Plotting and Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting and Regression\n",
    "\n",
    "Data from [the UCI ML database](https://archive.ics.uci.edu/ml/datasets/Daily+Demand+Forecasting+Orders)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('demand.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Week of the month (first week, second, third, fourth or fifth week</th>\n",
       "      <th>Day of the week (Monday to Friday)</th>\n",
       "      <th>Non-urgent order</th>\n",
       "      <th>Urgent order</th>\n",
       "      <th>Order type A</th>\n",
       "      <th>Order type B</th>\n",
       "      <th>Order type C</th>\n",
       "      <th>Fiscal sector orders</th>\n",
       "      <th>Orders from the traffic controller sector</th>\n",
       "      <th>Banking orders (1)</th>\n",
       "      <th>Banking orders (2)</th>\n",
       "      <th>Banking orders (3)</th>\n",
       "      <th>Target (Total orders)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>316.307</td>\n",
       "      <td>223.270</td>\n",
       "      <td>61.543</td>\n",
       "      <td>175.586</td>\n",
       "      <td>302.448</td>\n",
       "      <td>0.000</td>\n",
       "      <td>65556</td>\n",
       "      <td>44914</td>\n",
       "      <td>188411</td>\n",
       "      <td>14793</td>\n",
       "      <td>539.577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>128.633</td>\n",
       "      <td>96.042</td>\n",
       "      <td>38.058</td>\n",
       "      <td>56.037</td>\n",
       "      <td>130.580</td>\n",
       "      <td>0.000</td>\n",
       "      <td>40419</td>\n",
       "      <td>21399</td>\n",
       "      <td>89461</td>\n",
       "      <td>7679</td>\n",
       "      <td>224.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43.651</td>\n",
       "      <td>84.375</td>\n",
       "      <td>21.826</td>\n",
       "      <td>25.125</td>\n",
       "      <td>82.461</td>\n",
       "      <td>1.386</td>\n",
       "      <td>11992</td>\n",
       "      <td>3452</td>\n",
       "      <td>21305</td>\n",
       "      <td>14947</td>\n",
       "      <td>129.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>171.297</td>\n",
       "      <td>127.667</td>\n",
       "      <td>41.542</td>\n",
       "      <td>113.294</td>\n",
       "      <td>162.284</td>\n",
       "      <td>18.156</td>\n",
       "      <td>49971</td>\n",
       "      <td>33703</td>\n",
       "      <td>69054</td>\n",
       "      <td>18423</td>\n",
       "      <td>317.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.532</td>\n",
       "      <td>113.526</td>\n",
       "      <td>37.679</td>\n",
       "      <td>56.618</td>\n",
       "      <td>116.220</td>\n",
       "      <td>6.459</td>\n",
       "      <td>48534</td>\n",
       "      <td>19646</td>\n",
       "      <td>16411</td>\n",
       "      <td>20257</td>\n",
       "      <td>210.517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week of the month (first week, second, third, fourth or fifth week  \\\n",
       "0                                                  1                    \n",
       "1                                                  1                    \n",
       "2                                                  1                    \n",
       "3                                                  2                    \n",
       "4                                                  2                    \n",
       "\n",
       "   Day of the week (Monday to Friday)  Non-urgent order  Urgent order  \\\n",
       "0                                   4           316.307       223.270   \n",
       "1                                   5           128.633        96.042   \n",
       "2                                   6            43.651        84.375   \n",
       "3                                   2           171.297       127.667   \n",
       "4                                   3            90.532       113.526   \n",
       "\n",
       "   Order type A  Order type B  Order type C  Fiscal sector orders  \\\n",
       "0        61.543       175.586       302.448                 0.000   \n",
       "1        38.058        56.037       130.580                 0.000   \n",
       "2        21.826        25.125        82.461                 1.386   \n",
       "3        41.542       113.294       162.284                18.156   \n",
       "4        37.679        56.618       116.220                 6.459   \n",
       "\n",
       "   Orders from the traffic controller sector  Banking orders (1)  \\\n",
       "0                                      65556               44914   \n",
       "1                                      40419               21399   \n",
       "2                                      11992                3452   \n",
       "3                                      49971               33703   \n",
       "4                                      48534               19646   \n",
       "\n",
       "   Banking orders (2)  Banking orders (3)  Target (Total orders)  \n",
       "0              188411               14793                539.577  \n",
       "1               89461                7679                224.675  \n",
       "2               21305               14947                129.412  \n",
       "3               69054               18423                317.120  \n",
       "4               16411               20257                210.517  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "Let's do some quick visualization of these variables, just to make sure they do correlate with the variable we're targeting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of urgent and non-urgent orders\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True)\n",
    "\n",
    "sns.regplot(data = df, x = 'Urgent order', y = 'Target (Total orders)', ax = ax0)\n",
    "sns.regplot(data = df, x = 'Non-urgent order', y = 'Target (Total orders)', ax = ax1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Banking orders 1, 2, and 3\n",
    "\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharey=True)\n",
    "\n",
    "sns.regplot(data = df, x = 'Banking orders (1)', y = 'Target (Total orders)', ax = ax0)\n",
