Internet_Advertisement_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Internet Advertisements Data Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abstract:\n",
    "=\n",
    "This dataset represents a set of possible advertisements on Internet pages\n",
    "\n",
    "\n",
    "Data Set Information:\n",
    "=\n",
    "This dataset represents a set of possible advertisements on Internet pages. The features encode the geometry of the image (if available) as well as phrases occuring in the URL, the image's URL and alt text, the anchor text, and words occuring near the anchor text. The task is to predict whether an image is an advertisement (\"ad\") or not (\"nonad\").\n",
    "\n",
    "Attribute Information:\n",
    "=\n",
    "(3 continous; others binary; this is the \"STANDARD encoding\" mentioned in the [Kushmerick, 99].)\n",
    "\n",
    "One or more of the three continous features are missing in 28% of the instances; missing values should be interpreted as \"unknown\".\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python modules version used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.7.8 (tags/v3.7.8:4b47a5b6ba, Jun 28 2020, 08:53:46) [MSC v.1916 64 bit (AMD64)]\n",
      "Scipy: 1.5.2\n",
      "Pandas: 1.1.1\n",
      "Numpy: 1.19.1\n",
      "Matplotlib: 3.3.1\n",
      "Scikit-learn: 0.23.2\n"
     ]
    }
   ],
   "source": [
    "# Python version\n",
    "\n",
    "import sys\n",
    "print(\"Python: {}\".format(sys.version))\n",
    "#scipy\n",
    "import scipy\n",
    "print(\"Scipy: {}\".format(scipy.__version__))\n",
    "#pandas\n",
    "import pandas as pd\n",
    "print(\"Pandas: {}\".format(pd.__version__))\n",
    "#numpy\n",
    "import numpy as np\n",
    "print(\"Numpy: {}\".format(np.__version__))\n",
    "#matplotlib\n",
    "import matplotlib\n",
    "print(\"Matplotlib: {}\".format(matplotlib.__version__))\n",
    "#scikit-learn\n",
    "import sklearn\n",
    "print(\"Scikit-learn: {}\".format(sklearn.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries and toolkits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import collections\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading Dataset\n",
    "\n",
    "df = pd.read_csv(\"add.csv\", low_memory = False, header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Unnamed: 0     0     1       2  3  4  5  6  7  8  ...  1549  1550  1551  \\\n",
      "0              0   125   125       1  1  0  0  0  0  0  ...     0     0     0   \n",
      "1              1    57   468  8.2105  1  0  0  0  0  0  ...     0     0     0   \n",
      "2              2    33   230  6.9696  1  0  0  0  0  0  ...     0     0     0   \n",
      "3              3    60   468     7.8  1  0  0  0  0  0  ...     0     0     0   \n",
      "4              4    60   468     7.8  1  0  0  0  0  0  ...     0     0     0   \n",
      "...          ...   ...   ...     ... .. .. .. .. .. ..  ...   ...   ...   ...   \n",
      "3274        3274   170    94  0.5529  0  0  0  0  0  0  ...     0     0     0   \n",
      "3275        3275   101   140  1.3861  1  0  0  0  0  0  ...     0     0     0   \n",
      "3276        3276    23   120  5.2173  1  0  0  0  0  0  ...     0     0     0   \n",
      "3277        3277     ?     ?       ?  1  0  0  0  0  0  ...     0     0     0   \n",
      "3278        3278    40    40       1  1  0  0  0  0  0  ...     0     0     0   \n",
      "\n",
      "      1552  1553  1554  1555  1556  1557    1558  \n",
      "0        0     0     0     0     0     0     ad.  \n",
      "1        0     0     0     0     0     0     ad.  \n",
      "2        0     0     0     0     0     0     ad.  \n",
      "3        0     0     0     0     0     0     ad.  \n",
      "4        0     0     0     0     0     0     ad.  \n",
      "...    ...   ...   ...   ...   ...   ...     ...  \n",
      "3274     0     0     0     0     0     0  nonad.  \n",
      "3275     0     0     0     0     0     0  nonad.  \n",
      "3276     0     0     0     0     0     0  nonad.  \n",
      "3277     0     0     0     0     0     0  nonad.  \n",
      "3278     0     0     0     0     0     0  nonad.  \n",
      "\n",
      "[3279 rows x 1560 columns]\n"
     ]
    }
   ],
   "source": [
    "# Dataset \n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3279, 1560)\n"
     ]
    }
   ],
   "source": [
    "# Shape\n",
    "\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows: 3279 \n",
    "\n",
    "Columns: 1559 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "#### Removing unwanted column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "     0    1       2  3  4  5  6  7  8  9  ...  1549  1550  1551  1552  1553  \\\n",
       "0  125  125       1  1  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
       "1   57  468  8.2105  1  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
       "2   33  230  6.9696  1  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
       "3   60  468     7.8  1  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
       "4   60  468     7.8  1  0  0  0  0  0  0  ...     0     0     0     0     0   \n",
       "\n",
       "   1554  1555  1556  1557  1558  \n",
       "0     0     0     0     0   ad.  \n",
       "1     0     0     0     0   ad.  \n",
       "2     0     0     0     0   ad.  \n",
       "3     0     0     0     0   ad.  \n",
       "4     0     0     0     0   ad.  \n",
       "\n",
       "[5 rows x 1559 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(\"Unnamed: 0\", axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Typecasting all columns into integers\n",
    "\n",
    "df.columns = df.columns.astype(\"int\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column information:\n",
    "\n",
    "first three columns are important because most of variation can be seen \n",
    "\n",
    "column1 -> height of image\n",
    "\n",
    "column2 -> width of image\n",
    "\n",
    "column3 -> aspect ratio(proportional relationship between an image's width and height)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    object\n",
