{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.1 The Stock Market Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Year</th>\n",
       "      <th>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "      <th>Lag3</th>\n",
       "      <th>Lag4</th>\n",
       "      <th>Lag5</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Today</th>\n",
       "      <th>Direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>-1.055</td>\n",
       "      <td>5.010</td>\n",
       "      <td>1.1913</td>\n",
       "      <td>0.959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>-1.055</td>\n",
       "      <td>1.2965</td>\n",
       "      <td>1.032</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>1.4112</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>1.2760</td>\n",
       "      <td>0.614</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.614</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1.2057</td>\n",
       "      <td>0.213</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Lag1   Lag2   Lag3   Lag4   Lag5  Volume  Today  Direction\n",
       "0  2001  0.381 -0.192 -2.624 -1.055  5.010  1.1913  0.959          1\n",
       "1  2001  0.959  0.381 -0.192 -2.624 -1.055  1.2965  1.032          1\n",
       "2  2001  1.032  0.959  0.381 -0.192 -2.624  1.4112 -0.623          0\n",
       "3  2001 -0.623  1.032  0.959  0.381 -0.192  1.2760  0.614          1\n",
       "4  2001  0.614 -0.623  1.032  0.959  0.381  1.2057  0.213          1"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('smarket.csv')\n",
    "smarket_dat = df.drop(df.columns[0], axis=1)\n",
    "smarket_dat['Direction'] = smarket_dat['Direction'].map({'Up': 1, 'Down': 0})\n",
    "smarket_dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Year</th>\n",
       "      <th>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "      <th>Lag3</th>\n",
       "      <th>Lag4</th>\n",
       "      <th>Lag5</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Today</th>\n",
       "      <th>Direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.00000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "      <td>1250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2003.016000</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.003919</td>\n",
       "      <td>0.001716</td>\n",
       "      <td>0.001636</td>\n",
       "      <td>0.00561</td>\n",
       "      <td>1.478305</td>\n",
       "      <td>0.003138</td>\n",
       "      <td>0.518400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.409018</td>\n",
       "      <td>1.136299</td>\n",
       "      <td>1.136280</td>\n",
       "      <td>1.138703</td>\n",
       "      <td>1.138774</td>\n",
       "      <td>1.14755</td>\n",
       "      <td>0.360357</td>\n",
       "      <td>1.136334</td>\n",
       "      <td>0.499861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>-4.922000</td>\n",
       "      <td>-4.922000</td>\n",
       "      <td>-4.922000</td>\n",
       "      <td>-4.922000</td>\n",
       "      <td>-4.92200</td>\n",
       "      <td>0.356070</td>\n",
       "      <td>-4.922000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>-0.639500</td>\n",
       "      <td>-0.639500</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-0.640000</td>\n",
       "      <td>-0.64000</td>\n",
       "      <td>1.257400</td>\n",
       "      <td>-0.639500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>0.038500</td>\n",
       "      <td>0.038500</td>\n",
       "      <td>0.03850</td>\n",
       "      <td>1.422950</td>\n",
       "      <td>0.038500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>0.596750</td>\n",
       "      <td>0.596750</td>\n",
       "      <td>0.596750</td>\n",
       "      <td>0.596750</td>\n",
       "      <td>0.59700</td>\n",
       "      <td>1.641675</td>\n",
       "      <td>0.596750</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>5.733000</td>\n",
       "      <td>5.733000</td>\n",
       "      <td>5.733000</td>\n",
       "      <td>5.733000</td>\n",
       "      <td>5.73300</td>\n",
       "      <td>3.152470</td>\n",
       "      <td>5.733000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year         Lag1         Lag2         Lag3         Lag4  \\\n",
       "count  1250.000000  1250.000000  1250.000000  1250.000000  1250.000000   \n",
       "mean   2003.016000     0.003834     0.003919     0.001716     0.001636   \n",
       "std       1.409018     1.136299     1.136280     1.138703     1.138774   \n",
       "min    2001.000000    -4.922000    -4.922000    -4.922000    -4.922000   \n",
       "25%    2002.000000    -0.639500    -0.639500    -0.640000    -0.640000   \n",
       "50%    2003.000000     0.039000     0.039000     0.038500     0.038500   \n",
       "75%    2004.000000     0.596750     0.596750     0.596750     0.596750   \n",
       "max    2005.000000     5.733000     5.733000     5.733000     5.733000   \n",
       "\n",
       "             Lag5       Volume        Today    Direction  \n",
       "count  1250.00000  1250.000000  1250.000000  1250.000000  \n",
       "mean      0.00561     1.478305     0.003138     0.518400  \n",
       "std       1.14755     0.360357     1.136334     0.499861  \n",
       "min      -4.92200     0.356070    -4.922000     0.000000  \n",
       "25%      -0.64000     1.257400    -0.639500     0.000000  \n",
       "50%       0.03850     1.422950     0.038500     1.000000  \n",
       "75%       0.59700     1.641675     0.596750     1.000000  \n",
       "max       5.73300     3.152470     5.733000     1.000000  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smarket_dat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a63f7b8>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(smarket_dat.corr(), annot=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a0c9f98>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(10,10))\n",