    "sns.regplot(data = df, x = 'Banking orders (2)', y = 'Target (Total orders)', ax = ax1)\n",
    "sns.regplot(data = df, x = 'Banking orders (3)', y = 'Target (Total orders)', ax = ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Order types A, B, and C\n",
    "\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, sharey=True)\n",
    "\n",
    "sns.regplot(data = df, x = 'Order type A', y = 'Target (Total orders)', ax = ax0)\n",
    "sns.regplot(data = df, x = 'Order type B', y = 'Target (Total orders)', ax = ax1)\n",
    "sns.regplot(data = df, x = 'Order type C', y = 'Target (Total orders)', ax = ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll separate the data into `predictors` and `total_orders` for regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_orders = df['Target (Total orders)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Week of the month (first week, second, third, fourth or fifth week</th>\n",
       "      <th>Day of the week (Monday to Friday)</th>\n",
       "      <th>Non-urgent order</th>\n",
       "      <th>Urgent order</th>\n",
       "      <th>Order type A</th>\n",
       "      <th>Order type B</th>\n",
       "      <th>Order type C</th>\n",
       "      <th>Fiscal sector orders</th>\n",
       "      <th>Orders from the traffic controller sector</th>\n",
       "      <th>Banking orders (1)</th>\n",
       "      <th>Banking orders (2)</th>\n",
       "      <th>Banking orders (3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>316.307</td>\n",
       "      <td>223.270</td>\n",
       "      <td>61.543</td>\n",
       "      <td>175.586</td>\n",
       "      <td>302.448</td>\n",
       "      <td>0.000</td>\n",
       "      <td>65556</td>\n",
       "      <td>44914</td>\n",
       "      <td>188411</td>\n",
       "      <td>14793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>128.633</td>\n",
       "      <td>96.042</td>\n",
       "      <td>38.058</td>\n",
       "      <td>56.037</td>\n",
       "      <td>130.580</td>\n",
       "      <td>0.000</td>\n",
       "      <td>40419</td>\n",
       "      <td>21399</td>\n",
       "      <td>89461</td>\n",
       "      <td>7679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43.651</td>\n",
       "      <td>84.375</td>\n",
       "      <td>21.826</td>\n",
       "      <td>25.125</td>\n",
       "      <td>82.461</td>\n",
       "      <td>1.386</td>\n",
       "      <td>11992</td>\n",
       "      <td>3452</td>\n",
       "      <td>21305</td>\n",
       "      <td>14947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>171.297</td>\n",
       "      <td>127.667</td>\n",
       "      <td>41.542</td>\n",
       "      <td>113.294</td>\n",
       "      <td>162.284</td>\n",
       "      <td>18.156</td>\n",
       "      <td>49971</td>\n",
       "      <td>33703</td>\n",
       "      <td>69054</td>\n",
       "      <td>18423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.532</td>\n",
       "      <td>113.526</td>\n",
       "      <td>37.679</td>\n",
       "      <td>56.618</td>\n",
       "      <td>116.220</td>\n",
       "      <td>6.459</td>\n",
       "      <td>48534</td>\n",
       "      <td>19646</td>\n",
       "      <td>16411</td>\n",
       "      <td>20257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week of the month (first week, second, third, fourth or fifth week  \\\n",
       "0                                                  1                    \n",
       "1                                                  1                    \n",
       "2                                                  1                    \n",
       "3                                                  2                    \n",
       "4                                                  2                    \n",
       "\n",
       "   Day of the week (Monday to Friday)  Non-urgent order  Urgent order  \\\n",
       "0                                   4           316.307       223.270   \n",
       "1                                   5           128.633        96.042   \n",
       "2                                   6            43.651        84.375   \n",
       "3                                   2           171.297       127.667   \n",
       "4                                   3            90.532       113.526   \n",
       "\n",
       "   Order type A  Order type B  Order type C  Fiscal sector orders  \\\n",
       "0        61.543       175.586       302.448                 0.000   \n",
       "1        38.058        56.037       130.580                 0.000   \n",
       "2        21.826        25.125        82.461                 1.386   \n",
       "3        41.542       113.294       162.284                18.156   \n",
       "4        37.679        56.618       116.220                 6.459   \n",
       "\n",
       "   Orders from the traffic controller sector  Banking orders (1)  \\\n",
       "0                                      65556               44914   \n",
       "1                                      40419               21399   \n",
       "2                                      11992                3452   \n",
       "3                                      49971               33703   \n",
       "4                                      48534               19646   \n",
       "\n",
       "   Banking orders (2)  Banking orders (3)  \n",
       "0              188411               14793  \n",
       "1               89461                7679  \n",
       "2               21305               14947  \n",
       "3               69054               18423  \n",
       "4               16411               20257  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = df.drop('Target (Total orders)', axis=1)\n",
    "predictors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "Root Mean Squared Error: 1.9320840315887104e-12\n"
     ]
    }
   ],
   "source": [
    "# Create training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(predictors, total_orders, \n",
    "                                                    test_size = .2, random_state = 6)\n",
    "\n",
    "# Create the regressor\n",
    "reg_all = LinearRegression()\n",
    "\n",
