magazine_buy_logistic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read.csv('C:/Users/MANI/Documents/R sem 2/logistic_regression/Kid.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem_statement: If the customer will buy the magazine or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Obs.No.</th><th scope=col>Buy</th><th scope=col>Income</th><th scope=col>Is.Female</th><th scope=col>Is.Married</th><th scope=col>Has.College</th><th scope=col>Is.Professional</th><th scope=col>Is.Retired</th><th scope=col>Unemployed</th><th scope=col>Residence.Length</th><th scope=col>Dual.Income</th><th scope=col>Minors</th><th scope=col>Own</th><th scope=col>House</th><th scope=col>White</th><th scope=col>English</th><th scope=col>Prev.Child.Mag</th><th scope=col>Prev.Parent.Mag</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1    </td><td>0    </td><td>24000</td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>0    </td><td>0    </td><td>26   </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td></tr>\n",
       "\t<tr><td>2    </td><td>1    </td><td>75000</td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>0    </td><td>0    </td><td>15   </td><td>1    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>0    </td></tr>\n",
       "\t<tr><td>3    </td><td>0    </td><td>46000</td><td>1    </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>36   </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>0    </td><td>0    </td></tr>\n",
       "\t<tr><td>4    </td><td>1    </td><td>70000</td><td>0    </td><td>1    </td><td>0    </td><td>1    </td><td>0    </td><td>0    </td><td>55   </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>1    </td><td>0    </td></tr>\n",
       "\t<tr><td>5    </td><td>0    </td><td>43000</td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>27   </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>0    </td><td>1    </td></tr>\n",
       "\t<tr><td>6    </td><td>0    </td><td>24000</td><td>1    </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td><td>41   </td><td>0    </td><td>0    </td><td>1    </td><td>1    </td><td>0    </td><td>0    </td><td>0    </td><td>0    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllll}\n",
       " Obs.No. & Buy & Income & Is.Female & Is.Married & Has.College & Is.Professional & Is.Retired & Unemployed & Residence.Length & Dual.Income & Minors & Own & House & White & English & Prev.Child.Mag & Prev.Parent.Mag\\\\\n",
       "\\hline\n",
       "\t 1     & 0     & 24000 & 1     & 0     & 1     & 1     & 0     & 0     & 26    & 0     & 0     & 0     & 1     & 0     & 0     & 0     & 0    \\\\\n",
       "\t 2     & 1     & 75000 & 1     & 1     & 1     & 1     & 0     & 0     & 15    & 1     & 0     & 1     & 1     & 1     & 1     & 1     & 0    \\\\\n",
       "\t 3     & 0     & 46000 & 1     & 1     & 0     & 0     & 0     & 0     & 36    & 1     & 1     & 1     & 1     & 1     & 1     & 0     & 0    \\\\\n",
       "\t 4     & 1     & 70000 & 0     & 1     & 0     & 1     & 0     & 0     & 55    & 0     & 0     & 1     & 1     & 1     & 1     & 1     & 0    \\\\\n",
       "\t 5     & 0     & 43000 & 1     & 0     & 0     & 0     & 0     & 0     & 27    & 0     & 0     & 0     & 0     & 1     & 1     & 0     & 1    \\\\\n",
       "\t 6     & 0     & 24000 & 1     & 1     & 0     & 0     & 0     & 0     & 41    & 0     & 0     & 1     & 1     & 0     & 0     & 0     & 0    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Obs.No. | Buy | Income | Is.Female | Is.Married | Has.College | Is.Professional | Is.Retired | Unemployed | Residence.Length | Dual.Income | Minors | Own | House | White | English | Prev.Child.Mag | Prev.Parent.Mag |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1     | 0     | 24000 | 1     | 0     | 1     | 1     | 0     | 0     | 26    | 0     | 0     | 0     | 1     | 0     | 0     | 0     | 0     |\n",
       "| 2     | 1     | 75000 | 1     | 1     | 1     | 1     | 0     | 0     | 15    | 1     | 0     | 1     | 1     | 1     | 1     | 1     | 0     |\n",
       "| 3     | 0     | 46000 | 1     | 1     | 0     | 0     | 0     | 0     | 36    | 1     | 1     | 1     | 1     | 1     | 1     | 0     | 0     |\n",
       "| 4     | 1     | 70000 | 0     | 1     | 0     | 1     | 0     | 0     | 55    | 0     | 0     | 1     | 1     | 1     | 1     | 1     | 0     |\n",
       "| 5     | 0     | 43000 | 1     | 0     | 0     | 0     | 0     | 0     | 27    | 0     | 0     | 0     | 0     | 1     | 1     | 0     | 1     |\n",
       "| 6     | 0     | 24000 | 1     | 1     | 0     | 0     | 0     | 0     | 41    | 0     | 0     | 1     | 1     | 0     | 0     | 0     | 0     |\n",
       "\n"
      ],
      "text/plain": [
       "  Obs.No. Buy Income Is.Female Is.Married Has.College Is.Professional\n",
       "1 1       0   24000  1         0          1           1              \n",
       "2 2       1   75000  1         1          1           1              \n",
       "3 3       0   46000  1         1          0           0              \n",
       "4 4       1   70000  0         1          0           1              \n",
       "5 5       0   43000  1         0          0           0              \n",
       "6 6       0   24000  1         1          0           0              \n",
       "  Is.Retired Unemployed Residence.Length Dual.Income Minors Own House White\n",