    "sns.regplot(ax=ax, x='Year', y='Volume', data=smarket_dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11a8b7ef0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.regplot(ax=ax, x='index', y='Volume', data=smarket_dat.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.2 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691034\n",
      "         Iterations 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Direction</td>    <th>  No. Observations:  </th>  <td>  1250</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>               <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1243</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 19 Sep 2018</td> <th>  Pseudo R-squ.:     </th> <td>0.002074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>17:58:15</td>     <th>  Log-Likelihood:    </th> <td> -863.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -865.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.7319</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   -0.1260</td> <td>    0.241</td> <td>   -0.523</td> <td> 0.601</td> <td>   -0.598</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lag1</th>      <td>   -0.0731</td> <td>    0.050</td> <td>   -1.457</td> <td> 0.145</td> <td>   -0.171</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lag2</th>      <td>   -0.0423</td> <td>    0.050</td> <td>   -0.845</td> <td> 0.398</td> <td>   -0.140</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lag3</th>      <td>    0.0111</td> <td>    0.050</td> <td>    0.222</td> <td> 0.824</td> <td>   -0.087</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lag4</th>      <td>    0.0094</td> <td>    0.050</td> <td>    0.187</td> <td> 0.851</td> <td>   -0.089</td> <td>    0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lag5</th>      <td>    0.0103</td> <td>    0.050</td> <td>    0.208</td> <td> 0.835</td> <td>   -0.087</td> <td>    0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Volume</th>    <td>    0.1354</td> <td>    0.158</td> <td>    0.855</td> <td> 0.392</td> <td>   -0.175</td> <td>    0.446</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:              Direction   No. Observations:                 1250\n",
       "Model:                          Logit   Df Residuals:                     1243\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Wed, 19 Sep 2018   Pseudo R-squ.:                0.002074\n",
       "Time:                        17:58:15   Log-Likelihood:                -863.79\n",
       "converged:                       True   LL-Null:                       -865.59\n",
       "                                        LLR p-value:                    0.7319\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -0.1260      0.241     -0.523      0.601      -0.598       0.346\n",
       "Lag1          -0.0731      0.050     -1.457      0.145      -0.171       0.025\n",
       "Lag2          -0.0423      0.050     -0.845      0.398      -0.140       0.056\n",
       "Lag3           0.0111      0.050      0.222      0.824      -0.087       0.109\n",
       "Lag4           0.0094      0.050      0.187      0.851      -0.089       0.107\n",
       "Lag5           0.0103      0.050      0.208      0.835      -0.087       0.107\n",
       "Volume         0.1354      0.158      0.855      0.392      -0.175       0.446\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = smf.logit(formula='Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume', data=smarket_dat).fit()\n",
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>145</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>457</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down   Up\n",
       "Down   145  141\n",
       "Up     457  507"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = logit_model.predict(smarket_dat).map(lambda x: 1 if x > 0.5 else 0)\n",
    "conf_mtrx = metrics.confusion_matrix(smarket_dat['Direction'], pred)\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index= ['Down', 'Up']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5216"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(145 + 507) / (145 + 141 + 457 + 507)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5216"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pred == smarket_dat['Direction']).sum() / smarket_dat.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5216"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pred == smarket_dat['Direction']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691936\n",
      "         Iterations 4\n"
     ]
    },
    {
     "data": {
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       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
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       "      <td>34</td>\n",
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       "</div>"
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      "text/plain": [
       "      Down  Up\n",
       "Down    77  97\n",
       "Up      34  44"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_mask = smarket_dat['Year'] < 2005\n",
    "pre = smarket_dat[year_mask]\n",
    "post = smarket_dat[year_mask == False]\n",
    "\n",
    "logit_model = smf.logit(formula='Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume', data=pre).fit()\n",
    "pred = logit_model.predict(post).map(lambda x: 1 if x > 0.5 else 0)\n",
    "conf_mtrx = metrics.confusion_matrix(post['Direction'], pred)\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index= ['Down', 'Up']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4801587301587302"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pred == post['Direction']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692085\n",
      "         Iterations 3\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>76</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down   Up\n",
       "Down    35   35\n",
       "Up      76  106"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_mask = smarket_dat['Year'] < 2005\n",
    "pre = smarket_dat[year_mask]\n",
    "post = smarket_dat[year_mask == False]\n",
    "\n",
    "logit_model = smf.logit(formula='Direction ~ Lag1 + Lag2', data=pre).fit()\n",
