Chapter_4/Chapter_4_LAB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.6 Lab: Logistic Regression, LDA, QDA, and KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.1 The Stock Market Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1250, 9)\n"
     ]
    },
    {
     "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>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>1</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>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>Up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year   Lag1   Lag2   Lag3   Lag4   Lag5  Volume  Today Direction\n",
       "1  2001  0.381 -0.192 -2.624 -1.055  5.010  1.1913  0.959        Up\n",
       "2  2001  0.959  0.381 -0.192 -2.624 -1.055  1.2965  1.032        Up\n",
       "3  2001  1.032  0.959  0.381 -0.192 -2.624  1.4112 -0.623      Down\n",
       "4  2001 -0.623  1.032  0.959  0.381 -0.192  1.2760  0.614        Up\n",
       "5  2001  0.614 -0.623  1.032  0.959  0.381  1.2057  0.213        Up"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('E:\\programming\\dataset\\Into_to_statstical_learning\\Smarket.csv',index_col=0)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Lag1', 'Lag2', 'Lag3', 'Lag4', 'Lag5', 'Volume', 'Today',\n",
       "       'Direction'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "count  1250.00000  1250.000000  1250.000000  \n",
       "mean      0.00561     1.478305     0.003138  \n",
       "std       1.14755     0.360357     1.136334  \n",
       "min      -4.92200     0.356070    -4.922000  \n",
       "25%      -0.64000     1.257400    -0.639500  \n",
       "50%       0.03850     1.422950     0.038500  \n",
       "75%       0.59700     1.641675     0.596750  \n",
       "max       5.73300     3.152470     5.733000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029700</td>\n",
       "      <td>0.030596</td>\n",
       "      <td>0.033195</td>\n",
       "      <td>0.035689</td>\n",
       "      <td>0.029788</td>\n",
       "      <td>0.539006</td>\n",
       "      <td>0.030095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag1</th>\n",
       "      <td>0.029700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026294</td>\n",
       "      <td>-0.010803</td>\n",
       "      <td>-0.002986</td>\n",
       "      <td>-0.005675</td>\n",
       "      <td>0.040910</td>\n",
       "      <td>-0.026155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag2</th>\n",
       "      <td>0.030596</td>\n",
       "      <td>-0.026294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025897</td>\n",
       "      <td>-0.010854</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>-0.043383</td>\n",
       "      <td>-0.010250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag3</th>\n",
       "      <td>0.033195</td>\n",
       "      <td>-0.010803</td>\n",
       "      <td>-0.025897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.024051</td>\n",
       "      <td>-0.018808</td>\n",
       "      <td>-0.041824</td>\n",
       "      <td>-0.002448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag4</th>\n",
       "      <td>0.035689</td>\n",
       "      <td>-0.002986</td>\n",
       "      <td>-0.010854</td>\n",
       "      <td>-0.024051</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027084</td>\n",
       "      <td>-0.048414</td>\n",
       "      <td>-0.006900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lag5</th>\n",
       "      <td>0.029788</td>\n",
       "      <td>-0.005675</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>-0.018808</td>\n",
       "      <td>-0.027084</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022002</td>\n",
       "      <td>-0.034860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volume</th>\n",
       "      <td>0.539006</td>\n",
       "      <td>0.040910</td>\n",
       "      <td>-0.043383</td>\n",
       "      <td>-0.041824</td>\n",
       "      <td>-0.048414</td>\n",
       "      <td>-0.022002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Today</th>\n",
       "      <td>0.030095</td>\n",
       "      <td>-0.026155</td>\n",
       "      <td>-0.010250</td>\n",
       "      <td>-0.002448</td>\n",
       "      <td>-0.006900</td>\n",
       "      <td>-0.034860</td>\n",
       "      <td>0.014592</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year      Lag1      Lag2      Lag3      Lag4      Lag5    Volume  \\\n",
       "Year    1.000000  0.029700  0.030596  0.033195  0.035689  0.029788  0.539006   \n",
       "Lag1    0.029700  1.000000 -0.026294 -0.010803 -0.002986 -0.005675  0.040910   \n",
       "Lag2    0.030596 -0.026294  1.000000 -0.025897 -0.010854 -0.003558 -0.043383   \n",
       "Lag3    0.033195 -0.010803 -0.025897  1.000000 -0.024051 -0.018808 -0.041824   \n",
