Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Length of a British Monarch's Reign\n",
    "\n",
    "In this workbook I'm gonna do some analysis on British Monarchs, and see if we can identify factors that contribute to a longer or shorter time on the throne. The meat of the analysis is using simple linear regression to identify the contribution of various factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all kinds of stuff\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "import arrow\n",
    "import re\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and cleaning data\n",
    "\n",
    "There's four csvs of data I'm importing, and they all need greater or lesser degrees of cleaning. A lot of the features I'm pulling out of these datasets were actually the result of lots of faffing around trying various things, but I've put them all together here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Birth Dates\" is the birth dates of various monarchs\n",
    "\n",
    "birthdates = pd.read_csv('birth_dates.csv')\n",
    "birthdates['days_parsed'] = birthdates.Days.apply(lambda x: x.replace(',', '')).astype('int')\n",
    "birthdates.index = birthdates.Monarch.apply(lambda x: re.sub('King |Queen ', '', x)).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Wars\" is the dates of wars that ocurred on British soil\n",
    "\n",
    "wars = pd.read_csv('wars.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"Temperatures\" is the average temperature at 50-year intervals, \n",
    "# which I use for some ridiculous spurious correllations later.\n",
    "\n",
    "temperatures = pd.read_csv('warm_period.txt', delimiter=' ')\n",
    "\n",
    "# ugly little helper method\n",
    "def snip_number(x, n):\n",
    "    match = re.findall('(\\d+)', x)\n",
    "    if len(match) > n:\n",
    "        return match[n]\n",
    "\n",
    "temperatures['start'] = temperatures.year.apply(snip_number, args=(0,)).astype('int')\n",
    "temperatures['end'] = temperatures.year.apply(snip_number, args=(1,)).astype('int')\n",
    "temperatures['diff_from_mean'] = temperatures['adjusted_year'] - temperatures['adjusted_year'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the juice: The reigns of each monarch since 1066 or so.\n",
    "\n",
    "df = pd.read_csv('monarchs_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['start_date_parsed'] = df.apply(lambda x: arrow.get(\n",
    "                        x['Cleaned Start dates'] + ' ' + str(x['First regnal year']),\n",
    "                         'D MMMM YYYY')\n",
    "                                     , axis=1)\n",
    "df['Age at ascension'] = (df.Monarch.map(birthdates['days_parsed'].to_dict()) / 365).round()\n",
    "df['Minor'] = (df['Age at ascension'] <= 14)*1\n",
    "df['Woman'] = df.Monarch.apply(lambda x: any(i in x for i in ['Mary', 'Elizabeth', 'Victoria']))*1\n",
    "df['previous reign'] = df['Cleaned Years'].shift(1).fillna(0)\n",
    "df['Years since 1066'] = df['First regnal year'] - 1066\n",
    "\n",
    "# Some of the data was in a weird format, so I just fix some of those by hand\n",
    "df.iloc[0,11] = 38\n",
    "df.iloc[1,11] = 31\n",
    "df.iloc[5,11] = 31\n",
    "df.iloc[8,11] = 19\n",
    "df.iloc[27,11] = 38\n",
    "df.iloc[28,11] = 38"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A bunch of badly-written helpers here for figuring out if there were any wrs around the time the monarch took power. \n",
    "\n",
    "def on_or_between(row, year):\n",
    "    if pd.isnull(row.end):\n",
    "        return year >= int(row.start) and year <= int(row.start)\n",
    "    else:\n",
    "        return year > int(row.start) and year <= int(row.end)\n",
    "    \n",
    "def near(row, year):\n",
    "    if pd.isnull(row.end):\n",
    "        return year >= int(row.start) -3 and year <= int(row.start)\n",
    "    else:\n",
    "        return year > int(row.start) -3 and year <= int(row.end)\n",
    "\n",
    "def any_wars(year, func):\n",
    "    return wars.apply(func, axis=1, args=(year,)).any()\n",
    "\n",
    "def any_civil_wars(year, func):\n",
    "    return wars[wars.Notes == 'Civil war'].apply(func, axis=1, args=(year,)).any()\n",
    "    \n",
    "df['civil war during ascencion'] = df['First regnal year'].apply(any_civil_wars, args=(on_or_between,))*1\n",
    "df['war during ascencion'] = df['First regnal year'].apply(any_wars, args=(on_or_between,))*1\n",