    "pred = logit_model.predict(post).map(lambda x: 1 if x > 0.5 else 0)\n",
    "conf_mtrx = metrics.confusion_matrix(post['Direction'], pred)\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index= ['Down', 'Up']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5595238095238095"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pred == post['Direction']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.479146\n",
       "1    0.496094\n",
       "dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.predict(pd.DataFrame({'Lag1' : [1.2, 1.5], 'Lag2' : [1.1, -0.8]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.3 Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>0.042790</td>\n",
       "      <td>0.033894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>-0.039546</td>\n",
       "      <td>-0.031325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Lag1      Lag2\n",
       "Down  0.042790  0.033894\n",
       "Up   -0.039546 -0.031325"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(pre[['Lag1', 'Lag2']], pre['Direction'])\n",
    "\n",
    "# Group means\n",
    "pd.DataFrame(lda.means_, columns=['Lag1', 'Lag2'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Prior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>0.491984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>0.508016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Prior\n",
       "Down  0.491984\n",
       "Up    0.508016"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Priors\n",
    "pd.DataFrame(lda.priors_, index= ['Down', 'Up'], columns=['Prior'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>LD1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lag1</th>\n",
       "      <td>-0.642019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag2</th>\n",
       "      <td>-0.513529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LD1\n",
       "Lag1 -0.642019\n",
       "Lag2 -0.513529"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coefficients - NB: sklearn calls these `scalings`\n",
    "pd.DataFrame(lda.scalings_, columns=['LD1'], index=['Lag1', 'Lag2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120d86a58>"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LD1 = pre[['Lag1', 'Lag2']] @ lda.scalings_\n",
    "LD1.columns = ['LD1']\n",
    "LD1['Direction'] = pre['Direction']\n",
    "LD1['c'] = 0\n",
    "_, ax = plt.subplots(figsize=(10,5))\n",
    "sns.scatterplot('LD1', y='c', data=LD1, hue='Direction', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5595238095238095\n"
     ]
    },
    {
     "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>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>76</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down   Up\n",
       "Down    35   35\n",
       "Up      76  106"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction accuracy and confusion matrix\n",
    "preds = lda.predict(post[['Lag1', 'Lag2']])\n",
    "conf_mtrx = metrics.confusion_matrix(preds, post['Direction'])\n",
    "print((preds == post['Direction']).mean())\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49017925, 0.50982075],\n",
       "       [0.4792185 , 0.5207815 ],\n",
       "       [0.46681848, 0.53318152],\n",
       "       [0.47400107, 0.52599893],\n",
       "       [0.49278766, 0.50721234],\n",
       "       [0.49385615, 0.50614385],\n",
       "       [0.49510156, 0.50489844],\n",
       "       [0.4872861 , 0.5127139 ],\n",
       "       [0.49070135, 0.50929865],\n",
       "       [0.48440262, 0.51559738],\n",
       "       [0.49069628, 0.50930372],\n",
       "       [0.51199885, 0.48800115],\n",
       "       [0.48951523, 0.51048477],\n",
       "       [0.47067612, 0.52932388],\n",
       "       [0.47445929, 0.52554071],\n",
       "       [0.47995834, 0.52004166],\n",
       "       [0.49357753, 0.50642247],\n",
       "       [0.50308938, 0.49691062],\n",
       "       [0.49788061, 0.50211939],\n",
       "       [0.48863309, 0.51136691],\n",
       "       [0.50065681, 0.49934319],\n",
       "       [0.51087353, 0.48912647],\n",
       "       [0.50399248, 0.49600752],\n",
       "       [0.49163351, 0.50836649],\n",
       "       [0.50417721, 0.49582279],\n",
       "       [0.50267505, 0.49732495],\n",
       "       [0.49140429, 0.50859571],\n",
       "       [0.48059641, 0.51940359],\n",
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       "       [0.50059958, 0.49940042],\n",
       "       [0.49729649, 0.50270351],\n",
       "       [0.49585462, 0.50414538],\n",
       "       [0.48117774, 0.51882226],\n",
       "       [0.48414175, 0.51585825],\n",
       "       [0.47263882, 0.52736118],\n",
       "       [0.48364175, 0.51635825],\n",
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       "       [0.51359414, 0.48640586],\n",
       "       [0.49338391, 0.50661609],\n",
       "       [0.49268564, 0.50731436],\n",
       "       [0.4978472 , 0.5021528 ],\n",
       "       [0.49209142, 0.50790858],\n",
       "       [0.50563459, 0.49436541],\n",
       "       [0.50622877, 0.49377123],\n",
       "       [0.48818939, 0.51181061],\n",
       "       [0.47252929, 0.52747071],\n",
       "       [0.48323391, 0.51676609],\n",
       "       [0.48350857, 0.51649143],\n",
       "       [0.49133344, 0.50866656],\n",
       "       [0.48775664, 0.51224336],\n",
       "       [0.47243859, 0.52756141],\n",
       "       [0.48548774, 0.51451226],\n",
       "       [0.49329107, 0.50670893],\n",
       "       [0.48459731, 0.51540269],\n",
       "       [0.47237179, 0.52762821],\n",
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       "       [0.49140673, 0.50859327],\n",
       "       [0.4942755 , 0.5057245 ],\n",
       "       [0.48412321, 0.51587679],\n",
       "       [0.50260644, 0.49739356],\n",
       "       [0.50625572, 0.49374428],\n",
       "       [0.48218003, 0.51781997],\n",
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       "       [0.50005949, 0.49994051],\n",
