Internet-Advertisements.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import  seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "ads=pd.read_csv('adsdata.csv')\n",
    "#ads.head(500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from  pandas.plotting import scatter_matrixe\n",
    "######### train_test_split  ############\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#x=final.t\n",
    "#y=df_num\n",
    "#y_train , y_test ,x_train, x_test= train_test_split(y,x,test_size=0.2, random_state=30)\n",
    "#train_set.shap\n",
    "#print(\"x_train _set:\", y_train.shape)\n",
    "#print(\"x_test_set:\",y_test.shape)\n",
    "#print(\"y_train _set:\", x_train.shape)\n",
    "#print(\"y_test_set:\",x_test.shape)\n",
    "########\n",
    "df=train_set.copy()\n",
    "df_lable=df[\"t\"].copy()\n",
    "df= ads.drop((\"t\"),axis=1)\n",
    "df_num=df.drop(\"A\", axis=1)\n",
    "df_num=df_num.drop(\"B\", axis=1)\n",
    "df_num=df_num.drop(\"C\", axis=1)\n",
    "df_num=df_num.drop(\"D\", axis=1)\n",
    "#df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train _set: (2623, 1554)\n",
      "x_test_set: (656, 1554)\n",
      "y_train _set: (2623,)\n",
      "y_test_set: (656,)\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>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 1548</th>\n",
       "      <th>Unnamed: 1549</th>\n",
       "      <th>Unnamed: 1550</th>\n",
       "      <th>Unnamed: 1551</th>\n",
       "      <th>Unnamed: 1552</th>\n",
       "      <th>Unnamed: 1553</th>\n",
       "      <th>Unnamed: 1554</th>\n",
       "      <th>Unnamed: 1555</th>\n",
       "      <th>Unnamed: 1556</th>\n",
       "      <th>Unnamed: 1557</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3279 rows × 1554 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 4  Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  \\\n",
       "0              0           0           0           0           0           0   \n",
       "1              0           0           0           0           0           0   \n",
       "2              0           0           0           0           0           0   \n",
       "3              0           0           0           0           0           0   \n",
       "4              0           0           0           0           0           0   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "3274           0           0           0           0           0           0   \n",
       "3275           0           0           0           0           0           0   \n",
       "3276           0           0           0           0           0           0   \n",
       "3277           0           0           0           0           0           0   \n",
       "3278           0           0           0           0           0           0   \n",
       "\n",
       "      Unnamed: 10  Unnamed: 11  Unnamed: 12  Unnamed: 13  ...  Unnamed: 1548  \\\n",
       "0               0            0            0            0  ...              0   \n",
       "1               0            0            0            0  ...              0   \n",
       "2               0            0            0            0  ...              0   \n",
       "3               0            0            0            0  ...              0   \n",
       "4               0            0            0            0  ...              0   \n",
       "...           ...          ...          ...          ...  ...            ...   \n",
       "3274            0            0            0            0  ...              0   \n",
       "3275            0            0            0            0  ...              0   \n",
       "3276            0            0            0            0  ...              0   \n",
       "3277            0            0            0            0  ...              0   \n",
       "3278            0            0            0            0  ...              0   \n",
       "\n",
       "      Unnamed: 1549  Unnamed: 1550  Unnamed: 1551  Unnamed: 1552  \\\n",
       "0                 0              0              0              0   \n",
       "1                 0              0              0              0   \n",
       "2                 0              0              0              0   \n",
       "3                 0              0              0              0   \n",
       "4                 0              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "3274              0              0              0              0   \n",
       "3275              0              0              0              0   \n",
       "3276              0              0              0              0   \n",
       "3277              0              0              0              0   \n",
       "3278              0              0              0              0   \n",
       "\n",
       "      Unnamed: 1553  Unnamed: 1554  Unnamed: 1555  Unnamed: 1556  \\\n",
       "0                 0              0              0              0   \n",
       "1                 0              0              0              0   \n",