       "Lag4    0.035689 -0.002986 -0.010854 -0.024051  1.000000 -0.027084 -0.048414   \n",
       "Lag5    0.029788 -0.005675 -0.003558 -0.018808 -0.027084  1.000000 -0.022002   \n",
       "Volume  0.539006  0.040910 -0.043383 -0.041824 -0.048414 -0.022002  1.000000   \n",
       "Today   0.030095 -0.026155 -0.010250 -0.002448 -0.006900 -0.034860  0.014592   \n",
       "\n",
       "           Today  \n",
       "Year    0.030095  \n",
       "Lag1   -0.026155  \n",
       "Lag2   -0.010250  \n",
       "Lag3   -0.002448  \n",
       "Lag4   -0.006900  \n",
       "Lag5   -0.034860  \n",
       "Volume  0.014592  \n",
       "Today   1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#produces a correlation matrix for all the numrice columns \n",
    "corr = data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb27a709b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the best way to visualize corerelations matrices is heatmap\n",
    "plt.figure(figsize = (8,8))\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see from the above figure that correlations between veriablesa excpet Volume and Year are close to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Volume     Today\n",
      "Year    0.539006  0.030095\n",
      "Lag1    0.040910 -0.026155\n",
      "Lag2   -0.043383 -0.010250\n",
      "Lag3   -0.041824 -0.002448\n",
      "Lag4   -0.048414 -0.006900\n",
      "Lag5   -0.022002 -0.034860\n",
      "Volume  1.000000  0.014592\n",
      "Today   0.014592  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(corr.iloc[:,-2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb27f67668>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we choose the x axis as index, chossing year will give a discrete plot\n",
    "plt.figure(figsize = (12,6))\n",
    "sns.scatterplot(data.index,data['Volume'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.2 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#store a copy of the original data, as we are going to make some changes in the data\n",
    "data_orig = data.copy()\n",
    "data['Direction'] = data['Direction'].map({'Down':0,'Up':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2542: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691034\n",
      "         Iterations 4\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:                Tue, 30 Jun 2020   Pseudo R-squ.:                0.002074\n",
      "Time:                        09:31:47   Log-Likelihood:                -863.79\n",
      "converged:                       True   LL-Null:                       -865.59\n",
      "Covariance Type:            nonrobust   LLR p-value:                    0.7319\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -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"
     ]
    }
   ],
   "source": [
    "#logit = sm.Logit('Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume',data = data)\n",
    "#the above line was giving error\n",
    "\n",
    "X = data.iloc[:,1:-2]\n",
    "X = sm.add_constant(X)\n",
    "y = data['Direction']\n",
    "results = sm.Logit(y,X).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5070841334630003, 0.48146787817516973, 0.4811388348131379, 0.515222355792766, 0.5107811625911516, 0.5069564604552573, 0.49265087386752915, 0.5092291581449722, 0.5176135261687922, 0.4888377794207712]\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>Probabilities</th>\n",
       "      <th>Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.507084</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.481468</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.481139</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.515222</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.510781</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.506956</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.492651</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.509229</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.517614</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.488838</td>\n",
       "      <td>Down</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Probabilities Classes\n",
       "1        0.507084      Up\n",
       "2        0.481468    Down\n",
       "3        0.481139    Down\n",
       "4        0.515222      Up\n",
       "5        0.510781      Up\n",
       "6        0.506956      Up\n",
       "7        0.492651    Down\n",
       "8        0.509229      Up\n",
       "9        0.517614      Up\n",
       "10       0.488838    Down"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probablitie that the given oberstaion will result in 'UP'\n",
    "prob = results.predict(X)[:10]\n",
    "print(list(prob))\n",
    "\n",
    "predicted_classes = np.where(prob <=0.5,'Down','Up')\n",