       "       [0.50273766, 0.49726234],\n",
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       "       [0.49965006, 0.50034994],\n",
       "       [0.4818079 , 0.5181921 ],\n",
       "       [0.4651057 , 0.5348943 ],\n",
       "       [0.45778674, 0.54221326],\n",
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       "       [0.4964663 , 0.5035337 ],\n",
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       "       [0.4958518 , 0.5041482 ],\n",
       "       [0.51152122, 0.48847878],\n",
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       "       [0.48588431, 0.51411569],\n",
       "       [0.48269686, 0.51730314],\n",
       "       [0.47450117, 0.52549883],\n",
       "       [0.50085397, 0.49914603],\n",
       "       [0.51277655, 0.48722345],\n",
       "       [0.51354723, 0.48645277],\n",
       "       [0.50951274, 0.49048726],\n",
       "       [0.49502005, 0.50497995],\n",
       "       [0.49560882, 0.50439118],\n",
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       "       [0.49703392, 0.50296608],\n",
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       "       [0.49769137, 0.50230863],\n",
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       "       [0.48433658, 0.51566342],\n",
       "       [0.48606641, 0.51393359],\n",
       "       [0.49304173, 0.50695827],\n",
       "       [0.48872186, 0.51127814],\n",
       "       [0.49681471, 0.50318529],\n",
       "       [0.49449886, 0.50550114],\n",
       "       [0.49247425, 0.50752575],\n",
       "       [0.49801415, 0.50198585],\n",
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       "       [0.49943897, 0.50056103],\n",
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       "       [0.48832022, 0.51167978],\n",
       "       [0.48998014, 0.51001986],\n",
       "       [0.4771957 , 0.5228043 ],\n",
       "       [0.46940305, 0.53059695],\n",
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       "       [0.50009743, 0.49990257],\n",
       "       [0.48053033, 0.51946967],\n",
       "       [0.48769528, 0.51230472],\n",
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       "       [0.51355466, 0.48644534],\n",
       "       [0.50202175, 0.49797825],\n",
       "       [0.49568296, 0.50431704],\n",
       "       [0.49655358, 0.50344642],\n",
       "       [0.49645901, 0.50354099],\n",
       "       [0.48557189, 0.51442811],\n",
       "       [0.4951439 , 0.5048561 ],\n",
       "       [0.50600481, 0.49399519],\n",
       "       [0.48806432, 0.51193568],\n",
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       "       [0.5001986 , 0.4998014 ],\n",
       "       [0.50477457, 0.49522543],\n",
       "       [0.48752671, 0.51247329],\n",
       "       [0.48476481, 0.51523519],\n",
       "       [0.50284047, 0.49715953],\n",
       "       [0.50084349, 0.49915651],\n",
       "       [0.48255906, 0.51744094],\n",
       "       [0.47321244, 0.52678756],\n",
       "       [0.47977314, 0.52022686],\n",
       "       [0.49831721, 0.50168279],\n",
       "       [0.49688235, 0.50311765],\n",
       "       [0.49970307, 0.50029693],\n",
       "       [0.49147206, 0.50852794],\n",
       "       [0.48922997, 0.51077003],\n",
       "       [0.47876945, 0.52123055],\n",
       "       [0.47992336, 0.52007664],\n",
       "       [0.49138178, 0.50861822],\n",
       "       [0.49162875, 0.50837125],\n",
       "       [0.49487945, 0.50512055],\n",
       "       [0.48909001, 0.51090999],\n",
       "       [0.47909435, 0.52090565],\n",
       "       [0.4878531 , 0.5121469 ],\n",
       "       [0.48618381, 0.51381619],\n",
       "       [0.49355582, 0.50644418],\n",
       "       [0.49413286, 0.50586714],\n",
       "       [0.50207617, 0.49792383],\n",
       "       [0.50430515, 0.49569485],\n",
       "       [0.48904303, 0.51095697],\n",
       "       [0.50620061, 0.49379939],\n",
       "       [0.50927672, 0.49072328],\n",
       "       [0.48936695, 0.51063305],\n",
       "       [0.49877757, 0.50122243],\n",
       "       [0.4997456 , 0.5002544 ],\n",
       "       [0.48068521, 0.51931479],\n",
       "       [0.47905361, 0.52094639],\n",
       "       [0.48894962, 0.51105038],\n",
       "       [0.50394655, 0.49605345],\n",
       "       [0.49341736, 0.50658264],\n",
       "       [0.4748985 , 0.5251015 ],\n",
       "       [0.4706261 , 0.5293739 ],\n",
       "       [0.48689783, 0.51310217],\n",
       "       [0.49675542, 0.50324458],\n",
       "       [0.49294486, 0.50705514],\n",
       "       [0.49228531, 0.50771469],\n",
       "       [0.493369  , 0.506631  ],\n",
       "       [0.50536007, 0.49463993],\n",
       "       [0.50305521, 0.49694479],\n",
       "       [0.49058366, 0.50941634],\n",
       "       [0.47623902, 0.52376098],\n",
       "       [0.46033919, 0.53966081],\n",
       "       [0.46979321, 0.53020679],\n",
       "       [0.49253001, 0.50746999],\n",
       "       [0.48611431, 0.51388569],\n",
       "       [0.48113758, 0.51886242],\n",
       "       [0.48124736, 0.51875264],\n",
       "       [0.48423833, 0.51576167],\n",
       "       [0.50262179, 0.49737821],\n",
       "       [0.50523122, 0.49476878],\n",
       "       [0.4813184 , 0.5186816 ],\n",
       "       [0.50153968, 0.49846032],\n",
       "       [0.48771613, 0.51228387],\n",
       "       [0.47741706, 0.52258294],\n",
       "       [0.51688267, 0.48311733],\n",
       "       [0.507264  , 0.492736  ],\n",
       "       [0.48335152, 0.51664848],\n",
       "       [0.47267015, 0.52732985],\n",
       "       [0.5032667 , 0.4967333 ],\n",
       "       [0.52023495, 0.47976505],\n",
       "       [0.4950279 , 0.5049721 ],\n",
       "       [0.50187665, 0.49812335],\n",
       "       [0.50891419, 0.49108581],\n",
       "       [0.49689113, 0.50310887],\n",
       "       [0.49515948, 0.50484052],\n",
       "       [0.4895942 , 0.5104058 ],\n",
       "       [0.49046532, 0.50953468],\n",
       "       [0.50553179, 0.49446821],\n",
       "       [0.50554162, 0.49445838],\n",