    "df['war near ascencion'] = df['First regnal year'].apply(any_wars, args=(near,))*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the average temperature at the time the monarch took the throne.\n",
    "\n",
    "mask = temperatures.apply(lambda r: df['First regnal year'].between(r.start, r.end), axis=1).T\n",
    "df['temperature'] = mask.dot(mask.columns).apply(lambda x: temperatures.adjusted_year[x] if x > 0 else 9.41)\n",
    "df.temperature = df.temperature-df.temperature.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a bit overcomplicated: I'm pivoting out the \"claim\" column, \n",
    "# which is a text field with, like, \"inherited\", \"usurped\", etc. in it,\n",
    "# and turning it into a bunch of columns, one for each category, and containing ones and zeroes.\n",
    "# I bet there's a less obscure way of doing this.\n",
    "\n",
    "chocula = CountVectorizer()\n",
    "\n",
    "chocula.fit(df.Claim)\n",
    "\n",
    "df = df.join(pd.DataFrame(chocula.transform(df.Claim).toarray(), columns=chocula.get_feature_names()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monarch                                       William I\n",
       "No. of Years                                         21\n",
       "First regnal year                                  1066\n",
       "Regnal year start date                       14 October\n",
       "Regnal year end date                         13 October\n",
       "End of final year                            9 Sep 1087\n",
       "Cleaned Years                                        21\n",
       "Cleaned Start dates                          14 October\n",
       "Cleaned end date                             9 Sep 1087\n",
       "Claim                                          conquest\n",
       "start_date_parsed             1066-10-14T00:00:00+00:00\n",
       "Age at ascension                                     38\n",
       "Minor                                                 0\n",
       "Woman                                                 0\n",
       "previous reign                                        0\n",
       "Years since 1066                                      0\n",
       "civil war during ascencion                            0\n",
       "war during ascencion                                  0\n",
       "war near ascencion                                    1\n",
       "temperature                                   0.0782927\n",
       "conquest                                              1\n",
       "inherited                                             0\n",
       "treaty                                                0\n",
       "usurped                                               0\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at an example of a single monarch from our final data set\n",
    "\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Summary Statistics\n",
    "\n",
    "I'll start out with some basic description of the dataset and look for patterns or basic correllations in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cleaned Years</th>\n",
       "      <th>Age at ascension</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>4.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.560976</td>\n",
       "      <td>30.975610</td>\n",
       "      <td>-5.848980e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.903978</td>\n",
       "      <td>14.547316</td>\n",
       "      <td>4.570771e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.217073e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>-3.217073e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>-8.170732e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.782927e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>8.782927e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cleaned Years  Age at ascension   temperature\n",
       "count      41.000000         41.000000  4.100000e+01\n",
       "mean       23.560976         30.975610 -5.848980e-15\n",
       "std        17.903978         14.547316  4.570771e-01\n",
       "min         1.000000          1.000000 -6.217073e-01\n",
       "25%        10.000000         22.000000 -3.217073e-01\n",
       "50%        20.000000         31.000000 -8.170732e-02\n",
       "75%        35.000000         38.000000  1.782927e-01\n",
       "max        65.000000         65.000000  8.782927e-01"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing some of the numeric columns\n",
    "\n",
    "df[['Cleaned Years', 'Age at ascension', 'temperature']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36\n",