    "pd.DataFrame({'Probabilities':prob,'Classes':predicted_classes})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.05885099, -0.07281559, -0.0422629 ,  0.01098588,  0.00925089,\n",
       "         0.01024626,  0.12995149]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusion matrix and accuracy\n",
    "# Using sklearn\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X,y)\n",
    "lr.coef_\n",
    "# we can see that sklearn and statsmodel, don't have the exact same coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.5232\n",
      "confusion_matrix - \n",
      "[[143 459]\n",
      " [137 511]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y,lr.predict(X))\n",
    "accuracy = accuracy_score(y,lr.predict(X))\n",
    "\n",
    "print('Accuracy is ',accuracy)\n",
    "print('confusion_matrix - ')\n",
    "print(cm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE - Confusion matrix drawn in the book has actual values as columns, and predicted values as rows.\n",
    "       While in Sklearn, actual values are as rows, and predicted values are as columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[145., 457.],\n",
       "       [141., 507.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using statsmodel\n",
    "table = results.pred_table(threshold=0.5)\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computin train and test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train is (998, 9)\n",
      "Shape of test is  (252, 9)\n"
     ]
    }
   ],
   "source": [
    "train = data[data['Year']<2005]\n",
    "test = data[data['Year'] == 2005]\n",
    "print('Shape of train is',train.shape)\n",
    "print('Shape of test is ',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2542: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X_train = train.iloc[:,1:-2]\n",
    "X_train = sm.add_constant(X_train)\n",
    "y_train = train['Direction']\n",
    "\n",
    "X_test = test.iloc[:,1:-2]\n",
    "X_test = sm.add_constant(X_test)\n",
    "y_test = test['Direction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.691936\n",
      "         Iterations 4\n"
     ]
    }
   ],
   "source": [
    "results = sm.Logit(y_train,X_train).fit()\n",
    "pred_test = results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[77, 34],\n",
       "       [97, 44]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_classes = np.where(pred_test > 0.5,1,0)\n",
    "cm = confusion_matrix(y_test,pred_classes)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.4801587301587302\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test,pred_classes)\n",
    "print('Accuracy is ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The accuracy is just 48%, even random guessing will do a better job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using only Lag1 and lag 2\n",
    "predictors = ['Lag1','Lag2']\n",
    "X_train = train[predictors]\n",
    "X_train = sm.add_constant(X_train)\n",
    "\n",
    "X_test = test[predictors]\n",
    "X_test = sm.add_constant(X_test)\n",
    "# y_train and y_test remains the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.692085\n",
      "         Iterations 3\n"
     ]
    }
   ],
   "source": [
    "results = sm.Logit(y_train,X_train).fit()\n",
    "pred_test = results.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 35,  76],\n",
       "       [ 35, 106]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_classes = np.where(pred_test > 0.5,1,0)\n",
    "cm = confusion_matrix(y_test,pred_classes)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.5595238095238095\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test,pred_classes)\n",
    "print('Accuracy is ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the accuracy is better that before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.47914624, 0.49609387])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction\n",
    "results.predict([[1,1.2,1.1],\n",
    "                 [1,1.5,-0.8]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.3 Linear Discriment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use sklearn for LDA\n",
    "#from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['Lag1','Lag2']\n",
    "\n",
    "X_train = train[predictors]\n",
    "X_test = test[predictors]\n",
    "\n",
    "lda = LDA()\n",
    "# in the parameters we can also provide prior probabilities represented by pie in chapter\n",
    "lda.fit(X_train,y_train)\n",
    "pred = lda.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prior Probs are -  [0.49198397 0.50801603]\n",
      "Class Means are  -  [[ 0.04279022  0.03389409]\n",
      " [-0.03954635 -0.03132544]]\n",
      "Coeff are -  [[-0.05544078 -0.0443452 ]]\n"
     ]
    }
   ],
   "source": [
    "print('Prior Probs are - ',lda.priors_)\n",
    "print('Class Means are  - ',lda.means_)\n",