       "       [0.49424704, 0.50575296],\n",
       "       [0.48574952, 0.51425048],\n",
       "       [0.49016058, 0.50983942],\n",
       "       [0.506973  , 0.493027  ],\n",
       "       [0.50847644, 0.49152356],\n",
       "       [0.50412876, 0.49587124],\n",
       "       [0.50482987, 0.49517013],\n",
       "       [0.50238787, 0.49761213],\n",
       "       [0.49869029, 0.50130971],\n",
       "       [0.48247575, 0.51752425],\n",
       "       [0.48254694, 0.51745306],\n",
       "       [0.48316002, 0.51683998],\n",
       "       [0.50174966, 0.49825034],\n",
       "       [0.50587076, 0.49412924],\n",
       "       [0.48903208, 0.51096792],\n",
       "       [0.49110524, 0.50889476],\n",
       "       [0.48642499, 0.51357501],\n",
       "       [0.48470615, 0.51529385],\n",
       "       [0.49448897, 0.50551103],\n",
       "       [0.49622614, 0.50377386],\n",
       "       [0.50057022, 0.49942978],\n",
       "       [0.5039068 , 0.4960932 ],\n",
       "       [0.49463764, 0.50536236],\n",
       "       [0.48643657, 0.51356343],\n",
       "       [0.4807022 , 0.5192978 ],\n",
       "       [0.48514389, 0.51485611],\n",
       "       [0.49517341, 0.50482659],\n",
       "       [0.50058931, 0.49941069],\n",
       "       [0.497221  , 0.502779  ],\n",
       "       [0.4791988 , 0.5208012 ],\n",
       "       [0.48316733, 0.51683267],\n",
       "       [0.4892591 , 0.5107409 ]])"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get class probabilities\n",
    "lda.predict_proba(post[['Lag1', 'Lag2']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120a98d30>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BONUS: Simulation in 1D with good separation\n",
    "\n",
    "class_1 = pd.DataFrame({'class': 0,\n",
    "                        'x1': np.random.normal(1, 2, 100),\n",
    "                        'x2' : np.random.normal(2, 2, 100),\n",
    "                        'x3' : np.random.normal(3, 2, 100)})\n",
    "class_2 = pd.DataFrame({'class' : 1,\n",
    "                        'x1': np.random.normal(5, 2, 100),\n",
    "                        'x2' : np.random.normal(10, 2, 100),\n",
    "                        'x3' : np.random.normal(15, 2, 100)})\n",
    "\n",
    "df = pd.concat([class_1, class_2]).sample(frac=1)\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(df[['x1', 'x2', 'x3']], df['class'])\n",
    "\n",
    "LDs = df[['x1', 'x2', 'x3']] @ lda.scalings_\n",
    "LDs.columns = ['LD1']\n",
    "LDs['c'] = 0\n",
    "LDs['class'] = df['class']\n",
    "_, ax = plt.subplots(figsize=(10,5))\n",
    "sns.scatterplot(x='LD1', y='c', data=LDs, hue='class', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120c69ba8>"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BONUS: Simulation in 2D with good separation\n",
    "\n",
    "class_1 = pd.DataFrame({'class': 0,\n",
    "                        'x1': np.random.normal(1, 2, 100),\n",
    "                        'x2' : np.random.normal(2, 2, 100),\n",
    "                        'x3' : np.random.normal(3, 2, 100)})\n",
    "class_2 = pd.DataFrame({'class' : 1,\n",
    "                        'x1': np.random.normal(5, 2, 100),\n",
    "                        'x2' : np.random.normal(10, 2, 100),\n",
    "                        'x3' : np.random.normal(15, 2, 100)})\n",
    "class_3 = pd.DataFrame({'class' : 2,\n",
    "                        'x1': np.random.normal(10, 2, 100),\n",
    "                        'x2' : np.random.normal(20, 2, 100),\n",
    "                        'x3' : np.random.normal(30, 2, 100)})\n",
    "df = pd.concat([class_1, class_2, class_3]).sample(frac=1)\n",
    "\n",
    "lda = LinearDiscriminantAnalysis()\n",
    "lda.fit(df[['x1', 'x2', 'x3']], df['class'])\n",
    "\n",
    "LDs = df[['x1', 'x2', 'x3']] @ lda.scalings_\n",
    "LDs.columns = ['LD1', 'LD2']\n",
    "LDs['class'] = df['class']\n",
    "_, ax = plt.subplots(figsize=(10,10))\n",
    "sns.scatterplot(x='LD1', y='LD2', data=LDs, hue='class', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.4 Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "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>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>0.042790</td>\n",
       "      <td>0.033894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>-0.039546</td>\n",
       "      <td>-0.031325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Lag1      Lag2\n",
       "Down  0.042790  0.033894\n",
       "Up   -0.039546 -0.031325"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qda = QuadraticDiscriminantAnalysis()\n",
    "qda.fit(pre[['Lag1', 'Lag2']], pre['Direction'])\n",
    "\n",
    "# Group means\n",
    "pd.DataFrame(lda.means_, columns=['Lag1', 'Lag2'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5992063492063492\n"
     ]
    },
    {
     "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>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>81</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down   Up\n",
       "Down    30   20\n",
       "Up      81  121"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = qda.predict(post[['Lag1', 'Lag2']])\n",
    "conf_mtrx = metrics.confusion_matrix(preds, post['Direction'])\n",
    "print((preds == post['Direction']).mean())\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.5 K-Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    },
    {
     "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>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down  Up\n",
       "Down    43  58\n",
       "Up      68  83"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(pre[['Lag1', 'Lag2']], pre['Direction'])\n",
    "preds = knn.predict(post[['Lag1', 'Lag2']])\n",
    "print((preds == post['Direction']).mean())\n",
    "conf_mtrx = metrics.confusion_matrix(preds, post['Direction'])\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5317460317460317\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Down</th>\n",
       "      <th>Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Down</th>\n",