       "2                 0              0              0              0   \n",
       "3                 0              0              0              0   \n",
       "4                 0              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "3274              0              0              0              0   \n",
       "3275              0              0              0              0   \n",
       "3276              0              0              0              0   \n",
       "3277              0              0              0              0   \n",
       "3278              0              0              0              0   \n",
       "\n",
       "      Unnamed: 1557  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "...             ...  \n",
       "3274              0  \n",
       "3275              0  \n",
       "3276              0  \n",
       "3277              0  \n",
       "3278              0  \n",
       "\n",
       "[3279 rows x 1554 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from  pandas.plotting import scatter_matrixe\n",
    "######### train_test_split  ############\n",
    "from sklearn.model_selection import train_test_split\n",
    "y=final.t\n",
    "y_train , y_test ,df_num_train, df_num_test= train_test_split(y,df_num,test_size=0.2, random_state=30)\n",
    "#train_set.shap\n",
    "print(\"df_num_train _set:\", df_num_train.shape)\n",
    "print(\"df_num_test_set:\",df_num_test.shape)\n",
    "print(\"y_train _set:\", y_train.shape)\n",
    "print(\"y_test_set:\",y_test.shape)\n",
    "########\n",
    "df=train_set.copy()\n",
    "df_lable=df[\"t\"].copy()\n",
    "df= ads.drop((\"t\"),axis=1)\n",
    "df_num=df.drop(\"A\", axis=1)\n",
    "df_num=df_num.drop(\"B\", axis=1)\n",
    "df_num=df_num.drop(\"C\", axis=1)\n",
    "df_num=df_num.drop(\"D\", axis=1)\n",
    "df_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import OneHotEncoder \n",
    "#encoder_1hot =OneHotEncoder(sparse=False)\n",
    "#data_cat_1hot_tmp=encoder_1hot.fit_transform(ads[[\"t\"]])\n",
    "#data_cat_1hot=pd.DataFrame(data_cat_1hot_tmp)\n",
    "#data_cat_1hot.columns=encoder_1hot.get_feature_names(['test'])\n",
    "#data_cat_1hot.head()\n",
    "##############################final Data##########################\n",
    "#final=pd.concat([     df_num      ,  data_cat_1hot   ],axis=1)\n",
    "#final.head(10)                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 1550</th>\n",
       "      <th>Unnamed: 1551</th>\n",
       "      <th>Unnamed: 1552</th>\n",
       "      <th>Unnamed: 1553</th>\n",
       "      <th>Unnamed: 1554</th>\n",
       "      <th>Unnamed: 1555</th>\n",
       "      <th>Unnamed: 1556</th>\n",
       "      <th>Unnamed: 1557</th>\n",
       "      <th>test_ad.</th>\n",
       "      <th>test_nonad.</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",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\n",
       "      <td>3279.000000</td>\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>0.004270</td>\n",
       "      <td>0.011589</td>\n",
       "      <td>0.004575</td>\n",
       "      <td>0.003355</td>\n",
       "      <td>0.003965</td>\n",
       "      <td>0.011589</td>\n",
       "      <td>0.003355</td>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.009149</td>\n",
       "      <td>0.004575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003660</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.003050</td>\n",
       "      <td>0.006404</td>\n",
       "      <td>0.012809</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>0.139982</td>\n",
       "      <td>0.860018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.065212</td>\n",
       "      <td>0.107042</td>\n",
       "      <td>0.067491</td>\n",
       "      <td>0.057831</td>\n",
       "      <td>0.062850</td>\n",
       "      <td>0.107042</td>\n",
       "      <td>0.057831</td>\n",
       "      <td>0.069694</td>\n",
       "      <td>0.095227</td>\n",
       "      <td>0.067491</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060393</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.055148</td>\n",
       "      <td>0.079783</td>\n",
       "      <td>0.112466</td>\n",
       "      <td>0.115077</td>\n",
       "      <td>0.098320</td>\n",
       "      <td>0.039026</td>\n",
       "      <td>0.347021</td>\n",
       "      <td>0.347021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 1556 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 4   Unnamed: 5   Unnamed: 6   Unnamed: 7   Unnamed: 8  \\\n",
       "count  3279.000000  3279.000000  3279.000000  3279.000000  3279.000000   \n",
       "mean      0.004270     0.011589     0.004575     0.003355     0.003965   \n",
       "std       0.065212     0.107042     0.067491     0.057831     0.062850   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "        Unnamed: 9  Unnamed: 10  Unnamed: 11  Unnamed: 12  Unnamed: 13  ...  \\\n",
       "count  3279.000000  3279.000000  3279.000000  3279.000000  3279.000000  ...   \n",