       "1    object\n",
       "2    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3279 entries, 0 to 3278\n",
      "Columns: 1559 entries, 0 to 1558\n",
      "dtypes: int64(1554), object(5)\n",
      "memory usage: 39.0+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info()) #data set information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Information of First three continous variables\n",
    "\n",
    "28% data is missing for three continous attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3279 entries, 0 to 3278\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   0       3279 non-null   object\n",
      " 1   1       3279 non-null   object\n",
      " 2   2       3279 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 77.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.iloc[:, 0:3].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of missing data\n",
    "here '?' are string values for missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.loc[:, [0,1, 2, 3]]\n",
    "df1 = df1.applymap(lambda x:\"?\" in x)\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.title(\"Missing values HeatMap\")\n",
    "print((sns.heatmap(df1, cbar=False, yticklabels = False, cmap = \"viridis\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Frequency of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column[0] has missing values -903\n",
      "column[1] has missing values -901\n",
      "column[2] has missing values -910\n",
      "column[3] has missing values -15\n"
     ]
    }
   ],
   "source": [
    "for i in df1:\n",
    "    print('column[' + str(i) +'] has missing values -' + str(sum(df1[i])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling missing data with mean of each attribute\n",
    "\n",
    "1. Fill missing values with the aggregated mean of non null values\n",
    "2. convert data to float to get more precision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### replace_missing(df) is replacing missing values for top 4 features with maximum variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def replacing_missing(df):\n",
    "    for i in df:\n",
    "        df[i] = df[i].replace(\"[?]\", np.NAN, regex = True).astype(\"float\")\n",
    "        df[i] = df[i].fillna(df[i].mean())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calling replace_missing function in important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[[0, 1, 2, 3]] = replacing_missing(df.iloc[:, [0, 1, 2, 3]].copy()).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rounding off the values to Get a nominal output\n",
    "Here Lambda function is used to convert float value into rounded decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[3] = df[3].apply(lambda x : round(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis(E.D.A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Data is right skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>155.344828</td>\n",
       "      <td>3.911953</td>\n",
       "      <td>0.768222</td>\n",
       "      <td>0.004270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>110.729038</td>\n",
       "      <td>5.136153</td>\n",
       "      <td>0.422032</td>\n",
       "      <td>0.065212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.279400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>3.911953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>155.344828</td>\n",
       "      <td>3.911953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>640.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1            2            3            4\n",
       "count  3279.000000  3279.000000  3279.000000  3279.000000\n",
       "mean    155.344828     3.911953     0.768222     0.004270\n",
       "std     110.729038     5.136153     0.422032     0.065212\n",
       "min       1.000000     0.001500     0.000000     0.000000\n",
       "25%      90.000000     1.279400     1.000000     0.000000\n",
       "50%     150.000000     3.911953     1.000000     0.000000\n",
       "75%     155.344828     3.911953     1.000000     0.000000\n",
       "max     640.000000    60.000000     1.000000     1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[1, 2, 3, 4]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='3', ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 4)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(13)\n",
    "sns.distplot(df[0], ax = ax[0])\n",
    "sns.distplot(df[1], ax = ax[1])\n",
    "sns.distplot(df[2], ax = ax[2])\n",
    "sns.distplot(df[3], ax = ax[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relations between three continous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x299c8117488>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data = df.iloc[:, [0, 1, 2, 3, 1558]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting discrete data\n",
    "Strip plot is showing the variance in data\n",
    "\n",
    "Plot is showing relation between each column and all features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add/non-ad vs Features\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"add/non-ad vs Features\")\n",
    "fig, ax = plt.subplots(nrows = 4, ncols = 1)\n",
    "fig.set_figheight(15)\n",
    "fig.set_figwidth(18)\n",
    "sns.stripplot(y=1558,x=0,data=df,ax=ax[0])\n",
    "sns.stripplot(y=1558,x=1,data=df,ax=ax[1])\n",
    "sns.stripplot(y=1558,x=2,data=df,ax=ax[2])\n",
    "sns.stripplot(y=1558,x=3,data=df,ax=ax[3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency of target feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,y=1558,palette='husl')\n",
    "plt.title(\"Frequency of Targets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data and Central Tendoncy distribution\n",
    "Boxplot is a great a way to show the distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(x=1558,y=0,data=df)\n",
    "plt.xlabel('label-add/non-ad')\n",
    "plt.ylabel('width')\n",
