3350679066244866e-06)\n",
      "Q36 SpearmanrResult(correlation=-0.008183400148854008, pvalue=0.5966774713487297)\n",
      "Q37 SpearmanrResult(correlation=0.017141312488082115, pvalue=0.2676389619880042)\n",
      "Q38 SpearmanrResult(correlation=0.019970326669802446, pvalue=0.19653023211571796)\n",
      "Q39 SpearmanrResult(correlation=0.006148631679370548, pvalue=0.6909235354082377)\n",
      "Q40 SpearmanrResult(correlation=-0.02031640333986025, pvalue=0.18888455406785326)\n",
      "Q41 SpearmanrResult(correlation=-0.007129264809156809, pvalue=0.6447868830543678)\n",
      "Q42 SpearmanrResult(correlation=-0.00581000890740478, pvalue=0.7071365157715896)\n",
      "Q43 SpearmanrResult(correlation=0.012685384238812478, pvalue=0.4120295615955678)\n",
      "Q44 SpearmanrResult(correlation=0.010204477121652768, pvalue=0.5093277686498263)\n",
      "The question most correlated with left-handedness is Q35 with correlation of 0.0729262042820922 and p_value of 2.3350679066244866e-06.\n"
     ]
    }
   ],
   "source": [
    "corr = 0\n",
    "p_val = 1\n",
    "question = 'Q0'\n",
    "\n",
    "for i in lst:\n",
    "    spearman = spearmanr(lefty[i], lefty['hand'])\n",
    "    print(i, spearman)\n",
    "    if spearman[1] < p_val:\n",
    "        corr = spearman[0]\n",
    "        p_val = spearman[1]\n",
    "        question = i\n",
    "\n",
    "print(\"The question most correlated with left-handedness is \" + question + \" with correlation of \" + str(corr) + \" and p_value of \" + str(p_val) + \".\")"
   ]
  }
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