       "1     5\n",
       "Name: Woman, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Woman.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How monarchs took the throne\n",
    "\n",
    "df[['conquest', 'inherited', 'treaty', 'usurped']].sum().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Patterns over time, plus how monarchs took the throne\n",
    "\n",
    "cmap = df['usurped'].shift(1).fillna(0).astype(int).map({0:'green', 1:'red'})\n",
    "\n",
    "plt.bar(df.index, df['Cleaned Years'], color=cmap)\n",
    "plt.title('Years on Throne')\n",
    "plt.ylabel('Years')\n",
    "plt.xlabel('Monarch No. (in date order)')\n",
    "plt.annotate('Red bars indicate a monarch who was usurped', (1, 65))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age at ascension -48\n",
      "Minor 14\n",
      "Woman 27\n",
      "previous reign -8\n",
      "Years since 1066 -3\n",
      "civil war during ascencion 6\n",
      "war during ascencion -8\n",
      "war near ascencion -19\n",
      "temperature 23\n",
      "conquest -2\n",
      "inherited 27\n",
      "treaty -1\n",
      "usurped -29\n"
     ]
    }
   ],
   "source": [
    "# Corellations between various features and time on the throne.\n",
    "\n",
    "cols = df.columns[11:]\n",
    "for col in cols:\n",
    "    corr = float(df[col].corr(df['Cleaned Years']))\n",
    "    print(col, int(round(corr*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the cell above that there are a few features that are strongly correllated with the length of a monarch's reign. \"Age at ascencion\" is an obvious one, as is inheriting the throne, rather than usurping it. But there are other correllations which are a little more surprising. Being a woman improves your odds of a long reign. The most interesting correllation, for me, was the link between averge tempertures and a long, peaceful reign.\n",
    "\n",
    "We'll tease out more details of this in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Analysis\n",
    "\n",
    "Using linear regression to understand the relationships between some features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The biggest single influence on length of reign is age at ascension. Here we plot that relationship.\n",
    "\n",
    "x = df['Age at ascension']\n",
    "y = df['Cleaned Years']\n",
    "\n",
    "plt.scatter(x, y)\n",
    "\n",
    "z = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(x,p(x),\"r--\")\n",
    "\n",
    "for label, x, y in zip(df.Monarch, x, y):\n",
    "    if x < 5 or x > 60 or (y < 3 and x < 15):\n",
    "        plt.annotate(\n",
    "        label,\n",
    "        xy = (x, y+2),\n",
    "        ha = 'center'\n",
    "        )\n",
    "\n",
    "plt.xlabel('Age at ascension')\n",
    "plt.ylabel('Length of Reign (years)')\n",
    "\n",
    "plt.savefig('age.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age at ascension -0.7869079549838937\n",
      "Minor -12.951243707088503\n"
     ]
    }
   ],
   "source": [
    "# There's some outliers though - some young monarchs that ruled for a short time. \n",
    "# Let's see the effect of taking the throne very young/ \n",
    "\n",
    "lino = LinearRegression()\n",
    "\n",
    "cols = ['Age at ascension', 'Minor']\n",
    "\n",
    "lino.fit(df[cols], df['Cleaned Years'].values.reshape(-1,1))\n",
    "\n",
    "for feature, coef in zip(cols, lino.coef_[0]):\n",
    "    print(feature, coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looks like taking the throne under the age of 14 knocks about 13 years off your expected reign.\n",
    "# Let's look at the chart again, adjusting for this effect.\n",
    "\n",
    "x = df['Age at ascension']\n",
    "\n",
    "y = df['Cleaned Years'] - ((df['Age at ascension'] < 14) * lino.coef_[0][1])\n",
    "\n",
    "plt.scatter(x, \n",
    "            y,\n",
    "            c = (df['Age at ascension'] < 14).map({False:'blue', True:'red'})\n",
    "           )\n",
    "\n",
    "z = np.polyfit(x, y, 1)\n",
    "p = np.poly1d(z)\n",
    "plt.plot(x,p(x),\"r--\")\n",
    "\n",
    "label_names = ['Henry VI', 'William V', 'Richard III', 'Edward V', 'George III', 'Victoria', 'Elizabeth II']\n",
    "\n",
    "for label, x, y, pred_y in zip(df.Monarch, x, y, lino.predict(df[['Age at ascension', 'Minor']])):\n",
    "    if label in label_names:\n",
    "        plt.annotate(\n",
    "        label,\n",
    "        xy = (x, y+2),\n",