    "# Fit the regressor to the training data\n",
    "reg_all.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test data\n",
    "y_pred = reg_all.predict(X_test)\n",
    "\n",
    "# Compute and print R^2 and RMSE\n",
    "print(\"R^2: {}\".format(reg_all.score(X_test, y_test)))\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "print(\"Root Mean Squared Error: {}\".format(rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this is interesting. I can't say I've ever seen a simple linear regression produce an $R^2$ of 1. Let's do a little investigating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. 1. 1.]\n",
      "Average 5-Fold CV Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Create another LR, this time to do CV \n",
    "reg = LinearRegression()\n",
    "\n",
    "# Compute 5-fold cross-validation scores\n",
    "cv_scores = cross_val_score(reg, predictors, total_orders, cv = 5)\n",
    "\n",
    "# Print the 5-fold cross-validation scores\n",
    "print(cv_scores)\n",
    "\n",
    "print(\"Average 5-Fold CV Score: {}\".format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same result with multiple regressions. Let's take a peek inside reg_all to see what it learned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.45613837e-13 -6.13412346e-14  5.69477777e-15 -1.41673389e-14\n",
      "  1.00000000e+00  1.00000000e+00  1.00000000e+00 -8.39954515e-15\n",
      "  1.23660846e-17  3.73248824e-18 -3.81901337e-17  6.43188035e-17] -6.821210263296962e-13\n"
     ]
    }
   ],
   "source": [
    "# Print the coefficients and intercept from our original reg_all regressor\n",
    "print(reg_all.coef_, reg_all.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, we can see that the regressor seems to have learned that three variables account for -all- the variability in number of orders, and the other coefficients have been shrunk nearly to zero. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    539.577\n",
       "1    224.675\n",
       "2    129.412\n",
       "Name: Target (Total orders), dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_orders.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "539.576\n",
      "224.68\n",
      "129.47\n"
     ]
    }
   ],
   "source": [
    "# Values from the predictors dataframe below: Order Type A, B, and C\n",
    "print(61.54 + 175.586 + 302.45)\n",
    "print(38.06 + 56.04 + 130.58)\n",
    "print(21.84 + 25.13 + 82.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Week of the month (first week, second, third, fourth or fifth week</th>\n",
       "      <th>Day of the week (Monday to Friday)</th>\n",
       "      <th>Non-urgent order</th>\n",
       "      <th>Urgent order</th>\n",
       "      <th>Order type A</th>\n",
       "      <th>Order type B</th>\n",
       "      <th>Order type C</th>\n",
       "      <th>Fiscal sector orders</th>\n",
       "      <th>Orders from the traffic controller sector</th>\n",
       "      <th>Banking orders (1)</th>\n",
       "      <th>Banking orders (2)</th>\n",
       "      <th>Banking orders (3)</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>316.307</td>\n",
       "      <td>223.270</td>\n",
       "      <td>61.543</td>\n",
       "      <td>175.586</td>\n",
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       "      <td>14793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>128.633</td>\n",
       "      <td>96.042</td>\n",
       "      <td>38.058</td>\n",
       "      <td>56.037</td>\n",
       "      <td>130.580</td>\n",
       "      <td>0.000</td>\n",
       "      <td>40419</td>\n",
       "      <td>21399</td>\n",
       "      <td>89461</td>\n",
       "      <td>7679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43.651</td>\n",
       "      <td>84.375</td>\n",
       "      <td>21.826</td>\n",
       "      <td>25.125</td>\n",
       "      <td>82.461</td>\n",
       "      <td>1.386</td>\n",
       "      <td>11992</td>\n",
       "      <td>3452</td>\n",
       "      <td>21305</td>\n",
       "      <td>14947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Week of the month (first week, second, third, fourth or fifth week  \\\n",
       "0                                                  1                    \n",
       "1                                                  1                    \n",
       "2                                                  1                    \n",
       "\n",
       "   Day of the week (Monday to Friday)  Non-urgent order  Urgent order  \\\n",
       "0                                   4           316.307       223.270   \n",
       "1                                   5           128.633        96.042   \n",
       "2                                   6            43.651        84.375   \n",
       "\n",
       "   Order type A  Order type B  Order type C  Fiscal sector orders  \\\n",
       "0        61.543       175.586       302.448                 0.000   \n",
       "1        38.058        56.037       130.580                 0.000   \n",
       "2        21.826        25.125        82.461                 1.386   \n",
       "\n",
       "   Orders from the traffic controller sector  Banking orders (1)  \\\n",
       "0                                      65556               44914   \n",
       "1                                      40419               21399   \n",
       "2                                      11992                3452   \n",
       "\n",
       "   Banking orders (2)  Banking orders (3)  \n",
       "0              188411               14793  \n",
       "1               89461                7679  \n",
       "2               21305               14947  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can, in fact, find the number of orders simply by adding up the totals of the Order Type A, B, and C columns. The regressor is correct that most of the relevant information is provided by those three variables.\n",
    "\n",
    "A bit boring of a result (I didn't know this dataset would essentially have the answer built-in to it!), but at least we've verified here that linear regression does work in the most obvious of cases."
   ]
  }
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