    "print('Coeff are - ',lda.coef_)\n",
    "\n",
    "# Mean is the average value of observations for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 35  76]\n",
      " [ 35 106]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using LDA is  0.5595238095238095\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy using LDA is ',accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fb281afb38>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "tmp = pd.DataFrame({'Lag1':X_train['Lag1'],'Lag2':X_train['Lag2'],'Direction':y_train})\n",
    "sns.scatterplot(y = 'Lag2',x = 'Lag1',hue = 'Direction',data = tmp)\n",
    "\n",
    "x1 = X_train['Lag1']\n",
    "b, w1, w2 = lda.intercept_[0], lda.coef_[0][0], lda.coef_[0][1]\n",
    "y1 = -(b+x1*w1)/w2    \n",
    "plt.plot(x1,y1)\n",
    "\n",
    "#mean \n",
    "plt.scatter(lda.means_[0][0],lda.means_[0][1],marker = \"+\",color = 'blue',s = 800)\n",
    "plt.scatter(lda.means_[1][0],lda.means_[1][1],marker = \"+\",color = 'red',s = 800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.4 Quadratic Discriminant Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,\n",
       "                              store_covariance=False, tol=0.0001)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qda = QDA()\n",
    "qda.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean for class 0 is -  [0.04279022 0.03389409]\n",
      "Mean for class 1 is -  [-0.03954635 -0.03132544]\n",
      "Prior probalbilities -  [0.49198397 0.50801603]\n"
     ]
    }
   ],
   "source": [
    "print('Mean for class 0 is - ',qda.means_[0])\n",
    "print('Mean for class 1 is - ',qda.means_[1])\n",
    "print('Prior probalbilities - ',qda.priors_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 30  81]\n",
      " [ 20 121]]\n"
     ]
    }
   ],
   "source": [
    "pred = qda.predict(X_test)\n",
    "cm = confusion_matrix(y_test,pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using QDA is  0.5992063492063492\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy using QDA is ',accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QDA has the best performance with almost 60% accuracy\n",
    "# We can see from the graph that there is no possible linear spearation, and non linear models will perform better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.5 K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using KNN for 1 nieghbor is  0.5\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy using KNN for 1 nieghbor is ',accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using KNN for 1 nieghbor is  0.5317460317460317\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "print('Accuracy using KNN for 1 nieghbor is ',accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Even if we increase n to 3, accuracy is 53.17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.6 An Application to Caravan Insurance Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5822, 86)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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>1</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>2</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>3</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>4</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>5</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",
       "1       33         1        3         2         8       0       5       1   \n",
       "2       37         1        2         2         8       1       4       1   \n",
       "3       37         1        2         2         8       0       4       2   \n",
       "4        9         1        3         3         3       2       3       2   \n",
       "5       40         1        4         2        10       1       4       1   \n",
       "\n",
       "   MGODGE  MRELGE  ...  APERSONG  AGEZONG  AWAOREG  ABRAND  AZEILPL  APLEZIER  \\\n",
       "1       3       7  ...         0        0        0       1        0         0   \n",
       "2       4       6  ...         0        0        0       1        0         0   \n",
       "3       4       3  ...         0        0        0       1        0         0   \n",
       "4       4       5  ...         0        0        0       1        0         0   \n",
       "5       4       7  ...         0        0        0       1        0         0   \n",
       "\n",
       "   AFIETS  AINBOED  ABYSTAND  Purchase  \n",
       "1       0        0         0        No  \n",
       "2       0        0         0        No  \n",
       "3       0        0         0        No  \n",
       "4       0        0         0        No  \n",
       "5       0        0         0        No  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('E:\\programming\\dataset\\Into_to_statstical_learning\\Caravan.csv',index_col=0)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     0.940227\n",