       "1 0          0          26               0           0      0   1     0    \n",
       "2 0          0          15               1           0      1   1     1    \n",
       "3 0          0          36               1           1      1   1     1    \n",
       "4 0          0          55               0           0      1   1     1    \n",
       "5 0          0          27               0           0      0   0     1    \n",
       "6 0          0          41               0           0      1   1     0    \n",
       "  English Prev.Child.Mag Prev.Parent.Mag\n",
       "1 0       0              0              \n",
       "2 1       1              0              \n",
       "3 1       0              0              \n",
       "4 1       1              0              \n",
       "5 1       0              1              \n",
       "6 0       0              0              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t673 obs. of  18 variables:\n",
      " $ Obs.No.         : int  1 2 3 4 5 6 7 8 9 10 ...\n",
      " $ Buy             : int  0 1 0 1 0 0 0 0 0 0 ...\n",
      " $ Income          : int  24000 75000 46000 70000 43000 24000 26000 38000 39000 49000 ...\n",
      " $ Is.Female       : int  1 1 1 0 1 1 1 1 1 0 ...\n",
      " $ Is.Married      : int  0 1 1 1 0 1 1 1 0 1 ...\n",
      " $ Has.College     : int  1 1 0 0 0 0 1 0 1 0 ...\n",
      " $ Is.Professional : int  1 1 0 1 0 0 0 0 1 0 ...\n",
      " $ Is.Retired      : int  0 0 0 0 0 0 1 1 0 1 ...\n",
      " $ Unemployed      : int  0 0 0 0 0 0 0 0 0 0 ...\n",
      " $ Residence.Length: int  26 15 36 55 27 41 20 8 17 31 ...\n",
      " $ Dual.Income     : int  0 1 1 0 0 0 0 0 0 0 ...\n",
      " $ Minors          : int  0 0 1 0 0 0 1 0 0 0 ...\n",
      " $ Own             : int  0 1 1 1 0 1 1 1 0 1 ...\n",
      " $ House           : int  1 1 1 1 0 1 1 1 0 1 ...\n",
      " $ White           : int  0 1 1 1 1 0 1 1 1 1 ...\n",
      " $ English         : int  0 1 1 1 1 0 1 1 1 1 ...\n",
      " $ Prev.Child.Mag  : int  0 1 0 1 0 0 0 0 0 0 ...\n",
      " $ Prev.Parent.Mag : int  0 0 0 0 1 0 0 0 0 0 ...\n"
     ]
    }
   ],
   "source": [
    "str(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "df <- select(df, -Obs.No.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropped the column Obs.no(Observation numbers) because it is not required to build the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking for missing values using amelia library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(Amelia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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4ynlf8At/qsLvnNtb8xjGYBHncbRZEw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\nyL4o0pZDbQ2+RRXwXdryf8NXqnv18abuAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Magazine data\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missmap(df, main=\"Magazine data\", \n",
    "        col=c(\"yellow\", \"black\"), legend=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# As there are no missing values in the data. We can build the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lqFkI5CSKsQ0lEIaRVCOgohrUJIRyGkVQjpKIS0CiEdJRzS/S2EdAmEdJRsSPfP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\n/D/QEuig91RvCAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(Buy)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the target variable Buy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2NPZiNSJUKzRlbGnsxGpEqEZo2sjD2Z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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(Is.Retired)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are less retired guys in the town."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r1zNiGNVNisUMqxd84mpJEKmxVKOfbO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\nQICQAAFCAgQICRAgJECAkACB/wDSn4yswHiS8wAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(df,aes(Is.Married)) + geom_bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are more number of unmarried guys in the town."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(caTools)\n",
    "set.seed(101)\n",
    "\n",
    "split = sample.split(df$Buy, SplitRatio = 0.80)\n",
    "\n",
    "final.train = subset(df, split == TRUE)\n",
    "final.test = subset(df, split == FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data into train and test using sample.split function by 0.8 ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "final.log.model <- glm(formula= Buy ~ . , family = binomial(link='logit'),data = final.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = Buy ~ ., family = binomial(link = \"logit\"), data = final.train)\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-2.63022  -0.08797  -0.00809  -0.00136   2.17218  \n",
       "\n",
       "Coefficients:\n",
       "                   Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)      -1.813e+01  2.508e+00  -7.228 4.90e-13 ***\n",
       "Income            2.035e-04  2.679e-05   7.597 3.04e-14 ***\n",
       "Is.Female         1.183e+00  5.082e-01   2.328  0.01990 *  \n",
       "Is.Married        3.442e-01  6.640e-01   0.518  0.60415    \n",
       "Has.College       1.067e-01  4.859e-01   0.220  0.82622    \n",
       "Is.Professional   1.999e-01  5.281e-01   0.379  0.70504    \n",