    "plt.title(\"Boxplot-  Target Vs Height \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(x=1558,y=1,data=df)\n",
    "plt.xlabel('label-add/non-ad')\n",
    "plt.ylabel('width')\n",
    "plt.title(\"Boxplot-  Target Vs Width \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(x=1558,y=2,data=df)\n",
    "plt.xlabel('label-add/non-ad')\n",
    "plt.ylabel('width')\n",
    "plt.title(\"Boxplot-  Target Vs Aspect Ratio \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.boxplot(x=1558,y=3,data=df)\n",
    "plt.xlabel('label-add/non-ad')\n",
    "plt.ylabel('width')\n",
    "plt.title(\"Boxplot-  Target Vs Rest other features \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above boxplot we can conclude that the width of ad is much more than the width of non-ad.\n",
    "\n",
    "Also, there are lot of outliers in width of non-ad target."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changing categorical target data to numerical form:\n",
    "\n",
    "'0' - non advertisement image\n",
    "\n",
    "'1' - addvertisement image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:, -1] = df.iloc[:, -1].replace([\"ad.\", \"nonad.\"], [1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing our model, where \"x\" stores features and \"y\" stores the labels(ad/non-ad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:, :-1]\n",
    "y = df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Data\n",
    "Since the data is highly varied, scaling is needed to get all our data in a fixed range for better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled = StandardScaler()\n",
    "X = scaled.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 582.375x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df.iloc[:,[1, 0,2, -1]],hue=1558)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, after scaling EDA can be seen more clearly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model Pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Splitting of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting of training set and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 1, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I have training data in the X_train and y_train sets for training and preparing models, and a X_test and\n",
    "y_test sets for testing our model accuracy and predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don’t know which algorithms would be good on this problem or what configurations to use.\n",
    "\n",
    "Let’s test 6 different algorithms:\n",
    "\n",
    "1)Logistic Regression (LR)\n",
    "\n",
    "2)Linear Discriminant Analysis (LDA)\n",
    "\n",
    "3)K-Nearest Neighbors (KNN).\n",
    "\n",
    "4)Classification and Regression Trees (CART).\n",
    "\n",
    "5)Gaussian Naive Bayes (NB).\n",
    "\n",
    "6)Support Vector Machines (SVM).\n",
    "\n",
    "This is a good mixture of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.958930(0.011695)\n",
      "LDA: 0.963810(0.010967)\n",
      "KNN: 0.952832(0.009634)\n",
      "CART: 0.956487(0.012062)\n",
      "NB: 0.774302(0.016535)\n",
      "SVM: 0.963811(0.007357)\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv = kfold, scoring = \"accuracy\")\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    print(\"{}: {:.6f}({:.6f})\".format(name, cv_results.mean(), cv_results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, Support Vector Machine(SVM) and Linear Discriminant Analysis (LDA) have almost same accuracy 96.38%, but the standard deviation value of SVM is lower, making the dataset closer to mean.\n",
    "\n",
    "So, SVM looks like a better suit for the model.\n",
    "\n",
    "First let's see the result in visualiztion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Algorithm Comparison')"
      ]
     },
     "execution_count": 155,
     "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": [
    "# Compare Algorithms\n",
    "\n",
    "plt.boxplot(results, labels = names)\n",
    "plt.title(\"Algorithm Comparison\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the boxplots, we can see that most models have an accuracy of 95% and above, with exception Gaussian Naive Bayes(NB) to have a accuracy less than 80%. \n",
    "\n",
    "So, as you can see the LDA model has a larger interquartile range than the SVM model, making the SVM the best model for the model dataset classification.\n",
    "\n",
    "So, The best model for this classification dataset is SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Evaluating Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on test set\n",
    "\n",
    "model = SVC(gamma = \"auto\")\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9707317073170731\n"
     ]
    }
   ],
   "source": [
    "# Evaluate predictions\n",
    "\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model predicts a score of 0.9707 or about 97.1% accuracy on our hold out set(test set)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix\n",
    "So, Confusion Matrix shows that higher the diagonal values in the confusion matrix the better, indicating many\n",
    "correct predictions.\n",
    "\n",
    "So let's check our model using this confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[703   2]\n",
      " [ 22  93]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = plot_confusion_matrix(model, X_test, y_test,\n",
    "                            cmap = plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98       705\n",
      "           1       0.98      0.81      0.89       115\n",
      "\n",
      "    accuracy                           0.97       820\n",
      "   macro avg       0.97      0.90      0.93       820\n",
      "weighted avg       0.97      0.97      0.97       820\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Classification Report gives us the breakdown of each class(ad, non-ad) by\n",
    "precision, recall, f1-score and support showing excellent results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}