       "      <td>48</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up</th>\n",
       "      <td>63</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Down  Up\n",
       "Down    48  55\n",
       "Up      63  86"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(pre[['Lag1', 'Lag2']], pre['Direction'])\n",
    "preds = knn.predict(post[['Lag1', 'Lag2']])\n",
    "print((preds == post['Direction']).mean())\n",
    "conf_mtrx = metrics.confusion_matrix(preds, post['Direction'])\n",
    "pd.DataFrame(conf_mtrx, columns=['Down', 'Up'], index=['Down', 'Up'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.6 An Application to Caravan Insurance Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MOSTYPE</th>\n",
       "      <th>MAANTHUI</th>\n",
       "      <th>MGEMOMV</th>\n",
       "      <th>MGEMLEEF</th>\n",
       "      <th>MOSHOOFD</th>\n",
       "      <th>MGODRK</th>\n",
       "      <th>MGODPR</th>\n",
       "      <th>MGODOV</th>\n",
       "      <th>MGODGE</th>\n",
       "      <th>MRELGE</th>\n",
       "      <th>...</th>\n",
       "      <th>APERSONG</th>\n",
       "      <th>AGEZONG</th>\n",
       "      <th>AWAOREG</th>\n",
       "      <th>ABRAND</th>\n",
       "      <th>AZEILPL</th>\n",
       "      <th>APLEZIER</th>\n",
       "      <th>AFIETS</th>\n",
       "      <th>AINBOED</th>\n",
       "      <th>ABYSTAND</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MOSTYPE  MAANTHUI  MGEMOMV  MGEMLEEF  MOSHOOFD  MGODRK  MGODPR  MGODOV  \\\n",
       "0       33         1        3         2         8       0       5       1   \n",
       "1       37         1        2         2         8       1       4       1   \n",
       "2       37         1        2         2         8       0       4       2   \n",
       "3        9         1        3         3         3       2       3       2   \n",
       "4       40         1        4         2        10       1       4       1   \n",
       "\n",
       "   MGODGE  MRELGE    ...     APERSONG  AGEZONG  AWAOREG  ABRAND  AZEILPL  \\\n",
       "0       3       7    ...            0        0        0       1        0   \n",
       "1       4       6    ...            0        0        0       1        0   \n",
       "2       4       3    ...            0        0        0       1        0   \n",
       "3       4       5    ...            0        0        0       1        0   \n",
       "4       4       7    ...            0        0        0       1        0   \n",
       "\n",
       "   APLEZIER  AFIETS  AINBOED  ABYSTAND  Purchase  \n",
       "0         0       0        0         0        No  \n",
       "1         0       0        0         0        No  \n",
       "2         0       0        0         0        No  \n",
       "3         0       0        0         0        No  \n",
       "4         0       0        0         0        No  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caravan_dat = pd.read_csv('caravan.csv')\n",
    "caravan_dat = caravan_dat.drop(caravan_dat.columns[0], axis=1)\n",
    "caravan_dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MOSTYPE</th>\n",
       "      <th>MAANTHUI</th>\n",
       "      <th>MGEMOMV</th>\n",
       "      <th>MGEMLEEF</th>\n",
       "      <th>MOSHOOFD</th>\n",
       "      <th>MGODRK</th>\n",
       "      <th>MGODPR</th>\n",
       "      <th>MGODOV</th>\n",
       "      <th>MGODGE</th>\n",
       "      <th>MRELGE</th>\n",
       "      <th>...</th>\n",
       "      <th>ALEVEN</th>\n",
       "      <th>APERSONG</th>\n",
       "      <th>AGEZONG</th>\n",
       "      <th>AWAOREG</th>\n",
       "      <th>ABRAND</th>\n",
       "      <th>AZEILPL</th>\n",
       "      <th>APLEZIER</th>\n",
       "      <th>AFIETS</th>\n",
       "      <th>AINBOED</th>\n",
       "      <th>ABYSTAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.253349</td>\n",
       "      <td>1.110615</td>\n",
       "      <td>2.678805</td>\n",
       "      <td>2.991240</td>\n",
       "      <td>5.773617</td>\n",
       "      <td>0.696496</td>\n",
       "      <td>4.626932</td>\n",
       "      <td>1.069907</td>\n",
       "      <td>3.258502</td>\n",
       "      <td>6.183442</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076606</td>\n",
       "      <td>0.005325</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>0.004638</td>\n",
       "      <td>0.570079</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>0.006012</td>\n",
       "      <td>0.031776</td>\n",
       "      <td>0.007901</td>\n",
       "      <td>0.014256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.846706</td>\n",
       "      <td>0.405842</td>\n",
       "      <td>0.789835</td>\n",
       "      <td>0.814589</td>\n",
       "      <td>2.856760</td>\n",
       "      <td>1.003234</td>\n",
       "      <td>1.715843</td>\n",
       "      <td>1.017503</td>\n",
       "      <td>1.597647</td>\n",
       "      <td>1.909482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377569</td>\n",
       "      <td>0.072782</td>\n",
       "      <td>0.080532</td>\n",
       "      <td>0.077403</td>\n",
       "      <td>0.562058</td>\n",
       "      <td>0.022696</td>\n",
       "      <td>0.081632</td>\n",
       "      <td>0.210986</td>\n",
       "      <td>0.090463</td>\n",
       "      <td>0.119996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</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>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>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</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>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>max</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           MOSTYPE     MAANTHUI      MGEMOMV     MGEMLEEF     MOSHOOFD  \\\n",
       "count  5822.000000  5822.000000  5822.000000  5822.000000  5822.000000   \n",
       "mean     24.253349     1.110615     2.678805     2.991240     5.773617   \n",
       "std      12.846706     0.405842     0.789835     0.814589     2.856760   \n",