    "        ha = 'center'\n",
    "        )\n",
    "plt.title('Adjusted for Monarchs aged < 14yrs.')\n",
    "plt.xlabel('Age at ascension')\n",
    "plt.ylabel('Length of Reign (adjusted)')\n",
    "plt.annotate('(Red dots have adjusted values)', (65, 60), ha='right')\n",
    "plt.savefig('age_adjusted.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature\n",
    "\n",
    "One of the interesting correllations we saw was between average temperatures and length of reign. In this section I'll look at how temperature changes due to the \"Medieval Warm Period\" align with variance from an \"expected length of reign\" generated from the model we created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.Series([i[0] for i in lino.predict(df[['Age at ascension', 'Minor']])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_temp(year):\n",
    "    try:\n",
    "        return temperatures[(temperatures.start <= year) & (temperatures.end > year-1)].diff_from_mean.values[0]\n",
    "    except:\n",
    "        return 0.06\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['First regnal year'] + (df['First regnal year'].rank(method='first')/1000)\n",
    "y = df['Cleaned Years'] - predictions\n",
    "\n",
    "y_mean = y.rolling(6).mean()\n",
    "y_low = y_mean - y.rolling(6).std()\n",
    "\n",
    "y_low_smooth = UnivariateSpline(x, y_low.fillna(y_low[5]), k=4, s=200)(range(1066,1952))\n",
    "y_high = y_mean + y.rolling(6).std()\n",
    "y_high_smooth = UnivariateSpline(x, y_high.fillna(y_high[5]), k=4, s=200)(range(1066,1952))\n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax1.fill_between(range(1066,1952), y_high_smooth, y_low_smooth, color='red', alpha=0.25)\n",
    "ax1.scatter(\n",
    "        x=x,\n",
    "        y=y,\n",
    "        c='red')\n",
    "\n",
    "ax2.plot(range(1066,1952), \n",
    "            pd.Series(range(1066,1952)).apply(year_temp),\n",
    "         c='blue'\n",
    "           )\n",
    "ax2.set_ylim([-1,1])\n",
    "\n",
    "for label, x, y in zip(df.Monarch, x, y):\n",
    "    if label in ['Richard III', 'Edward V', 'George III', 'Victoria']:\n",
    "        ax1.annotate(\n",
    "        label,\n",
    "        xy = (x, y+(x/900)),\n",
    "        ha = 'center'\n",
    "        )\n",
    "\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Difference from expected reign length (years)')\n",
    "ax2.set_ylabel('Temperature difference from mean (degrees)')\n",
    "plt.title('Effect of Medieval Climatic Anomaly on Length of Regnancy')\n",
    "\n",
    "plt.savefig('temp_error_correllation.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Final Model\n",
    "\n",
    "Let's make a model that tries to predict how long a monarch will reign. We'll use some of the features we explored previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(df, test_size = 0.2)\n",
    "linus = LinearRegression()\n",
    "features = ['Minor', 'Age at ascension', 'Years since 1066', 'temperature', 'usurped']\n",
    "linus.fit(train[features], train['Cleaned Years'])\n",
    "predictions = linus.predict(test[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    test['Cleaned Years'],\n",
    "    predictions  \n",
    "    )\n",
    "\n",
    "for label, x, y in zip(test.Monarch, test['Cleaned Years'], predictions):\n",
    "        plt.annotate(\n",
    "        label,\n",
    "        xy = (x, y),\n",
    "        ha = 'center'\n",
    "        )\n",
    "\n",
    "plt.ylabel('Predicted Length of Reign')\n",
    "plt.xlabel('Actual Length of Reign')\n",
    "plt.savefig('predicted_vs_actual_test.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So our model is not exactly _super_ accurate, but it does ok?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline:  48.950258803532996\n",
      "Minor -21.67\n",
      "Age at ascension -1.0\n",
      "Years since 1066 0.03\n",
      "temperature 12.86\n",
      "usurped -10.8\n"
     ]
    }
   ],
   "source": [
    "# Here's the coeficients for each of our features. \n",
    "# A neat thing about linear regression is that the coeficients are in the same units as the value we're predicting.\n",
    "# That means we can translate directly from the coeficients \n",
    "# to a number of years added or subtracted from a monarch's reign.\n",
    "\n",
    "print(\"Baseline: \", linus.intercept_)\n",
    "for feature, coef_ in zip(features, linus.coef_):\n",
    "    print(feature, round(coef_, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}