       "Yes    0.059773\n",
       "Name: Purchase, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Purchase'].value_counts() / len(data)\n",
    "# Only around 6% of the people purchased the insurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MOSTYPE     24.253349\n",
       "MAANTHUI     1.110615\n",
       "MGEMOMV      2.678805\n",
       "MGEMLEEF     2.991240\n",
       "MOSHOOFD     5.773617\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From a sample of means if the observations it can be seen that the variables falls in different ranges\n",
    "# We want all the variables to have a same range. \n",
    "data.mean()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will applt standard scaler which will transform the data such that it has 0  mean, and std deviation of 1\n",
    "# but before that lets split the data into test and train data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = data.iloc[:1000,:]\n",
    "train = data.iloc[1000:,:]\n",
    "\n",
    "X_train = train.drop('Purchase',axis = 1)\n",
    "y_train = train['Purchase']\n",
    "X_test = test.drop('Purchase',axis = 1)\n",
    "y_test = test['Purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the standard Scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing labels from string to binary\n",
    "y_train = np.where(y_train == 'Yes',1,0)\n",
    "y_test = np.where(y_test == 'Yes',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN with n_neighbors = 1\n",
    "knn_1 = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "knn_1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for k = 1 is  0.883\n",
      "Confusion Matrix\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>874</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   874   67\n",
       "Yes   50    9"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = knn_1.predict(X_test)\n",
    "cm = confusion_matrix(y_test,pred)\n",
    "print('The accuracy for k = 1 is ',accuracy_score(y_test,pred))\n",
    "print('Confusion Matrix')\n",
    "pd.DataFrame(cm,columns = ['No','Yes'],index = ['No','Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Out of 76 predicted values, 9 are in True in real.\n",
    "# accuracy = 11%, which is better than random guesing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for k = 3 is  0.926\n",
      "Confusion Matrix\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>920</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   920   21\n",
       "Yes   53    6"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN with n_neighbors = 3\n",
    "knn_3 = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "knn_3.fit(X_train,y_train)\n",
    "\n",
    "pred = knn_3.predict(X_test)\n",
    "cm = confusion_matrix(y_test,pred)\n",
    "print('The accuracy for k = 3 is ',accuracy_score(y_test,pred))\n",
    "print('Confusion Matrix')\n",
    "pd.DataFrame(cm,columns = ['No','Yes'],index = ['No','Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for 27 totol predicted values, 6 are Yes in real\n",
    "# Accuracy is 22%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy for k = 5 is  0.934\n",
      "Confusion Matrix\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>931</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      No  Yes\n",
       "No   931   10\n",
       "Yes   56    3"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN with n_neighbors = 5\n",
    "knn_5 = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "knn_5.fit(X_train,y_train)\n",
    "\n",
    "pred = knn_5.predict(X_test)\n",
    "cm = confusion_matrix(y_test,pred)\n",
    "print('The accuracy for k = 5 is ',accuracy_score(y_test,pred))\n",
    "print('Confusion Matrix')\n",
    "pd.DataFrame(cm,columns = ['No','Yes'],index = ['No','Yes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for k = 5, accuracy is 33.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional  - Finding the optimal value of k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_knn(n_neighbors):\n",
    "    accuracy_list = []\n",
    "    for n in n_neighbors:\n",
    "        knn = KNeighborsClassifier(n_neighbors=n)\n",
    "\n",
    "        knn.fit(X_train,y_train)\n",
    "\n",
    "        pred = knn.predict(X_test)\n",
    "        accuracy_list.append(accuracy_score(y_test,pred))\n",
    "    return accuracy_list    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fb2cc76588>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbors = [i for i in range(1,10)]\n",
    "accuracy_list = run_knn(neighbors)\n",
    "plt.plot(neighbors,accuracy_list)\n",
    "plt.xlabel('N_neighbors')\n",
    "plt.ylabel('Test Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}