       "min       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "25%      10.000000     1.000000     2.000000     2.000000     3.000000   \n",
       "50%      30.000000     1.000000     3.000000     3.000000     7.000000   \n",
       "75%      35.000000     1.000000     3.000000     3.000000     8.000000   \n",
       "max      41.000000    10.000000     5.000000     6.000000    10.000000   \n",
       "\n",
       "            MGODRK       MGODPR       MGODOV       MGODGE       MRELGE  \\\n",
       "count  5822.000000  5822.000000  5822.000000  5822.000000  5822.000000   \n",
       "mean      0.696496     4.626932     1.069907     3.258502     6.183442   \n",
       "std       1.003234     1.715843     1.017503     1.597647     1.909482   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     4.000000     0.000000     2.000000     5.000000   \n",
       "50%       0.000000     5.000000     1.000000     3.000000     6.000000   \n",
       "75%       1.000000     6.000000     2.000000     4.000000     7.000000   \n",
       "max       9.000000     9.000000     5.000000     9.000000     9.000000   \n",
       "\n",
       "          ...            ALEVEN     APERSONG      AGEZONG      AWAOREG  \\\n",
       "count     ...       5822.000000  5822.000000  5822.000000  5822.000000   \n",
       "mean      ...          0.076606     0.005325     0.006527     0.004638   \n",
       "std       ...          0.377569     0.072782     0.080532     0.077403   \n",
       "min       ...          0.000000     0.000000     0.000000     0.000000   \n",
       "25%       ...          0.000000     0.000000     0.000000     0.000000   \n",
       "50%       ...          0.000000     0.000000     0.000000     0.000000   \n",
       "75%       ...          0.000000     0.000000     0.000000     0.000000   \n",
       "max       ...          8.000000     1.000000     1.000000     2.000000   \n",
       "\n",
       "            ABRAND      AZEILPL     APLEZIER       AFIETS      AINBOED  \\\n",
       "count  5822.000000  5822.000000  5822.000000  5822.000000  5822.000000   \n",
       "mean      0.570079     0.000515     0.006012     0.031776     0.007901   \n",
       "std       0.562058     0.022696     0.081632     0.210986     0.090463   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       7.000000     1.000000     2.000000     3.000000     2.000000   \n",
       "\n",
       "          ABYSTAND  \n",
       "count  5822.000000  \n",
       "mean      0.014256  \n",
       "std       0.119996  \n",
       "min       0.000000  \n",
       "25%       0.000000  \n",
       "50%       0.000000  \n",
       "75%       0.000000  \n",
       "max       2.000000  \n",
       "\n",
       "[8 rows x 85 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caravan_dat.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>MOSTYPE</th>\n",
       "      <th>MAANTHUI</th>\n",
       "      <th>MGEMOMV</th>\n",
       "      <th>MGEMLEEF</th>\n",
       "      <th>MOSHOOFD</th>\n",
       "      <th>MGODRK</th>\n",
       "      <th>MGODPR</th>\n",
       "      <th>MGODOV</th>\n",
       "      <th>MGODGE</th>\n",
       "      <th>MRELGE</th>\n",
       "      <th>...</th>\n",
       "      <th>APERSONG</th>\n",
       "      <th>AGEZONG</th>\n",
       "      <th>AWAOREG</th>\n",
       "      <th>ABRAND</th>\n",
       "      <th>AZEILPL</th>\n",
       "      <th>APLEZIER</th>\n",
       "      <th>AFIETS</th>\n",
       "      <th>AINBOED</th>\n",
       "      <th>ABYSTAND</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.680848</td>\n",
       "      <td>-0.272557</td>\n",
       "      <td>0.406662</td>\n",
       "      <td>-1.216859</td>\n",
       "      <td>0.779338</td>\n",
       "      <td>-0.694251</td>\n",
       "      <td>0.217425</td>\n",
       "      <td>-0.068705</td>\n",
       "      <td>-0.161802</td>\n",
       "      <td>0.427633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073159</td>\n",
       "      <td>-0.081048</td>\n",
       "      <td>-0.059915</td>\n",
       "      <td>0.764905</td>\n",
       "      <td>-0.022704</td>\n",
       "      <td>-0.073644</td>\n",
       "      <td>-0.150608</td>\n",
       "      <td>-0.08734</td>\n",
       "      <td>-0.118806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.992212</td>\n",
       "      <td>-0.272557</td>\n",
       "      <td>-0.859426</td>\n",
       "      <td>-1.216859</td>\n",
       "      <td>0.779338</td>\n",
       "      <td>0.302526</td>\n",
       "      <td>-0.365379</td>\n",
       "      <td>-0.068705</td>\n",
       "      <td>0.464119</td>\n",
       "      <td>-0.096069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073159</td>\n",
       "      <td>-0.081048</td>\n",
       "      <td>-0.059915</td>\n",
       "      <td>0.764905</td>\n",
       "      <td>-0.022704</td>\n",
       "      <td>-0.073644</td>\n",
       "      <td>-0.150608</td>\n",
       "      <td>-0.08734</td>\n",
       "      <td>-0.118806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.992212</td>\n",
       "      <td>-0.272557</td>\n",
       "      <td>-0.859426</td>\n",
       "      <td>-1.216859</td>\n",
       "      <td>0.779338</td>\n",
       "      <td>-0.694251</td>\n",
       "      <td>-0.365379</td>\n",
       "      <td>0.914094</td>\n",
       "      <td>0.464119</td>\n",
       "      <td>-1.667175</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073159</td>\n",
       "      <td>-0.081048</td>\n",
       "      <td>-0.059915</td>\n",
       "      <td>0.764905</td>\n",
       "      <td>-0.022704</td>\n",
       "      <td>-0.073644</td>\n",
       "      <td>-0.150608</td>\n",
       "      <td>-0.08734</td>\n",
       "      <td>-0.118806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.187335</td>\n",
       "      <td>-0.272557</td>\n",
       "      <td>0.406662</td>\n",
       "      <td>0.010754</td>\n",
       "      <td>-0.970896</td>\n",
       "      <td>1.299302</td>\n",
       "      <td>-0.948183</td>\n",
       "      <td>0.914094</td>\n",