       "mean      0.011589     0.003355     0.004880     0.009149     0.004575  ...   \n",
       "std       0.107042     0.057831     0.069694     0.095227     0.067491  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "       Unnamed: 1550  Unnamed: 1551  Unnamed: 1552  Unnamed: 1553  \\\n",
       "count    3279.000000    3279.000000    3279.000000    3279.000000   \n",
       "mean        0.003660       0.002440       0.003050       0.006404   \n",
       "std         0.060393       0.049341       0.055148       0.079783   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "       Unnamed: 1554  Unnamed: 1555  Unnamed: 1556  Unnamed: 1557  \\\n",
       "count    3279.000000    3279.000000    3279.000000    3279.000000   \n",
       "mean        0.012809       0.013419       0.009759       0.001525   \n",
       "std         0.112466       0.115077       0.098320       0.039026   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "          test_ad.  test_nonad.  \n",
       "count  3279.000000  3279.000000  \n",
       "mean      0.139982     0.860018  \n",
       "std       0.347021     0.347021  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     1.000000  \n",
       "50%       0.000000     1.000000  \n",
       "75%       0.000000     1.000000  \n",
       "max       1.000000     1.000000  \n",
       "\n",
       "[8 rows x 1556 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final.head(10)\n",
    "#final.describe()\n",
    "#import  seaborn as sns\n",
    "#sns.countplot(x='test', data=final ,palette='hls')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3279 entries, 0 to 3278\n",
      "Columns: 1555 entries, Unnamed: 4 to t\n",
      "dtypes: int32(1), int64(1554)\n",
      "memory usage: 38.9 MB\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "data_adc=ads[\"t\"]\n",
    "data_adc_encode=encoder.fit_transform(data_adc)\n",
    "data_adc_encode= pd.DataFrame(data_adc_encode,columns=[\"t\"])\n",
    "#data_adc_encode.head(500)\n",
    "##############################final Data##########################\n",
    "final=pd.concat([     df_num      , data_adc_encode   ],axis=1)\n",
    "final.head(1000) \n",
    "final.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pct_of Data_ad: 13.998170173833484\n",
      "pct_of Data_nonad: 86.00182982616651\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "#df_num.hist(bins=40, figsize=(40,40))\n",
    "#plt.show()\n",
    "import  seaborn as sns\n",
    "sns.countplot(x='t', data=final ,palette='hls' )\n",
    "#plt.bar(lable='o')\n",
    "plt.legend('ad')\n",
    "plt.show()\n",
    "count_ads_sub=len(final[final['t']==0])\n",
    "count_ads=len(final[final['t']==1])\n",
    "pct_of_ad=count_ads_sub/(count_ads_sub+count_ads)\n",
    "print(\"pct_of Data_ad:\",pct_of_ad*100)\n",
    "pct_of_noad=count_ads/(count_ads_sub+count_ads)\n",
    "print(\"pct_of Data_nonad:\",pct_of_noad*100)\n",
    "#sns.countplot(x='test_ad.', data=final ,palette='hls')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(df.corr(),cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.82      0.83        79\n",
      "           1       0.98      0.98      0.98       577\n",
      "\n",
      "    accuracy                           0.96       656\n",
      "   macro avg       0.91      0.90      0.91       656\n",
      "weighted avg       0.96      0.96      0.96       656\n",
      "\n",
      "Accuacy of Desition Tree Classifire on test set:0.96\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "dtc=DecisionTreeClassifier()\n",
    "dtc=dtc.fit(df_num_train,y_train)\n",
    "dt=dtc.predict(df_num_test)\n",
    "from sklearn.metrics import classification_report\n",
    "print (classification_report(y_test,dtc.predict(df_num_test)))\n",
    "print('Accuacy of Desition Tree Classifire on test set:{:.2f}' .format(dtc.score(df_num_test,y_test)))\n",
    "from sklearn import tree\n",
    "plt.figure(figsize=(20,20))\n",
    "temp=tree.plot_tree(dtc.fit(df_num,y),fontsize=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.86      0.47        79\n",
      "           1       0.98      0.75      0.85       577\n",
      "\n",
      "    accuracy                           0.76       656\n",
      "   macro avg       0.65      0.81      0.66       656\n",
      "weighted avg       0.90      0.76      0.80       656\n",
      "\n",
      " Naive Bayes test accuracy:  0.76 \n"
     ]
    }
   ],
   "source": [
    "#*************      Naive Bayes clasifier       *******************\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "#gnb.fit(df_num_train, y_train())\n",
    "y_pred =gnb.fit(df_num_train, y_train).predict(df_num_test)\n",
    "#y_pred = gnb.predict(df_num_test)\n",
    "print (classification_report(y_test,gnb.predict(df_num_test)))\n",
    "print (' Naive Bayes test accuracy:  {:.2f} '.format(gnb.score(df_num_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mlp Classifier  test accuracy:  0.9634146341463414\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.81      0.84        79\n",
      "           1       0.97      0.98      0.98       577\n",
      "\n",