       "Is.Retired       -2.101e+00  1.041e+00  -2.018  0.04354 *  \n",
       "Unemployed        9.858e-01  6.118e+00   0.161  0.87199    \n",
       "Residence.Length  4.064e-02  1.677e-02   2.424  0.01537 *  \n",
       "Dual.Income       7.717e-01  5.763e-01   1.339  0.18055    \n",
       "Minors            1.679e+00  5.583e-01   3.007  0.00264 ** \n",
       "Own               1.036e+00  6.609e-01   1.568  0.11696    \n",
       "House            -9.010e-01  6.960e-01  -1.295  0.19547    \n",
       "White             1.784e+00  5.844e-01   3.052  0.00227 ** \n",
       "English           1.376e+00  9.733e-01   1.413  0.15751    \n",
       "Prev.Child.Mag    2.141e+00  8.617e-01   2.485  0.01297 *  \n",
       "Prev.Parent.Mag   1.220e-01  7.015e-01   0.174  0.86193    \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 516.68  on 537  degrees of freedom\n",
       "Residual deviance: 148.70  on 521  degrees of freedom\n",
       "AIC: 182.7\n",
       "\n",
       "Number of Fisher Scoring iterations: 9\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(final.log.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model using final.train data the target variable is Buy. The independent variables income,white,is retired has less p value from summary function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted.probabilities <- predict(final.log.model,newdata=final.test,type='response')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted.results <- ifelse(fitted.probabilities > 0.5,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Accuracy 0.940740740740741\"\n"
     ]
    }
   ],
   "source": [
    "misClasificError <- mean(fitted.results != final.test$Buy)\n",
    "print(paste('Accuracy',1-misClasificError))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So the accuracy of a model has 94% that it can predict accurately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   \n",
       "    FALSE TRUE\n",
       "  0   104    6\n",
       "  1     2   23"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(final.test$Buy, fitted.probabilities > 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# confusion matrix\n",
    "predicted true negative - 104,\n",
    "predicted true positive - 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = 23\n",
    "TN = 104\n",
    "FP = 6\n",
    "FN = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Error Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0592592592592593"
      ],
      "text/latex": [
       "0.0592592592592593"
      ],
      "text/markdown": [
       "0.0592592592592593"
      ],
      "text/plain": [
       "[1] 0.05925926"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error.rate = (FP+FN) / (TP+TN+FN+FP)\n",
    "error.rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.05 is the error rate that the number of all incorrect predictions made by the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.92"
      ],
      "text/latex": [
       "0.92"
      ],
      "text/markdown": [
       "0.92"
      ],
      "text/plain": [
       "[1] 0.92"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SN = (TP) / (TP + FN)\n",
    "SN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 92% sensitivity is the number of correct positive predictions made by the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.945454545454545"
      ],
      "text/latex": [
       "0.945454545454545"
      ],
      "text/markdown": [
       "0.945454545454545"
      ],
      "text/plain": [
       "[1] 0.9454545"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SP = (TN) / (TN + FP)\n",
    "SP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 94% is the percentage of true negative predictions made by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.793103448275862"
      ],
      "text/latex": [
       "0.793103448275862"
      ],
      "text/markdown": [
       "0.793103448275862"
      ],
      "text/plain": [
       "[1] 0.7931034"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prec = (TP) / (TP + FP)\n",
    "prec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# when the model predicts yes.We calculate how often it is correct.So the model is 79% correct.¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# False Positive Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0545454545454545"
      ],
      "text/latex": [
       "0.0545454545454545"
      ],
      "text/markdown": [
       "0.0545454545454545"
      ],
      "text/plain": [
       "[1] 0.05454545"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FPR = (FP) / (TN +  FP)\n",
    "FPR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# false positive rate is when its actually no how often does it predict yes.so the model has 5% predicted yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(pROC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m2T6QIeIeVgtB+27DcUHPqN2XNDGvYW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\nQEiAAEICBBASIICQAAGEBAj4DxyiKFGaWzYpAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_roc = roc(final.test$Buy ~ fitted.probabilities, plot =TRUE, print.auc = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area Under Curve got 97.5% so the model can able to distinguish between positive class 1 and negative class 0."
   ]
  }
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