       "      <td>0.464119</td>\n",
       "      <td>-0.619771</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073159</td>\n",
       "      <td>-0.081048</td>\n",
       "      <td>-0.059915</td>\n",
       "      <td>0.764905</td>\n",
       "      <td>-0.022704</td>\n",
       "      <td>-0.073644</td>\n",
       "      <td>-0.150608</td>\n",
       "      <td>-0.08734</td>\n",
       "      <td>-0.118806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.225735</td>\n",
       "      <td>-0.272557</td>\n",
       "      <td>1.672750</td>\n",
       "      <td>-1.216859</td>\n",
       "      <td>1.479432</td>\n",
       "      <td>0.302526</td>\n",
       "      <td>-0.365379</td>\n",
       "      <td>-0.068705</td>\n",
       "      <td>0.464119</td>\n",
       "      <td>0.427633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073159</td>\n",
       "      <td>-0.081048</td>\n",
       "      <td>-0.059915</td>\n",
       "      <td>0.764905</td>\n",
       "      <td>-0.022704</td>\n",
       "      <td>-0.073644</td>\n",
       "      <td>-0.150608</td>\n",
       "      <td>-0.08734</td>\n",
       "      <td>-0.118806</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    MOSTYPE  MAANTHUI   MGEMOMV  MGEMLEEF  MOSHOOFD    MGODRK    MGODPR  \\\n",
       "0  0.680848 -0.272557  0.406662 -1.216859  0.779338 -0.694251  0.217425   \n",
       "1  0.992212 -0.272557 -0.859426 -1.216859  0.779338  0.302526 -0.365379   \n",
       "2  0.992212 -0.272557 -0.859426 -1.216859  0.779338 -0.694251 -0.365379   \n",
       "3 -1.187335 -0.272557  0.406662  0.010754 -0.970896  1.299302 -0.948183   \n",
       "4  1.225735 -0.272557  1.672750 -1.216859  1.479432  0.302526 -0.365379   \n",
       "\n",
       "     MGODOV    MGODGE    MRELGE    ...     APERSONG   AGEZONG   AWAOREG  \\\n",
       "0 -0.068705 -0.161802  0.427633    ...    -0.073159 -0.081048 -0.059915   \n",
       "1 -0.068705  0.464119 -0.096069    ...    -0.073159 -0.081048 -0.059915   \n",
       "2  0.914094  0.464119 -1.667175    ...    -0.073159 -0.081048 -0.059915   \n",
       "3  0.914094  0.464119 -0.619771    ...    -0.073159 -0.081048 -0.059915   \n",
       "4 -0.068705  0.464119  0.427633    ...    -0.073159 -0.081048 -0.059915   \n",
       "\n",
       "     ABRAND   AZEILPL  APLEZIER    AFIETS  AINBOED  ABYSTAND  Purchase  \n",
       "0  0.764905 -0.022704 -0.073644 -0.150608 -0.08734 -0.118806         0  \n",
       "1  0.764905 -0.022704 -0.073644 -0.150608 -0.08734 -0.118806         0  \n",
       "2  0.764905 -0.022704 -0.073644 -0.150608 -0.08734 -0.118806         0  \n",
       "3  0.764905 -0.022704 -0.073644 -0.150608 -0.08734 -0.118806         0  \n",
       "4  0.764905 -0.022704 -0.073644 -0.150608 -0.08734 -0.118806         0  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = caravan_dat.drop('Purchase', axis=1)\n",
    "caravan_dat_std = (tmp - tmp.mean()) / tmp.std()\n",
    "caravan_dat_std['Purchase'] = caravan_dat['Purchase'].map({'Yes' : 1, 'No' : 0})\n",
    "caravan_dat_std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.882"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = caravan_dat_std.iloc[0:1000]\n",
    "train = caravan_dat_std.iloc[1000:]\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(train.drop('Purchase', axis=1), train['Purchase'])\n",
    "preds = knn.predict(test.drop('Purchase', axis=1))\n",
    "\n",
    "# knn prediction accuracy with k=1\n",
    "(preds == test['Purchase']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>873</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>68</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   873   50\n",
       "Yes   68    9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_mtrx = metrics.confusion_matrix(preds, test['Purchase'])\n",
    "pd.DataFrame(conf_mtrx, columns=['No', 'Yes'], index=['No', 'Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.941"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# naive prediction accuracy\n",
    "(test['Purchase'] == 0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11688311688311688"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn positive predictive value with k = 1\n",
    "9 / (68 + 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>930</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   930   55\n",
       "Yes   11    4"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(train.drop('Purchase', axis=1), train['Purchase'])\n",
    "preds = knn.predict(test.drop('Purchase', axis=1))\n",
    "conf_mtrx = metrics.confusion_matrix(preds, test['Purchase'])\n",
    "pd.DataFrame(conf_mtrx, columns=['No', 'Yes'], index=['No', 'Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26666666666666666"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn postivie predictive value with k = 5\n",
    "4 / (11 + 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.192013\n",
      "         Iterations: 35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>934</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   934   59\n",
       "Yes    7    0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'Purchase ~ ' + ' + '.join(train.drop('Purchase', axis=1).columns)\n",
    "logit_model = smf.logit(formula= formula, data=train).fit()\n",
    "\n",
    "preds = (logit_model.predict(test) > 0.5)\n",
    "conf_mtrx = metrics.confusion_matrix(preds, test['Purchase'])\n",
    "pd.DataFrame(conf_mtrx, columns=['No', 'Yes'], index=['No', 'Yes'])\n",
    "\n",
    "# positive predictive value is zero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>919</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   919   48\n",
       "Yes   22   11"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = (logit_model.predict(test) > 0.25)\n",
    "conf_mtrx = metrics.confusion_matrix(preds, test['Purchase'])\n",
    "pd.DataFrame(conf_mtrx, columns=['No', 'Yes'], index=['No', 'Yes'])\n",
    "\n",
    "# positive predictive value is (11 / 33) = 1/3"
   ]
  }
 ],
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