      "    accuracy                           0.96       656\n",
      "   macro avg       0.93      0.90      0.91       656\n",
      "weighted avg       0.96      0.96      0.96       656\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#******************          Mlp Classifier       ********************\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "mpc=MLPClassifier(hidden_layer_sizes=(5,2), max_iter=1000)\n",
    "mpc.fit(df_num_train, y_train)\n",
    "y_predm=mpc.predict(df_num_test)\n",
    "print (\"Mlp Classifier  test accuracy: \", mpc.score(df_num_test, y_test))\n",
    "print (classification_report(y_test,adsk.predict(df_num_test)))\n",
    "print(\"knn(k=5) test accuracy;\", adsk.score(df_num_test  , y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "fit() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-167-29cb39e45767>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0madsr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'1bfgs'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0madsr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0my_predr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0madsr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: fit() missing 1 required positional argument: 'y'"
     ]
    }
   ],
   "source": [
    "#******************    <    logistic Regreession Classifier   >     ************************\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "adsr=LogisticRegression(solver='1bfgs')\n",
    "adsr.fit(y_train.ravel())\n",
    "y_predr=adsr.predict(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "when k=5 neighnors , knn test accuracy:0.9527439024390244\n",
      "when k=5 neighnors , knn test accuracy:0.9672131147540983\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.63      0.76        79\n",
      "           1       0.95      1.00      0.97       577\n",
      "\n",
      "    accuracy                           0.95       656\n",
      "   macro avg       0.96      0.81      0.87       656\n",
      "weighted avg       0.95      0.95      0.95       656\n",
      "\n",
      "knn(k=5) test accuracy; 0.9527439024390244\n",
      "when k=0.9573170731707317 neighnors , knn test accuracy:4\n",
      "when k=0.9939001143728555 neighnors , knn test accuracy:1\n"
     ]
    }
   ],
   "source": [
    "#**********************        knn          ***************************\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k=5\n",
    "adsk=KNeighborsClassifier(n_neighbors=k)\n",
    "adsk.fit(df_num_train,y_train)\n",
    "y_predk=adsk.predict(df_num_test)\n",
    "print(\"when k={} neighnors , knn test accuracy:{}\".format(k , adsk.score(df_num_test ,y_test)))\n",
    "print(\"when k={} neighnors , knn test accuracy:{}\".format(k , adsk.score(df_num_train ,y_train)))\n",
    "print(classification_report(y_test,adsk.predict(df_num_test)))\n",
    "print(\"knn(k=5) test accuracy;\",adsk.score(df_num_test, y_test))\n",
    "ran=np.arange(1,30)\n",
    "train_list=[]\n",
    "test_list=[]\n",
    "for i  , each in enumerate(ran):\n",
    "    adsk=KNeighborsClassifier(n_neighbors=each)\n",
    "    # adsk.fit(df_num_train,y_train)\n",
    "    adsk.fit(df_num_train,y_train)\n",
    "    \n",
    "    test_list.append(adsk.score(df_num_test, y_test))\n",
    "    train_list.append(adsk.score(df_num_train , y_train))\n",
    "print(\"when k={} neighnors , knn test accuracy:{}\".format(np.max(test_list) ,test_list.index (np.max(test_list))+1))\n",
    "print(\"when k={} neighnors , knn test accuracy:{}\".format(np.max(train_list) ,train_list.index (np.max(train_list))+1))\n",
    "plt.figure(figsize=[15,15])\n",
    "plt.plot(ran,test_list,label='test score')\n",
    "plt.plot(ran,train_list,label='train score')\n",
    "plt.xlabel('number of neighbers')\n",
    "plt.ylabel('numer/count')\n",
    "plt.xticks(ran)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.80      0.88        79\n",
      "           1       0.97      1.00      0.99       577\n",
      "\n",
      "    accuracy                           0.97       656\n",
      "   macro avg       0.98      0.90      0.93       656\n",
      "weighted avg       0.97      0.97      0.97       656\n",
      "\n",
      "Accuracy of logistic regression classifier on test set:0.97\n"
     ]
    }
   ],
   "source": [
    "#****************              logistic Regreession Classifier              *****************\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "#adsr=LogisticRegression(solver='1bfgs')\n",
    "#adsr.fit(df_num_train,y_train())\n",
    "#y_predr=adsr.predict(y_test)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "#x_train, x_test, y_train, y_test = train_test_split(df_num, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(df_num_train, y_train)\n",
    "y_pred = logreg.predict(df_num_test)\n",
    "print(classification_report(y_test,logreg.predict(df_num_test)))\n",
    "print('Accuracy of logistic regression classifier on test set:{:.2f}'.format(logreg.score(df_num_test, y_test)))"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}